Imaging of Neurodegenerative Disorders

       

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Imaging of Neurodegenerative Disorders

Best Evidence Recommendations

2nd Edition

Sangam G. Kanekar, MD

Associate Professor of Radiology and Neurology Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Seung-Ho Shin, MD

Assistant Professor of Otology
and Neurotology
Department of Otolaryngology–Head and Neck Surgery Cha University
Seongnam, Republic of Korea

1874 illustrations

Thieme
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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

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Library of Congress Cataloging-in-Publication Data

Imaging of neurodegenerative disorders / [edited by] Sangam G. Kanekar.

p. ; cm.
Includes bibliographical references and index.

ISBN 978-1-60406-854-2 (hardcover : alk. paper) – ISBN 978-1- 60406-855-9 (ebook)

I. Kanekar, Sangam G., editor.
[DNLM: 1. Neurodegenerative Diseases–diagnosis. 2. Neuroimaging–

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methods. WL 358.5] RC376.5 616.8’307548–dc23

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

I dedicate this book to
“MahaSaraswati”
and to my parents Gurudas and the late Meerabai Kanekar

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Contents

Foreword 1……………………………………………………………………………….. xi Foreword 2……………………………………………………………………………….xiii Preface…………………………………………………………………………………… xv Acknowledgments ………………………………………………………………………. xvii

Contributors………………………………………………………………………………xix Part I. Introduction

Chapter 1:

Chapter 2: Chapter 3: Chapter 4: Chapter 5: Chapter 6: Chapter 7:

Chapter 8: Chapter 9:

Overview of Neurodegenerative Diseases ……………………………………………… 2 Sangam G. Kanekar and Maya L. Lichtenstein

Part II. Imaging Techniques

Structural Imaging of Dementia………………………………………………………. 14 Sangam G. Kanekar and Vijay Mittal

Magnetic Resonance Spectroscopy in Neurodegenerative Disorders …………………… 24 Tushar Chandra, Suyash Mohan, Sanjeev Chawla, and Harish Poptani

SPECT and PET Imaging of Neurotransmitters in Dementia……………………………. 34 Mateen Moghbel, Andrew Newberg, Mijail Serruya, and Abass Alavi

Diffusion Tensor Imaging in Neurodegenerative Disorders…………………………….. 42 Dhiraj Baruah, Suyash Mohan, and Sumei Wang

Functional Imaging of the Brain………………………………………………………. 51 Leslie Hartman and Aaron S. Field

Role of Noninvasive Angiogram and Perfusion in the Evaluation of Neurodegenerative Disorders ……………………………………………………………………………. 60 Sangam G. Kanekar and Puneet Devgun

Part III. Normal Aging

Imaging of the Normal Aging Brain…………………………………………………… 70 Ruth A. Wood, Ludovico Minati, and Dennis Chan

Iron Accumulation and Iron Imaging in the Human Brain……………………………… 80 Stefan Ropele and Christian Langkammer

Part IV. Alzheimer’s Disease
Chapter 10: Mild Cognitive Impairment………………………………………………………….. 90

Kei Yamada and Koji Sakai

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Contents

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Chapter 11: Overview of Alzheimer’s Disease ………………………………………………….. 113 Leonardo Cruz de Souza and Marie Sarazin

Chapter 12: Genetics, Neuropathology, and Biomarkers in Alzheimer’s Disease …………………. 119 Maria Martinez-Lage Alvarez and Rashmi Tondon

Chapter 13: Imaging of Alzheimer’s Disease: Part 1…………………………………………….. 124 Donald G. McLaren, Guofan Xu, and Vivek Prabhakaran

Chapter 14: Imaging of Alzheimer’s Disease: Part 2…………………………………………….. 133 Christian La, Wolfgang Gaggl, and Vivek Prabhakaran

Chapter 15: Magnetic Resonance Imaging and Histopathological Correlation in
Alzheimer’s Disease……………………………………………………………….. 139

Mark D. Meadowcroft and Qing X. Yang

Part V. Non-Alzheimer’s Cortical Dementia
Chapter 16: Dementia with Lewy Body Disease ………………………………………………… 150

Aristides A. Capizzano and Toshio Moritani

Chapter 17: Frontotemporal Lobar Degeneration ………………………………………………. 157 Aristides A. Capizzano and Toshio Moritani

Part VI. Dementia with Extrapyramidal Syndromes

Chapter 18: Parkinson’s Disease ……………………………………………………………….. 166 Jennifer G. Goldman, John W. Ebersole, Douglas Merkitch, and Glenn T. Stebbins

Chapter 19: Atypical Parkinsonian Syndromes………………………………………………….. 180 Nicola Pavese and David J. Brooks

Chapter 20: Secondary Parkinsonism ………………………………………………………….. 186 Thyagarajan Subramanian, Kala Venkiteswaran, and Elisabeth Lucassen

Part VII. Vascular Dementia

Chapter 21: Vascular Dementia………………………………………………………………… 194 A.M. Barrett and Vahid Behravan

Chapter 22: Neuroimaging of Vascular Dementias ……………………………………………… 199 Amit Agarwal and Sangam G. Kanekar

Chapter 23: Imaging of Specific Hereditary Microangiopathies …………………………………. 210 Kenneth Lury and Mauricio Castillo

Chapter 24: Vasculitis and Dementia…………………………………………………………… 216 Sampson K. Kyere, Olaguoke Akinwande, Dheeraj Gandhi, and Gaurav Jindal

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Part VIII. Infection and Inflammatory Conditions Associated with Dementia
Chapter 25: Human Immunodeficiency Virus (HIV) Dementia…………………………………… 226

Toshio Moritani, Aristides Capizzano, and Sangam G. Kanekar

Chapter 26: Non-Human Immunodeficiency Virus (HIV) Infectious Dementia …………………… 232 Krishan K. Jain, Jitendra K. Saini, and Rakesh K. Gupta

Chapter 27: Prion Disease……………………………………………………………………… 239 Toshio Moritani, Aristides Capizzano, Girish Bathla, and Yoshimitsu Ohgiya

Chapter 28: Immune-Mediated Dementias …………………………………………………….. 245 Sangam G. Kanekar, Vinod Maller, and Amit Agarwal

Part IX. Normal Pressure Hydrocephalus

Chapter 29: Normal Pressure Hydrocephalus…………………………………………………… 256 Ritu Shah, Fathima Fijula Palot Manzil, and Surjith Vattoth

Part X. Tumor-Related Cognitive Dysfunction

Chapter 30: Brain Tumors and Cognitive Dysfunction…………………………………………… 266 Sangam G. Kanekar and Hazem Matta

Chapter 31: Paraneoplastic Syndrome …………………………………………………………. 276 Toshio Moritani, Aristides A. Capizzano, and Yoshimitsu Ohgiya

Part XI. Trauma

Chapter 32: Posttraumatic Cognitive Disorders ………………………………………………… 284 Inga Koerte, Alexander Lin, Marc Muehlmann, Boris-Stephan Rauchmann, Kyle Cooper,

Michael Mayinger, Robert A. Stern, and Martha E. Shenton

Part XII. Endocrine and Toxins-Related Dementia

Chapter 33: Endocrine-, Metabolic-, Toxin-, and Drug-Related Dementia ……………………….. 296 Sangam G. Kanekar and Brian S. Bentley

Part XIII. Inborn Errors of Metabolism

Chapter 34: Inborn Errors of Metabolism ………………………………………………………. 306 Sangam G. Kanekar and Dejan Samardzic

Part XIV. Cerebellar Degeneration and Dysfunction

Chapter 35: Normal Anatomy and Pathways of Cerebellum…………………………………….. 318 Sangam G. Kanekar and Jeffrey D. Poot

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Contents

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x

Contents

Chapter 36: Imaging of Cerebellar Degeneration and Cerebellar Ataxia…………………………. 328 Sangam G. Kanekar and Kyaw Tun

Part XV. Motor Neuron Disorders

Chapter 37: Overview of Motor Neuron Disorders ……………………………………………… 340 Divisha Raheja and Zachary Simmons

Chapter 38: Neuroimaging of Motor Neuron Disorders…………………………………………. 349 Divisha Raheja and Zachary Simmons

Part XVI. Clinical Approach and Treatment

Chapter 39: Reversible versus Nonreversible Dementia: Practical Approach …………………….. 362 Sol De Jesus and Sangam G. Kanekar

Chapter 40: Advances in the Treatment of Dementia…………………………………………… 371 Madhav Thambisetty, Néstor Gálvez-Jiménez, and Thyagarajan Subramanian

Chapter 41: Imaging of Deep Brain Stimulation………………………………………………… 378 Falgun H. Chokshi

Index …………………………………………………………………………………………. 386

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Foreword 1

Modern advances in extending the human lifespan are, in part, accountable for the steady rise in neurodegenerative disease worldwide. While there are many tomes written on Alzheimer’s disease and related dementias, until this one I hadn’t encountered a book that broadly emphasizes the full range of neurodegenerative syndromes, including chronic head injury, vascular causes, viral syndromes (e.g., HIV encephalopathy), prion disease, paraneoplastic syndromes, and toxin and drug-related conditions. This thoughtful and comprehensive approach has produced a unique and highly useful body of work. Over the past several decades, the imaging tools at our disposal for evaluating neurodegener- ative disorders have dramatically evolved. A half-century ago, there was no effective way to visualize the brain through an intact skull. Now, the coordinated use of structural and functional modalities permits diagnostic and prognostic assessments and provides specific biomarkers poised to monitor the success of therapeutic strategies that as yet remain largely rudimentary.

Sangam G. Kanekar’s Imaging of Neurodegenerative Dis- orders clearly fills an important gap in the literature as it uniquely offers a broader lens through to consider neurode- generative conditions. The introductory chapter, written by Dr. Kanekar and Maya L. Lichtenstein, provides the context and rationale for the work and its organizational structure. Imaging technology has contributed to discriminating

among underlying etiologies for what was previously an array of poorly understood and overlapping signs and symp- toms that affected a person’s mood, memory and personality. The integration of genetic, epidemiologic and underlying neuropathologic information is critical as well. Part II addresses imaging techniques relevant to the disorders in the book, such as diffusion tensor imaging with MRI and amyloid PET imaging. The book is grounded in the funda- mental dictum that neuroimaging evaluation of the brain requires a thorough understanding of how the brain’s appearance and physiology change with normal aging.

Dr. Kanekar has done a stellar job of gathering a large multidisciplinary group of experts from 21 institutions around the globe to contribute to this book. Dr. Kanekar is Associate Professor of Radiology and Neurology at Penn State College of Medicine and a prolific writer and editor. The enormous value of this book should be appreciated by clin- icians, students, and neuroscientists alike.

Carolyn Cidis Meltzer, MD, FACR

William P. Timmie Professor and Chair of Radiology and Imaging Sciences Emory University School of Medicine Atlanta, Georgia

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Foreword 2

I have to admit that when Sangam G. Kanekar asked me to write this Foreword for his book I felt some unease about doing it. Wouldn’t it be strange to have a person who co- authored a chapter in this book praise it here? But after reading the materials that form the rest of the book, all uneasiness disappeared and I am glad to be writing this short message of introduction. Imaging neurodegenerative disorders seems to be one of the most difficult clinical neuroradiological tasks yet one that has enormous impor- tance for patients and their families. We are beginning to move beyond the limited information offered by anatomical/ structural imaging and embracing newer techniques that shed light into the physiology of these disorders, provide guidance in achieving a correct diagnosis and may even help monitor the effects of therapies. Here 41 chapters authored by 82 experts worldwide explore, explain, try to make sense of, and teach us about these devastating conditions. This book is thus, a veritable “what’s what” by “who’s who.”

A few years ago, my mother who had had multiple mye- loma for many years (basically and fortunately asymptom-

atic) developed a rapidly progressive dementia characterized by bizarre behavior. For her physicians, family and friends the situation became a true “casse-tete.” No explanation for it was ever found. I re-tell this painful episode because many if not most of us will be confronted with a loved one facing and battling a neurodegenerative disorder. Their diagnosis is slippery, their treatment is generally non-existing, and the pain and cost they result in, are enormous.

Dr. Kanekar and all of the authors in this book are to be congratulated for producing an excellent and readable oeu- vre that I hope and expect will help neuroradiologists, neu- rologists, and many others who deal with these terrible diseases to understand them better.

Mauricio Castillo, MD, FACR

Professor of Radiology Chief, Division of Neuroradiology University of North Carolina Chapel Hill, North Carolina

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Preface

The World Health Organization estimated in 2010 that there were 36.5 million people worldwide living with dementia, with global cost for care of 604 billion dollars per year. A new case of dementia is diagnosed approximately every 4 sec- onds. With the continuing increase of the elderly population and a corresponding increase in neurodegenerative disor- ders, it is important for all physicians to be familiar with the various types of dementia. The diagnosis of dementia historically involved clinical suspicion alone, with, when available, confirmation via postmortem neuropathological analysis. The advancement in neuroimaging has afforded significant insight into progressive neurodegenerative disorders and their mimics. Distinguishing between prevent- able, potentially reversible, and irreversible (progressive) etiologies has serious implications for future planning in regard to the patient’s medical, social, and economic spheres.

In recent years, numerous new developments have occurred in neuroimaging. Besides improvement in structural imaging with thinner sections, 3D volume, and higher-resolution imaging, molecular and cellular imaging

have made a big impact on how we look at the brain and its function. MR spectroscopy, DTI, perfusion imaging, fMRI, and PET scans have further increased our understanding of the pathological processes of the brain, neurodegenerative dis- eases in particular. However, in spite of the wealth of new concepts that have evolved from these resources, there have been no dedicated textbooks on the imaging of neurodegen- erative diseases until now. Imaging of Neurodegenerative Disorders covers the application of these fascinating techni- ques, along with basic structural imaging in the diagnosis of various neurodegerative disorders. This book has many con- tributors who have brought fresh insights and expertise that encompass more disease entities. We attempt, at least in part, to fill the gap of knowledge that exists in the imaging and understanding of neurodegenerative diseases.

The author expects you will find this book enjoyable and educational, and hopes it guides you toward a better under- standing of neurodegenerative diseases.

Sangam G. Kanekar, MD

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Acknowledgments

Working on this book with so many outstanding contributors has been an enjoyable and immensely informative experience. I thank them, the staff at Thieme Publishers Inc., and my family for their overwhelming support.

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xvii

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Contributors

Amit K. Agarwal, MD

Assistant Professor of Radiology
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Olaguoke Akinwande, MD

Fellow
Department of Radiology Johns Hopkins University Baltimore, Maryland

Abass Alavi, MD, PhD(Hon), DSc(Hon)

Professor of Radiology and Neurology
Director of Research Education
Department of Radiology
University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania

Girish Bathla, FRCR, DMRD, MMeD

Department of Radiology
University of Iowa Hospitals and Clinics Iowa City, Iowa

A. M. Barrett, MD

Director, Stroke Rehabilitation Research Kessler Foundation
Chief, Neurorehabilitation Program Innovation Kessler Institute of Rehabilitation
Professor, Physical Medicine and Rehabilitation Rutgers-New Jersey Medical School
West Orange, New Jersey

Dhiraj Baruah, MD, PDCC

Assistant Professor of Radiology Medical College of Wisconsin Milwaukee, Wisconsin

Vahid Behravan, MD

Private practice Kensington, Maryland

Brian S. Bentley, DO

Chief Resident
Department of Radiology
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

David J. Brooks, MD, DSc, FRCP FMedSci

Hartnett Professor of Neurology Department of Medicine Imperial College London London, United Kingdom

Aristides A. Capizzano, MD

Assistant Professor of Radiology University of Iowa Hospitals and Clinics Iowa City, Iowa

Mauricio Castillo, MD, FACR

Professor of Radiology
Chief, Division of Neuroradiology University of North Carolina Chapel Hill, North Carolina

Dennis Chan, MD, PhD, FRCP

University Lecturer and Honorary Consultant in Clinical Neurosciences

University of Cambridge Cambridge, United Kingdom

Tushar Chandra, MD

Pediatric Neuroradiologist Department of medical Imaging Nemours Children’s Hospital Orlando, Florida

Sanjeev Chawla, PhD

Senior Research Investigator
Department of Radiology
University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania

Falgun H. Chokshi, MD, MS, DABR

Department of Radiology and Imaging Sciences Emory University School of Medicine
Atlanta, Georgia

Jeffrey Kyle Cooper, BA

Harvard Medical School Boston, Massachusetts

Sol De Jesus, MD

Adjunct Clinical Post-Doctoral Associate
Center for Movement Disorders and Neurorestoration University of Florida
Gainesville, Florida

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xx

Contributors

Leonardo Cruz de Souza, MD, PhD

Neurologist, Faculty of Medicine Federal University of Minas Gerais Belo Horizonte, Brazil

Puneet S. Devgun, DO

Department of Radiology
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

John W. Ebersole, MD

Resident
Department of Radiology
Rush University Medical Center Chicago, Illinois

Aaron S. Field, MD, PhD

Professor of Radiology and Biomedical Engineering Chief of Neuroradiology
School of Medicine and Public Health
University of Wisconsin

Madison, Wisconsin

Wolfgang Gaggl, PhD

Department of Radiology
School of Medicine and Public Health University of Wisconsin
Madison, Wisconsin

Néstor Gálvez-Jiménez, MD, MSc, MS(HSA), FACP

Professor of Medicine (Neurology-Florida) Cleveland Clinic Lerner College of Medicine

Chairman, Department of Neurology Director, Neurosciences Center Chief, Movement Disorders Program Cleveland Clinic

Weston, Florida

Clinical Professor and Associate Chair of Neurology Herbert Wertheim College of Medicine
Florida International University
Miami, Florida

Dheeraj Gandhi, MD

Director, Division of Interventional Neuroradiology Professor of Radiology, Neurology, and Neurosurgery University of Maryland School of Medicine Baltimore, Maryland

Jennifer G. Goldman, MD, MS

Associate Professor
Department of Neurological Sciences Rush University Medical Center Chicago, Illinois

Rakesh K. Gupta, MD

Director and Head, Department of Radiology and Imaging Fortis Memorial Research Institute
Gurgaon, Haryana, India

Leslie Hartman, MD

Department of Radiology
School of Medicine and Public Health University of Wisconsin
Madison, Wisconsin
Staff Radiologist
Regional Diagnostic Radiology
St. Cloud, Minnesota

Krishan K. Jain, MD, PDCC(Neuroradiology)

Consultant
Department of Radiology and Imaging Fortis Memorial Research Institute Gurgaon, Haryana, India

Gaurav Jindal, MD

Assistant Professor of Radiology
Division of Interventional Neuroradiology University of Maryland Medical Center Baltimore, Maryland

Sangam G. Kanekar, MD

Associate Professor of Radiology and Neurology Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Inga Katharina Koerte, MD

Professor of Neurobiological Research Department of Child and Adolescent Psychiatry,

Psychosomatic, and Psychotherapy Ludwig-Maximilian-University Munich, Germany

and

Psychiatry Neuroimaging Laboratory Department of Psychiatry
Brigham and Women’s Hospital Harvard Medical School

Boston, Massachusetts

Sampson K. Kyere, MD, PhD

Resident
Department of Radiology
University of Maryland Medical Center Baltimore, Maryland

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Christian La, BA

Department of Radiology
School of Medicine and Public Health University of Wisconsin
Madison, Wisconsin

Christian Langkammer, PhD

Department of Neurology Medical University of Graz Graz, Austria

Maya Lichtenstein, MD

Clinical Fellow in Behavioral Neurology
Clinic for Alzheimer’s Disease and Related Disorders University of British Columbia
Vancouver, British Columbia, Canada

Alexander P. Lin, PhD

Director, Center for Clinical Spectroscopy Brigham and Women’s Hospital Assistant Professor of Radiology
Harvard Medical School

Boston, Massachusetts

Elisabeth B. Lucassen, MD

Assistant Professor of Neurology
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Kenneth M. Lury, MD

Assistant Professor of Radiology – Retired Division of Neuroradiology
University of North Carolina School of Medicine Chapel Hill, North Carolina

Vinod G. Maller, MD

Fellow in Interventional Radiology
University of Tennessee Health Science Center Memphis, Tennessee

Maria Martinez-Lage Alvarez, MD

Assistant Professor of Pathology and Laboratory Medicine University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania

Hazem M. Matta, DO

Department of Radiology
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Michael Mayinger

Department of Child and Adolescent Psychiatry, Psychosomatic, and Psychotherapy

Ludwig-Maximilian-University Munich, Germany

Donald G. McLaren, PhD

Clinical Imaging Scientist Biospective, Inc. Montreal, Canada

Mark D. Meadowcroft, PhD

Assistant Professor of Neurosurgery and Radiology Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Douglas V. Merkitch, BA

Research Assistant
Department of Neurological Sciences Rush University Medical Center Chicago, Illinois

Ludovico Minati, PhD

Researcher
Fondazione IRCCS Istituto Neurologico Carlo Besta Milan, Italy

Vijay K. Mittal, MD

Department of Radiology
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Mateen C. Moghbel, BS

Stanford University School of Medicine Stanford, California

Suyash Mohan, MD, PDCC

Assistant Professor of Radiology
Division of Neuroradiology
Perelman School of Medicine at University of Pennsylvania Philadelphia, Pennsylvania

Toshio Moritani, MD, PhD

Department of Radiology
University of Iowa Hospitals and Clinics Iowa City, Iowa

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Contributors

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Contributors

Marc Mühlmann, MD

Institute for Clincal Radiology Ludwig-Maximilian-University Munich, Germany

Andrew Newberg, MD

Department of Radiology and Emergency Medicine Thomas Jefferson University
Philadelphia, Pennsylvania

Yoshimitsu Ohgiya, MD

Associate Professor of Radiology Showa University School of Medicine Tokyo, Japan

Fathima Fijula Palot Manzil, MBBS, DMRT, ABNM certified Nuclear Medicine/Clinical Imaging
Hamad Medical Corporation
Doha, Qatar

Nicola Pavese, MD, PhD

Clinical Senior Lecturer and Consultant in Neurology Neurology Imaging Unit (NIU)
Imperial College London
Division of Brain Sciences

Hammersmith Campus London, United Kingdom

Jeffrey D. Poot, DO

Diagnostic Radiology Resident Department of Radiology
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Harish Poptani, PhD

Research Associate Professor
Department of Radiology and Radiation Oncology University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania

Vivek Prabhakaran, MD, PhD

Assistant Professor of Radiology and Neurology School of Medicine and Public Health University of Wisconsin
Madison, Wisconsin

Divisha Raheja, MD

Assistant Professor of Neurology
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Boris-Stephan Rauchmann

Institute for Clinical Radiology Ludwig-Maximilian-University Munich, Germany

Stefan Ropele, PhD

Associate Professor of Medical Physics Department of Neurology
Medical University of Graz
Graz, Austria

Jitender Saini, MD, MBBS

Associate Professor
Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore, India

Koji Sakai, PhD

Associate Professor
Advanced MR Imaging Research Laboratory Department of Radiology
Graduate School of Medical Science
Kyoto Prefectural University of Medicine Kyoto, Japan

Dejan Samardzic, MD

Department of Radiology
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Marie Sarazin, MD, PhD

Professor of Neurology
Unité de Neurologie de la Mémoire et du langage Centre Hospitalier Sainte Anne
Université Paris Descartes, Sorbonne Paris Cité Paris, France

Mijail Serruya, MD, PhD

Assistant Professor of Neurology Kimmel Medical College Thomas Jefferson University Philadelphia, Pennsylvania

Ritu Shah, MD

Radiology Associates of Florida Tampa, Florida

Martha E. Shenton, PhD

Professor, Departments of Psychiatry and Radiology Director, Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital
Harvard Medical School

VA Healthcare System Boston, Massachusetts

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Zachary Simmons, MD

Professor of Neurology and Humanities
Director, Neuromuscular Program and ALS Center Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Glenn T. Stebbins, PhD

Professor
Department of Neurological Sciences Rush University Medical Center Chicago, Illinois

Robert A. Stern, PhD

Professor of Neurology, Neurosurgery, and Anatomy and Neurobiology

Clinical Core Director, BU Alzheimer’s Disease Center Clinical Research Director, BU CTE Center
Boston University School of Medicine
Boston, Massachusetts

Thyagarajan Subramanian, MD

Professor of Neurology and Neural and Behavioral Sciences Director, Central PA APDA Informational Center

and Movement Disorders Program Penn State University College of Medicine Hershey, Pennsylvania

Rashmi Tondon, MD

Surgical Pathology Fellow
Department of Pathology and Laboratory Medicine University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania

Madhav Thambisetty, MD, PhD

Clinical Investigator and Chief
Unit of Clinical and Translational Neuroscience Laboratory of Behavioral Neuroscience National Institute on Aging
National Institutes of Health
Baltimore, Maryland

Kyaw Nyan Tun, DO

Neuroradiology Fellow
Department of Radiology
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Surjith Vattoth, MD, DNB, FRCR, DABR

Senior Consultant Neuroradiologist Hamad Medical Corporation
Doha, Qatar

Kala Venkiteswaran, PhD

Assistant Professor
Departments of Neurology and Neural and

Behavioral Sciences
Milton S. Hershey Medical Center
Penn State University College of Medicine Hershey, Pennsylvania

Sumei Wang, MD

Department of Radiology
University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania

Ruth A. Wood, BM BCh, MRCP(UK)

MRC Clinical Research Training Fellow Sainsbury Wellcome Centre for Neural Circuits

and Behaviour University College London London, United Kingdom

Guofan Xu, MD

Department of Radiology
University of Wisconsin Hospital and Clinics Madison, Wisconsin

Kei Yamada, MD, PhD

Professor and Chairman
Department of Radiology
Kyoto Prefectural University of Medicine Kyoto, Japan

Qing X. Yang, PhD

Professor of Radiology, Biogengineering, Engineering Sciences, and Neurosurgery

Center for NMR Research
Department of Radiology
Penn State University College of Medicine Hershey, Pennsylvania

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Contributors

xxiii

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Part I Introduction

1 Overview of Neurodegene Diseases

I

rative

2

2

Introduction

1 Overview of Neurodegenerative Diseases

Sangam G. Kanekar and Maya L. Lichtenstein

1.1 History

Neurodegenerative diseases comprise a broad swath of differ- ent neurologic diseases, all of which have one thing in com- mon: the pathology is ultimately the loss of neurons in the central nervous system. Onset can be acute but is more often chronic, and the symptoms tend to get progressively worse over time. The diseases are difficult to talk about broadly because they manifest with such a myriad of signs and symptoms. The most common neurodegenerative disease is the dementing dis- ease of Alzheimer’s. There are many other dementing diseases as well that affect different parts of the brain, causing different symptoms. Dementia is commonly understood as the loss of function of at least two cognitive domains that is severe enough to cause loss of daily function in social or occupational spheres.1 Some neurodegenerative diseases are primarily motor disor- ders, such as Parkinson’s disease and amyotrophic lateral scle- rosis (ALS). The underlying cause of the neuronal loss that ties these diseases together is different in each case. Some diseases are caused primarily by proteins, for example through abnor- mal accumulation or misfolding. These protein accumulations disrupt the normal function of the cells and ultimately lead to cell death. Examples of these “proteinopathies” include tau, amyloid, TDP-43, and α-synuclein. Some overlap is seen between diseases and pathologies, but overall pathology is usu- ally distinctive enough for a definitive diagnosis. Other neuro- degenerative diseases are caused by inflammation or infection, toxins, or vitamin deficiencies. Some diseases are primarily genetic, caused by deletions or trinucleotide repetitions. This is where we are now in understanding these diseases.

The latter part of the 20th century up to the present has pro- vided an enormous amount of understanding of these various diseases, but much work remains to be done toward a better understanding to be able ultimately to treat the people who have these diseases more effectively. One of the biggest advances in understanding these diseases has been the role of neuroimaging. Although there was an American Society of Neu- roradiology made up of 14 neuroradiologists in 1962, before the 1970s, this field was not widely recognized.2 Skull films had been done since the advent of the roentgenogram in the early 20th century, but they could really only be used to detect skull fractures or calcifications in the head. In the early 20th century, the pneumoencephalogram was developed by Walter Dandy, but it was uncomfortable and dangerous for the patient. In the mid-20th century, angiography was developed and used by radiologists and neurosurgeons to look at the blood vessels in the brain by using contrast material injected into the arteries. Angiography has become safer in the last few decades but ini- tially carried substantial risks. These methods were invasive and did not provide a good image of the brain itself. Angiogra- phy, for example, was used not only to look at blood vessels in the brain but also to detect masses by visualizing the vessels to determine whether any had shifted from their usual locations. In 1971, the first computed tomography (CT) scan was intro- duced by Godfrey Hounsfield in a South London hospital for a woman in whom a frontal brain tumor was suspected

(▶Fig. 1.1). Since that time, the technology continues to improve, with the CT scan being used as the underlying modal- ity for positron emission tomography (PET), single-photon emission computed tomography (SPECT), and noninvasive angi- ography. However, because of the same underlying technology, there are limitations to what can be seen in the brain using a CT scan. Magnetic resonance imaging (MRI) was developed in the 1980s and has emerged as the gold standard for looking at brain structures, having increased sensitivity for brain struc- tures compared to the CT scan. Structures as small as 1 mm can be detected on MRI, and quantitative measurements can be made reliably. In addition to structural imaging, MRI can be used for functional imaging. Innovations in the field of neuro- imaging have provided ways to see neurodegenerative diseases in vivo in a way that is not possible at autopsy. Imaging also has provided a new lens for understanding and monitoring the pro- gression of these diseases, not just clinically, as in the past.

We have come a long way since the ancient Egyptians, who believed that dementia was the end result of aging, and since the Middle Ages, when health fell into the realm of the church

Fig. 1.1 Reproduction of first clinical head computed tomography scan in South London, 1971; suspected frontal brain tumor, later confirmed at biopsy, in woman. (Brain scan from Atkinson Morley Hospital, as appears in Beckmann EC. CT scanning: the early days. Br J Radiol 2006;79(937):5-8.)

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

and senility was considered a “fading away from spirituality.”3 The term senility, which is defined by Merriam-Webster as “the physical and mental infirmity of old age,” continues to be used casually as a synonym for dementia, although dementia is a pathology and not necessarily part of normal aging. Until rela- tively recently, in fact, Alzheimer’s was termed presenile demen- tia to set it apart from the regular dementia that any old person was expected to develop along with aging. Until the late 19th century, it was primarily through careful clinical description that individual diseases were able to be split out from the “black box” of any era’s belief about what caused any pro- gressive debilitation. James Parkinson wrote his essay on the “shaking palsy” in 1817, and some of his descriptions were observations of people walking around his neighborhood. Earlier descriptions in the literature of “rest tremor” and “festi- nation” can be found,4 but it was through Parkinson’s more detailed accounts that later investigators, importantly Jean- Martin Charcot, launched their research. Charcot was much more descriptive, naming rigidity as being a cardinal feature of the disease. He made differentiation between the features of resting versus action tremor and other features, such as pos- ture, gait, lack of actual weakness, and rigidity, which we see with what we now call Parkinson’s disease.5 Once that arche- type of the disease was confidently described, Charcot’s team could find variants of that archetype, including descriptions of what today would be called Parkinson-plus syndromes. Without any ancillary tests, these kinds of clinical observations were how diseases were described and defined. There was some gross central nervous system pathology, but no stains for neu- rons until the late 19th century, when Camillo Golgi discovered a silver stain for neurons.6 This was used by him and Santiago Ramon y Cajal for the first time to see and describe neurons, axons, dendrites, and other parts of the central nervous system, thus opening the field of neuropathology.

To give an example of the field around the turn of the 20th century, in 1894, Otto Binswanger described a case of progres- sive dementia with stroke symptoms, which he called encepha- litis subcorticalis chronica progressiva.7 His study of that patient’s brain was thereby the first to state that white matter atrophy caused by vascular insufficiency can result in dementia.

The disease was described without use of histopathology, using only gross pathology. The disease was later called Binswanger’s disease by Alzheimer, a term that is sometimes still used to refer to someone with severe vascular dementia (▶Fig. 1.2). Alois Alzheimer described a patient he saw in 1901 with short-term memory loss and behavioral disorders and was able to examine her brain after her death in 1906. He was able to identify amy- loid plaques with Nissl stain, and possibly Mann stain, and pre- sented his findings at a conference. This was one of the first clinicopathologic neurologic cases, and Emil Kraepelin named the disease after Alzheimer.8 Around the same time, Arnold Pick also described the disease later named after him on both clini- cal and pathological grounds: a patient with speech and behav- ioral problems, progressing to dementia, who had argyrophilic spherical inclusions (Pick bodies) and globose neurons on pathology.9 During this period, most dementia was considered to be due to syphilis, although there had not yet been any path- ological proof of this (this proof came in 1913 with Hideyo Noguchi’s contribution), and later Binswanger’s disease. Alz- heimer’s and Pick’s disease were considered interesting outliers of dementing illnesses. In the last 30 years, as Alzheimer’s has become recognized as the most prevalent and most studied cause of dementia, and many patients with other dementing ill- nesses are often generically labeled as having Alzheimer’s dis- ease.10 The term became the late 20th century’s version of neu- rosyphilis as a catch-all diagnosis.

The study of neurodegenerative diseases in the early 20th century did not differ much from that of the hundred years preceding it. There was no good way to differentiate some diseases in vivo and no treatment or cure for them. Dementias were classified in the back of psychiatric manuals as organic brain diseases, and no special attention was given to them. Demented people, regardless of the underlying cause or dis- ease, were often treated much the same as psychotic patients and were placed in institutions. The latter 20th century showed witnessed the advent of more refined diagnostic tools in the form of neuroimaging and neuropsychology, as well as discov- eries in molecular and cellular pathways and genetics. For example, there was the discovery of dopamine pathways and the development of levodopa in the 1960s to treat patients with

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Overview of Neurodegenerative Diseases

Fig.1.2 Binswanger’sdisease.(a)Axialcomputed tomography scan shows diffuse hypodensity in the cerebral white matter (white arrows).
(b) Axial fluid-attenuated inversion recovery (FLAIR) image shows generalized prominence of convexity sulci. Diffuse hyperintensity is seen

in the cerebral white matter (white arrows), indicating small vessel disease.

3

Introduction

Fig. 1.3 Progressive supranuclear palsy. Sagittal T1-weighted image shows moderate atrophy of the midbrain, often called “bird’s-beak” appearance (white arrow).

Fig. 1.4 Huntington disease. Axial T2-weighted image shows atrophy of the caudate nuclei bilaterally (arrows) with dilatation of the frontal horns of the lateral ventricles.

4

Parkinson’s disease.11 Oliver Sacks wrote the book Awakenings about treating the institutionalized patients with levadopa and how they came to life with the medication. The drug also helped to differentiate idiopathic Parkinson’s disease from other, less typical presentations. Since the 19th century, there were descriptions from Charcot about atypical Parkinson-type patients, but only in 1964 was there a distinct clinical and path- ological entity of what is now called progressive supranuclear palsy, described by Steele-Richardson-Olszewski, differentiating this syndrome from Parkinson’s disease (▶Fig. 1.3).12 This description was not immediately accepted by all of the neuro- logic community, and the disease was considered by some to be more of a “subspecies” of Parkinson’s disease, not its own dis- ease species. The atypically presenting progressive supranu- clear palsy patients were grouped together with typical Parkin- son’s disease patients for the initial drug trials of levodopa. The drug had different effects on the two populations: it worked well for the symptoms of the idiopathic Parkinson’s diseases patients, and it worked poorly or not at all for the progressive supranuclear palsy patients.13 The logic of this finding ulti- mately allowed for wider acceptance of progressive supranu- clear palsy as a separate disease from Parkinson’s. This method of drug trial and error is still used in patients to differentiate types of underlying disease pathology that clinically may look similar.

Although Huntington’s disease had been described clinically and pathologically in 1872, and was known to be inherited in an autosomal dominant fashion, it was only in 1983 that the Huntington gene was mapped to human chromosome 4p; it was the first autosomal dominant disease to be mapped. It was

a full 10 years later that the pathogenic mutation was identified as a CAG-repeat expansion (▶Fig. 1.4).14 This mapping and identification allowed for a whole new way of studying and understanding the disease and held promise as a way for defini- tively diagnosing other diseases. Genetic testing for some dis- eases still provides some of the only other definitive diagnoses besides pathology. For example, genetics has allowed precise delineations of the myriad groups of disorders called the spino- cerebellar ataxias. Because they are caused by different genes, they can be classified as different entities, and can be studied and their courses and prognoses further elucidated with some confidence. This has helped enormously in allowing clinicians’ observations to have more definitive validation or a way to be “checked” against an objective test during the patient’s life.

Because of the advent of these diverse modalities (i.e., imag- ing, blood and fluid assays, genetics, pharmacology, immunol- ogy) and the overwhelming amount of research in these fields over the course of the 20th, and now the 21st, century, there have also been more consensus committees, standardizing test results, diagnostic criteria, and the definition of disease, so that researchers and clinicians can speak in the same language about these diseases. These tasks remains difficult because the neuro- degenerative diseases are often quite heterogeneous. Although adjuvant testing has helped to make up diagnostic criteria, many of these modalities are still in the research phase only, and the diagnoses for these diseases remain largely clinical- pathological. The role of neuroimaging in helping to differentiate and define diseases in vivo is evidenced by the fact that imaging correlates are used for diagnostic criteria of many neurodegenerative diseases.

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Overview of Neurodegenerative Diseases

The aim of this book is to cover each disease as it is cur- rently understood and to show what it might look like using various techniques of neuroimaging. It is of enormous impor- tance that we start to look for not just what is there in imag- ing, but what is missing, as well to be able to see and quantify patterns of atrophy, where and why there is signal change, increased or decreased uptake, which tests are most helpful, which tests are available in research or clinically, and what tests can help differentiate between diseases. Clinically, neuroimaging is crucial for ruling out tumor, stroke, bleeding, normal-pressure hydrocephalus, or other potentially treatable or reversible causes of dementia. In the past it was thought that primary causes of dementia and other neurodegenerative diseases were progressive and relentless, and besides being used as a modality for “ruling out” other diseases, neuro- imaging did not have a role in these diseases. Increasingly, however, the role of imaging has expanded and is more acknowledged. In the clinical realm, as well as for research and knowledge for its own sake, imaging helps enormously in affirming suspicion, differentiating entities that have clinical overlap, and charting progression of neurodegenerative dis- eases. It is hoped that someday these modalities can be used to chart the effects of treatments as well. This book can be used by radiologists, neuroradiologists, neurologists, other internal medicine doctors as well as by anyone seeking to understand these diseases through the lens of the image, which is increasingly becoming more sophisticated.

1.2 Epidemiology

Recognition and diagnosis of neurodegenerative diseases are crucial because for these diseases the greatest risk factor is age, and we have an increasingly older population. As people are surviving infections, heart attacks, cancer, accidents, and other hazards of being alive, they are living longer. Along with living longer, there is an increased risk of developing a neuro- degenerative disease. This is especially true for in many low- to middle-income nations, with a major expected rise in the prev- alence of dementing illnesses in their populations in the coming years as their populations grow and more people live longer (▶Fig. 1.5).15 The difficulty in assessing the prevalence of dementia is twofold, as identified in a 2013 meta-analysis: dementia is difficult to diagnose: it often requires multi-domain specialties; batteries of tests; and, to be definite, genetic testing or autopsy, any or all of which are not always performed. Another problem has been in the study designs themselves and in misapplication of study designs involving two or more phases, which leads to underestimation of prevalence and over- precision.15 Another issue is that not all neurodegenerative dis- eases are classified primarily as dementias, and so diseases like ALS, Parkinson’s disease, and secondary causes of neuro- degenerative disease, such as alcohol or vasculitis, are often not included in those studies because they are grouped differently.

Alzheimer’s disease is the most prevalent neurodegenerative disease and accounts for 60 to 80% of all dementias. It is esti- mated that in 2010 there were 36.5 million people worldwide living with dementia. A case of dementia is diagnosed every 4 seconds.16 The World Health Organization estimated that the annual global cost for dementia in 2010 was 604 billion

dollars.15 Medical complications from neurodegenerative dis- eases are common, and these patients are hospitalized more often and for longer periods than other people in their age groups. These diseases impose an enormous burden on the economy as well as on social and family structures. As the dis- eases progress, families and caregivers must often give up their jobs to take care of the patients, and the patients often need an increased level of care in nursing homes and other assisted- living facilities.15 These costs are expected to continue to grow along with the aging population; the number of people living with dementia is predicted to double every 20 years to 65.7 million in 2030.15 Although there are variable rates of disease based on epidemiologic studies, there is not a race, country, gender, or socioeconomic class that is not at risk for developing neurodegenerative diseases.

The most common neurodegenerative disease in any age group is Alzheimer’s disease, but the proportions are different when the age for the patient is younger than 65. In one British population study, younger patients with Alzheimer’s disease accounted for only 34% of young-onset dementia. In patients younger than 65, other causes (such as those from metabolic, toxic, or systemic illnesses) are more common; but even among young-onset dementias, it seems that Alzheimer’s, fol- lowed by vascular dementia, FTD, and dementia with Lewy bodies, is the most prevalent, as is true for patients older than 65, although Lewy body dementia is the second most common cause of dementia in patients older than 65. Although there is a paucity of epidemiology relating to young-onset dementias, a British study showed an overall prevalence of 54 per 100,000 in patients age 30 to 65, and a Japanese study showed a similar overall prevalence of 43 per 100,000 in people age 18 to 65. Still, the overwhelming burden of disease is carried by the older population, with an estimated prevalence of 1 to 2%atage65,10to15%byage80,andashighas40%in 90-year-olds.1

Fig. 1.5 The growth in numbers of people with dementia in high- income (HIC) and low- and middle-income countries (LMIC). (Used with permission from Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, and Ferri CP. The global prevalence of dementia. Alzheimer Dementia 2013;9:70.)

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

5

Introduction

1.3 Clinical Approach

Neurodegenerative diseases are defined by loss of neurons, which can occur anywhere in the central nervous system: corti- cal and subcortical areas, brainstem, cerebellum, and spinal cord. It is the “where” of the neuronal loss that gives us the clin- ical presentation: both what the patient and the family notice and what we see from history and examination. The “where” can also be seen on imaging, either by injury to the area or atro- phy of the area, with concurrent loss of function.

The approach to the patient with a neurodegenerative dis- ease involves first and foremost the suspicion that this may be the cause of the patient’s complaints. The chief complaints are quite variable, and the presentations are as well: although vary- ingly acute, subacute, or chronic, they tend to be progressive, but some relapse and remit, and some seem to plateau. Because we deal with loss of neurons in the central nervous system, we expect the complaints to pertain chiefly to one or more cogni- tive functions or a motor function; included in these overbroad categories are dysfunctions that we know as clinicians to be caused by damage to a particular part of the brain, but patients do not necessarily know that memory and concentration are

two different things anatomically, or that being unsteady is not always at all a sign of being weak (▶ Table 1.1). Often there are many complaints, and they accrue over the years. Often there are disturbances that were overlooked or ignored or not consid- ered relevant by the patient or the family when the symptoms first appeared; so a careful history must be obtained, as well as a full medical and family history, and a social history that includes exposures and prior level of function. While examining the patient, the clinician will tend to concentrate on the area of chief complaint, but a full general, neurologic, and psychiatric examination also must be obtained. It is helpful for the clinician to know that many diseases may manifest with a nonspecific memory complaint or cognitive slowing. It is important to look for other neurologic signs that may help to narrow the differen- tial diagnosis. For example, a dementia with ataxia may lead the clinician to think of spinocerebellar ataxia, paraneoplastic diseases, alcoholic dementia, multiple sclerosis, prion disease, and others (▶ Fig. 1.6).17 Such an approach will help guide fur- ther testing.

In general, if the patient is older than 65 and the clinical suspicion is a primary neurodegenerative disease, such as Alzheimer’s disease, the diagnosis is clinical; but some basic

Table 1.1 Cognitive and motor complaints, with examples and differential diagnosis

Complaints Examples of symptoms Examples of possible syndromes

   

Cognitive

 

Behavior/personality

Inappropriate behavior, decline in social behavior, emotional blunting, decline in grooming, mental rigidity, hallucinations

FTD, DLB, CBS, HD, PDD, CJD, vitamin deficiency, toxins, VaD, AD

  

Executive skills

Problems with cooking, multi-tasking, using computer, keep- ing up with bills and finances, judgment

FTD, later-stage AD, PDD, ALS

  

Visual-spatial

Trouble recognizing faces, getting lost, seeing things properly, judging distances

PCA, AD

   

Memory

Repeating questions, forgetting appointments, no recall of events or shows/movies

AD, PD, DLB, PDD, VaD, vitamin deficiencies

  

Attention

Does not attend to things said, walks into rooms but doesn’t remember why/what was wanted, easily distractible

TBI, PDD, DLB, CJD, NPH

  

Speech/language

Speech apraxia, phoneme &/or syntax errors, poor naming, impaired comprehension, hesitant speech, severe word- finding difficulties

PPA [PNFA, SD, LPA], CBS, PSP

 

Praxis

Have trouble doing things on command, have trouble completing multi-step tasks in the right order, have trouble using tools, using the hands or legs properly

CBS, AD, PD, HD, PCA

  

Motor

Examples of symptoms

Coordination trouble with any or all of following: speech, arms, legs, gait, trunk, eye movements

Examples of possible syndromes

 

Flinging limb movements, tremor, jerking of limb or body (myoclonus), abnormal posturing

Masked face, decreased arm swing, less spontaneous move- ment; slow moving or talking, smaller steps.

Trouble going up or down stairs, trouble getting up from chairs, falling backward, difficulty lifting arms and holding onto objects

Dysphagia for solids or liquids, tongue weakness, decreased gag or cough reflex, changed (hoarse or quiet) voice, inappropriate emotionality

Abbreviations: AD, Alzheimer’s dementia; ALS, amyotrophic lateral sclerosis; CBD, corticobasal degeneration; CBS cortical basal syndrome, CJD, Creutzfelt-Jakob disease; DLB, dementia with Lewy bodies; FTD, frontotemporal dementia; HD, Huntington’s disease; LPA, logopenic aphasia; MSA, multisystem atrophy; NPH, normal pressure hydrocephalus; PCA, posterior cortical atrophy; PD, Parkinson’s disease; PDD, Parkinson’s disease with dementia; PNFA, progressive nonfluent aphasia; PSP, progressive supranuclear palsy; SCA, spinocerebellar ataxia; SD, semantic dementia; VaD, vascular disease.

Unsteadiness/ataxia Abnormal movements
Less movement/hypokinesis Weakness/falls

Bulbar problems

Vitamin deficiencies, heavy metals, toxins, SCA, HD, NPH, PD, PSP, DLB, VaD, MSA

HD, CBS, PD, CJD, SCA, heavy metal, vitamin deficiency, toxins

PD, PDD, DLB, PSP, CBS, VaD, heavy metals

VaD, PSP, PD, NPH, ALS, SCA, HD, MSA, vitamin deficiencies, toxins

VaD, ALS, MSA

       

6

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Overview of Neurodegenerative Diseases

Fig. 1.6 Spinocerebellar atrophy. Axial T2-weighted image shows prominence of cerebellar folia suggestive of cerebellar atrophy. Pons is normal in morphology and signal intensity.

laboratory studies must be obtained, including complete blood cell count, complete metabolic panel, vitamin B12, and thyroid- stimulating hormone.18 Also required is an imaging study, either head CT or MRI of the brain. If the patient is younger or there is doubt about the diagnosis, further testing should be obtained (▶ Fig. 1.7). As may be indicated, appropriate labora- tory, electroencephalographic, electromyographic, sleep, and imaging studies should be obtained to confirm or rule out other possible causes of complaint.

Throughout life, however, the accumulation of clinical data tends to trump any test we do, and we often wait for families to allow pathological evidence or genetic testing for confirmation of our clinical suspicion. The hope is that we can use already developed tools more wisely, and further hope is that there continue to be more innovative approaches to diagnose more definitively and earlier in the course of disease. As research moves toward identifying diseases at earlier stages for treat- ment, we must develop new tools to identify correctly the patients and patient groups for study.

An enormously powerful tool has been and continues to be neuroimaging; it can act as a surrogate for pathology, both in the gross pathological sense in quantitative measures but also increasingly as a histopathological marker of disease in vivo. It has great advantages as well, even over pathology: images can be made showing function in different parts of the brain or highlighting loss of function; and images can be taken longitu- dinally, showing changes over time (▶Fig. 1.8). Studies can

show quantitative measures of atrophy in various parts of the brain even before a patient has clinical symptoms, such as in mild cognitive impairment. Potentially, imaging may be able to show objective efficacy of treatments.

1.4 Pathology

It would be a lot simpler if, given a thorough history and exami- nation, the astute clinician could always know what disease is causing the symptoms or, short of that, could obtain the one test that makes the diagnosis. Neurodegenerative diseases are devastating for patients and families, and often the not knowing adds to the difficulty. There is, of course, clinical overlap, and as the disease progresses, the signs and symptoms overlap even more, as more of the brain becomes engorged by disease and areas are damaged, connections are lost. A strange homogene- ity and heterogeneity can exist pathologically as well. The loss of neurons causing disease can be caused by many different processes, such as abnormal protein accumulation, vascular damage, inflammation, vitamin deficiencies, toxins, infections, or some combination thereof. These entities cause the pathol- ogy that make the disease. Pathologically, as diseases progress, there is an end pattern of neuronal and synaptic loss, as well as laminar spongiosis and astrocytosis,19 and the causative agent is not always evident. Grossly, there is often atrophy in regions typical for each disease. For example, atrophy is present in fron- tal and/or temporal regions in frontotemporal degeneration; this can be seen in imaging during life, as well as at autopsy (▶Fig. 1.9). Early in the disease, different brain regions are more affected than others, depending on the clinical subtype; for example, a substantial decrease in weight and volume in the dominant frontal lobe is expected in progressive nonfluent aphasia. As the neurodegenerative disease progresses, however, often an increase in atrophy occurs more globally, although usually the initial area remains the most affected.

Many of the neurodegenerative diseases are defined patho- logically by the type and distribution of protein accumulation. Diseases that clinically resemble one another may have a totally different underlying pathology. An example is the “Parkinson- plus” disease progressive supranuclear palsy, which clinically can resemble idiopathic Parkinson’s disease in its extrapyrami- dal rigidity, bradykinesia, and gait impairment. Although often progressive supranuclear palsy also involves dementia, bulbar palsy, and the characteristic supranuclear ophthalmoplegia, these are not always present, or at least present initially.14,15 It is certainly a disease that clinically can be confused with Par- kinson’s disease.20 At autopsy, however, the pathology of Par- kinson’s disease shows loss of dopaminergic cells in the mid- brain and Lewy body deposits made of α-synuclein. Although in progressive supranuclear palsy there is also loss of midbrain neurons and loss of substantia nigra pigment seen at autopsy, this disease also has characteristic tau pathology (▶Fig. 1.10). In contrast, some diseases have a quite different clinical presen- tation, such as in patients with ALS or FTD, who can have the same pathology in TDP-43; these patients may also share the same genetic mutation in C9ORF72.21 By no means, however, do all patients with FTD or ALS exhibit that pathology; for example, some FTDs show tau protein deposition, and other familial forms of ALS have aggregates of superoxide dismutase in cell bodies.22 The difference in protein pathology and

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7

Introduction

Fig. 1.7 Flowchart for the assessment and investigation of young-onset dementia. This algorithm provides an overview of the diagnostic approach to patients with young-onset dementia; it is only a general guide. In amnestic young-onset dementia, first-line genetic testing is for amyloid precursor protein (APP), presenilin-1 (PSEN1), presenilin-2 (PSEN2), and prion. In behavioral cases, first-line testing is for MAPT (particularly if symmetrical atrophy on magnetic resonance imaging [MRI]) and granulin (GRN, particularly if asymmetric pattern of atrophy). Aβ, amyloid β; CADASIL, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CJD, Creutzfeldt-Jakob disease; EEG, electroencephalograph; FDG, fluorodeoxyglucose; FTLD, frontotemporal lobar degeneration; SPECT, single-photon emission computed tomography. VGKC, voltage-gated potassium channel. (Used with permission from Rossor MN, Fox NC, Mummery CJ, Schott JM, Warren JD. The diagnosis of young-onset dementia. Lancet Neurol 2010; 9:802.)

Fig. 1.8 Alzheimer’s disease. Coronal T1- weighted imaging shows severe atrophy of the bilateral hippocampi (arrows). There is mild generalized atrophy of the frontotemporal lobes. Coronal positron emission tomography image shows decreased uptake in the medial temporal lobes, indicating hypometabolism typical for Alzheimer’s disease.

8

distribution allows for definitive diagnoses to be made in many of the primary neurodegenerative diseases and has helped to differentiate and redefine some of these diseases (▶ Fig. 1.11). Pathology remains the gold standard for definitive diagnosis in many primary neurodegenerative diseases. Not all neuro-

degenerative diseases are caused by abnormal protein accumu- lation and deposits, however, and not all diseases carry a read- able “protein signature” pathologically. Some are caused by infection, inflammation, or neuronal death by direct toxic injury or less directly from ischemia or hypoxia, and other

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Overview of Neurodegenerative Diseases

Fig. 1.9 Frontotemporal dementia. (a) Coronal computed tomography image shows severe atrophy of the frontal and temporal lobes bilaterally (arrows) with marked dilatation of the cerebrospinal fluid spaces. (b) Sagittal T1WI shows selective atrophy of the frontal lobe with normal parietal and occipital lobes. (c) Gross pathology of patient with pathologically proven frontotemporal dementia. Arrows indicate frontal lobe atrophy (a,b). (parts used by permission from Jennifer W. Baccon, MD, PhD, Penn State Hershey Medical Center.)

diseases have unknown causes or are caused by deleterious gain or loss of genetic function. There is a broad range of what can be seen pathologically with these diseases.

1.5 Genetics

Although it was known that there seemed to be a hereditary component to some neurodegenerative diseases, such as Hun- tington disease, which for the vast majority of cases seemed to be inherited in an autosomal dominant fashion, it was not until the 1980s and 1990s that the ability to map genes became pos- sible. Huntington disease, as mentioned earlier herein, was the first disease gene discovered. Since then, the discovery of genes that either cause, predispose to, or protect from disease has provided a whole new way of understanding and looking at dis- eases. It is important for the nongeneticist to understand a few fundamentals of the genetics behind disease. It is not always as simple as a defect in gene X causing disease Y, but this is the case in some diseases with a high degree of penetrance. In dis- eases like Huntington and the spinocerebellar ataxias, a gene

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9

Fig. 1.10 Immunohistochemical stain for tau. (Pathology slide used by permission from Jennifer W. Baccon, MD, PhD, Penn State Hershey Medical Center)

10

Introduction

Fig. 1.11 The overlap between clinical and pathological descriptions of neurodegenerative diseases: Some proteinopathies and clinical entities.

mutation causes disease (▶ Table 1.2). Other genes that behave like this are the APP gene on chromosome 21 or the presenilin 1 gene on chromosome 14, both of which cause rare forms of familial Alzheimer’s disease, less than 5% of all Alzheimer’s dis- ease. These genes are also inherited in an autosomal dominant fashion. Some genes that are known to cause disease may not cause disease in each carrier, or they may cause a modified dis- ease. This is the case of the progranulin mutation, which causes FTD, in which the chance of developing the disease increases as the carrier of the mutation ages, but it is not 100% penetrant. Then there are genes that have been discovered that seem to confer a risk for developing disease, such as apolipoprotein (Apo) E4 and Alzheimer’s disease. Each person has two copies of ApoE, and it comes in three forms: E2, E3, and E4. A person with a copy of ApoE4 is considered at increased risk for devel- oping Alzheimer’s disease. A person with two copies of ApoE4 is considered to have an even greater risk and is likely to develop the disease at an earlier age14; but this is only a risk fac- tor, just as traumatic brain injury, insulin resistance, cerebral vascular disease, and smoking may be risk factors: none of these risks guarantees the development of Alzheimer’s disease. The person who has two copies of ApoE4 and is suffering from dementia might not be suffering from Alzheimer’s disease, and the reverse is also true: a person suffering from Alzheimer’s dis-

ease does not necessarily have even one copy of the ApoE4 gene. For a small subset of patients with neurodegenerative dis- eases, however, genetic testing can provide a definitive diagno- sis, and genes likely play a much greater role than we now understand in who develops disease and why.

1.6 Summary

Although it can be quite useful to have constructs in mind, it is important not to be too rigid about how we classify neuro- degenerative disorders. The definitions of these diseases and how we understand them shift like tidal sands as we learn more about each disease individually and then step back and refit our new knowledge into the greater patterns. Of course, it also depends on through which lens we are looking at them. Clinicians, geneticists, pathologists, radiologists, molecular biol- ogists, and chemists all have different ways of sorting and understanding these diseases. It is only through the effort of each part of this multidisciplinary approach that we can hope to gain a better understanding of these diseases. In doing so, we will be able to better care for and communicate with the increasing number of patients who suffer from these illnesses. The aim of this book is to develop a better understanding of neurodegenerative diseases through neuroimaging. Chapters

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Overview of Neurodegenerative Diseases

Table 1.2 Overview of genetics of some established neurodegenerative diseases

Disease Gene Protein Chromosome Inheritance

Alzheimer’s

APP

A-β precursor

21

Dominant

APOE

Apolipoprotein E

19

Risk factor

PSEN1

Presenilin 1

14

Dominant

PSEN2

Presenilin 2

1

Dominant

Parkinson’s

SNCA

α-synuclein

4

Dominant

PRKN

Parkin

6

Recessive

DJ1

DJ-1

1

Recessive

PINK1

PTEN-induced putative kinase 1

1

Recessive

LRRK2

Leucine-rich repeat kinase 2; dardarin

12

Dominant

FTD

MAPT

Microtubule-associated protein tau

17

Dominant

PRG

Progranulin

17

Dominant

FTD with IBM and early Paget disease

VCP

Valosin-containing protein

9

Dominant

FTD and MND

C9ORF72

C9ORF72-encoded protein (unknown)

9

Dominant

ALS

SOD1

Superoxide dismutase 1

21

Dominant and Recessive

Huntington

ALS2

Alsin

2

Recessive

spinocerebellar

HTT

Huntingtin

4

Dominant

ataxias

ATXN I, II, and III,

Ataxin 1, 2, and 3,

6, 12, 14, respectively

Dominant

Wilson’s

ATP7B

P-type ATPase

13

Recessive

Prion

PRNP

Prion protein

20

Dominant and Risk factor

CADASIL

NOTCH3

Neurogenic locus notch homolog protein 3

19

Dominant

CARASIL

HTRA1

HTRA serine protease

10

Recessive

Abbreviations: ALS, amyotrophic lateral sclerosis; ATPase, adenosine triphosphatase; CADASIL, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CARASIL, cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy; FTD, frontotemporal dementia; IBM, inclusion body myositis; MND, motor neuron disease.

are arranged by disease, and with a brief discussion of each dis- ease as it is now understood are the images themselves. Along with the images are discussions of what tests to order, what to look for, what is expected to be seen in each disorder, and new clinical and research modalities.

References

. [1]  McKhann GM, Knopman DS, Chertkow H et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 263–269

. [2]  Leeds NE, Kieffer SA. Evolution of diagnostic neuroradiology from 1904 to 1999. Radiology 2000; 217: 309–318

. [3]  Albert ML, Mildworf B. The concept of dementia. J Neurolinguist 1989; 4: 301–308

. [4]  Pearce JMS. Aspects of the history of Parkinson’s disease. J Neurol Neurosurg Psychiatry 1989; 52 Suppl: 6–10

. [5]  Goetz CG. The history of Parkinson’s disease: early clinical descriptions and neurological therapies. Cold Spring Harb Perspect Med 2011; 1: a008862

. [6]  Henry JM. Neurons and Nobel Prizes: a centennial history of neuropathology.
Neurosurgery 1998; 42: 143–156

. [7]  Mast H, Tatemichi TK, Mohr JP. Chronic brain ischemia: the contributions of
Otto Binswanger and Alois Alzheimer to the mechanisms of vascular demen-
tia. J Neurol Sci 1995; 132: 4–10

. [8]  Graeber MB, Kösel S, Egensperger R et al. Rediscovery of the case described
by Alois Alzheimer in 1911: historical, histological and molecular genetic
analysis. Neurogenetics 1997; 1: 73–80

. [9]  Pan XD, Chen XC. Clinic, neuropathology and molecular genetics of fronto-
temporal dementia: a mini-review. Transl Neurodegener 2013; 2: 8

[10] [11] [12] [13] [14] [15]

[16] [17] [18] [19]

[20] [21] [22]

Snowden JS, Neary D, Mann DM. Frontotemporal dementia. Br J Psychiatry 2002; 180: 140–143
Hornykiewicz O. A brief history of levodopa. J Neurol 2010; 257 Suppl 2: S249–S252

Colosimo C, Bak TH, Bologna M, Berardelli A. Fifty years of progressive supra- nuclear palsy. J Neurol Neurosurg Psychiatry 2014; 85: 938–944
Daroff RB. Progressive supranuclear palsy: a brief personalized history. Yale J Biol Med 1987; 60: 119–122

Bertram L, Tanzi RE. The genetic epidemiology of neurodegenerative disease. J Clin Invest 2005; 115: 1449–1457
Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global preva- lence of dementia: a systematic review and metaanalysis. Alzheimers Dement 2013; 9: 63–75, e2

World Health Organization. Dementia: a public health priority. http://apps. who.int/iris/bitstream/10665/75263/1/9789241564458_eng.pdf. 2012
Rossor MN, Fox NC, Mummery CJ, Schott JM, Warren JD. The diagnosis of young-onset dementia. Lancet Neurol 2010; 9: 793–806

Galasko D. The diagnostic evaluation of a patient with dementia. Continuum (Minneap Minn) 2013; 19 2 Dementia: 397–410
Duyckaerts C. Neuropathologic classification of dementias: introduction. In: Duyckaerts C, Litvan I, eds. Handbook of Clinical Neurology. Vol 89 (3rd Series) Dementias. New York, NY: Elsevier; 2008; 147–159

Bower JH, Dickson DW, Taylor L, Maraganore DM, Rocca WA. Clinical corre- lates of the pathology underlying parkinsonism: a population perspective. Mov Disord 2002; 17: 910–916
Hsiung GY, DeJesus-Hernandez M, Feldman HH et al. Clinical and patho- logical features of familial frontotemporal dementia caused by C9ORF72 mutation on chromosome 9p. Brain 2012; 135: 709–722

Mackenzie IR, Bigio EH, Ince PG et al. Pathological TDP-43 distinguishes sporadic amyotrophic lateral sclerosis from amyotrophic lateral sclerosis with SOD1 mutations. Ann Neurol 2007; 61: 427–434

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Part II Imaging Techniques

. 2  Structural Imaging of Dementia 14

. 3  Magnetic Resonance Spectroscopy in
Neurodegenerative Disorders 24

. 4  SPECT and PET Imaging of
Neurotransmitters in Dementia 34

. 5  Diffusion Tensor Imaging in
Neurodegenerative Disorders 42

. 6  Functional Imaging of the Brain 51

. 7  Role of Noninvasive Angiogram and
Perfusion in the Evaluation of Neurodegenerative Disorders 60

II

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14

Imaging Techniques

2 Structural Imaging of Dementia

Sangam G. Kanekar and Vijay Mittal

Dementia is derived from Latin as “away from the mind,” and it encompasses a vast spectrum of diseases, which can be divided into reversible and irreversible causes. Diagnosis remains a chal- lenging task to the clinician because patients with different diseases can have similar signs and symptoms. Because thera- pies have become more specific with advances in research, accurate diagnosis is paramount. Fortunately, cross-sectional imaging has evolved and has proved an invaluable tool in diag- nostic work-up. When combined with clinical signs and symp- toms, structural imaging establishes the cause of dementia and allows focused treatments.

The two most compelling arguments against routine imaging of dementia include cost and case management. Rough esti- mates of diagnostic imaging tests in dementia may range from $350 to $700 million per year in the near future.1,2,3 Use of imaging may decline if it becomes a “rule-out” tool rather than a diagnostic tool. Additionally, many findings are equivocal and therefore would not significantly change patient treatment. For example, findings of cortical atrophy on head computed tomog- raphy (CT) are problematic because the degree of atrophy has overlap with absent disease state.4 Lastly, cellular and func- tional imaging with fluorodeoxyglucose F18, single-photon emission computed tomography (SPECT) perfusion studies with technetium-hexamethylpropylenamine oxime, and magnetic resonance imaging functional studies (fMRI)—including perfu- sion MRI, blood oxygenation level–dependent fMRI, and MRI spectroscopy—are extremely resource limited and expensive. The high expense and limited availability of these functional and cellular imaging tests have resulted in their being used less often, even though they are the most sensitive tools in early diagnosis of many diseases, such as the parkinsonian syndromes.5

Structural imaging is more than an exclusionary tool, how- ever, and provides valuable diagnostic information as well. Despite the argument about equivocal imaging, CT separates normal subjects from those with true dementia with more than 89% accuracy,6 or a specificity of more than 95%.7 The identification of pseudodementia, or dementia as a symptom of depression, can indicate an easily treatable condition. Also, MRI has emerged as a more specific and sensitive modality that can provide excellent diagnostic yield. It quantifies gray and white matter structures8; for example, it has been able to quantify hippocampal volume, which is highly accurate in diagnosing Alzheimer’s disease (AD),9,10 in addition to correlating with clinical progression.4

Although routine clinical scanning of patients may not pro- vide immediate benefit, our longitudinal knowledge of various pathological processes and early alterations in brain anatomy on imaging can enable us to identify abnormalities much earlier during the clinical setting, which may benefit future at-risk patients who could undergo directed therapy. Unfortunately, postmortem examination does not allow such treatment. An example is our evolution of knowledge to distinguish AD, nor- mal pressure hydrocephalus (NPH), and microvascular disease, which have different clinical outcomes and treatments.4 Our understanding of microvascular disease, so-called unidentified

bright objects, has expanded, and we now understand that these areas of demyelination correlate with clinical signs, such as delayed reaction time or falls. Additionally, chronic micro- vascular disease can be differentiated from more acute sub- cortical infarcts by MRI.11

2.1 Imaging Modalities

A decade ago, the primary role of imaging in a suspected case of dementia or neurodegenerative disease was limited mainly to the exclusion of treatable (reversible) causes of dementia, such as tumor, subdural hematoma, infection, and stroke. With advances in technology, identifying the minute brain details at the structural and functional level has changed the role of neuroimaging (▶ Fig. 2.1). Although imaging still plays a greater role in distinguishing reversible causes from irreversible causes of dementia, this role is relatively small because reversible causes constitute only around 1% of the causes of dementia. Today, neuroimaging helps in differentiating and classifying various irreversible causes of dementia. This is even more important because concordant advances have been made in pharmaceutical, behavioral, and cognitive therapies to treat and prevent various types of dementia. Most of the functional tech- niques are new and not widely available or understood. Addi- tionally, their role in the diagnosis of neurodegenerative dis- eases is still not well established for clinical practice. Structural imaging is more readily available and easy to interpret. The development of diagnostic clinical criteria has improved diag- nostic accuracy, but these criteria are still far from perfect. For example, the accuracy of the criteria for diagnosis of AD is lim- ited and depends on the expertise of the clinical center, with specificity ranging between 76 and 88% and sensitivity between 53 and 65%. With newer structural and functional imaging techniques, the diagnosis of many dementias can be suspected or established in the early stages and helps clinicians tailor treatment as well as understand the heritance and prog- nosis of the disease, which facilitates discussion with patients and relatives.

2.1.1 Computed Tomography Versus

Magnetic Resonance Imaging

Computed tomography is fast and relatively inexpensive com- pared with MRI, and its clinical utility revolves around exclu- sion of disease rather than diagnosis. Whereas CT relies on volume changes, MRI adds soft tissue information and thus allows radiologists to assess the disease characteristics accu- rately.12 Besides the basic T1- and T2-weighted images in axial, sagittal, and coronal planes, gradient-echo T2* imaging and volumetric MRI using three-dimensional (3D) T1-weighted sequences play an important role in the evaluation of neuro- degenerative diseases. Molecular and cellular imaging tech- niques, such as diffusion tensor imaging, iron-quantifying techniques, spectroscopy, and perfusion may be added to improve the sensitivity and specificity of the diagnosis.

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

T1- and T2-weighted imaging is used to assess the gross anat- omy of the brain and to exclude the presence of subdural hema- toma, mass effect, hydrocephalus, or other anomalies. In addi- tion, T2-weighted sequences are sensitive to changes in tissue properties, including tissue damage, resulting from changes in the transverse magnetization or T2 decay.12 This property of T2 is helpful in evaluating neurodegenerative diseases, which are mostly characterized by cell loss, astrogliosis, microglial prolif- eration, and increased deposition of iron or other paramagnetic substances. Nonheme iron in ferritin and hemosiderin is seen as signal loss on T2-weighted imaging; this loss results from shortening of T2. The sensitivity for signal changes resulting from iron deposition in the brain can be increased by using T2*- weighted gradient-echo sequences or susceptibility-weighted imaging.12 By using an inversion pulse, the contrast of T1- weighted images can be improved, as in a magnetization- prepared rapid acquisition with gradient-echo sequence of high-resolution 3D data sets. This sequence is helpful in volu- metric analysis of the brain.

Simply put, structural imaging using MRI plays a vital role in evaluation of neurodegenerative diseases, demonstrating supe- rior ability to distinguish various degenerative diseases from each other and from age-related changes. As a prognostic tool, it estimates the future likelihood of clinical progression based on the current extent and severity of disease. Finally, it also acts as an indicator of the disease progression over time, derived from serial measurements.

2.2 Voxel-Based Methods

One of the primary findings in most neurodegenerative dis- eases on pathology is selective atrophy of a specified anatomi- cal structure early in the disease. These changes have been well studied using histopathology. Efforts are continuously being made to use imaging to quantify early neuron loss in specific

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Structural Imaging of Dementia

Fig. 2.1 Coronal T2 magnetic resonance imaging through the hippocampus and entorhinal cortex. (a) Head. (b) Body. (c) Hippocampal tail. Images show: (1), hippocampus; (2), amygdala; (3), temporal horn; (red arrow), subiculum; (4), parahippocampal gyrus; (yellow arrows), entorhinal cortex; (5), fornix.

locations, which would provide the likely diagnosis. Different techniques exist, ranging from simple quantitative measures of diameter, area, and volume to the most advanced voxel-based morphometry (VBM) and voxel-based relaxometry (VBR), both of which require high-quality 3D sampling of the entire brain.13 The goal of VBM and VBR is to provide superior gray/white mat- ter differentiation, to define cortical and deep gray matter struc- tures, and to outline the cerebrospinal fluid (CSF)-filled spaces (▶ Fig. 2.2). The sequence and the slice partitions depend on the institution and capabilities of the particular scanner.

The sophistication of image-processing techniques with 3D volume acquisition has allowed accurate characterization of brain shape (deformation-based morphometry) and brain tissue composition (voxel-based morphometry) after macroscopic dif- ferences in shape have been discounted. Using these tech- niques, information about overall shape (deformation fields) and residual anatomical differences inherent in the data (nor- malized images) can be partitioned. VBM is based on coregis- tration of high-resolution 3D datasets, which are normalized to a study-specific template for detection of volume differences between two or more groups.13,14 Normalization is based on intracranial volume and has proven to reduce interindividual variations and account for gender differences. VBM involves a voxel-wise comparison of the local concentration of gray mat- ter, white matter, and CSF between two subjects. The procedure involves spatially normalizing the images, smoothing, correct- ing interindividual variations in gyral anatomy, and then voxel- wise analyzing the data. VBM has shown to be more sensitive than the normal two-dimensional measurements of structures. Although manual segmentation is time consuming and has lim- ited intraobserver and interobserver reliability, it remains the gold standard in quantitative AD imaging studies. In contrast, VBM has the advantage of being automatic, not requiring expert-dependent manual delineation of structural boundaries, and having no intraobserver and interobserver limitations.

15

16

Imaging Techniques

Fig. 2.2 Voxel-based morphometry in mild cognitive impairment (MCI) and Alzheimer’s disease (AD). In MCI, the gray matter loss is predominately seen in the medial basal and lateral temporal lobes. In AD, loss of gray matter is more extensive and involves the medial temporal lobe, basal temporal lobe, lateral temporal and parietal neocortex, posterior cingulate, temporal parietal association neocortex, and prefrontal cortex. L, left; R, right.

2.3 Structural Imaging in Aging

Differentiating normal age-related physiologic changes from early neurodegenerative disease is challenging clinically as well as on imaging. With increasing age, normal structural changes may overlap with the spectrum of neurodegenerative diseases on various imaging modalities. Common imaging and patholog- ical findings in aging include brain atrophy, white matter lesions, cerebral microbleeds, silent brain infarcts, and enlarged perivascular spaces.

Various cross-sectional imaging studies have shown smaller brain volumes with increasing age, especially in a person older than 55 years. Brain volume is expressed as a percentage of intracranial volume. A mean rate of brain volume loss of 0.4 to 0.5% per year has been described as normal.15 Hippocampal volume is shown to decline approximately 1.4 to 1.6% per year in normal aging compared with AD, which shows volume loss of 4.7% per year.16

T2-weighted hyperintense white matter changes are the most common finding in aging. These changes are due mainly to hypoxic/ischemic injury. T2*-weighted gradient-echo tech- nique allows easy identification of microbleeds. The prevalence of microbleeds has been estimated to be more than 20% in per- sons aged 60 years and older, increasing to nearly 40% in those older than 80 years.17 In the aging population, microbleeds are lobar in location. These lobar microbleeds correlate well with worse cognitive function. Lastly, dilated perivascular spaces, which can be seen at all ages, become more prominent with aging and are associated with the presence of silent brain

infarcts and hyperintense white matter changes. Dilated peri- vascular spaces are thought to be associated with cognitive deficits, independent of white matter changes and infarcts.

2.4 Role of Structural Imaging in Irreversible Dimentia

2.4.1 Mild Cognitive Impairment

Mild cognitive impairment (MCI) is defined as a mild but defi- nite decline from previous cognitive ability, confirmed by a reli- able observer and substantiated by deficits on neurocognitive testing. According to Petersen and colleagues,18 the criteria for amnestic MCI require (1) memory complaints, (2) difficulties with normal activities of daily living, (3) normal general (non- memory) cognitive function decline, (4) abnormal memory scores, and (5) no dementia. Early identification is crucial because 50 to 75% of elderly patients with MCI are at increased risk for developing of AD. Compared with normal controls, significant atrophy was identified in the hippocampus and entorhinal cortex of patients with MCI but not in the parahip- pocampal gyrus, fusiform gyri, and temporal gyri. Patients with MCI have less severe hippocampal atrophy (-12 to -14%) than those with AD (-22 to -23%), as well as less entorhinal cortex volume losses (-21%) than those with AD (-38%).19 The VBM method applied to MCI and normal groups confirmed atrophy of the hippocampus, medial temporal lobe, parahippocampal gyrus, and amygdala but also revealed differences in volumes.

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Structural Imaging of Dementia

2.4.2 Alzheimer’s Disease

The main pathological features in AD are neuronal loss with gliosis in the temporal cortex, neurofibrillary tangles (NFTs) formed by tau protein aggregates, granulovacuolar degenera- tion of neurons, senile plaques, and amyloid angiopathy formed mainly by β-amyloid deposits. NFTs and neuropil threads first appear in the transentorhinal and entorhinal areas (parahippo- campal gyrus), increasing in density during the course of the disease. These changes progress to involve the hippocampus, limbic system, temporal and parietal cortices, and finally the entire neocortex.20 Historically, AD has been a clinical diagnosis that uses neuropsychiatric tests. Over the past decade, however, neuroimaging has become a more direct diagnostic tool in which specific changes may suggest the diagnosis of AD.

The findings in AD can be largely classified according to the stage of the disease. In the earliest transentorhinal stage, volume changes are confined primarily to transentorhinal and entorhinal regions, with mild involvement of the hippo- campus. During the limbic stage, the imaging and pathologic changes involve larger parts of the hippocampal formation, sub- cortical structures (thalamus, amygdala), and basal forebrain (▶ Fig. 2.3). In the later stages, there is widespread cortical atro- phy. These changes can be characterized using nonvolumetric assessment or may be quantified using newer techniques of volumetric measurements.

Early atrophic changes in the medial temporal lobe, which include the height of the hippocampal formation and the sizes of the choroidal fissure and the temporal horn, can suggest the presence of AD. The diagnostic accuracy of the visual rating was reported at 95%, which was higher than the 85% accuracy of the hippocampal volumetry in differentiating AD patients from control subjects.20 Sensitivity and specificity in distinguishing patients with AD from healthy controls are in the range of 85 and 88%, respectively.

The entorhinal cortex is affected earlier than the hippocam- pus by NFTs and has a greater potential as an early marker, but it is a more challenging region to assess on imaging. Volumetric analysis techniques have shown the reduction in entorhinal cortex volume in AD to be approximately 35 to 40% compared with healthy controls.19 Some authorities believe that the diag-

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

nostic accuracy of the entorhinal cortex volume alone is close to 100% and superior to the hippocampus; however, this is debat- able. In AD, hippocampal neuronal atrophy strongly correlates with NFT pathology. The percentage reduction in total number of hippocampal neurons correlates with the percentage of neu- rons with NFTs. Based on histologic volumetry, a difference of 30% between healthy controls and age-matched AD subjects was found, which correlated well with MRI volumetric studies.

As the name suggests, various parts of the limbic system show NFT and atrophic changes in the limbic stage before spreading to the neocortex. There is 20 to 33% volume reduc- tion with prolonged T2 relaxation time in the amygdala.19 T2 changes are thought to be due to increased free water content in the tissue. Changes of atrophy may also be seen in the para- hippocampal gyrus (left > right) and in the temporal gyri. Longitudinal studies have shown rapid enlargement of the ven- tricular size and evolution of brain atrophy in individuals with dementia compared with controls (▶Fig. 2.4). Using an auto- mated body substance isolation method applied to serial MRIs acquired 1 year apart, Fox and colleagues21 described median amygdala volume loss of 12.3 mL per year in the AD group and 0.3mL in controls. On fluorodeoxyglucose-positron emission tomography (FDG-PET) scan, hypometabolism is seen in the medial temporal and parietal lobes (▶ Fig. 2.5).

2.4.3 Non-Alzheimer’s Dementia Frontotemporal Degeneration

Frontotemporal degeneration (FTD) is a common cause of dementia, especially in patients younger than 70 years. It typi- cally presents between ages 45 and 65 years. Clinically, FTD can be classified into three types: frontotemporal dementia, seman- tic dementia, and nonfluent aphasia.22 FTD is characterized pathologically by extensive loss of pyramidal neurons in the frontotemporal cortex, severe gliosis within the gray and white matter, spongiosis, and the presence of argyrophilic intraneuro- nal inclusion bodies (Pick bodies). Imaging does play a role in differentiating FTD from other neurodegenerative changes. There is preferential atrophy of the frontal and anterior tempo- ral lobes, which helps distinguish it from AD.22 The three

Fig. 2.3 Alzheimer’s disease. (a) Coronal com- puted tomography scan image shows severe atrophy of amygdala and marked bilateral atro- phy of the hippocampal formations with dilata- tion of the temporal horns. There is dilatation of the lateral ventricles. (b) Axial fluid-attenuated inversion recovery (FLAIR) image of the same patients shows severe atrophy of the amygdala and head and body of the hippocampi bilaterally (arrows).

17

Imaging Techniques

Fig. 2.4 Serial changes in the hippocampus in patient with Alzheimer’s disease. Serial coronal magnetic resonance images of a patient with Alzheimer’s disease in (a) 2004, (b) 2006, and (c) 2008 show progressive hippocampal atrophy (arrows) with dilatation of the temporal horn. (d) Axial fluid-attenuated inversion recovery (FLAIR) image from the 2008 study shows severe atrophy of amygdala (arrows) and marked bilat- eral atrophy of the hippocampal formations (arrowheads) with dilatation of the temporal horns.

Fig. 2.5 Magnetic resonance imaging and positron emission tomography (PET) correlation in Alzheimer’s disease. (a) Coronal T1-weighted image shows moderate atrophy of the hippocampi in a 71-year-old man with memory loss, classic for Alzheimer’s disease. (b) Coronal and (c) axial fluorodeoxyglucose (FDG)-PET images of the brain show bilateral low uptake in the temporal (arrows in coronal) and parietal lobes (arrows in axial).

18

different types of FTD may show different appearance on MRI: (1) frontotemporal dementia is characterized clinically by behavioral disturbances, antisocial behavior, and disinhibition owing to primary involvement of the frontal lobes. There is atrophy primarily affecting the frontal lobes and anterior por- tions of the temporal lobes in the late stages of the disease (▶ Fig. 2.6). (2) Semantic dementia typically manifests with pro- gressive anomia resulting from loss of long-term memory of language comprehension and object recognition. Unlike in AD patients, short-term memory is usually intact. Structural imag- ing shows atrophy of the frontal and temporal lobes, more pro- nounced in the temporal lobes, and often asymmetric, affecting the left temporal lobe more. (3) Nonfluent progressive aphasia is characterized by the preservation of verbal comprehension with severe disruption of conversational speech, speech dysflu- ency, and phonologic errors. MRI in these patients shows atro- phy in the perisylvian regions of the frontal and temporal lobes. Severe thinning of the cortical gyri, giving a “knife-blade” appearance, is seen, especially in the anterior portion the supe- rior temporal gyrus (▶ Fig. 2.7).

Lewy Body Dementia

Lewy body dementia is a neurodegenerative disease with the histopathological hallmark of the intraneuronal aggregation of

α-synuclein protein inclusions (Lewy bodies). Patients have fluc- tuations in cognition, visual hallucinations, depression, and nighttime agitation. Antidopaminergic and anticholinergic neuroleptics may cause irreversible extrapyramidal symptoms in Lewy body dementia, making diagnosis crucial. Volumetric studies have shown that gray matter structures may be more affected than white matter structures. Conventional CT and MRI findings, although nonspecific, include atrophy of the putamen and cortical atrophy, predominantly in the occipital lobe.23,24

Corticobasal Degeneration

Corticobasal degeneration (CBD) manifests in late adulthood. Patients with CBD have asymmetric limb apraxia, rigidity, or akinesia. Severe depression and cognitive decline, leading to dementia, may also be seen. Clinically, CBD is difficult to differ- entiate from FTD and progressive supranuclear palsy (PSP). No specific imaging appearances are identified, but progressive atrophy of the parietal lobes and caudate nuclei favor CBD (▶ Fig. 2.8). The cerebral hemispheres are often asymmetric and contralateral to the clinically affected side.25 Putaminal hypoin- tensity, as well as hyperintense signal changes in the motor cor- tex or subcortical white matter on T2-weighted images, may also be seen in CBD.24 The asymmetric cerebral atrophy seen in

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Structural Imaging of Dementia

Fig. 2.6 Frontotemporal dementia in patient with behavioral disturbances. Axial (a) computed tomography and (b) T2-weighted magnetic resonance images show mild frontotemporal atrophy with sparing of the occipitoparietal lobes. Sagittal (c) single-photon emission computed tomography images show hypometabolism in the frontal lobe (arrow) with normal uptake in the rest of the cerebral parenchyma.

Fig. 2.7 Frontotemporal dementia in a
61-year old patient with nonfluent aphasia. Axial (a) T2-weighted and (b) T1-weighted images show severe atrophy of the anterior temporal lobes bilaterally left more than right. There is severe thinning of the superior temporal gyrus, giving a “knife-blade” (black arrows in T2 and white arrows in FLAIR images) appearance.

CBD is believed to distinguish it from AD. However, none of these structural MRI abnormalities seems to be of diagnostic relevance for CBD.

Huntington Disease

Huntington disease is an autosomal dominant neuro- degenerative disorder that typically manifests with chorea and dementia. The classic signs of Huntington disease include cho- rea (diffuse, involuntary, rapid, irregular, jerky movements) and a gradual loss of thought processing and acquired intellectual

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abilities (dementia). The neurodegeneration associated with Huntington disease affects primarily the basal ganglia (espe- cially the caudate nucleus) and the cerebral cortex. The characteristic imaging finding in Huntington disease is marked atrophy of the caudate nuclei and corpus striatum (▶ Fig. 2.9).24 The larger bicaudate and bifrontal ratios in Huntington disease patients are due to caudate atrophy and ventricular enlargement, respectively. Diffuse cerebral volume loss also may be seen and can be more pronounced in the frontal lobes than elsewhere. Preferential gray-matter atrophy also is described in the opercular cortex, hypothalamus, and right paracentral

19

Imaging Techniques

Fig. 2.8 Corticobasal degeneration in 65-year-old man. (a) Axial and (b) coronal T1-weighted images show symmetric atrophy of the parietal lobes (arrows).

Fig. 2.9 Huntington disease. Axial T2-weighted images reveal atrophy of the head of the caudate nuclei (white arrows), with enlargement of the frontal horns of the lateral ventricles. The right putamen is slightly atrophic (arrowheads).

20

lobule. Patients who have the juvenile form of Huntington disease may also demonstrate hyperintense T2 signal in the caudate nuclei and putamina.25 Simmons et al26 showed that putamena- trophy (~50.1%) exceeded caudate changes (~27.7%), and volumetric measurement of the putamen was a more sensitive indicator of brain abnormalities in patients with mild Huntington disease than were measures of caudate atrophy. Data have also suggested that putamen volume measured with MRI is a prefera- ble marker of preclinical Huntington disease.

Studies have indicated that putamen atrophy occurs first and faster in Huntington disease than does caudate atrophy. Cau- date atrophy is more prominent in the late stage of the disease.

Parkinsonian Disorders

Brain MRI techniques have more easily demarcated the lines between the various parkinsonian disorders rather than the clinical symptoms, which overlap too much and have different prognoses and management.5 The parkinsonian diseases include idiopathic Parkinson’s disease, multiple-system atrophy (MSA), PSP, CBD, and manganese-induced parkinsonism.

Idiopathic Parkinson’s Disease

Idiopathic Parkinson’s disease (IPD) is a movement disorder that is clinically characterized by resting tremor, rigidity, brady- kinesia, and postural instability resulting from loss of dopamin- ergic neurons in the substantia nigra (SN) pars compacta.27,28 Pathological characteristics include the loss of pigmented dopa- minergic neurons of the pars compacta of the SN and loss of pigmented cells of the locus ceruleus and dorsal motor nucleus of the vagus. In addition, reactive astrocytosis and intraneuro- nal aggregations of Lewy bodies are seen in the pars compacta. About 40 to 70% of patients with PD have dementia, which is mainly subcortical, resulting from dopaminergic insufficiency. The dementia in these patients is characterized predominantly by attention deficits and impairment in executive functions, whereas memory impairment may be secondary. On histology, PD patients show higher concentrations of Lewy bodies in the transrhinal and entorhinal cortices, the hippocampi, and the amygdala than do PD patients without dementia.

Although conventional MRI is usually normal in early PD, it excludes the other possible causes of secondary parkinsonism (including vascular disorders, hydrocephalus, and neoplasms). On higher magnetic fields (3 tesla [T]) right-left asymmetry of the pars compacta may be a feature in early stages of the dis- ease, especially in patients who have hemi-parkinsonism symp- toms. Narrowing of the pars compacta of the SN may be seen in patients who have long-standing PD. The normal width of the pars compacta has been reported to be 4mm, whereas in PD,

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Structural Imaging of Dementia

the average width is 2.7mm.29,30 Advanced cases of PD may show distinct abnormalities of the SN, including signal increase on T2-weighted MRIs or smudging of the hypointensity in the SN toward the red nucleus. Reduction or absence of normal hypointensity on T2-weighted images is seen in the pars reticu- lata of the SN as a result of selective neuronal loss. Segmented inversion recovery ratio imaging may demonstrate a significant decrease SN:midbrain ratio. Mild, nonspecific cortical and sub- cortical volume loss is observed in some patients.

Multiple-System Atrophy

Multiple-system atrophy, characterized by autonomic dys- function, pyramidal tract dysfunction, and cerebellar ataxia,31,32 is due to neuronal loss and gliosis of the nigrostriatal tract in the MSA-parkinsonism type (MSA-P) and olivopontocerebellar tract in the MSA-cerebellar type (MSA-C).5 Pathology demon- strates glial cytoplasmic inclusion bodies. MSA is often confused with PD.

Structural MRI findings that point toward MSA-P include atrophy and signal alteration in the putamen. Putaminal hypo- intensities and putaminal rim hyperintensities (“slit-like” mar- gin) on T2-weighted imaging correspond to neuronal loss, iron deposition, and gliosis. Putaminal hyperintense rim helps to differentiate MSA from IPD, but it does not help in differentiat- ing MSA from PSP and CBD. On 3.0 T, a hyperintense putaminal rim on T2-weighted imaging is thought to be nonspecific and may be a normal finding in elderly patients. Putaminal hypoin- tensity is not unique to MSA but may rarely be seen in IPD because iron accumulation occurs in both.33,34 Specificity of these findings for differentiating MSA-P from PD and healthy controls is considered high, whereas sensitivity, especially in the early disease stages, seems insufficient. T2-weighted gradi- ent-echo putaminal hypointensity and fluid-attenuated inver- sion recovery (FLAIR) putaminal rim hyperintensity constitute the most accurate method for differentiating MSA from IPD.35

The MSA cerebellar type (MSA-C) can involve early childhood or old age. Its first sign is ataxia, first in the legs then in the arms and hands, and finally it shows bulbar manifestation.5 The primary degeneration involves pontine nuclei, with subsequent progressive antegrade degeneration of the pontocerebellar tracts and the cerebellar cortex hemispheric greater than ver- mian. Later in the disease, the inferior olive loses its normal

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bulge because of neuronal loss and gliosis. MRI shows atrophy of the pons with flattening of the inferior part (loss of normal pregnant belly of pons) (▶ Fig. 2.10a).36 Atrophy of the cerebellar cortex (hemispheric greater than vermian), MCP, and inferior olives is also seen. Degeneration of pontine neurons and trans- verse pontocerebellar fibers with normal signal intensity in the surrounding parenchyma give a classic “hot-cross bun” sign of the pons on axial T2-weighted images (▶ Fig. 2.10b).37 The aver- age MCP width was significantly smaller in patients (cutoff value of 8 mm) with MSA than in those with PD or in control subjects.38

Progressive Supranuclear Palsy

Progressive supranuclear palsy occurs in late adulthood and is characterized by vertical gaze palsy, slow vertical saccades, pos- tural instability, and frequent falls. Dementia is mild and is seen in the late stages of the disease. PSP is histologically character- ized by tau-positive NFTs and glial and neuronal loss, mainly in the basal ganglia and brainstem.5 It is important to differentiate PSP from other forms of movement disorders because PSP patients typically do not respond well to dopamine replace- ment therapy.

Structural MRI findings that point toward PSP include sym- metric progressive atrophy of the midbrain, superior cerebellar peduncles, thalami, and caudate nuclei.39,40 There is associated enlargement of the third ventricle and tegmental atrophy, with increase signal intensity in the midbrain. On sagittal images, the superior contour of the midbrain may have a flattened or concave profile, a finding believed highly specific for PSP. A reduced anteroposterior midbrain diameter of less than 14 mm has been proposed to optimally separate PSP from other types of neurodegenerative parkinsonism and healthy controls.36,41 Another indirect sign of midbrain atrophy in patients with PSP is the “penguin silhouette” or “hummingbird” sign, correspond- ing to the shape of the midbrain tegmentum (the bird’s head) and pons (the bird’s body) on midsagittal MRI (▶Fig. 2.11).42 Visual assessment of atrophy of the superior cerebellar pedun- cle (SCP) can distinguish PSP patients from controls and from patients with other parkinsonian disorders, including MSA and PD, with a sensitivity of 74% and a specificity of 94%. Ratios and indices of the pons and midbrain are also used to distinguish PD, MSA, and PSP from each other. Calculation of the ratio

Fig. 2.10 Multiple-system atrophy (MSA)-cerebellar type (MSA-C). Olivopontocere- bellar degeneration. (a) Sagittal T1-weighted and (b) T2-weighted images show atrophy of the pons (white wide arrow), the middle cerebellar peduncles, and the cerebellar hemispheres (white thin arrow). Axial T2-weighted image shows classic cruciform pattern of the pontine fibers called “hot-cross bun sign” (black arrow).

21

22

Imaging Techniques

Fig. 2.11 Serial changes in the midbrain in a patient with progressive supranuclear palsy (PSP). (a) Sagittal T1-weighted image in 2009 shows mild atrophy in the midbrain, early changes of PSP. (b) Magnetic resonance imaging done in 2010 shows concave profile (white arrow) of the superior surface of the midbrain. In 2011 (c) midsagittal image shows atrophy of midbrain, dilatation of the third ventricle, and widening of the interpeduncular fossa, giving a ‘‘hummingbird’’ appearance (arrow).

between pontine and midbrain areas has been demonstrated to discriminate between PSP patients and patients with PD, MSA-P, or healthy controls. Quattrone and colleagues43 have proposed an index termed the MRI parkinsonism index (MRPI), which is calculated by multiplying the pontine:midbrain area ratio by the ratio of the MCP:SCP width (MCP/superior cerebel- lar puduncle). The MRPI is shown to be significantly larger in patients with PSP than in healthy controls or in PD and MSA-P patients. Atrophic changes are also seen in the inferior olives and frontal and temporal lobes. Atrophy of the frontal lobes is particularly seen in the orbitofrontal and medial cortex, which may help in distinguishing PSP from PD. The degree of atrophy seen in the frontal lobes correlates well with the level of behav- ioral disturbance seen clinically, as does the degree of atrophy in the caudate nuclei and brainstem with the severity of motor function impairment.

2.5 Reversible Dementia

Imaging plays an important role in diagnosing and differentiat- ing the various causes of reversible or preventable dementia. Clinical history, examinations, various laboratory tests, and imaging can easily pinpoint the diagnosis of reversible demen- tia. Medications, nutritional abnormalities, endocrine dys- function, infection and inflammatory conditions, vascular problems, and toxins are a few of the common causes of revers- ible dementia. Space-occupying lesions, such as subdural hema- toma, large intra-axial or extra-axial masses, and NPH, are well-known causes of reversible dementia and can be diag- nosed easily by imaging. Imaging findings of these pathologies are discussed in detail in subsequent chapters. Cognitive decline and dementia resulting from medications, nutritional abnor- malities, or endocrine dysfunction are predominantly sus- pected or diagnosed based on clinical history, examination, and laboratory analysis. Imaging plays a role of exclusion in the diagnosis of these conditions.

Infection and inflammatory conditions, such as human immunodeficiency (HIV) dementia, Creutzfeldt-Jakob disease

(CJD), progressive multifocal leukoencephalopathy (PML), Lyme disease, and multiple sclerosis, may be diagnosed with a combi- nation of imaging and blood and CSF examinations. HIV demen- tia (AIDS dementia complex) is caused by direct infection of the macrophages and microglia of the central nervous system by the HIV retrovirus. With the advent of highly active antiretrovi- ral therapy, there has been a significant decrease in the inci- dence of HIV dementia. Gray as well as white matter may be affected with HIV infection, leading to generalized cortical atro- phy and diffuse bilateral white-matter abnormalities. These abnormalities are seen most commonly in the peritrigonal and subinsular white matter, although they can progress to a more confluent and diffuse pattern of leukoencephalopathy. PML is seen mostly in the setting of HIV or in patients undergoing immunosuppressive therapy or who have hematologic malig- nancies. PML is caused by reactivation of the Jamestown Can- yon virus. The diagnosis of PML is confirmed by detection of JCV DNA by polymerase chain reaction in CSF. However, imag- ing, especially MRI, certainly leads to the diagnosis of PML from the pattern and distribution of the abnormality. The diagnostic hallmark of PML is the presence of multiple foci of demyelina- tion found initially sparsely distributed in the subcortical white matter but also in the cortex and deep gray structures. These lesions are frequently bilateral and multiple with involvement of the subcortical U fiber. Mass effect and hemorrhage are unusual. Demyelination is predominantly seen involving the parietal, occipital, and frontal lobes. Lesions lack enhancement and restricted diffusion. CJD is caused by a protease-resistant prion protein. Clinically, it presents with triad of myoclonus, progressive dementia, and periodic sharp-wave patterns on electroencephalography. The disease is characterized histo- pathologically by neuronal destruction, gemistocytic astrocyto- sis, spongiform changes, and prion deposition. On MRI, signal abnormalities are seen, most commonly in the gray-matter structures, including the cerebral cortex, basal ganglia, and thalami. Diffusion-weighted imaging may show restricted diffu- sion with bright signal on T2-weighted images in these areas. The variant form of CJD has a characteristic appearance on conventional MRI sequences (called the pulvinar sign), sym-

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metric high signal on T2-weighted images, and FLAIR sequences in the posterior thalami.

Intracranial space-occupying lesions causing dementia are discussed in detail in Chapters 30 and 39. Cross-sectional imag- ing by either CT or MRI is sensitive and diagnostic of these con- ditions. NPH shows the classic imaging appearance of ventricu- lar dilatation out of proportion to the convexity sulci, which becomes significant with the appropriate clinical setting: the triad of dementia, recent-onset gait apraxia, and urinary incontinence. Dilation of the temporal horns occurs without the hippocampal atrophy seen in AD. Additionally, the parahippo- campal fissure is spared in NPH compared with hydrocephalus of other causes. Radioisotope cisternography demonstrates decreased CSF flow with delayed transit of the radiotracer to the subarachnoid space over the cerebral convexities. NPH can be identified on routine imaging, and patients achieve substan- tial benefit from CSF shunting.

Vascular dementia is the term used to define the cognitive impairment resulting from cerebrovascular disease and ische- mic or hemorrhagic brain injury. Pathophysiology, causes, crite- ria, and imaging findings are discussed in detail in Chapters 21 and 22.

References

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Tartaglia MC. Frontotemporal lobar degeneration: new understanding brings new approaches. Neuroimaging Clin N Am 2012; 22: 83–97, viii
Keyserling H, Mukundan S, Jr. The role of conventional MR and CT in the work-up of dementia patients. Magn Reson Imaging Clin N Am 2006; 14: 169–182

Gallucci M, Limbucci N, Catalucci A, Caulo M. Neurodegenerative diseases. Radiol Clin North Am 2008; 46: 799–817, vii
Tokumaru AM, O’uchi T, Kuru Y, Maki T, Murayama S, Horichi Y. Corticobasal degeneration: MR with histopathologic comparison. AJNR Am J Neuroradiol 1996; 17: 1849–1852

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Geser F, Seppi K, Stampfer-Kountchev M et al. EMSA-SG. The European Multi- ple System Atrophy-Study Group (EMSA-SG). J Neural Transm 2005; 112: 1677–1686
Kraft E, Schwarz J, Trenkwalder C, Vogl T, Pfluger T, Oertel WH. The combina- tion of hypointense and hyperintense signal changes on T2-weighted mag- netic resonance imaging sequences: a specific marker of multiple system atrophy? Arch Neurol 1999; 56: 225–228

Lee EA, Cho HI, Kim SS, Lee WY. Comparison of magnetic resonance imaging in subtypes of multiple system atrophy. Parkinsonism Relat Disord 2004; 10: 363–368
von Lewinski F, Werner C, Jörn T, Mohr A, Sixel-Döring F, Trenkwalder C. T2*- weighted MRI in diagnosis of multiple system atrophy: a practical approach for clinicians. J Neurol 2007; 254: 1184–1188

Schrag A, Good CD, Miszkiel K et al. Differentiation of atypical parkinsonian syndromes with routine MRI. Neurology 2000; 54: 697–702
Abe K, Hikita T, Yokoe M, Mihara M, Sakoda S. The “cross” signs in patients with multiple system atrophy: a quantitative study. J Neuroimaging 2006; 16: 73–77

Nicoletti G, Fera F, Condino F et al. MR imaging of middle cerebellar peduncle width: differentiation of multiple system atrophy from Parkinson’s disease. Radiology 2006; 239: 825–830
Savoiardo M, Girotti F, Strada L, Ciceri E. Magnetic resonance imaging in pro- gressive supranuclear palsy and other parkinsonian disorders. J Neural Transm Suppl 1994; 42: 93–110

Aiba I, Hashizume Y, Yoshida M, Okuda S, Murakami N, Ujihira N. Relation- ship between brainstem MRI and pathological findings in progressive supra- nuclear palsy—study in autopsy cases. J Neurol Sci 1997; 152: 210–217
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Structural Imaging of Dementia

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24

Imaging Techniques

3 Magnetic Resonance Spectroscopy in Neurodegenerative Disorders

Tushar Chandra, Suyash Mohan, Sanjeev Chawla, and Harish Poptani

Magnetic resonance spectroscopy (MRS) has evolved as a useful technique to complement the anatomical information obtained from magnetic resonance imaging (MRI) with quantitative information on the chemical composition of brain in vivo. The fundamental theory of nuclear magnetic resonance (NMR) is the same for both MRI and MRS. MRI relies on obtaining ana- tomical information from hydrogen protons of water, whereas MRS provides information about the chemical environment of hydrogen protons of other brain metabolites. MRS has long been used in chemistry to characterize the synthesis and purity of chemical compounds; however, it has taken a long time for MRS to evolve to the extent that it can be relevant to diagnostic evaluation of patients and helpful in clinical decision-making. In current clinical practice, MRS is a useful noninvasive diagnos- tic tool to provide information about the metabolites in the brain and how they are affected in disease processes. Although the sensitivity of MRS to differentiate disease processes remains limited, it is a useful technique for complementing critical chemical information with the anatomical information obtained from MRI, and in many cases it can clinch the diagnosis.

The goal of this chapter is to familiarize readers with basic concepts of MRS and to analyze the role MRS plays in evalua- tion of the plethora of neurodegenerative disorders that affect the brain. This requires a thorough understanding of the basic principles that govern the technique, knowledge about the neu- ron-specific markers, and, most importantly, knowing how to implement the technique according to clinical requirements.

3.1 Basic Principles and Technique

Many fundamental physics concepts need to be understood before we can really look at how MRS gives us neurome- tabolic information. The first of these is the concept of nuclear magnetism.

3.1.1 Nuclear Magnetism

The basic concept of electromagnetism is that a charged particle has a magnetic field around it. This concept applies to biological tissues as well. Atoms possessing an even number of protons and neutrons are not magnetic and therefore cannot be used with this technique. The nuclei that can be used in MRS studies include hydrogen (H1), phosphorus (P31), C13, F19, and Na23; however, only H1 and P31 exist in biological tissues in high enough concentrations to obtain a spectrum. Proton spectros- copy that is based on H1, the most abundant nuclei in the body, has been most widely used to date.

3.1.2 Chemical Shift

The protons in various biological tissues are in a state of rota- tion (or precession) around an axis and get aligned to the direc- tion of the externally applied magnetic field. On application of a

radiofrequency pulse that matches the frequency of the exter- nal magnetic field, there is resonance. MRI uses this phenome- non to generate signals from protons in vivo.

The frequency of precession of an atom is given by the Lar- mor frequency, which is described by the following equation:

W 1⁄4 γBo ð3:1Þ

where γ = gyromagnetic ratio, Bo = magnetic field strength.1 For hydrogen (H1) nucleus at 1.5 tesla (T), the Larmor frequency is 63.5 MHz, whereas for phosphorus (P31), it is 25 MHz. Selec- tively applying radiofrequency pulses to match the Larmor frequency of a given nucleus allows for specific observation of different nuclei in MRS.

The magnetic field experienced by a nucleus depends not only on the external magnetic field but also on the small mag- netic fields that are generated by the electron clouds that sur- round the nucleus. These electron clouds shield the nucleus from the external magnetic field and result in a slightly differ- ent magnetic field actually experienced by the nucleus. As dif- ferent nuclei in biological tissues have different microenviron- ments (because of the electron cloud), the shielding effect is dif- ferent. Hence, based on the local chemical environment, the magnetic field experienced by the nuclei differs; the difference in local magnetic field is quite small and is called the chemical shift. This chemical shift can be expressed in terms of 1 Hz per million Hertz Hz, or simply parts per million (ppm). The chemi- cal shift specific for a given metabolite is independent of the external magnetic field strength and can help in identifying the compound on the MR spectrum. In the spectrum, the frequency characterized by chemical shift in parts per million is depicted on the x-axis, and the amplitude is depicted on the y-axis. The quantification of metabolite concentration can be made by the area under the peak.

3.1.3 Data Acquisition

Acquiring data is similar to MRI with a few additional steps. Shimming is the first step required to produce MRS data. Shim- ming refers to the process of creating a homogeneous magnetic field. The inhomogeneity can be minimized by tuning various field gradients in the x-, y-, and z-axis. This is usually done automatically but may also be done manually.

The second step is water and fat suppression. The concentra- tion of water protons is about ten thousand times the concen- tration of other metabolites in a biological tissue.2,3,4,5 The predominant spectrum, therefore, is of water; unless it is sup- pressed, the other metabolite cannot be observed to the extent of obtaining meaningful data. This can be done by adding water-suppressing pulses. Chemical shift selective water sup- pression is the most commonly applied technique for this pur- pose. Additionally, frequency-selective fat-suppression pulse is used to suppress the lipid and fat signal from the skull and marrow. Most of the lipid inside the brain is in the membrane- bound form and is not visible on in vivo MRS.

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Magnetic Resonance Spectroscopy in Neurodegenerative Disorders

3.2 Techniques

Typically, MRS is performed after obtaining anatomical infor- mation by MRI. A suitable volume of interest is selected for placement of a voxel to obtain a spectrum from the region of interest.

Each of the many different MRS techniques that can be per- formed has its merits and limitations. MRS can be done using a single-voxel or multivoxel technique using short or long echo time (TE). Knowledge of these techniques and choosing the appropriate technique in a given clinical scenario are vital for successful implementation of MRS.

3.2.1 Single-Voxel Spectroscopy Versus

Chemical Shift Imaging

As the name suggests, single-voxel spectroscopy provides data from a single voxel at a time. The region of interest is selected based on the clinical question being addressed. It is highly accu- rate (with minimal partial volume) and provides good field homogeneity. The other technique is multivoxel spectroscopy, also known as chemical shift imaging or magnetic resonance spectroscopic imaging. This technique allows evaluation of a larger area of interest that encompasses multiple voxels and can be used with either a two-dimensional or three-dimen- sional technique. The tradeoff is longer acquisition time and a slightly less accurate voxel localization as the data from each voxel “bleed” into the neighboring voxel.

3.2.2 Short Versus Long Echo Time

In general, clinical MRS data vary with the choice of the TE used in the pulse sequence. Short TE techniques generally use a TE of approximately 20 to 40ms, which permits detection of additional metabolites that have a relatively short T2 compared with sequences with long TE. Furthermore, because of the lower TE value used, the signal-to-noise ratio (SNR) in these techniques is higher compared with long TE techniques;

however, the spectra can be “crowded” by the larger number of metabolite peaks. Thus, several metabolite peaks can appear as overlapping signals on short TE techniques.

Magnetic resonance spectroscopy can be performed with intermediate and long TE as well, in the range of 135 to 288 ms, which results in lesser metabolite peaks and a “cleaner” spec- trum. However, the SNR is worse compared with that with short TE techniques. An advantage of long TE techniques is that the lactate peak is inverted below the baseline in the form of a doublet at 135 to 144 ms, and this can help in separating the lipid spectrum from lactate, which remains over the baseline.

3.3 Metabolite Peaks

The following are the major metabolite peaks observed in proton MRS (▶ Fig. 3.1):
● N-acetylaspartate(NAA):NAAisthelargestmetabolitepeak

and resonates at 2.02 ppm. It is the marker of neuronal and axonal integrity. NAA is decreased in any pathological condi- tion that results in neuronal loss; hence, a decrease in NAA is seen in almost all neurodegenerative disorders. An increase in NAA is observed in Canavan’s disease (an autosomal reces- sive leukodystrophy), however, because it is caused by a defi- ciency of the enzyme aspartoacylase, which leads to elevation of NAA in the brain and urine.

●  Creatine(Cr):Creatineandphosphocreatineresonateat 3.0 ppm. Cr is a marker for brain energy metabolism and is thought to be stable; it is used as an internal reference for other brain metabolites.

●  Choline(Cho):Choline-containingcompounds(freecholine, phosphocholine, and glycerophosphocholine) resonate at 3.2 ppm. Cho is a constituent of cell membrane and is a marker for membrane turnover. The Cho level is increased in conditions with rapid cell membrane turnover or an increased number of cells. Cho levels are high in tumors and demyelinating conditions.

●  Lipids:Lipids(orfreefattyacids)resonatefrom0.9to
1.5 ppm. Lipids are markers of severe cell stress and tissue

Fig. 3.1 Axial T1-weighted image (a) from a normal healthy subject demonstrating voxel position from right frontal lobe. Proton magnetic resonance spectra acquired with positron-resolved spectroscopy sequence using short echo time (TE; 30 ms); (b) long TE (135 ms); (c) displaying characteristic resonances: N-acetyl aspartate (NAA) (2.02 parts per million [ppm]), creatine (Cr, 3.02 ppm), choline (Cho, 3.22 ppm), glutamate (Glx, 2.35 ppm), and myo-inositol (mI, 3.56 ppm) from the voxel shown in (a). Note the spectra acquired with TE = 30 ms. (b) Broader resonances along with appreciable baseline distortion, mainly because of contamination of signals from shorter T2 components such as macromolecules. Also because of shorter T2 value of Cr than that of Cho, a higher Cho:Cr ratio is observed at longer TE spectra (c) compared with shorter TE spectra (b).

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Imaging Techniques

damage, such as the liberation of membrane lipids that is seen

in the necrotic brain tumors.

●  Lactate: Lactate is seen as an inverted doublet at 1.3 ppm on
MRS performed at a TE of 135 to 144 ms. At low TE values (20 to 40 ms) and higher TE values (270 ms), lactate is seen as a doublet peak above the baseline that overlaps with the lipid peak at short TE spectra. Lactate is not normally detectable in the brain spectra, and the presence of lactate signifies lack of oxidative phosphorylation and onset of anaerobic glycolysis. Increased lactate levels are seen in ischemia, hypoxia, brain tumors, and mitochondrial disorders.

●  Myo-inositol (mI): mI resonates at 3.56 ppm and is seen when using a short TE; it is an osmolyte and astrocytic marker. An increase in mI is seen in Alzheimer’s disease (AD) and fronto- temporal dementias (FTDs).

●  Glutamine and glutamate (Glx): These metabolites resonate from 2.2 to 2.4 ppm. Increased levels are noted in metabolic conditions resulting in hyperammonemia, such as hepatic encephalopathy.
3.4 Normal Aging
The normal process of aging induces many microstructural changes in the brain that involve both the cortex as well as the white matter. The volume of the brain decreases by approxi- mately 5% every decade after the age of 40.6 Structurally, in addition to the volume loss, there is increased iron deposition and increased white matter hyperintensities. In terms of chemi- cal composition, there is decreased brain water content and increased cerebrospinal fluid (CSF).7 Within the brain, however, not all structures are affected equally by aging. The earliest affected area is the prefrontal cortex, followed by the striatum, temporal lobe, cerebellar vermis, cerebellar hemispheres, and hippocampus. The occipital cortex is the least affected.8
Individual variations in neurometabolite levels correlate sig- nificantly with cognitive function in the elderly. Maintenance of creatine level is important in the pathophysiology of normal aging. Proton MRS studies have shown higher Cr levels in healthy aging brains compared with healthy young brains.9,10 Creatine is a sum of phosphocreatine and creatine. Phospho- creatine is converted to adenosine triphosphate by creatine kinase, an enzyme that decreases with aging. Therefore, it is logical that Cr concentration increases with age. Furthermore, increased Cr level may be a marker of decreased brain energy metabolism and may be related to age-related mild cognitive impairment or even frank dementia.11,12 Kadota et al have also demonstrated a steady and almost linear decrease in the white matter NAA:Cr ratio starting in the third decade and continuing into old age.13 No correlation has been found between NAA or Cho levels and the process of aging.
3.5 Alzheimer’s Disease
Alzheimer’s disease is the leading cause of dementia in the elderly. Typically, there is progressive dementia that most profoundly affects the declarative memory, especially early in the disease process. The disease is diagnosed based on clini- cal criteria that require exclusion of other causes of dementia and demonstration of progressive loss in more than one

domain. Clinical diagnosis of AD is currently made by the Diagnostic and Statistical Manual of Mental Disorders, 4th edi- tion text revision and the National Institute of Neurological and Communicative Disorders and Stroke Alzheimer’s Crite- ria. The definitive diagnosis of AD, however, can be made only at autopsy. Pathological changes develop first in the hip- pocampus and the entorhinal cortex and include a combina- tion of neuronal loss, amyloid deposition, glial proliferation, decreased synaptic density, and vascular changes with forma- tion of senile plaques and NFTs.14,15,16

The role of imaging in AD cases is to diagnose the condition before the onset of overt symptoms to provide a therapeutic window for drug treatment. Anatomical changes in the brain develop late in the disease process, and findings on MRI can be nonspecific. Although hippocampal atrophy is the hallmark of AD, it can be seen in various other forms of neurodegeneration.

The temporal evolution of neuropathological changes in AD is thought to follow a distinct pattern. The earliest changes of AD in the preclinical stage develop in the entorhinal cortex and hippocampus. Subsequently, there is involvement of the neo- cortex and development of overt dementia. Many studies have correlated the neuropathological findings to the development of dementia.17,18 MRS also mirrors these findings, with abnor- mal spectra from the posterior cingulate gyrus and hippocam- pus in early AD.

For a long time, MRS has been used in neurodegenerative dis- orders. Klunk et al were probably first to demonstrate decreased NAA levels on spectra from perchloric extracts in patients with AD.19 The primary neurometabolites affected in AD are NAA and mI. Because NAA is found in all neurons, a decrease in NAA is expected in any condition that involves neu- ronal loss. It is a marker of neuronal viability and functionality. Increased levels of mI reflect glial proliferation or increased glial size.20 Elevation of mI in AD is thought to represent glial activa- tion and microglial proliferation.21

The role of other neurometabolites in the diagnosis of AD is not certain. Creatine levels are used as an internal reference to calculate ratios. Regarding Cho, results have been conflicting among various studies, and no clear consensus of authorities has been established as to whether it is increased, decreased, or unchanged in AD.

The hallmark of spectroscopic alterations in AD is elevation of mI:Cr and a decrease in NAA:Cr ratios in various anatomical regions within the brain.19–23 It has also been found that mI:Cr is elevated in mild cognitive impairment and mild AD, even in the absence of a decrease in NAA:Cr.21,24 Therefore, the initial change in the progression of AD is elevation of mI:Cr, and a decrease in NAA:Cr develops later. Furthermore, the decrease in NAA:Cr correlates with dementia severity and cognitive symp- toms, indicating that decreased NAA is the marker to quantita- tively assess disease severity.25,26 MRS has not been widely used to assess treatment response, although a few single-site trials have shown improvement in NAA:Cr ratios after therapy. No multicentric data have reliably demonstrated improvement in NAA:Cr or mI:Cr ratios after drug therapy.

3.6 Dementia with Lewy Bodies

Dementia with Lewy bodies (DLB) is the second most common cause of dementia, after AD, and it frequently coexists with AD.

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Magnetic Resonance Spectroscopy in Neurodegenerative Disorders

The classic clinical picture is a triad of fluctuating cognitive impairment, recurrent visual hallucinations, and parkinsonism. The symptoms overlap with both AD and Parkinson’s disease. DLB is pathologically characterized by the finding of Lewy bod- ies in the cortex. Lewy bodies are seen in the substantia nigra in Parkinson’s disease. In patients with DLB, loss of cholinergic neurons is thought to account for degeneration of cognitive function, and the death of dopaminergic neurons appears to be responsible for degeneration of motor control.

The most important spectroscopic discriminating feature of DLB from other forms of dementias is the finding of a normal NAA:Cr ratio in the posterior cingulate gyrus. Patients with AD, FTD, or vascular dementia have decreased NAA:Cr ratios in this region.27 Molina et al demonstrated significantly lower mean NAA:Cr, Glx:Cr, and Cho:Cr ratios in the white matter in patients with DLB compared with controls.28 The spectra obtained from the gray matter were normal, suggesting involvement of white matter in DLB, a finding subsequently confirmed by diffusion tensor imaging.29,30 Kantarcki et al dem- onstrated increased Cho:Cr in the posterior cingulate gyrus in patients with DLB. The Cho level was also elevated in DLB as well as in AD.27 Xuan et al showed that patients with DLB had significantly lower NAA:Cr ratios in the bilateral hippocampi, whereas the Cho:Cr ratio did not differ from the control group.31 However, AD can coexist in many patients with DLB, and thus the hippocampal spectrum in these patients may reflect pathological changes as a result of AD rather than of DLB.

3.7 Frontotemporal Dementia

Frontotemporal dementia is a progressive neurodegenerative disorder characterized by tau- or ubiquitin-positive spherical cortical inclusions, gliosis, and microvacuolar degeneration predominantly involving the frontal and anterior temporal lobes.32,33,34,35 FTD accounts for nearly 20% of presenile demen- tia cases. The disease has three major variants: the behavioral variant, semantic dementia, and progressive nonfluent aphasia. On the basis of cognitive neuropsychological evidence, the ven- tromedial prefrontal cortex is a major locus of dysfunction early in the course of the behavioral variant of FTD.36

Proton MRS studies have demonstrated a decrease in NAA levels and an increase in Cho and mI from many sites, includ- ing the anterior and posterior cingulate cortex, medial frontal cortex, and temporal cortex. This pattern is similar to the findings observed in patients with AD, and there is consider- able overlap in the neurometabolite abnormalities observed in these conditions. Chawla et al demonstrated similar find- ings in spectra obtained from the dorsolateral prefrontral cortex as well as the motor cortex in these patients (▶ Fig. 3.2, ▶ Fig. 3.3).37 They suggested a possible association between FTD and motor neuron disease (MND) in view of the similar metabolic alterations in the motor cortex from this subset of patients. This is supported by the fact that some FTD patients with clinically normal motor examination dem- onstrate abnormal electromyography of the tongue and extremity muscles, as seen in MND.

Fig. 3.2 Proton magnetic resonance spectro- scopic imaging grids overlaid over axial T2- weighted images demonstrating the location of voxels from dorsolateral prefrontal cortex region from a frontotemporal dementia (FTD) patient (a) and from a healthy controls (b). Correspond- ing spectra (echo time [TE] = 30 ms) from the voxels demonstrating various metabolites. Note the reduced N-acetyl aspartate (NAA) and ele- vated resonances of choline (Cho) and myo- inositol (mI) in FTD patients compared with controls.

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Imaging Techniques

Fig. 3.3 Proton magnetic resonance spectro- scopic imaging grids overlaid over axial T2- weighted images demonstrating the location of voxels from motor cortex region from a fronto- temporal dementia (FTD) patient (a) and from healthy controls (b). Corresponding spectra (echo time [TE] = 30 ms) from the voxels demonstrating various metabolites. Please notice reduced N- acetyl aspartate (NAA) and elevated resonances of choline (Cho) and myo-inositol (mI) in FTD patients compared with controls.

3.8 Creutzfeldt-Jakob Disease

Creutzfeldt-Jakob disease (CJD) is an incurable and invariably fatal neurodegenerative disease caused by infection with agents called prions. Prions are misfolded proteins, and they cause the properly folded proteins in their host to become misfolded, leading to rapid neurodegeneration. Clinical presentation is rapidly progressive dementia and myoclonus. Apart from the clinical signs and symptoms, diagnosis can be made by demon- strating characteristic triphasic spikes on electroencephalogra- phy and 14–3-3 protein in CSF analysis. The disease has four subtypes: sporadic, variant, iatrogenic, and familial. It is impor- tant to differentiate the variant form because it is transmitted by cattle infected by bovine spongiform encephalopathy virus. Variant CJD has a pathognomic “pulvinar” sign on MRI, defined as high T2 signal in the pulvinar thalami, which is higher than that in the basal ganglia. The other subtypes of the disease demonstrate high T2 signal and restricted diffusion in the stria- tum, thalamus, and cortex.

The characteristic histopathological features are spongiform degeneration of the neurons, astrocytic gliosis, amyloid plaque formation, and neuronal loss. Spongiform degeneration is seen in the cortex, putamen, caudate nucleus, thalamus, and hippo- campus. Spongiform change or vacuolization restricts free dif- fusion of protons, leading to hyperintensity of lesions on diffu- sion-weighted imaging (DWI).38 The characteristic findings are restricted diffusion in the basal ganglia and the cortex. On DWI, changes are detected earlier during the disease course com- pared with T2 and fluid-attenuated inversion recovery (FLAIR)

sequences.39,40 The disease can be followed up with serial MRI using DWI.41,42

As with all other forms of neurodegeneration, MRS demon- strates decreased NAA from the involved regions in patients with this disease. Various authorities have noted that the decrease in NAA occurs relatively late during the course of dis- ease.43 In cases of sporadic CJD, involvement of basal ganglia has been noted to correlate with rapid progression.44 Kim et al demonstrated that basal ganglia involvement was strongly associated with lower NAA:Cr ratios and shorter disease dura- tion. Therefore, NAA:Cr ratios of the affected brain at the early stage of sporadic CJD can be a useful parameter in predicting the clinical course.45

3.9 Huntington Disease

Huntington disease (HD) is a genetic neurodegenerative dis- order that affects muscle coordination and leads to cognitive decline and psychiatric symptoms. It is the most common genetic cause of involuntary writhing movements called chorea. The disease is caused by expansion of a CAG triplet repeat stretch within the HD or IT15 gene located on the short arm of chromosome 4, which encodes a protein called huntingtin. This expansion results in synthesis of an abnormal protein that causes neuronal degeneration and brain atrophy.

Striatal atrophy is considered the hallmark of pathological findings in HD.46 MRI demonstrates atrophy in the caudate nucleus and putamen, much earlier than clinical manifestations of the disease.47

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Magnetic Resonance Spectroscopy in Neurodegenerative Disorders

Several investigators have shown MRS to demonstrate altera- tions in NAA and Cr levels in the striatum.48,49 A decrease in NAA corresponds to neuronal loss, and decreased Cr is consist- ent with impaired energy metabolism seen in this disease. San- chez et al demonstrated decreased Cr and NAA in the striatum in patients with HD.50 This was also confirmed by several other investigators, including a study by Bogaard et al, in which a high-field 7T magnet was used.51 Bogaard et al also demon- strated a relationship between the differences in NAA and Cr levels and clinical measures of disease severity. Therefore MRS potentially could be used to monitor the disease process.

Another postulated mechanism of development of HD is the theory of abnormal excitotoxicity of neurons, which states that abnormal activation of neurons leads to cell death.52 This event is caused by an increase in glutamate levels, which is thought to be an excitotoxic neurometabolite. Taylor et al demonstrated increased glutamate:Cr levels in HD,53 supporting this hypothe- sis; however, Bogaard et al51 found decreased glutamate levels in the striatum in patients with HD, a finding that can be explained by a decrease in the number of viable neurons to the extent that glutamate is lowered along with the neuron count.

3.10 Parkinson’s Disease

and Related Disorders

Parkinson’s disease is a progressive neurodegenerative disorder characterized by bradykinesia, rigidity, tremor, gait disorders, and cognitive dysfunction. Dementia can occur late in course of the disease. The disease is diagnosed by history and the clinical examination. The pathological hallmark of Parkinson’s disease is selective loss of dopaminergic neurons in the pars compacta of substantia nigra. As the disease progresses, there is involve- ment of the basal forebrain and the neocortex. Another impor- tant pathological feature is the presence of Lewy bodies.

Magnetic resonance spectroscopy is a powerful tool for quan- tification of brain metabolites that gives us insight into the pathophysiology of these disorders. Both proton and phospho- rus spectroscopy have been used by several authors for Parkin- son’s disease and related disorders. Mitochondrial dysfunction in the neostriatal dopaminergic neurons has been implicated in the disease pathogenesis, and MRS can target this condition.

Early studies showed no significant reduction in NAA in the striatum,54 putamen, and globus pallidus.55 Hattingen et al per- formed combined phosphorus and proton MRS in the neostria- tal region in 16 patients with early and 13 patients with advanced Parkinson’s disease and in 19 age-matched controls. They found bilateral reduction of high-energy phosphates such as adenosine triphosphate and phosphocreatine with normal levels of low-energy metabolites, such as adenosine diphosphate and inorganic phosphate.56 They concluded that mitochondrial dysfunction is an early and persistent event in the pathophysiology of dopaminergic degeneration in Parkin- son’s disease.

Recently, Zhou et al performed proton MRS in the substantia nigra in patients with Parkinson’s disease and found signifi- cantly lower NAA:Cr, NAA:Cho, NAA:(Cho + Cr) levels in Parkin- son’s disease patients compared with healthy controls. They also observed significantly lower NAA:Cr, NAA:Cho, NAA:

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(Cho + Cr) in patients with severe Parkinson’s disease compared with patients with mild Parkinson’s disease.57

Clinically, diagnosis of Parkinson’s disease can be quite chal- lenging, and the differential diagnosis includes multisystem atrophy (MSA), progressive supranuclear palsy (PSP), and corti- cobasal degeneration (CBD). In MSA, the middle cerebellar peduncle and pontine nuclei are severely involved, whereas in PSP, the dentate nuclei and superior cerebellar peduncles are afflicted. In CBD, there is severe involvement of the thalamus and pontocerebellar locations. The specific diagnosis of these diseases is difficult and calls for quantitative biomarkers. MRS studies focusing on differentiating these disorders are sparse and do not provide consistent results. Further multicenter trials and prospective studies are required to evaluate the role of MRS in discriminating these disorders.

3.11 Amyotrophic Lateral

Sclerosis

Amyotrophic lateral sclerosis (ALS), or Lou Gehrig disease, is a progressive neurodegenerative MND that involves the motor cortex, corticospinal tract, upper brainstem, and spinal cord anterior horn cells.58 The disease is uniformly fatal and involves both the upper motor neurons and lower motor neurons. The precise cause of this devastating neurodegenerative disorder is not yet known. The pathogenesis of this disease involves loss of neuronal integrity in the corticospinal tracts. Because NAA is a surrogate marker for neuronal integrity and viability, MRS can be helpful in providing critical information that might not be available on conventional MRI sequences (▶Fig. 3.4). Jones et al59 performed MRS on ALS and reported reduction of NAA and NAA:Cho ratios in motor cortex and adjacent cortex Many studies have shown decreased NAA:Cr ratios in areas of the brain that contribute significantly to corticospinal tracts in patients with ALS.60,61,62,63

In addition to decreased NAA, recent focus has been on the role of glutamate (glu) in the pathogenesis of ALS. The levels of glu have been found to be elevated in the plasma and CSF of patients with ALS.64,65 Glutamate is a neurometabolite that takes part in synaptic transmission. In patients with ALS, there is decreased reuptake of glu by postsynaptic receptors, which leads to increased activation of excitatory amino acid receptors, causing increased calcium ion uptake by the neurons. This is lethal for the cell and can cause activation of catabolic enzymes such as protein kinases and phospholipases that can lead to neuronal death.66

Glu and glutamine (gln) levels are thought to be relatively constant in the brain, and these metabolites appear as overlap- ping multiple peaks at 2.35 and 3.75 ppm. The combined peak from glu and gln is generally also referred to as Glx. Han at el demonstrated increased glu:Cr and Glx:Cr ratios in spectra obtained from the posterior limb of the internal capsule in patients with ALS.67

Therefore, for clinical evaluation of ALS, glu:Cr, Glx:Cr, and NAA:Cr ratios are ideal indexes. The ability of MRS to provide qualitative information that can be monitored for disease pro- gression over time makes it an ideal modality for evaluation of these patients.

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Imaging Techniques

Fig. 3.4 Axial T1-weighted images demonstrat- ing region of interests from precentral gyrus (pre-CG, red), postcentral gyrus (post-CG, green), and posterior limb of internal capsule (IC, yellow) from a representative amyotrophic laterial sclerosis (ALS) patient. Occipital region (OR, orange) may be considered as an internal control as this region has been reported to be relatively spared from atrophy and abnormal glucose metabolism in ALS patients. Proton magnetic resonance spectra from these regions displaying different metabolites. Compared with occipital region, reduced NAA and elevated choline (Cho) resonances are discernible from other locations.

Fig. 3.5 Axial T2 fluid-attenuated inversion recovery (FLAIR) image demonstrating hyperintense multiple sclerosis lesions in periventricular white matter regions. A repre- sentative voxel encompassing multiple sclerosis plaque is shown, along with corresponding spectrum (echo time [TE] = 30 ms) displaying various metabolites. Please note the diminished signal from N-acetyl aspartate (NAA) and elevated signals from choline (Cho) and myo-inositol (mI). Glx, glutamate.

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3.12 Multiple Sclerosis

Multiple sclerosis (MS) is the most common autoimmune neuro- degenerative/complex inflammatory disorder, especially in young adults. Most patients with MS follow a relapsing-remit- ting course characterized by relapses of variable severity fol- lowed by remissions of varying duration. An increasing body of evidence suggest sthat MS is characterized by demyelination, axonal loss, inflammation, gliosis, and edema.

Contrast-enhancing acute MS lesions typically show eleva- tions in Cho and lactate and lipid levels during the first 6 to 10 weeks after their appearance. The NAA concentration in the acute phase of lesion development is highly variable, ranging from almost no change to significant decreases. Creatine, which is generally higher in glial cells than in neurons, usually remains stable; however, significant increases68 or decreases69 have been observed in MS. These changes may be related to varying amounts of neuronal and oligodendroglial loss and astrocytic proliferation rather than altered energy metabolism. Increases have also been reported in mI levels, likely a result of microglial proliferation and in Glx levels secondary to active inflammatory infiltrates (▶ Fig. 3.5).

Acute MS plaques usually progress to chronic plaques that appear hypointense on T1-weighted images, also commonly referred to as “black holes.” These lesions harbor varying degrees of neuronal and axonal loss as inflammatory process decreases, edema resolves, and reparative mechanisms such as remyelination become active. These pathological changes can be seen as alterations in the metabolite pattern. There is a progressive return of lactate levels to normal levels within weeks, whereas Cho and lipid levels decrease for some months but do not always return to normal values. A moder- ate increase in Cr may also be observed secondary to gliosis and remyelination.70 NAA may further decrease, indicating progressive neuronal or axonal damage or show partial recovery over several months without reaching normality. Several mechanisms have been proposed to explain this behavior, such as resolution of edema and inflammation, an increase in the diameter of previously shrunken axons sec- ondary to remyelination, and reversible metabolic changes in neuronal mitochondria.71

It is now widely accepted that normal-appearing white matter (NAWM) and normal-appearing gray matter (NAGM) regions, which appear normal both macroscopically and

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Magnetic Resonance Spectroscopy in Neurodegenerative Disorders

3.13 Human Immunodeficiency

Virus Infection

Involvement of the central nervous system is a common feature of human immunodeficiency virus (HIV) infection, and in par- ticular subcortical gray matter regions carry a heavy HIV load. Neurons have not been appreciably infected by HIV owing to a lack of CD4 + cell surface receptors. However, an inflammatory response involving microglial cells and perivascular macro- phages leads to neuronal dysfunction and ultimately neuronal loss. In the initial phase, brain inflammation caused by the HIV is clinically asymptomatic and turns to mild-to-advanced HIV- associated neurocognitive impairments (HNCIs) and finally in about 20% of patients to dementia or encephalopathy in the course of HIV infection.80

Several 1H MRS studies81,82,83 have reported reduced NAA suggestive of axonal loss along with increased Cho secondary to infiltration by inflammatory cells and increased mI related to gliosis from patients with HNCIs. Furthermore, abnormal metabolite pattern has also been observed, even from neuro- logically asymptomatic HIV patients who do not show any abnormalities on conventional MRI, suggesting a higher sensi- tivity of 1 H MRS in the detection of early brain damage induced by HIV (▶ Fig. 3.6). In a cohort of HIV-positive patients treated with highly active antiretroviral therapy (HAART), Roc et al84 observed elevated levels of lipids and lactate from lenticular nuclei, suggesting that HIV-induced oxidative stress and inflammation occur even after initiation of HAART. Taken

on conventional MRI, are actually not normal. Several studies72,73,74 have also reported abnormal metabolite pat- tern from NAWM and NAGM regions in MS patients com- pared with normal subjects.

To investigate the course of metabolism from MS plaques at different stages of evolutionary development, several longitudi- nal studies have been performed.70,75 A reduction in NAA:Cr ratio was reported by most of these studies during the course of the disease. A few investigators found a subsequent recovery of NAA:Cr over time, leading to the suggestion that axonal loss is not the only mechanism of reduction in the NAA:Cr ratio. An increase in the Cho:Cr and its subsequent normalization has also been reported. A small number of studies have reported that Cr concentration does not remain stable over time.70 In a study performed by Narayana et al,76 NAA levels reached their minimum value when lesion volume reached its maximum. In another serial study, increased Cho and lipid levels were observed from NAWM regions that subsequently went on to develop MRI visible lesions.77

Using whole-brain MRS, Gonen et al observed lower global NAA in MS patients compared with controls.78 This difference was greater among older than among younger subjects. Another study observed a 3.5 times faster decrease in global NAA levels compared with atrophy in MS patients, implying that neuronal cell injury precedes atrophy and that degener- ating axons may leave behind their empty myelin sheaths. This study suggests that NAA is a more sensitive indicator of disease progression than either lesion load or atrophy in MS.79

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31

Fig. 3.6 T1-weighted images (first column) and corresponding spectra from a representative control subject (a), human immunodeficiency virus (HIV) + subsyndromic (b), HIV + sympto- matic (c) patients are shown. Proton magnetic resonance spectroscopic imaging grid is centered over subcortical gray matter region for each of the subjects. Spectra shown on the right were taken from voxels overlapping the lenticular nuclei (blue squares). The x-axis for all the spectra ranged from 0.2 to 4.3 parts per million (ppm). Spectra acquired at echo time (TE) = 135 ms (second column) and at TE = 30 m (third column) display resonances of N-acetyl aspartate (NAA, 2.02 ppm), creatine (Cr, 3.02 ppm), choline (Cho, 3.22 ppm), lactate (Lac), and lipid (Lip) at

1.33 ppm. Note that the peak of lactate is inverted below the baseline from spectra acquired at TE = 135 ms (second column).

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Imaging Techniques

together, these studies suggest that quantitative 1 H MRS may play a role in the objective assessment of the presence, magni- tude, and progression of brain involvement in HIV infection.

3.14 Summary and Future

Perspectives

Magnetic resonance spectroscopy offers a noninvasive means of assessing in vivo brain function and dysfunction, both in nor- mal aging as well as in a plethora of neurodegenerative disor- ders. Studies obtained at higher field strengths have resulted in sampling of smaller tissue volumes, greater SNR, and higher metabolic spatial resolution. Despite these significant technical advancements in the acquisition and analysis of proton MRS, translation of MRS in clinical practice is still not seamless, mainly because of the lack of normative data and an insufficient understanding of the pathologic basis of proton MRS metabolite changes.

We believe further advances in these areas would expand the impact of proton MRS as a biomarker for the early detection of neurodegenerative diseases and in monitoring the potential neuroprotective effects of newer experimental therapy in this era of personalized medicine.

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[31] Xuan X, Ding M, Gong X. Proton magnetic resonance spectroscopy detects a relative decrease of N-acetylaspartate in the hippocampus of patients with dementia with Lewy bodies. J Neuroimaging 2008; 18: 137–141

[32] Grossman M. Frontotemporal dementia: a review. J Int Neuropsychol Soc 2002; 8: 566–583

[33] Forman MS, Farmer J, Johnson JK et al. Frontotemporal dementia: clinico- pathological correlations. Ann Neurol 2006; 59: 952–962

[34] Jackson M, Lowe J. The new neuropathology of degenerative frontotemporal dementias. Acta Neuropathol 1996; 91: 127–134

[35] Mann DM. Dementia of frontal type and dementias with subcortical gliosis. Brain Pathol 1998; 8: 325–338

[36] Rahman S, Sahakian BJ, Hodges JR, Rogers RD, Robbins TW. Specific cognitive deficits in mild frontal variant frontotemporal dementia. Brain 1999; 122: 1469–1493

[37] Chawla S, Wang S, Moore P et al. Quantitative proton magnetic resonance spectroscopy detects abnormalities in dorsolateral prefrontal cortex and motor cortex of patients with frontotemporal lobar degeneration. J Neurol 2010; 257: 114–121

[38] Mittal S, Farmer P, Kalina P, Kingsley PB, Halperin J. Correlation of diffusion- weighted magnetic resonance imaging with neuropathology in Creutzfeldt- Jakob disease. Arch Neurol 2002; 59: 128–134

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[77] Tartaglia MC, Narayanan S, De Stefano N et al. Choline is increased in pre- lesional normal appearing white matter in multiple sclerosis. J Neurol 2002; 249: 1382–1390

[78] Gonen O, Catalaa I, Babb JS et al. Total brain N-acetylaspartate: a new mea- sure of disease load in MS. Neurology 2000; 54: 15–19

[79] Ge Y, Gonen O, Inglese M, Babb JS, Markowitz CE, Grossman RI. Neuronal cell injury precedes brain atrophy in multiple sclerosis. Neurology 2004; 62: 624–627

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[64] Rooney WD, Miller RG, Gelinas D, Schuff N, Maudsley AA, Weiner MW. Decreased N-acetylaspartate in motor cortex and corticospinal tract in ALS. Neurology 1998; 50: 1800–1805

[65] Rule RR, Suhy J, Schuff N, Gelinas DF, Miller RG, Weiner MW. Reduced NAA in motor and non-motor brain regions in amyotrophic lateral sclerosis: a cross-sectional and longitudinal study. Amyotroph Lateral Scler Other Motor Neuron Disord 2004; 5: 141–149

[66] Heath PR, Shaw PJ. Update on the glutamatergic neurotransmitter system and the role of excitotoxicity in amyotrophic lateral sclerosis. Muscle Nerve 2002; 26: 438–458

[67] Han J, Ma L. Study of the features of proton MR spectroscopy ((1)H-MRS) on amyotrophic lateral sclerosis. J Magn Reson Imaging 2010; 31: 305–308
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glutamate in multiple sclerosis using magnetic resonance spectroscopy at 3 T.

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[71] Arnold DL, De Stefano N, Narayanan S, Matthews PM. Proton MR spectros- copy in multiple sclerosis. Neuroimaging Clin N Am 2000; 10: 789–798, ix–x

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[74] Inglese M, Li BS, Rusinek H, Babb JS, Grossman RI, Gonen O. Diffusely elevated cerebral choline and creatine in relapsing-remitting multiple sclerosis. Magn Reson Med 2003; 50: 190–195

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4 SPECT and PET Imaging of Neurotransmitters in Dementia

Mateen Moghbel, Andrew Newberg, Mijail Serruya, and Abass Alavi

Positron emission tomography (PET) and single-photon emis- sion computed tomography (SPECT) have contributed sub- stantially to uncovering the basis of various neuropsychiatric disorders. Our understanding of the pathophysiology and treat- ment of these complex diseases has been informed by studies on cerebral metabolism, blood flow, and neurotransmitters that have been carried out using these functional imaging modal- ities. As novel radiotracers are developed and innovative appli- cations are devised, PET and SPECT will continue to provide tre- mendous insight into the causes, diagnosis, and treatment of neurologic and psychiatric diseases. Perhaps the most common disorders studied with PET and SPECT are those that result in dementia symptoms. Thus, PET and SPECT have been used extensively in the study of Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD), dementia with Lewy bodies (DLB), and other related disorders. Although much of the focus has been on the evaluation of cerebral blood flow and glucose metabolism, a wide array of studies have explored various neurotransmitter systems in these disorders. This chapter reviews the current literature regarding neuro- transmitter imaging with PET and SPECT in the evaluation of dementia.

Fluorine 18 (18F)-labeled glucose is the radioligand most commonly used in clinical brain PET. Glucose is radiolabeled with 18F by substituting the hydroxyl group with 18F to create the radioligand 2-deoxy-2-fluorodeoxyglucose ([18F]FDG). [18F]FDG is taken up by brain cells in the same way as unlabeled glucose, but after phosphorylation to [18F]FDG-6-phosphate, it cannot continue glycolysis and becomes trapped in the brain cell. The PET scanner detects the amount of labeled glucose taken up by the brain because the 18F isotope, as well as the other PET isotopes, undergoes radioactive decay to emit a posi- tron and neutrino, the process of positive beta decay. The emit- ted positron travels through tissues before colliding with an electron in its path, causing both particles to be annihilated. The nuclei of positron emitters are generally rich in protons and consequently attempt to maintain stability by gaining neutrons and losing excess protons. This can be accomplished in one of two isobaric decay processes: positron emission or electron capture. The mass number in both the parent and daughter nuclei remains the same in either process. A PET scan- ner then detects the photons that are released by the annihila- tion in coincidence, forming a PET image. The resulting image is a map of the distribution of the annihilations occurring within the organ of interest. The map illustrates the particular tissues in which the tracer has become concentrated. The result is a detailed evaluation of the pattern of cerebral metabolism (▶Fig. 4.1). A nuclear medicine physician can then analyze these results in the context of the patient’s diagnosis and treat- ment plan. For brain imaging, FDG is the most common tracer for clinical purposes, but there are many experimental tracers that have been used to evaluate different neurotransmitter sys- tems in patients with neurodegenerative disorders. These trac- ers bind to specific receptors in the brain, and the amount of radioactivity detected in specific structures is correlated with the receptor availability.

For SPECT imaging, the two most common tracers are hexam- ethylpropyleneamine (HMPAO) and ethyl cysteine dimer, both of which are used for evaluating cerebral blood flow. The basic SPECT imaging technique requires injection of a gamma- emitting radioisotope combined with a particular molecule that follows some type of neurophysiologic process, including binding to neurotransmitter receptors. Subsequently, because of the gamma emission of the isotope, the ligand concentration is visualized by a gamma camera. Tomography enables the localization of radioactivity and, hence, the location of the tracer concentration. Although SPECT imaging requires longer acquisition times, has poorer spatial resolution, and has greater susceptibility to artifacts, technical advancements in the instru- ments of SPECT have begun to markedly improve these limita- tions. Most clinical SPECT systems that are used to perform patient studies still utilize scintillation cameras with NaI(Tl) (thallium-activated sodium iodide) detectors. These systems consist of one or more scintillation camera heads attached to a gantry that revolves around the patient to collect projection views. The most common configuration has two scintillation cameras that are fixed at either 90 or 180 degrees or have the capability to be positioned at selected orientations. The projec- tion information required for SPECT is acquired by gamma-ray detectors, and much of the quality of the projection depends on the properties of these detectors.

Over the years, numerous tracers have been developed for both PET and SPECT imaging for neurologic applications. After their injection into a subject, many of these experimental trac- ers function by binding to receptors for neurotransmitters, such as serotonin and dopamine. These tracers for both SPECT and PET imaging of neurotransmitters in dementia are particu- larly useful for the study of neurodegenerative disorders. This chapter reviews some of the major applications and findings of

Fig. 4.1 Normal fluorodeoxyglucose (FDG) positron emission tomog- raphy scan from a healthy control without any neuropsychiatric disorders. The scan reveals relatively uniform metabolism in all cortical and subcortical structures.

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

SPECT and PET Imaging of Neurotransmitters in Dementia

PET imaging in the evaluation of neurodegenerative disorders that result in dementia.

4.1 Alzheimer’s Disease

The criteria for the diagnosis of AD were originally defined by the Working Group of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) in 1984.1 The criteria for the diagnosis of AD include progressive, chronic cognitive deficits in the middle-aged and elderly patients without any identifiable underlying cause. Although patients in the advanced stages of dementia can often be accu- rately diagnosed, it is a challenge to differentiate between AD and other forms of dementia in the earlier stages.2,3 With the aid of functional imaging modalities like PET, the diagnostic and etiologic questions that continue to surround AD may be answered in years to come.

Most PET studies of AD have focused on glucose metabolism and have found that whole-brain glucose metabolism (CMRGlc) is reduced in AD patients; the bilateral parietal and temporal lobes are especially affected.4,5,6,7,8,9,10 This parietal hypometab- olism (▶ Fig. 4.2) is often considered the “typical” presentation of AD and may be particularly pronounced in patients under the age of 65 years.11,12,13 Based on a large number of studies, this pattern of parietal hypometabolism carries a general sensi- tivity and specificity of approximately 85 and 60%, respectively. However, the pattern is not pathognomonic for AD and might also be observed in patients with PD, bilateral parietal subdural hematomas, bilateral parietal stroke, and bilateral parietal radi- ation therapy ports.14 It has also been reported that the magni- tude and extent of hypometabolism correlate with the severity of the dementia symptoms. Patients with moderate dementia have been found to have significant hypometabolism in the left midfrontal lobes, bilateral parietal lobes, and the superior tem- poral regions. In more advanced cases of AD, the same regions have an even greater reduction in metabolism.

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Another application of PET is the measurement of changes in various neurotransmitter systems that are associated with AD. It has been reported in the literature that the neocortex, hippo- campus, and amygdala of AD patients demonstrate significantly reduced acetylcholinesterase activity, which suggests that cho- linergic innervation to the basal forebrain has been lost in these patients.15 The regions that were most affected were the tem- poral and parietal cortices. A study by Kuhl et al showed that the onset of AD before 65 years of age correlated with reduced binding of iodobenzovesamicol (an in vivo marker of the vesicu- lar acetylcholine transporter) throughout the cerebral cortex and hippocampus. However, when onset of disease occurred after age 65, binding reductions were limited to the temporal cortex and hippocampus.16

A small PET study of nine AD patients, eight patients with mild cognitive impairment (MCI), and seven age-matched healthy controls showed a significant reduction in 2-[18F]FA- 85380 BP(ND), a marker of nicotinic acetylcholine receptor activity, in typical AD-affected brain regions.17 The 2-[18F]FA- 85380 BP(ND) correlated with the severity of cognitive impair- ment, and only MCI patients who subsequently converted to AD had a reduction in 2-[18F]FA-85380 BP(ND). Thus, the nicotinic receptors in dementia may not only reflect the degree of impairment, but may also predict the clinical course of disease.

A related SPECT study investigated in vivo changes in the α4β2-nicotinic acetylcholine receptor in 16 AD patients and 16 controls.18 Subjects also underwent perfusion imaging with 99mTc-hexamethylenepropyleneamineoxime SPECT. The results showed significant bilateral reductions in nicotinic receptor binding in the frontal lobe, striatum, right medial temporal lobe, and pons in patients with AD compared with controls. However, unlike the PET study already mentioned, no signifi- cant correlations were made with clinical or cognitive mea- sures. Although this was a small sample size, both 123I-5IA- 85380 and 99mTc-HMPAO SPECT imaging demonstrated similar diagnostic performance in correctly classifying controls and patients with AD.

A study of 27 patients with mild AD underwent PET scanning with 15O-water for regional cerebral blood flow and (S)(-)[11C] nicotine for the assessment of nicotine binding.19 Mean cortical [11C]nicotine binding significantly correlated with the results of attention tests such as the Digit Symbol test and Trail Making Test A, but [11C]nicotine binding was not significantly corre- lated with the results of tests of episodic memory or visuo- spatial ability. No correlations were observed between cerebral blood flow and cognition. Thus, the cortical nicotinic receptors appear to be related to the cognitive function of attention in patients with AD.

A number of other neurotransmitter systems have also been evaluated in patients with AD. The serotonin and dopamine sys- tems have been of particular interest. For example, an early study of nine AD patients using [18F]setoperone PET revealed markedly decreased 5-hydroxytrypamine (5HT)2 binding in the temporal, frontal, parietal, and occipital cortices in patients with AD relative to control values.20 Another study on sero- tonin-2A receptors in AD patients demonstrated a temporal pattern of reduced receptor density in early stages of the dis- ease, followed by a plateauing effect as the disease progresses.21 However, studies using [11C]DASB PET have shown that this decrease in neocortical serotonin 2A receptor binding that has

Fig. 4.2 Fluorodeoxyglucose (FDG) positron emission tomography scan of an Alzheimer’s disease patient shows moderately decreased metabolism in the bilateral temporoparietal regions.

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been observed in early AD is not the result of a primary loss of serotonergic neurons or their projections.22 In yet another study, hippocampal dopamine D2 receptors density was shown to be reduced, correlating with impairments in memory in AD patients.23

Other studies have revealed decreases in postsynaptic sero- tonin receptor binding in AD patients. A study of nine AD patients and 26 controls using 123I-5-I-R91150 SPECT to evalu- ate the 5-HT2A receptors demonstrated an age-related decline of neocortical binding potential (11.6% per decade).24 Further- more, AD patients had a significant regional reduction in the 5-HT2A binding in the orbitofrontal, prefrontal, lateral frontal, cingulate, sensorimotor, parietal inferior, and occipital regions. One study using [18F]deuteroaltanserin PET showed a signifi- cant decrease in the binding potential in 5-HT2A receptors in the anterior cingulate in AD patients, but this decrease did not correlate with behavioral measures such as depressive and psy- chotic symptoms.25 Another study using [18F]altanserin and [11C]DASB PET of early AD patients and controls demonstrated a decrease of roughly 30% in cortical 5-HT2A receptor binding in patients with MCI compared with healthy controls.22 In AD patients, decreases were marked in [18F]altanserin binding but largely insignificant in [11C]DASB binding. The only exception was in the mesial temporal cortex, where a 33% reduction was observed in [11C]DASB binding.

A [18F]altanserin PET study of MCI patients and healthy age- matched controls for 2 years reported that 8 of the 14 MCI patients had progressed to probable AD by the end of the fol- low-up period.21 In patients as well as controls, no significant changes were detected in 5-HT2A receptor binding over the 2-year period. Thus, despite the marked decreases in cortical 5-HT2A receptor binding that are seen in early MCI, further reductions have not been associated with progression from MCI to AD.

One study of the 5-HT1A binding in 10 AD patients and 10 controls revealed significantly decreased 5-HT1A binding poten- tial in the right medial temporal lobe, but not in the other regions such as the frontal, lateral temporal, parietal, and cere- bellar cortices.26 Another PET study of the 5-HT1A in AD, MCI, and controls showed that significantly decreased receptor den- sities in both hippocampi and the raphe nuclei in AD patients.27 The authors also reported a strong correlation between 5-HT1A receptor decreases in the hippocampus and worsening Mini- Mental State Examination (MMSE) scores. Additionally, decreased 5-HT1A receptor measures correlated with decreased cerebral glucose metabolism as measured by FDG-PET. A separate study of 5-HT1A receptor density in the hippocampus using a voxel-based analysis revealed decreased whole-brain binding in AD brains but increased whole-brain binding in the brain of patients with amnestic MCI.28 More specifically, they noted a significant decrease of binding potential in the hippo- campus and parahippocampal gyri of AD patients, whereas there was a significant increase of binding potential in the inferior occipital gyrus in amnestic MCI patients. The authors suggest that this difference in serotonergic receptor labeling may help distinguish amnestic MCI patients from mild AD patients.

Of note, PET imaging has been useful for evaluating medica- tion for the potential treatment of AD. For example, one study used 11C-labeled WAY-100635 PET to evaluate the binding of

lecozotan, a 5-hydroxytryptamine-1A (5-HT1A) antagonist under development as therapy for AD.29 The results demon- strated that lecozotan binds to 5-HT1A receptors in the brain with a maximum observed receptor occupancy of 50 to 60% after a single 5-mg dose in elderly subjects and AD patients. Such studies can help to further identify and evaluate treatment interventions for AD and other dementing illnesses.

Another PET study assessed 5-HT4 binding and cortical Aβ burden using [11C]SB207145 and [11C]PIB, respectively.30 No significant difference in 5-HT4 receptor binding was seen between patients and healthy subjects when the diagnosis of AD was made using clinical criteria. However, when patients were assessed based on their Aβ burden, those who had posi- tive findings on Pittsburgh compound B (PIB) studies showed a 13% increase in 5-HT4 receptor binding. In summary, this study found a positive correlation between 5-HT4 receptor binding and Aβ burden in AD patients, as well as a negative correlation between 5-HT4 receptor binding and MMSE scores. The authors indicated that the data suggest that cerebral 5-HT4 receptor upregulation begins before the onset of clinical symptoms and progresses while dementia is still in its early stages. They spec- ulated that this may be a compensatory effect in response to decreased levels of interstitial 5-HT. Such a compensatory effect might help to improve cognitive function transiently, increase acetylcholine release, or counteract Aβ accumulation.

The dopaminergic system has also been evaluated in AD patients. For example, one study31 investigated the relationship between striatal DA (D2) receptor availability using [11C]- raclopride PET and compared the imaging results to measures of cognition (sustained visual attention, spatial planning, word recognition) and motor (speed and dexterity) function in 24 patients with mild to moderate AD. In this study, higher D2 binding was associated with increased motor speed and, para- doxically, poorer attentional performance. The authors argued that these findings suggest that the use of DA (D2) receptor ago- nists as an adjunctive treatment in AD may have dissociable effects on cognitive function.

A study of 27 MCI patients were evaluated using PET with [11C]dihydotetrabenazine to measure striatal dopamine terminal integrity and [11C]PIB to measure cerebral amyloid burden.32 The results showed that 11 subjects were initially classified clinically as amnestic MCI, 7 as multidomain MCI, and 9 as nonamnestic MCI. At a mean follow-up of 3 years, 18 sub- jects converted to dementia with significant cerebral amyloid deposition or nigrostriatal denervation as a strong predictor of conversion to dementia. As with most of the other studies described in this chapter, there was only moderate concordance between the clinical classifications and PET-based classification of dementia subtypes.

The benzodiazepine/γ-aminobutyric (GABA) receptor system has also been evaluated in AD patients. For example, a small study33 of six early AD patients and six controls evaluated GABA binding using [11C]flumazenil PET and found decreased binding in the inferomedial temporal cortex, hippocampus, retrosple- nial cortex, and posterior perisylvian regions. In addition, [11C] flumazenil hippocampal binding correlated with memory per- formance. Interestingly, the authors report that [11C]- flumazenil binding was decreased, particularly in the brain regions with the greatest degree of neuronal loss in postmor- tem studies of early AD. The authors suggest that despite the

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SPECT and PET Imaging of Neurotransmitters in Dementia

small sample size of their study, [11C]flumazenil binding could be a useful marker of neuronal loss in early AD.

However, a SPECT study using [123I]Iomazenil and [99mTc] HMPAO in 16 patients with amnestic MCI and 14 elderly control subjects revealed no significant difference in GABA binding.34 Furthermore, hypoperfusion of the precuneus and posterior cingulate cortex suggested that GABA receptors are preserved in early dementia and that functional changes precede neuronal or synaptic loss in neocortical posterior regions. Clearly, future studies are needed to better evaluate the use of GABA receptor imaging in MCI and AD.

Perhaps one of the most important potential roles for PET or SPECT imaging is in the evaluation of therapeutic interventions for AD. The relatively recent development of several pharma- ceuticals for AD provides an important area for PET imaging. Patients can be imaged before therapy to determine who might be the best candidates for therapy. Patients can also be followed up longitudinally to determine the effectiveness of the pharma- ceutical intervention. Also, PET imaging can be useful in the physiologic evaluation of various pharmacologic interventions. A PET study by Kuhl et al aimed to elucidate the pharmacologic mechanism by investigating the effects of donepezil on acetyl- cholinesterase activity.35 It was reported that donepezil hydro- chloride inhibits cerebral cortical acetylcholinesterase activity in AD patients; on average, acetylcholinesterase activity was decreased by 27%. This finding suggests that the clinical trials of donepezil are not reflecting the actual degree of pharmacologic activity and that further investigation of the effects of this drug are warranted.

A PET study of the use of tacrine in patients with AD demon- strated improvement of nicotinic receptors (measured as [11C] nicotine binding), cerebral blood flow, and cognitive tests (Trail Making Test and block design test) that preceded improve- ments in glucose metabolism.36 These improvements were observed in both short- and long-terms treatment regimens. Propentofylline (PPF) has been explored as a potential pharma- cologic intervention in patients with both vascular dementia and AD because of the elaboration of inflammatory cytokines and neurotoxic free radicals, decreased secretion of nerve growth factor by astrocytes, excess release of glutamate with associated neurotoxicity, and loss of cholinergic neurons in these two types of dementia. A phase II study using PET showed significant improvements in cerebral glucose metabolism in patients with both vascular dementia and AD after treatment with PPF. Patients treated with a placebo had significant decreases in cerebral metabolism during the same period.37

Thus, PET and SPECT imaging have been used extensively in patients with AD, both in its early stages as well as in later stages in which treatment is attempted. It is likely that as more research is performed to understand the pathophysiology and management of AD, receptor studies with PET or SPECT will play an important role.

4.2 Frontotemporal Dementia

Frontotemporal dementia is a clinical neurologic disorder that results from the degeneration of the frontal and temporal ante- rior lobes of the brain. The classification of FTD has remained controversial for years, but the current definition includes Pick

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Fig. 4.3 Fluorodeoxyglucose (FDG) positron emission tomography scan of a patient with frontal lobe dementia showing hypometabolism in the bilateral frontal lobes and the anterior temporal lobes. The remainder of the cortex in this patient has preserved metabolism.

disease, primary progressive aphasia, and semantic dementia as defining characteristics. The two clinical patterns that the symptoms of FTD fall into are behavioral changes and aphasia.

Identification of the regions of the brain that are affected by FTD has been aided by FDG-PET, allowing for improved accu- racy in diagnosis. Several studies have demonstrated hypome- tabolism and deficits in perfusion, primarily in the frontal lobes of FTD patients (▶ Fig. 4.3). Diehl et al38 reported an association between FTD and metabolism in the frontal lobe. Grimmer et al39 showed that FTD patients have substantial deficits in the metabolism of the frontal cortices, as well as the caudate nuclei and the thalami.

A focal loss of serotonin receptors has been identified by PET studies as a critical aspect of the pathophysiology of FTD, a find- ing that is in line with postmortem reports. A study of 5-HT1 receptor distribution in FTD patients demonstrated marked reductions in bilateral [11C]WAY-100635 binding in the frontal, medial, and lateral temporal regions.40 Similarly, a study of 5-HT2 receptor distribution in FTD using [11C]MDL 100,907 PET demonstrated substantial decreases in binding in the orbi- tofrontal, frontal medial, and cingulate cortices.41

A PET study of four patients with frontotemporal lobar dementia (FTLD) using [11C]WAY-100635 demonstrated that the FTLD patients had significantly decreased serotonin 5-HT1A binding potential compared with controls in the frontal, temporal, and occipital regions.40 The FTLD patients had binding potential values that were 50 to 69% that of controls and suggest that profound 5-HT1A binding potential decreases may be present and contribute to the symptoms in these patients.

4.3 Parkinson’s Disease

PD is a neurologic disorder with a clinical triad of bradykinesia, tremor, and rigidity resulting from neuronal loss in the substan- tia nigra and locus ceruleus. The destruction of pigmented neu- rons in these regions leads to reduced production and storage of dopamine, as well as dysfunction of the nigrostriatal system. PD can also manifest with cognitive impairment in as many as 30% of patients.

37

Imaging Techniques

Fig. 4.4 A fluorodopa positron emission tomography scan of a patient with Parkinson’s disease (right scan) reveals markedly reduced uptake in the putamen and only mild uptake in the caudate nuclei compared with a healthy control subject with robust uptake throughout the basal ganglia (left scan).

38

Multiple PET studies in the literature report hyper- metabolism in the basal ganglia in the early stages of PD.42,43 Patients have also displayed mild and diffuse cortical hypome- tabolism that correlates with the severity of their bradykinesia. There is evidence that hemi-parkinsonism is related to hyper- metabolism in the contralateral basal ganglia.

The PET radiotracers targeting the dopaminergic system pre- synaptically and postsynaptically—through [18F]fluorodopa and 18F-N-methylspiperone, respectively—are most suitable for the diagnosis, management, and follow-up of PD and other move- ment disorders. The pathophysiology of PD is dependent on the progressive deterioration of dopaminergic neurons in the sub- stantia nigra; therefore, the uptake of [18F]fluorodopa is consis- tently reduced in the striatum (▶Fig. 4.4) and abnormal in extrastriatal regions to varying degrees.44 Whole-brain PET imaging has demonstrated additional differences in [18F]- fluorodopa uptake between PD patients and healthy controls in extrastriatal regions, which may underlie the impaired cogni- tion associated with PD. Studies have shown marked decreases in [18F]fluorodopa uptake in the frontal cortex,45 while others have reported lower uptake in the midbrain and anterior cingu- late.46 On the other hand, some studies have demonstrated that despite decreased [18F]fluorodopa uptake in the striatum, patients with early stage PD manifest substantially higher bilat- eral uptake in dorsolateral prefrontal regions.47,48

A study using FP-CIT (DaTscan) SPECT of seven patients with PD without dementia, 17 with PD plus dementia, and 18 healthy controls revealed no difference in dopamine trans- porter (DAT) binding in the striatum between PD patients with and without dementia.49 Although this sample is small, the results suggest that DAT binding is not associated with demen- tia symptoms in PD patients. A [99mC]raclopride study investi- gated the possibility that frontal lobe dysfunction is responsible for cognitive impairment in PD, either as a direct result of hin- dered transmission in the mesocortical dopaminergic system or as an indirect result of altered dopaminergic function in the substantia nigra.50 This study involved a spatial working mem- ory task (SWT) as well as a visuomotor control task (VMT). In controls, raclopride binding in the dorsal caudate was lower in SWTs than in VMTs, a finding that is consistent with the

heightened release of endogenous dopamine during executive functions. However, this difference in binding in the dorsal cauadate was not observed in PD patients. Both patients and controls demonstrated reduced racolpride binding in the ante- rior cingulate cortex during SWTs. Furthermore, dopamine release in the dorsal caudate was markedly decreased in PD patients, but it remained steady in the medial prefrontal cortex. The results of this study suggested that executive deficits in the early stages of PD are related to reduced nigrostriatal dopamin- ergic function resulting in abnormal processing in the cortico- basal ganglia circuit. However, dopaminergic transmission appears well preserved in the mesocortices of patients with early PD. This study demonstrates not only a deficit of dopamin- ergic function in PD patients but also specific effects that directly relate to cognitive impairment in these patients.

Additional studies have investigated the relationship between PD and the serotonin system. Politis et al used [11C]DASB PET to demonstrate that a progressive nonlinear reduction in serotonin transporter binding occurs in PD but is not signifi- cantly correlated with the severity of the condition.51 In con- trast, another [11C]DASB PET study showed increased serotonin transporter binding in similar patients.52

As with the other disorders, it appears that neurotransmitter studies with PET and SPECT imaging will be highly useful in both clinical and research applications in patients with PD. It is also interesting to note that neurotransmitters other than dopamine, the primary target of PD, may also be of significance in understanding the pathophysiology of the disorder, specifi- cally when it results in dementia.

4.4 Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is a disorder that results in cognitive impairment but is found to have Lewy bodies on his- topathological evaluation, differentiating the disorder from other dementing illnesses. The dopamine receptor system is primarily affected in DLB, and it thus has been a primary focus of neuroimaging studies. For example, in a multicenter study53 using DaTscan SPECT in 326 patients with a clinical diagnosis of probable (n=94) or possible (n=57) DLB or non-DLB dementia (n=147), established by a consensus panel, the authors reported a mean sensitivity of 78% for detecting clinically probable DLB and a specificity of 90% for excluding non-DLB dementia, which was predominantly due to AD. In this study, the positive predic- tive value was 82%, and the negative predictive value was 88%. There was also relatively high inter-rater reliability in reading the scans.

A smaller study compared DaTscan and 99mTc-exametazime blood flow SPECT in 33 controls, 33 AD patients, and 28 DLB patients.54 Agreement between raters in categorizing scans was found to be “moderate” (mean kappa = 0.53) for 99mTc-exameta- zime and “excellent” (mean kappa = 0.88) for 123I-FP-CIT. In AD and DLB patients, the consensus rating was in line with the clinical diagnosis in 56% of cases using 99mTc-exametazime and 84% using 123I-FP-CIT. Receiver operator characteristic analysis revealed superior diagnostic accuracy with 123I-FP-CIT (sensitiv- ity 79%, specificity 88%) compared with occipital 99mTc-exame- tazime (sensitivity 64%, specificity 64%). Thus, this study showed that DaTscan SPECT had significantly greater diagnostic

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accuracy compared with 99mTc-exametazime in the differentia- tion of DLB from AD.

A similar earlier study evaluated DaTscan in 164 older sub- jects (33 healthy older control subjects, 34 with AD, 23 with DLB, 38 with PD, and 36 with PD plus dementia).55 The results revealed a significant reduction in DAT binding in subjects with DLB compared with subjects with AD and controls but decreased binding similar to that seen in PD. Interestingly, the DLB patients had a flatter rostrocaudal (caudate-putamen) gradient compared with PD patients consistent with the patho- physiologic progression of the two disorders. The greatest loss in all three striatal regions was seen in those who had PD and dementia.

Perhaps the largest analysis to date is a systematic meta- analysis of studies in the literature that assess the accuracy of presynaptic dopaminergic imaging with 123I-FP-CIT (DaTscan) in the diagnosis of patients with DLB.56 The meta-analysis included studies in which DaTscan was performed in cases of diagnostic uncertainty and studies in which patients already had established diagnoses of DLB or non-DLB dementia or controls. Four studies with a total of 419 subjects were deemed suitable by the authors for the meta-analysis. The meta- analysis demonstrated a pooled sensitivity of DaTscan in differ- entiating DLB versus no DLB was 86.5% and a specificity of 93.6%. The authors concluded that DaTscan provided high diag- nostic accuracy for the diagnosis of DLB, especially in terms of specificity.

Another study57 compared FDG PET and 123I-β-CIT SPECT for differentiating DLB from AD and found that the most sensitive indicator (88%) was hypometabolism in the lateral occipital cortex, whereas the most specific sign (100%) was preservation of the mid or posterior cingulate gyrus. How- ever β-CIT achieved 100% accuracy and greater effect size than did [18F]FDG-PET.

Although the dopamine system has been the primary focus in DLB, other neurotransmitters are likely also affected. To investigate in vivo differences in the distribution of α4β2 sub- types of nicotinic acetylcholine receptors, one study used the ligand 123I-5-Iodo-3-[2(S)-2-azetidinylmethoxy] pyridine (5IA- 85380) SPECT in 15 patients with DLB and 16 controls.18 Com- pared with controls, there were significant reductions in α4β2 nicotinic acetylcholine receptors in the frontal, striatal, tempo- ral, and cingulate regions in DLB patients. Also, there was increased uptake of 123I-5IA-85380 in the occipital cortex in DLB patients relative to controls. This increase was particularly noted to be associated with DLB subjects with a recent history of visual hallucinations. The authors suggested that these find- ings indicate a link between cholinergic changes in occipital lobe and visual hallucinations in DLB.

In a related study, PET imaging with N-[11C]-methyl-4- piperidyl acetate to measure brain acetylcholinesterase activity was performed in 18 patients with PD, 21 patients with PD with dementia (PDD) or DLB, and 26 healthy controls.58 The PDD/ DLB group consisted of 10 patients with PDD and 11 patients with DLB. Among the PD patients, acetylcholinesterase activity was significantly reduced in the cerebral cortex, especially in the medial occipital cortex, but it was even lower in patients with PDD/DLB. However, there was no significant difference in regional AChE activity deficits between early PD and advanced PD groups or between DLB and PDD groups.

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SPECT and PET Imaging of Neurotransmitters in Dementia

Thus, neurotransmitter imaging using both PET and SPECT might be helpful in the clinical evaluation and research investi- gation of patients with DLB.

4.5 Studies Comparing Different

Dementias

In this final section, we review several studies, in addition to those described already, that specifically compare patients across multiple types of dementia. Such studies are particularly important for differentiating the disorders from one another and also helping to determine the most useful imaging studies for that purpose.

A study of 27 subjects with neurodegenerative dementia associated with parkinsonism evaluated the use of FDG-PET and DaTscan SPECT.59 The subjects were placed in groups according to their clinical diagnoses of probable AD (five sub- jects), corticobasal degeneration (six subjects), DLB (eight sub- jects), FTD (four subjects), or PD with dementia (four subjects). Using discriminant analysis of the two scans, the authors reported that 85% of the patients were correctly classified using FDG-PET alone. When DATscan was evaluated alone, 59% were correctly classified, but the combination of both DAT and nor- malized FDG uptake yielded 100% accurate classifications. The authors concluded that an automated analysis approach com- bining FDG uptake and DAT binding may be the most effective approach for classifying individual patients with dementia and parkinsonism.

To assess several different physiologic parameters in demen- tia, one study used PET with FDG, [18F]fluorodopa, and N-11C- methyl-4-piperidyl acetate (MP4A) to measure cholinergic function in eight patients with PDD, six patients with DLB, and nine patients with PD without dementia, all compared with age-matched controls.60 The results found that patients with DLB and PDD share the same profile of dopaminergic and cho- linergic deficits in the brain. The authors argued that the two disorders may represent two sides of the “same coin” in a con- tinuum of DLBs. The authors also suggested that cholinergic deficits, in addition to motor symptoms, are crucial for the development of dementia.

An interesting study of the serotonin transporter binding in the midbrain of 53 patients with PD (15), DLB (15), PSP (8), and essential tremor (15) were evaluated with FP-CIT SPECT imaging.61 Patients with PD demonstrated a moderately lower serotonin level than patients with essential tremors and con- trols. However, patients with PSP and DLB showed substantially lowered to undetectable levels of serotonin, respectively. The authors suggested that their findings indicate that the neuro- degenerative process affects serotoninergic neurons in parkin- sonian syndromes, with much more severe involvement in DLB than in PD patients, despite a comparable loss of striatal DATs.

An assessment of several different types of dementia patients demonstrated the problem in comparing clinical and imaging findings for diagnosis. In this study,62 75 subjects with mild dementia underwent a conventional clinical evalua- tion followed by PET imaging with [11C]-dihydrotetrabenazine and [11C]PIB. Based on clinical evaluation, 36 subjects were clas- sified as having AD, 25 as having FTD, and 14 as having DLB. Based on PET imaging, 47 subjects were classified as having AD,

39

40

Imaging Techniques

15 as having DLB, and 13 as having FTD. This study found that clinical consensus and neuroimaging classifications were in limited agreement in all types of dementia, with discordance of classifications occurring in approximately 35% of subjects. This study did not compare the clinical and PET findings with post- mortem diagnosis, which complicates the ability to understand the findings. However, it appears that both clinical and PET findings may be helpful in more accurately classifying dementia patients.

4.6 Conclusion

Overall, neurotransmitter imaging with PET and SPECT has been a powerful tool for evaluating patients with neurologic disorders associated with dementia. PET and SPECT imaging have shed light on the causes and pathophysiology of numerous disease processes. In the clinical settings, these functional imaging modalities prove valuable in initial diagnoses and evaluation of diseases. In the years to come, the development of radiopharmaceuticals targeting specific disorders, as well as the neurotransmitter systems they involve, will expand the scope of applications for these modalities, both clinically and in research. Moreover, functional imaging will continue to improve its abilities to assess the suitability of medical and surgical interventions for patients, to determine prognosis, and to evaluate the response to treatment. Thus, PET and SPECT imaging will continue to be a critical asset for studying the brain in patients with dementia.

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positron emission tomographic studies. Mov Disord 1990; 5: 203–213

. [44]  Heiss WD, Hilker R. The sensitivity of 18-fluorodopa positron emission tomography and magnetic resonance imaging in Parkinson’s disease. Eur J
Neurol 2004; 11: 5–12

. [45]  Kaasinen V, Rinne JO. Functional imaging studies of dopamine system and
cognition in normal aging and Parkinson’s disease. Neurosci Biobehav Rev
2002; 26: 785–793

. [46]  Ito K, Nagano-Saito A, Kato T et al. Striatal and extrastriatal dysfunction in
Parkinson’s disease with dementia: a 6-[18F]fluoro-L-dopa PET study. Brain
2002; 125: 1358–1365

. [47]  Rakshi JS, Uema T, Ito K et al. Frontal, midbrain and striatal dopaminergic
function in early and advanced Parkinson’s disease A 3D [18F]dopa-PET study.
Brain 1999; 122: 1637–1650

. [48]  Brück A, Aalto S, Nurmi E, Bergman J, Rinne JO. Cortical 6-[18F]fluoro-L-dopa

[52] Boileau I, Warsh JJ, Guttman M et al. Elevated serotonin transporter binding in depressed patients with Parkinson’s disease: a preliminary PET study with [11C]DASB. Mov Disord 2008; 23: 1776–1780

[53] McKeith I, O’Brien J, Walker Z et al. DLB Study Group. Sensitivity and specificity of dopamine transporter imaging with 123I-FP-CIT SPECT in dementia with Lewy bodies: a phase III, multicentre study. Lancet Neurol 2007; 6: 305–313

[54] Colloby SJ, Firbank MJ, Pakrasi S et al. A comparison of 99mTc-exameta- zime and 123I-FP-CIT SPECT imaging in the differential diagnosis of Alz- heimer’s disease and dementia with Lewy bodies. Int Psychogeriatr 2008; 20: 1124–1140

[55] O’Brien JT, Colloby S, Fenwick J et al. Dopamine transporter loss visualized with FP-CIT SPECT in the differential diagnosis of dementia with Lewy bodies. Arch Neurol 2004; 61: 919–925

[56] Papathanasiou ND, Boutsiadis A, Dickson J, Bomanji JB. Diagnostic accuracy of 123I-FP-CIT (DaTSCAN) in dementia with Lewy bodies: a meta-analysis of published studies. Parkinsonism Relat Disord 2012; 18: 225–229

[57] Lim SM, Katsifis A, Villemagne VL et al. The 18F-FDG-PET cingulate island sign and comparison to 123I-β-CIT SPECT for diagnosis of dementia with Lewy bodies. J Nucl Med 2009; 50: 1638–1645

[58] Shimada H, Hirano S, Shinotoh H et al. Mapping of brain acetylcholinesterase alterations in Lewy body disease by PET. Neurology 2009; 73: 273–278
[59] Garibotto V, Montandon ML, Viaud CT et al. Regions of interest-based dis-

criminant analysis of DaTscan SPECT and FDG-PET for the classification of

dementia. Clin Nucl Med 2013; 38: e112–e117
[60] Klein JC, Eggers C, Kalbe E et al. Neurotransmitter changes in dementia with

Lewy bodies and Parkinson’s disease dementia in vivo. Neurology 2010; 74:

885–892
[61] Roselli F, Pisciotta NM, Pennelli M et al. Midbrain SERT in degenerative

parkinsonisms: a 123I-FP-CIT SPECT study. Mov Disord 2010; 25: 1853– uptake and frontal cognitive functions in early Parkinson’s disease. Neurobiol 1859

Aging 2005; 26: 891–898
[49] Song IU, Chung YA, Oh JK, Chung SW (2014). An FP-CIT PET comparison of

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SPECT and PET Imaging of Neurotransmitters in Dementia

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Imaging Techniques

5 Diffusion Tensor Imaging in Neurodegenerative Disorders Dhiraj Baruah, Suyash Mohan, and Sumei Wang

Neurodegeneration results in deterioration of neurons in the brain and spinal cord. Neurodegenerative disorder is defined as a progressive condition of the nervous system associated with destruction or loss of selective neurons associated with func- tions like movement and cognition, ultimately leading to death.1 These disorders may be hereditary or sporadic. People suffering with these conditions place a significant amount of physical and emotional burden on their family and caregivers. The increasing prevalence of neurodegenerative diseases is a major health problem that uses a significant amount of health-related expenditures.2 Depending on loss of function, neurodegenerative disorders are classified mainly into two cat- egories: affecting cognition (such as Alzheimer’s disease [AD]) and affecting movement (such as Parkinson’s disease [PD]). Although significant progress has been made in recent years for understanding the pathophysiology of these diseases, treat- ment of these conditions is still symptomatic rather than effecting a cure.

Conventional imaging techniques, including magnetic reso- nance imaging (MRI), are limited in understanding these condi- tions and usually show abnormalities only in advanced stages of the disease. Understanding the parenchymal changes in the brain at a microstructural level helps to understand the differ- ent disorders in this group and might help in developing cura- tive or preventive treatments in the future. One of these advanced imaging techniques is diffusion tensor imaging (DTI), which evaluates the microstructural changes in the brain.3,4

Our aim in this chapter is to describe the recent advances in understanding changes of white matter (WM) in patients with some common neurodegenerative disorders. Before going to the disorders, we first look at some basic facts about DTI.

5.1 DiffusionTensorImaging:

Basic Concepts

Diffusion tensor imaging uses the property of random motion (Brownian motion) of water molecules in vivo. Water diffusion in WM is constrained by the physical boundaries, including the axon sheath, leading the movement to be greater along the z (long) axis of the fiber than across it. This asymmetric property of water diffusion in WM is known as anisotropy. Color maps can be generated by using this information to localize WM tracts (▶ Fig. 5.1). Conventional DWI gives information in only one direction. DTI helps to quantify this property at the voxel level by using a tensor. Tensor is a mathematical concept that not only allows quantification of molecular motion in each direction but also gives an average magnitude of water diffusion.5

The two commonly used indices are mean diffusivity (MD) and fractional anisotropy (FA), which can be calculated using the following equations:

MD1⁄4ð!1 þ!2 þ!3Þ 3

Fig. 5.1 Diffusion tensor imaging (DTI)-based color map of a healthy subject. Colors indicate directions as follows: red, left-right; green, anterior-posterior; blue, superior-inferior. White line delineates man- ually segmented corticospinal tract (CST) (a) Reconstructed CSTs (green) are overlaid on color maps (b) (Reprinted with permission from Wang S, Poptani H, Bilello M, Wu X, Woo JH, Elman LB et al. AJNR Am J Neuroradiol. 2006 Jun-Jul;27(6):1234-8.)

vu ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

FA1⁄4ut3 ð!1 !Þ2 þð!2 !Þ2 þð!3 !Þ2 2 !12 þ!22 þ!32

 

Where λ1 , λ2 , and λ3 are three eigenvalues of the diffusion ten- sor, and λ denotes the mean of the three eigenvalues. MD is a measure of the directionally averaged magnitude of diffusion and is related to the integrity of the local brain tissue. FA repre- sents the degree of anisotropy in the diffusion and reflects the degree of alignment of cellular structure. DTI indirectly assesses the integrity of tissue and could be useful in characterizing neu- rodegenerative disorders.

5.2 Aging Brain

It is important to understand normal age-related microstructural changes of WM before exploring neurodegenerative diseases, which is usually not possible with conventional MRI. Degenera- tive WM changes with normal aging include a decrease in mye- lin density and alterations in myelin structure.6,7 Conventional MRI is helpful in evaluating volumetric changes of the aging brain. Microstructural disruption of WM in the aging brain can be detected using DTI. In a study of 38 participants, Salat et al8 have shown significant age-related decline of FA in frontal WM, the posterior limb of the internal capsule, and the genu of the corpus callosum, with preservation of temporal and posterior WM. Other studies have also shown more anterior WM changes associated with the aging brain compared with posterior WM.9 The subtle and probably preclinical changes with aging seen using DTI may enable monitoring of WM recovery in normal aging, trauma, and disease.10

5.3 Alzheimer’s Disease
Alzheimer’s disease is the most common form of dementia and

has been defined pathologically by the presence of intracellular

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Diffusion Tensor Imaging in Neurodegenerative Disorders

neurofibrillary tangles and extracellular neuritic plaques.

There is an accelerated loss of cortical neurons compared with

age-matched nondemented persons.11 Many authorities

have documented that changes in WM are also more pro-

nounced in patients with AD than in age-matched nonde-

mented persons.12,13 These WM changes are the focus of evalu-

ation using DTI. Studies have reported changes seen using DTI,

including decreased FA, increased MD, and decreased lattice index.14,15,16,17,18,19,20

Slight impairment of cognitive function without a full-blown picture of dementia is defined as mild cognitive impairment (MCI). Patients with MCI carry a higher risk of developing AD in later life (10 to 15% conversion rate).21 Researchers have generated in vivo quantitative DTI markers to identify patients with high risk (▶ Fig. 5.2).22,23,24 Most of the changes in MCI and AD patients are posteriorly located (involving the hippocampus, pallidum, thala- mus, and caudate), whereas changes in the normal aging brain are commonly seen anteriorly (involving frontal WM).20,25,26,27

Fig. 5.2 Results from group comparisons of fractional anisotropy (FA) and mean diffusivity (MD). The anatomical underlay is the MNI (Montreal Neurological Institute) space registered target FA image. Maps are referenced to a standard human white matter atlas (Mori et al., 2005).
The group at high risk for AD showed decreased FA (shown in red) compared with the low-risk group in a number of regions, prominently including the fornix and inferior longitudinal fasciculus (ILF) in the temporal lobe and anterior portions of the inferior fronto-occipital fasciculus (IFOF)/uncinate fasciculus (UNC) in the frontal lobe. There were no regions in which the low-risk group showed decreased FA compared with the high-risk group. Within regions of decreased FA, there were only two regions of increased MD in the high-risk group (shown in orange): the genu and the right IFOF/ILF. CING, cingulum; L, left; R, right. (Reprinted with permission from Gold BT, Powell DK, Andersen AH, Smith CD.Neuroimage. 2010 Oct 1;52(4):1487-94.)

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5.4 Dementias Other than Alzheimer’s Disease

5.4.1 Dementia with Lewy Bodies

Dementia with Lewy bodies (DLB), also known as Lewy body variant of AD, is the second most common form of dementia in elderly patients after AD.28 The pathophysiology of DLB is likely due to neuronal synaptic dysfunction rather than to neuronal loss. Clinical differentiation between AD and DLB is not always possible. Three main clinical features described with DLB are impairment in cognitive function, visual hallucinations, and spontaneous parkinsonism.28 Posterior predominance of changes in DTI are more common in DLB than are frontal changes29; WM in the region of parieto-occipital and temporal lobes is commonly involved. As already stated, changes of FA in the posterior WM are also common in AD; however, posterior to anterior preferential involvement occurs in a significantly greater extent with DLB compared with AD. Researchers have shown significant association of decreased FA with DLB involv- ing occipital areas (precuneus) and inferior longitudinal fascicu- lus (ILF).30,31 Although preferential FA changes were seen poste- riorly in DLB, an increase in MD was rather diffuse than regional.29 Rosie et al found reduced FA in the left thalamic WM in DLB compared with that in AD patients.29

5.4.2 Frontotemporal Dementia

Frontotemporal dementia (FTD) is a neurodegenerative condi- tion that is characterized by involvement of the frontal and anterior temporal lobes.32 Depending on the predominant involvement of frontal or temporal lobe, FTD is divided into two main categories: frontal lobe variant and temporal lobe variant.33 Patients with the frontal variant of FTD usually have gradually worsening change in personality and behavior. Patients with the temporal variant of FTD have gradually wor- sening fluent aphasia.34 The first observation of decreased FA was shown in a postmortem brain by Larsson et al.35 In 36 patients with FTD, Borroni et al found significant involvement of the superior longitudinal fasciculus (SLF) with frontal variant FTD and bilateral ILF involvement in temporal variant FTD.33 Elise et al have shown decreased FA and increased radial diffu- sivity in frontotemporal WM and reduced connectivity between frontoinsula and anterior mid-cingulate cortex on resting state functional MRI in presymptomatic FTD patients years before symptom onset.36

5.5 Human Prion Disease 5.5.1 Creutzfeldt-Jakob Disease

Creutzfeldt-Jakob disease (CJD) was first described in the 1920s by German neurologists Hans Gerhard Creutzfeldt and Alfons Maria Jakob. Its pathogenesis is not completely clear; however, researchers have shown that CJD is transmissible to nonhuman primates and other animals on filtration of the inoculum, indi- cating that the agent is small and “replicating.”37 Patients with this rapidly progressive fatal neurodegenerative disease typi- cally have progressive dementia, generalized myoclonus, and

mutism. Cerebrospinal fluid examination for 14–3-3 protein is a highly sensitive and specific marker for CJD in the appropriate scenario.38 In a blinded study, Steinhoff et al have shown high diagnostic value of electroencephalography (EEG), with sensi- tivity and specificity of periodic sharp wave complexes of 67 and 86%, respectively, for the diagnosis of CJD.39 Although these laboratory examinations are helpful for diagnosis of CJD, they do not have reliable markers to assess progression of the disease.

Another option for diagnosis is brain biopsy; however, this procedure is invasive and risky. Among the routine MRI sequences, DWI is accepted as the most helpful imaging modal- ity for the diagnosis of CJD, with studies showing benefit of DWI in early stages with or without changes in EEG (▶ Fig. 5.3).40,41 Using 3-tesla (T) MRI in three patients with CJD, Fujita et al have shown significant lower MD values than those found in control patients in the striatum, caudate nucleus, puta- men, globus pallidus, and thalamus; however, they found no significant abnormality of FA compared with the control group.42

5.6 Parkinson’s and Related

Movement Disorders

Parkinson’s disease (PD) is a common chronic, progressive neu- rologic disease with classic findings including resting tremor, rigidity, bradykinesia, and postural instability. Among all the clinical symptoms, resting tremor is the most characteristic of PD.43 If tremor is absent, evaluation of conditions that can show signs of parkinsonism, including multiple system atrophy, progressive supranuclear palsy, and striatonigral degeneration, should be considered.44 Characteristic pathologic finding for the diagnosis of PD is loss of dopaminergic neurons in the pars compacta of the substantia nigra. The usefulness of positron emission tomography and single-photon emission computed tomography for the diagnosis of PD is described in the literature.45

Routine MRI is usually unremarkable in patients with PD, even in advanced stage, but it is useful for ruling out secondary causes of parkinsonism-type symptoms.46,47 Predominant fron- tal lobe atrophy has been consistently found in patients with PD without dementia, but GM volume reduction is seen in the parietal and temporal lobes more specifically associated with PD with dementia.48,49,50,51

Measurement of MD and FA using DTI helps differentiate PD patients from control groups and those with progressive supra- nuclear palsy.52,53,54 In patients with PD, decreased FA is seen in the frontal lobes, premotor areas, and cingulum.53,54 The thala- mus, globus pallidus, putamen, and caudate nucleus are com- monly involved in atypical parkinsonisms as opposed to PD.55,56 Increased diffusivity (increased MD) in the substantia nigra may be due to significant loss of dopaminergic neurons, leading to decreased cellular matrix.57 In PD patients without dementia, correlation of executive impairment with reduced FA in the parietal WM has been seen.58 FA and MD are not significantly changed in the corticospinal tract (CST) of PD patients.59,60 Although changes in the DTI parameters in the genu of corpus callosum have been described, the splenium is not involved.61 Prodoehl et al have shown that DTI of the basal ganglia and

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Diffusion Tensor Imaging in Neurodegenerative Disorders

Fig. 5.3 Sporadic Creutzfeldt-Jakob disease in a 34-year-old man who had abnormal leg movement and slow thinking. (a) Axial T2-weighted magnetic resonance image shows an area of subtle abnormal signal hyperintensity in the right putamen and caudate nuclei. (b) Axial diffusion-weighted images show bilateral areas of abnormal high signal intensity at the putamen and caudate nuclei, particularly in the right. (c) Axial apparent diffusion coefficient (ADC) map from diffusion-weighted imaging demonstrates reduced ADC value.

5.8 Motor Neuron Disease 5.8.1 Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative dis- order and the most common motor neuron disease. ALS has a rapidly progressive course, usually leading to death in 2 to 3 years.80,81 Because ALS is a disease of the motor system, patients have muscle weakness and paralysis; however, cogni- tive and behavioral symptoms are also described with this motor neuron disease.82

Pathologically, ALS causes damage of upper motor neurons in the cerebral cortex and lower motor neurons in the brain- stem and spinal cord. There are different genetic subtypes.83 The familial form of ALS usually has autosomal dominant inheritance. Genetic mutations are described in familial ALS patients involving multiple locations.84 Electromyography can help identify the involvement of lower motor neurons. However, neurologic examination is the only way to detect the upper motor neuron involvement but is subjective and unreliable.

Among imaging techniques, advanced MRI, including DTI, has the potential to be used as an objective diagnostic or prognostic marker of ALS. DTI color-based map can show thinning of the CST (▶ Fig. 5.4). Reduced FA along the CST is the predominant abnormality shown in most DTI studies.85,86,87,88,89,90,91,92,93,94,95 The CST damage observed in ALS patients possibly correlates with the rate of disease progression,96 although some dis- agreement remains regarding whether the CST damage corre- lates with disease severity.97,98,99,100

Verstraete et al have shown disconnection of motor systems in patients with ALS and concluded that the disease progresses along structural brain connections rather than only through the involvement of CST.101 Moreover, ALS is a multisystem disorder. Decreased FA has also been described in areas other than motor

cerebellum is useful in classifying PD and atypical parkinsonism accurately and also in distinguishing them from nonmovement disorders.62

5.7 Huntington’s Disease

Huntington’s disease (HD) is a devastating late-onset autosomal dominant trinucleotide CAG repeat neurodegenerative disorder that involves chromosome 4, leading to abnormal elongation of the polyglutamine stretch of huntingtin, which becomes increasingly toxic. Although this mutation and resultant elon- gated huntingtin are seen in the tissues of almost all organs of HD patients, pathological changes are seen only in the brain. The main structure involved is striatum, including caudate and putamen.

Symptoms of HD depend on the length of CAG repeat, with longer CAG expansion causing earlier disease onset.63 Clinical pictures include the triad of progressive cognitive, psychiatric, and motor symptoms. Among the motor signs, chorea is consid- ered the classic manifestation of HD. Although pathological changes in HD, including neuronal loss, are described as occur- ring predominantly in the striatum, significant changes are also described in brain structures other than striatum.64,65

Studies have shown a valuable role of MRI in characterizing

changes in the brain of presymptomatic and symptomatic HD

patients.66,67,68,69 Many cross-sectional volumetric studies have

shown decreased WM volume in patients with HD.70,71,72 The

interpretation of DTI parameters is complex in HD patients

because multiple parenchymal changes occur, including demye-

lination, axon damage, neuronal loss, and gliosis.73 Decreased

FA on DTI is observed in presymptomatic patients and in

patients at early stage compared with control subjects.74,75,76 As

decreasing FA is consistently shown in patients with HD, DTI

may become an important biomarker for the early detection of HD.77,78,79

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Imaging Techniques

tracts in patients with ALS by different voxel-based DTI studies. Kassubek et al have shown widespread WM involvement in ALS patients compared with control subjects, including CST, adja- cent subcortical WM, and corpus callosum (▶Fig. 5.5).93,94,102 Also, Agosto et al have shown that subtle involvement of the right uncinate fasciculus may precede the appearance of behav- ioral symptoms in patients with ALS.96

5.9 Multiple Sclerosis

Multiple sclerosis (MS) is a chronic inflammatory immune- mediated demyelinating and neurodegenerative disease.103,104 MS is the most common cause of nontraumatic disability in young and middle-aged adults.105 The exact causes and patho- genesis of MS are not clear; however, experimental models sug- gest autoimmunity as the basis of WM injury.106 Demyelinated plaque is the predominant pathologic hallmark of MS, with a

Fig. 5.4 Diffusion tensor imaging–based color maps of a healthy subject (a) and an amyotrophic lateral sclerosis (ALS) patient (b). The left corticospinal tract (arrows) appears thinner in the ALS patient (b). (Reprinted with permission from Wang S, Poptani H, Bilello M, Wu X, Woo JH, Elman LB et al. AJNR Am J Neuroradiol. 2006 Jun-Jul;27 (6):1234-8.)

Fig. 5.5 Comparison of fractional anisotropy (FA) maps based on diffusion tensor imaging (DTI) data of 20 patients with amyotrophic lateral sclerosis (ALS) and 20 age- and gender-matched controls. Upper panel: Group-averaged FA maps of controls (left) and patients with ALS (right) in coronal (large) and axial/sagittal view. FA display threshold is 0.2. Lower left: Comparison between the ALS group and controls by whole-brain–based statistical voxel-wise comparison at group level at P < 0.05 after correction for multiple comparisons. The areas with decreased FA in ALS are displayed, with the significance of the alterations coded by temperature of the color bar. Right: Fiber tracking of the corticospinal tract (CST) in group-averaged DTI data sets. The underlying FA values were averaged and statistically compared. Differences between group-averaged ALS FA maps and group-averaged control FA maps were highly significant, as indicated. (Reprinted with permission from Kassubek J, Ludolph AC, Müller HP. Ther Adv Neurol Disord. 2012 Mar;5(2):119-27.)

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special predilection for periventricular WM, corpus callosum, optic nerves, and spinal cord.106

Most patients with MS have a relapsing-remitting clinical picture with episodic onset of symptoms followed by residual deficits or full recovery. Complete recovery is common in the early stage of the disease.107 A secondary progressive course after many episodes of relapse and recovery is more common than a primary progressive course from the onset.108

Multiple sclerosis is diagnosed clinically. Because of its sensi- tivity in detection and characterization of demyelinating areas, however, MRI is integrated in the diagnostic criteria for MS.109 MRI with conventional sequences is used routinely for diagno- sis and for monitoring treatment response and disease progres- sion. Association of conventional MRI with clinical status is lim- ited, and DTI gives better information about WM damage com- pared with conventional imaging; FA and MD values are more useful in the assessment of MS patients (▶Fig. 5.6).110 DTI shows reduced FA and increased MD in MS lesions; studies have shown a reduction of FA in enhancing compared with nonen- hancing lesions.111,112,113 Although DTI shows WM damage in MS more accurately than does conventional MRI, these changes do not always correlate with clinical disability.114 Genova et al have shown a relationship between executive functioning and processing speed with changes in FA in WM regions in patients with MS.115 Moreover, the difference in WM tract disruption can be directly visualized using diffusion tensor tractography (▶ Fig. 5.7).

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Fig. 5.6 Axial fluid-attenuated inversion recovery (FLAIR) image (a), mean diffusivity (MD) map (b), fractional anisotropy (FA) map (c), and diffusion tensor imaging (DTI)-based color map (d) from the brain of a patient with multiple sclerosis. The lesions demonstrate increased MD value and reduced FA value.

Besides involvement of WM in the brain, involvement of the spinal cord leading to weakness and loss of proprioception fre- quently causes significant disability in patients with MS.116,117,118 In MS, spinal cord involvement is seen by conventional MRI in 80% of patients and in 99% of patients at autopsy.116,118 Conven- tional T2-weighted and contrast-enhanced MRI sequences are routinely used for diagnosis, progression, and monitoring of disease response with new treatment modalities. However, advanced imaging markers, including DTI, are more valuable in showing disease extent.

More precise DTI methods like tract-specific DTI may become helpful in assessing changes with new neuroprotec- tion and neural repair treatments, particularly in patients with progressive MS, in whom the criteria of enhancement are always useful.119

5.10 Summary

Although neurodegenerative disorders are diagnosed mainly clinically, imaging may be helpful in providing adjunctive information. Standard MRI has limited usefulness for evaluating these diseases, but microstructural information provided by DTI may impact early diagnosis and better understanding of the pathogenesis of neurodegenerative dis- eases, thus impacting the formulation of new treatment modalities.

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Fig. 5.7 Diffusion tensor tractography of corpus callosum (CC) in the same patient as in ▶ Fig. 5.6. Region of interest (ROI) is placed at the midsagittal level. Note that the fibers of CC are disrupted in the location of the lesions.

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[76] Reading SA, Yassa MA, Bakker A et al. Regional white matter change in pre- symptomatic Huntington’s disease: a diffusion tensor imaging study. Psychia- try Res 2005; 140: 55–62

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Functional Imaging of the Brain

6 Functional Imaging of the Brain

Leslie Hartman and Aaron S. Field

Since its discovery as a viable technique to noninvasively image the functioning brain, functional magnetic resonance imaging (fMRI) has become an increasingly popular tool for researchers, clinical neuroscientists, neuroradiologists, and others. This chapter provides an overview of the physics and technical aspects of fMRI, introduces fMRI paradigms and resting-state fMRI, reviews the advantages and disadvantages of fMRI, dis- cusses the role of fMRI in neurodegenerative disorders, and briefly touches on the future of fMRI, especially as it relates to neurodegenerative disorders.

6.1 Overview of the Physiology and Physics Underlying Functional MRI

An understanding of how fMR images are created is critical to appropriate interpretation. A complete discussion of the physi- ology and physics is beyond the scope of this introductory chapter, but excellent resources are available for a more detailed explanation, including Huettel and McCarthy (2008)1 and Jezzard et al (2001).2 The most common method for fMRI is based on the blood-oxygen-level–dependent (BOLD) contrast, the fMRI technique that is the focus of this chapter.

Understanding the physiology behind fMRI dates back to 1890 and the experiments of Roy and Sherrington at Cambridge University, where the idea that regional cerebral blood flow could reflect neuronal activity was first investigated.3 It is this basic principle that makes fMRI possible. The BOLD effect is a

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

complex biophysical phenomenon. The origin of BOLD fMRI signal change lies in the different magnetic properties of oxy- genated hemoglobin (O-Hb), which is diamagnetic, and deoxy- genated hemoglobin (D-Hb), which is slightly paramagnetic relative to brain tissue.4 Vessels that contain oxygenated arterial blood cause little or no distortion to the magnetic field in their vicinity, whereas field inhomogeneities resulting from the pres- ence of D-Hb lead to shortening of the T2* relaxation time and thus a reduction of signal on any MRIs sensitive to magnetic susceptibility effects (▶Fig. 6.1). As a result, the MRI signal increases with an increase in the ratio of O-Hb to D-Hb.5 In an fMRI experiment, a series of images is rapidly acquired as the subject performs a task that shifts brain activity between two or more well-defined states. Via neurovascular coupling, as the neuronal activity in a region of brain tissue increases with a specific task, there is an increase in the supply of oxygenated blood, a decrease in concentration of D-Hb, and thus an increase in MRI signal in any regions of brain associated with the task (the BOLD signal, ▶Fig. 6.1).6 Alternatively, so-called resting-state fMRI can be performed with a subject resting in the MRI scanner with eyes closed but without sleeping; in this case, BOLD signal fluctuations that demonstrate synchrony between regions are thought to reflect functional connectivity. This technique is increasingly popular and is discussed in a later section in this chapter. Additionally, many fMRI studies have demonstrated decreased task-related activity in certain brain regions, a phenomenon typically explained on the basis of a so- called default mode of resting brain activity.7,8,9,10,11

The most common MRI sequence used in BOLD fMRI is a T2* gradient-echo sequence using single-shot echo planar imaging

Fig. 6.1 Illustration of neurovascular coupling and resultant changes in the blood-oxygen-level–dependent (BOLD) signal. In the resting or basal state, there is a greater proportion of deoxyhemoglobin in capillaries and venules, which cause microscopic-field inhomogeneities that lead to decreased signal in the gradient echo magnetic resonance (MR) image (a). In the activated state, there is an increase in the flow but only a modest increase in oxygen consumption, which leads to a decrease in the concentration of deoxyhemoglobin (b). The signal difference in the gradient echo planar imaging (EPI) has been exaggerated for illustration purposes. The actual signal change is on the order of 1 to 5% at 1.5 T and 2–10% at 3.0 T and requires statistical analysis to detect. The deoxyhemoglobin is labeled as D-Hb (blue ovals) while the oxyhemoglobin is labeled as O-Hb (red ovals).

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Fig. 6.2 Example of the distortion and loss of signal in the anteroinferior temporal regions resulting from susceptibility-related field inho- mogeneities in these regions (e.g., air in the frontal sinuses, mastoid cavities). Unfortunately, blood-oxygen-level–dependent (BOLD) function- al magnetic resonance imaging (fMRI) relies on such susceptibility effects to detect regional changes in deoxyhemoglobin concentrations.

(EPI), which allows whole-brain data collection in a few sec- onds or less, as is necessary to capture brain function in near “real time.”12 This high speed comes at the expense of spatial resolution, which is substantially lower than for a conventional MRI scan, typically only 3 to 5mm.13,14 Another drawback to EPI is distortion and signal loss in the frontotemporal regions secondary to the sensitivity of EPI to magnetic susceptibility differences (▶ Fig. 6.2).

6.2 Blocked Paradigm Designs

and Postprocessing

When designing a paradigm for BOLD fMRI, tasks are chosen that can be performed in the scanner and that are expected to activate the region of interest. For example, if the concern is related to language, typically tasks that use the Broca and Wernicke areas are chosen, targeting expressive and recep- tive language, respectively. The most commonly used para- digms are “blocked” designs, in which the subject repeatedly performs a task for a specified time, resting for a similar time between repetitions (i.e., alternating “task” and “control” blocks) (▶Fig. 6.3). This repetition is required for statistical processing to detect the small signal changes that character-

ize the BOLD response, on the order of 1 to 5% at 1.5 tesla (T) or 2 to 10% at 3.0T, against a background of physiologic noise.15,16 Postprocessing of the data typically includes head motion correction and both spatial and temporal filtering of the data. Most commonly, the blocked time course of the task paradigm is convolved with a model of the hemodynamic response to generate an expected time course for BOLD signal in activated regions. Statistical tests based on the correlation or regression are then performed to identify voxels in which the signal changes over time correlate with the timing of the alternating task and control blocks, accounting for the hemo- dynamic response timing. This results in a voxel-wise statisti- cal map (e.g., t-statistic),17 to which a significance threshold is applied to determine which voxels will be considered “acti- vated” (▶Fig. 6.4). Those activated voxels are typically over- laid in color onto a higher-spatial-resolution anatomical brain image (▶ Fig. 6.5). The specifics of these postprocessing tech- niques are beyond the scope of this introductory chapter, but several excellent resources are available, including Jezzard et al (2001)2 and Huettel and McCarthy (2008).1 Several soft- ware packages are also freely available with tools for analysis of fMRI data (e.g., SPM at http://www.fil.ion.ucl.ac.uk/spm, Brain Voyager at http://www.brainvoyager.com/, AFNI at http://afni.nimh.nih.gov/afni).

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Functional Imaging of the Brain

Fig. 6.3 Schematic example of a block-design functional magnetic resonance imaging (fMRI) experiment. In this case, finger tapping is used. Each block in a typical task-based fMRI experi- ment is approximately 20 to 30 seconds in duration. The “off” block is with the subject at rest; the “on” block is with the subject tapping his or her finger.

Fig. 6.4 Convolving the block design with the hemodynamic response function (HRF) yields the theoretically expected time course of signal change in an “activated” voxel. This “reference waveform” is then compared (statistically, e.g., correlation or regression analysis) to the blood- oxygen-level–dependent (BOLD) signal variations voxel by voxel; those voxels for which the similarity between the actual and expected signal changes is above the statistical threshold are considered to be activated (typically after a voxel-clustering procedure to exclude spurious “positives”).

6.3 Resting-State Functional

Magnetic Resonance Imaging and

Functional Connectivity

Interest has been increasing about using fMRI to learn how dif- ferent brain regions interact with one another, how these inter-

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

actions relate to observable behaviors, and how they may be compromised in various neurodegenerative or psychiatric dis- orders. Resting-state fMRI (RS-fMRI) is used to study these interactions, commonly known as functional connectivity, which is defined as the temporal dependency between spatially remote neurophysiologic events18,19; in RS-fMRI, synchronous neuronal activity between regions of the brain is sought

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Fig. 6.5 Example of overlaying thresholded functional magnetic resonance imaging (fMRI) statistical maps onto anatomical MRI. This is a resting-state fMRI obtained in a 5-year-old child under general anesthesia. The case illustrates the ability to obtain meaningful fMRI data in individuals who cannot cooperate for a task-based fMRI. (Courtesy of Dr. V. Prabhakaran, University of Wisconsin–Madison.)

through the BOLD signal at rest and is presumed to reflect func- tional connectivity.20,21,22,23 Low-frequency oscillations (~0.01 to 0.1 Hz) have been identified in the resting state (RS) that help to identify these functional networks.20,22,24 A precise neuronal basis for these low-frequency oscillations has not been eluci- dated but is presumed to exist because most RS patterns tend to occur between regions that overlap in function and neuro- anatomy.22,25,26,27 In addition, studies have shown a strong asso- ciation between spontaneous BOLD fluctuations and simulta- neously measured fluctuations in neuronal spiking.10 Other studies have illustrated an indirect association between the amplitude of RS-fMRI correlations and electrophysiologic recordings of neuronal firing.28 Commonly used methods to assess functional connectivity maps for a specific region of interest in RS-fMRI is the seed voxel approach or independent component analysis.29,30,31,32,33,34 RS-fMRI is also appealing in that it requires minimal cooperation and motivation from sub- jects and is ideal in those who cannot fully engage enough to perform task-based fMRI studies (e.g., sedated or comatose patients).

There are at least seven commonly reported, functionally linked RS networks (▶ Fig. 6.6), including the default-mode net- work (DMN), primary visual and extrastriatal visual networks, executive control network, bilateral lateralized frontoparietal networks, primary sensorimotor network, and auditory-phono- logic network.25,26,29,34,35,36,37,38,39 One of the most studied net- works is the DMN, which has been shown to have an elevated level of neuronal activity at rest normally.40,41 Connectivity and activity of the DMN have been linked to human cognitive pro- cesses, including monitoring the world around us, integration of emotional and cognitive processing, and mind-wandering with stimulus-independent thoughts.21,40,42 These findings have piqued investigators’ interest in these networks, especially the DMN, in neurologic and psychiatric brain disorders. Some of the psychiatric disorders that have been studied with fMRI include depression, schizophrenia, autism, posttraumatic syn- dromes, attention-deficit hyperactivity disorder, and dys- lexia.43,44,45,46,47,48,49 The use of fMRI in neurodegenerative dis- orders is discussed in the next section of this chapter.

6.4 Functional Magnetic Resonance Imaging in Neurodegenerative Disorders

The most common clinical application of fMRI is to localize the eloquent cortex during neurosurgical planning for intractable epilepsy or tumor resections, for example, to lateralize language before temporal lobectomy for epilepsy.50 However, many fMRI studies have sought to identify changes in brain activity in neu- rodegenerative disorders, particularly in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) in preclinical stages. fMRI has demonstrated potential in diagnosing early AD in apo- lipoprotein-E4 gene carriers without clinical symptoms and in predicting the development of AD in individuals with MCI.51,52,53 fMRI studies demonstrating a reduction in cortico- cortical connectivity in AD are supported by electrophysiologic studies, including electroencephalography (EEG) and magneto- encephalography-based studies.54 Altered levels of functional connectivity on RS-fMRI in neurodegenerative disease have been reported in the DMN and other RS networks. Some of the neurodegenerative diseases studied with RS-fMRI include AD, frontotemporal dementia, dementia, multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), dementia with Lewy bodies, and Huntington’s disease.55–66 Together, these studies suggest that neurodegenerative diseases target interconnected cortical networks rather than single regions within the brain.57 An example of the strength of RS functional connectivity between certain regions of the brain in association with verbal episodic memory tasks in patients with MS is illustrated in ▶ Fig. 6.7.

In addition to RS-fMRI studies, fMRI studies can demonstrate alterations in brain activity with specific tasks in ALS, AD, PD, Huntington’s disease, semantic dementia in the frontotemporal lobar degeneration spectrum, and human immunodeficiency virus positivity.52,67,68,69,70,71,72,73,74,75 For example, Tessitore et al demonstrated reduced activity in brain regions encom- passing the primary motor and premotor cortex and the right parietal association cortex but heightened activity in the

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anterior putamen in an area involved in motor execution in individuals with sporadic ALS.68

Various pharmacologic agents cause changes in the physiol- ogy of the central nervous system, and this is the basis of phar- macologic fMRI (Ph-fMRI). Over the past decade, there has been increasing interest in the application of Ph-fMRI to evaluate changes in brain activity with pharmacologic agents in those

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Functional Imaging of the Brain

Fig. 6.6 The most consistently reported resting-state (RS) networks with major components detailed. This is a composite from multiple RS functional magnetic resonance imaging (fMRI) studies that used different groups of subjects and acquisition protocols. Networks include the default mode network (DMN) consisting of precuneus/posterior cingulate, medial frontal, and inferior parietal cortical regions and medial temporal lobe (a); primary visual (orange) and extrastriatal visual (gold) networks comprising retinotropic occipital cortex and temporo-occipital regions (b); executive control network composed of the superior and middle prefrontal cortices, anterior cingulate, and ventrolateral prefrontal cortex (c); left and right lateralized network, including inferior and medial frontal gyri, precuneus, inferior parietal, and angular gyrus (d), primary sensorimotor network (e), and auditory- phonological network consisting of superior temporal, insular, and postcentral cortex (f).

with and without neurodegenerative disease. fMRI signal changes have been reported with administration of D-ampheta- mine, dextroamphetamine, haloperidol, and dopamine via methylphenidate, among others.76,77,78,79 Ph-fMRI has also been used to study changes in various neurodegenerative states in response to a pharmacologic agent or by using the pharmaco- logic agent as a stimulus. Examples include the use of levodopa

55

Imaging Techniques

Fig. 6.7 The strength of resting-state functional connectivity between the left middle frontal gyrus (posterior Brodmann area 9) and the right middle temporal gyrus is associated with peak performance on a verbal episodic memory task in patients with multiple sclerosis (peak r = 0.56, P = 1 × 10-4, corrected for multiple comparisons) (a). (b) Illustrates the strength of resting-state functional connectivity between the left middle frontal gyrus (posterior Brodmann area 9) and the anterior left middle frontal gyrus and shows an inverse association with perform- ance on a verbal episodic memory task in patients with multiple sclerosis (r = –0.63, P = 1 × 10-5, corrected for multiple comparisons). (Courtesy of Drs. M. Phillips and M. Lowe, Cleveland Clinic.)

56

in hemiparkinson syndrome and PD and of rivastigmine in AD, MS, and MCI.80,81,82,83,84,85,86 A study by Mattay et al in 2002 on the use of dopaminergic modulation in evaluating cortical func- tion in subjects with PD showed promising results, with evi- dence supporting that the hypodopaminergic state is associated with decreased efficiency of prefrontal cortical information processing and that dopaminergic therapy improves the physi- ologic efficiency of this region.87 A review article by Jenkins on Ph-fMRI provides a good overview of its potential uses and fac- tors to consider when designing a Ph-fMRI study and evaluating the data.88

6.5 Advantages and Limitations

Functional MRI is a powerful, noninvasive method to identify and evaluate brain responses to cognitive tasks and stimuli, to promote understanding of complex networks and the interac- tions of various brain regions, and to assess RS networks in “normal” subjects and those with neurologic and psychiatric disorders. The use of fMRI in presurgical planning is now com- monplace in the clinic. For example, in presurgical planning for intractable epilepsy or brain tumors, fMRI can lateralize lan- guage noninvasively, has a high correspondence to Wada test- ing (intracarotid sodium amobarbital procedure), and provides detailed information about the language network that the Wada test cannot provide.89,90 The advantages of fMRI have made the technique increasingly popular over the last several years, particularly given its now wide availability as a “turnkey” add-on to MRI scanners, including pulse sequences, task para- digms, devices for presentation of stimuli to subjects in the scanner, and devices to record the subject’s responses.91

The main advantages of fMRI in brain mapping are its nonin- vasiveness and relatively high spatial resolution on the order of a few millimeters, which is superior to the spatial resolution in positron emission tomography (PET).92 In addition, fMRI has a relatively high temporal resolution compared with PET, lacks ionizing radiation, and does not need external contrast agents

or tracers. BOLD sensitivity for signal change via neurovascular coupling, and thus brain activation with a specific task or at rest, is directly proportional to magnetic field strength. There- fore, a higher field strength (i.e., 3.0 T and greater) will be more sensitive than a 1.5-T scanner.16 Thus, as MRI technology continues to evolve and higher field strengths become more commonly used, BOLD sensitivity for signal change will also continue to improve.

Functional MRI has several inherent disadvantages and pit- falls that are important to understand. Some of these can be mitigated with specific techniques. One must remember that BOLD fMRI detects only changes, rather than the absolute level, of brain activity and that it does so indirectly through neu- rovascular coupling, presuming a stable relationship between neural activation and resulting changes in absolute D-Hb con- centration. Unfortunately, this coupling may be variable across individuals or across brain regions within an individual. It may be compromised by pharmacologic modulations, such as medi- cations used during anesthesia; it may change with aging; and it may be altered by the pathology under study, such as a vascu- lar tumor.93,94,95,96 There is often reduced signal intensity and geometric distortion in the frontal and temporal regions or in the vicinity of surgical changes, blood products, and so forth, secondary to magnetic susceptibility effects, potentially leading to false-negatives. Parallel acquisition techniques have been developed that not only reduce image acquisition time but also reduce susceptibility artifacts, improving signal detection in basal frontal and mesial temporal regions and benefitting stud- ies involving memory, emotion, and executive function.97 Decreasing voxel size (e.g., through decreasing slice thickness in EPI acquisitions) can decrease distortion in hippocampus and amygdala.98,99

An obvious limitation of fMRI is that MRI is contraindicated in some individuals (e.g., those with cardiac pacemaker, implanted metal, claustrophobia). BOLD fMRI is highly sensitive to head motion, including task-related head motion or motion associated with cardiac or respiratory cycles, which can cause both false-positives and false-negatives. Task-related fMRI relies on the subject to understand and to complete the required tasks, which can also lead to false-negatives if the sub- ject is unable to cooperate sufficiently. Lastly, fMRI acquisition generates loud noise, up to 120dB, which can interfere with paradigms involving auditory processing.100,101,102 One can min- imize this effect by directly reducing the source of the noise or using the hemodynamic delay of BOLD response and inserting silent periods in the acquisition process.103,104

6.6 The Future of fMRI in

Neurodegenerative Disorders

The wealth of information fMRI provides about structure- function relationships in the brain may go beyond the immedi- ate implications of identifying brain regions responsible for executing certain functions. Functional brain networks eval- uated with fMRI, including RS networks like the DMN, are emerging as potential neuroimaging biomarkers for various neurodegenerative disorders and improved early diagnosis. They are providing new insights into functional effects on the brain in different diseases and may have implications in the

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

development and assessment of pharmacologic therapy in neu- rodegenerative disorders. Whereas many fMRI studies have been conducted on AD, MCI, and Huntington’s disease, further fMRI research is still needed, such as in frontotemporal lobar degeneration spectrum, dementia with Lewy bodies, and PD. Several recent studies have elucidated an RS network involving the basal ganglia, promising new applications of fMRI to study neurodegenerative changes involving the basal ganglia during disease progression or pharmacologic therapy.105,106,107 Ph-fMRI methods and results will need to be considered carefully with each potential application, as there may be many confounding factors; pharmacologic effects may be direct or indirect, short- and long-term pharmacologic responses may be different, and results in study populations may not apply in individual sub- jects. Although Ph-fMRI is promising, further data are still needed to make its use clinically relevant.

Functional MRI is well-suited to identifying imaging bio- markers for longitudinal, disease-monitoring studies because it is safe and easily repeatable. Whether fMRI methods will prove successful in reliably detecting preclinical stages of various neu- rodegenerative diseases and monitoring meaningful changes are questions awaiting future studies. With ongoing advances in fMRI acquisition and analysis techniques, fMRI is likely to become an increasingly powerful tool for understanding and evaluating neurodegenerative diseases and their progressive impact on brain networks. With ultrahigh-field fMRI on the horizon, spatial resolution will likely improve to submillimeter level, and it may ultimately be possible to identify the specific cortical layer(s) altered by neurodegenerative diseases.108,109 Finally, the increasing combination of fMRI with other advanced MRI techniques (e.g., diffusion tensor imaging) and with EEG is likely to yield new insights into the structural and functional changes of neurodegenerative disorders beyond what is possible by fMRI alone.

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. [92]  Connelly A, Jackson GD, Frackowiak RS, Belliveau JW, Vargha-Khadem F, Gadian DG. Functional mapping of activated human primary cortex with a clinical MR imaging system. Radiology 1993; 188: 125–130

. [93]  Martin E, Thiel T, Joeri P et al. Effect of pentobarbital on visual processing in man. Hum Brain Mapp 2000; 10: 132–139

. [94]  Ross MH, Yurgelun-Todd DA, Renshaw PF et al. Age-related reduction in func- tional MRI response to photic stimulation. Neurology 1997; 48: 173–176

. [95]  Hesselmann V, Zaro Weber O, Wedekind C et al. Age related signal decrease
in functional magnetic resonance imaging during motor stimulation in
humans. Neurosci Lett 2001; 308: 141–144

. [96]  Fujiwara N, Sakatani K, Katayama Y et al. Evoked-cerebral blood oxygenation
changes in false-negative activations in BOLD contrast functional MRI of
patients with brain tumors. Neuroimage 2004; 21: 1464–1471

. [97]  Golay X, de Zwart JA, Ho YC, Sitoh YY. Parallel imaging techniques in func-
tional MRI. Top Magn Reson Imaging 2004; 15: 255–265

. [98]  Merboldt KD, Finsterbusch J, Frahm J. Reducing inhomogeneity artifacts in
functional MRI of human brain activation-thin sections vs gradient compen-
sation. J Magn Reson 2000; 145: 184–191

. [99]  Merboldt KD, Fransson P, Bruhn H, Frahm J. Functional MRI of the human
amygdala? Neuroimage 2001; 14: 253–257

[100] Mansfield P, Glover PM, Beaumont J. Sound generation in gradient coil struc- tures for MRI. Magn Reson Med 1998; 39: 539–550

[101] Anderson AW, Marois R, Colson ER et al. Neonatal auditory activation detected by functional magnetic resonance imaging. Magn Reson Imaging 2001; 19: 1–5

[102] Bandettini PA, Jesmanowicz A, Van Kylen J, Birn RM, Hyde JS. Functional MRI of brain activation induced by scanner acoustic noise. Magn Reson Med 1998; 39: 410–416

[103] Amaro E, Jr, Williams SC, Shergill SS et al. Acoustic noise and functional mag- netic resonance imaging: current strategies and future prospects. J Magn Reson Imaging 2002; 16: 497–510

[104] Mansfield P, Chapman BL, Bowtell R, Glover P, Coxon R, Harvey PR. Active acoustic screening: reduction of noise in gradient coils by Lorentz force balancing. Magn Reson Med 1995; 33: 276–281

[105] Damoiseaux JS, Beckmann CF, Arigita EJS et al. Reduced resting-state brain activity in the “default network” in normal aging. Cereb Cortex 2008; 18: 1856–1864

[106] Di Martino A, Scheres A, Margulies DS et al. Functional connectivity of human striatum: a resting state fMRI study. Cereb Cortex 2008; 18: 2735– 2747

[107] Robinson S, Basso G, Soldati N et al. A resting state network in the motor con- trol circuit of the basal ganglia. BMC Neurosci 2009; 10: 137

[108] Koopmans PJ, Barth M, Norris DG. Layer-specific BOLD activation in human V1. Hum Brain Mapp 2010; 31: 1297–1304

[109] Yacoub E, Harel N, Ugurbil K. High-field fMRI unveils orientation columns in humans. Proc Natl Acad Sci U S A 2008; 105: 10607–10612

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Functional Imaging of the Brain

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7 Role of Noninvasive Angiogram and Perfusion in Evaluation of Neurodegenerative Disorders

Sangam G. Kanekar and Puneet Devgun

Computed tomography (CT) and magnetic resonance (MR) angiograms are well-established noninvasive techniques in the evaluation of the intracranial and extracranial vessels. Today, accuracy in the evaluation of extracranial and medium-sized intracranial vessel pathologies is comparable to the conven- tional angiogram. Cerebral perfusion is another noninvasive technique commonly used in the evaluation of stroke that gives information regarding the structural as well as the molecular functioning of the brain tissue. CT and MR perfusion both pro- vide insight into capillary-level hemodynamics and cerebral perfusion. Compared with CT perfusion (CTP), magnetic reso- nance perfusion (MRP) does not require radiation.

Today cerebrovascular disease (CVD) is thought to be the sec- ond most common cause of dementia. There is also ongoing debate as to whether Alzheimer’s disease (AD) and vascular dementia combined are more common than AD alone. It has been suggested that CVD may play an important role in deter- mining the presence and severity of clinical symptoms of AD. Clinicians believe that the prevalence of AD with CVD is grossly underestimated. The prevalence of vascular dementia rises from 0 to 2% in the 60- to 69-year-old age group, up to 16% (3 to 6% for men) age 80 to 89.1 In addition, risk factors for vascu- lar dementia are the same as those for CVD, stroke, and white matter lesions, which include arterial hypertension, atrial fibril- lation, myocardial infarction, coronary artery disease, diabetes, generalized atherosclerosis, lipid abnormalities, smoking, family history, and specific genetic features. All this suggests the importance of vascular imaging in evaluation of a patient with dementia. Single-photon emission computed tomography (SPECT) and positron emission tomography (PET) have been widely used in the evaluation of dementia patients to estimate cerebral perfusion. Over the last decade, CT and MR angiogram and perfusion techniques have also been successfully used in the evaluation of the neurodegenerative disorders, especially AD and vascular dementia. In this chapter, we discuss the prin- ciples, techniques, and applications of these noninvasive angi- ography and perfusion techniques.

7.1 Noninvasive Angiography

7.1.1 Computed Tomography

Angiography

Computed tomography angiography (CTA) is a noninvasive imaging technique typically used to evaluate large cervical and intracranial arteries. CTA is a thin-section volumetric CT exami- nation performed with intravenous contrast medium used to enhance the carotid, vertebral arteries, and the circle of Willis. CTA typically involves a volumetric helical acquisition that extends from the aortic arch to the circle of Willis (▶ Fig. 7.1). Many different scan parameters must be balanced to produce a diagnostic CTA, including contrast administration, reformatting, and reconstruction parameters. Ideal CTA imaging requires

intravenous iodinated contrast opacification of the arterial tree with no venous enhancement. The contrast opacification is dependent on the type and timing of contrast, and the optimal arterial opacification is dependent on the volume, rate, and administration of contrast. Various contrast timing strategies are available for optimal arterial opacification, including fixed delay, bolus tracking, and test bolus (GE Healthcare ).2,3,4 Fixed

Fig. 7.1 Normal neck computed tomography angiography using a bone subtracted technique reveals normal carotid and vertebral arteries in the neck.

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Role of Noninvasive Angiogram and Perfusion in Evaluation

delay, the simplest of the timing strategies, uses a fixed delay from the time of contrast injection to imaging. A slightly more complex strategy is bolus tracking, where scanning starts once a pretest HU (hounsfield) opacification is reached in a vessel of interest (typically the ascending aorta). A disadvantage of this technique is the inherent lag between the time to start scan- ning to actually acquiring images. An alternative to bolus track- ing is a test bolus, in which 10 mL of contrast is injected while a region of interest (ROI) is set in the proximal internal carotid artery. Using a low-level radiation scan, the ROI is scanned con- tinuously to determine the predelay, which corresponds to 50% of the maximal test vessel opacification.

Postprocessing of the data is extremely important for correct interpretation of the images. Various techniques have been developed for postprocessing, including maximum intensity projection (MIP), multiplanar volume reformat (MPR), curved reformat (CR), shaded surface display, or volume rendering, where it is possible to evaluate the vessel in its entirety.5,6,7,8 MIP constructs a two-dimensional (2D) image by displaying only pixels with a maximum CT attenuation (▶ Fig. 7.2). Because this technique relies on detecting the highest pixel on a given ray, it is sensitive to overlap from adjacent bony and opacified venous structures. MPR, unlike MIP, constructs a 2D image from the mean of CT attenuation compared with the maximum. In the CR technique, the vessel is traced along its course, with the user selecting the pixels to display on consecutive axial images. It is mostly useful for long, tortuous vessels, such as the carotid or vertebral arteries (▶Fig. 7.3). CR is a time-consuming tech- nique and is also subject to interpretative error.

Compared with other techniques, CTA has both advantages and disadvantages. With advances in technology and multide- tector row CT (MDCT), examination can be completed in a shorter time. The vessel from the arch of the aorta to the intra- cranial arteries can be scanned in less than 15 seconds using a 64-slice MDCT. Given the speed of the examination, CTA is less prone to motion artifact and can provide true anatomical repre- sentation of stenosis, lumen diameters, and calcifications. Unlike MR, there is no restriction on patients with pacemakers, ventilators, or monitoring devices or on claustrophobic patients. The greatest disadvantages of CTA are the radiation dose and iodinated contrast.

7.1.2 Magnetic Resonance Angiography

Like CTA, magnetic resonance angiography (MRA) is a non- invasive imaging technique used to depict the extracranial and intracranial circulation. Neck and intracranial MRA can be obtained using various imaging techniques, which include time of flight (TOF), multiple overlapping thin slab acquisition (MOTSA), phase-contrast (PC), and contrast-enhanced MRA. TOF can be obtained as either a 2D or 3D TOF.9,10

Time of Flight

Time-of-flight MRA is a gradient-echo sequence that depicts vascular flow by repeatedly applying a radiofrequency (RF) pulse to a volume of tissue, followed by dephasing and rephas- ing. TOF uses the difference in longitudinal magnetization between unsaturated (high signal) and saturated spins. Station- ary tissues in this volume become saturated by the repeated RF pulses and demonstrate low signal, whereas blood flowing in vessels carries unsaturated spins and has relatively high signal intensity.9,10 2D TOF MRA is typically performed in the neck, using a large flip angle. Blood flowing perpendicular to the multiple thin slices is well imaged and produces a bright signal compared with the stationary tissue. 3D TOF MRA is performed in the head (mostly for the circle of Willis) and uses a smaller flip angle, which reduces saturation artifacts (▶Fig. 7.4). The smaller flip angle and the addition of magnetization transfer decrease the background saturation. 3D TOF, compared with 2D TOF, has better spatial resolution and better signal-to-noise res- olution, but it covers only a small volume. MOTSA is a hybrid technique between 2D and 3D TOF and has higher spatial reso- lution than 2D TOF while covering a larger area than 3D TOF MRA with less saturation artifact.

Contrast-Enhanced Magnetic Resonance Angiography

Contrast-enhanced (CE) MRA is performed with a rapid, short repetition time (TR) gradient-echo sequence (10ms) after an intravenous bolus of gadolinium. Contrast-enhanced MRA, like CTA, requires a balance of diagnostic techniques of contrast

Fig. 7.2 Normal head computed tomography angiography (CTA). Axial (a) and coronal
(b) reformatted maximum intensity projection images from CTA demonstrate normal course and caliber of intracranial vessels.

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Fig.7.3 Curved reformatted image of neck computed tomography angiography shows the course of the entire neck portion of the internal carotid artery in a single image. Large ulcerative plaque (white arrow) is seen in the distal portion of the common carotid artery.

Fig. 7.4 Maximum intensity projection images of the three-dimen- sional time-of-flight magnetic resonance angiograms of the head show normal course and caliber of intracranial vessels around the circle of Willis.

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administration, reformatting, and reconstruction. Injected gad- olinium shortens the T1 to less than 10 ms so that opacified ves- sels are hyperintense.9 The timing of CE MRA can be a test bolus or automatic bolus technique, as described earlier. CE MRA can cover a much larger area of anatomy in a much shorter time and is less susceptible to motion artifact (▶ Fig. 7.5). CE MRA is obtained with intravenous contrast, which produces shortened T1, giving an anatomical representation of the vessel, whereas TOF anatomy is inferred from physiology (velocity dependent).

One of the disadvantages of CE MRA and CTA is that the data must be acquired during a narrow window of contrast enhance- ment, which relies on proper contrast bolus and image acquisi- tion. TOF and MRA can both be postprocessed into MIP images. MIP produces 3D images by a set of parallel rays drawn along the highest intensity in the source images. Multiple different projections are taken to construct a rotating 3D image.

Phase Contrast

Phase-contrast MRA is a gradient-echo sequence that depicts blood flow by quantifying the difference in the transverse mag- netization between stationary and moving tissue. After an RF pulse, a pair of symmetric but opposed phase-encoding gradi- ents are applied in one direction within the image voxel.9 The first gradient dephases, and the second rephases the transverse magnetizations. Stationary tissues have no net change in phase because they experience equal but opposite magnetic-field environments during the dephasing and then rephasing gradi- ents. Moving blood experiences different magnetic fields as each gradient is applied. The net phase shift, either positive or negative, determines the direction of flow and the amount of phase shift, which is directly proportional to the velocity of the blood flow.

Phase-contrast (PC) MRA capitalizes on the change in trans- verse magnetization (phase shift) that occurs when flowing protons encounter changes in gradient strength, produced by bipolar gradient pulses. By applying a bipolar gradient echo to the tissue, a phase shift is induced in moving spins but not in stationary tissue. PC MRA demonstrates flow directionality and

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Role of Noninvasive Angiogram and Perfusion in Evaluation

7.1.3 Perfusion
Computed Tomography Perfusion

Computed tomography perfusion expands the role of CT by providing insight into capillary-level hemodynamics and the brain parenchyma. Cerebral CTP is a functional imaging tech- nique that reflects cerebral microcirculation and reveals changes in cerebral microcirculation and metabolism that can- not be detected by conventional CT or MRI scans. The general principles underlying the computation of perfusion parameters, such as cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT), are the same for both MR and CT. CTP and MR perfusion-weighted imaging (PWI) both attempt to evaluate the capillary-level hemodynamics using different tech- niques. CTP relies on direct visualization of the contrast mate- rial, whereas MR PWI techniques rely on the indirect T2* effect induced in adjacent tissues.

A basic principle in CTP is monitoring the first pass of an iodinated contrast agent through the cerebral circulation. This is accomplished by continuous cine imaging for 45 seconds over the same volume of tissue during the rapid administration of a small, high-flow contrast material. A transient hyperattenua- tion caused within the brain tissue is directly proportional to the amount of contrast material in the vessels and blood for that region. This provides insight into the delivery of blood to the brain parenchyma. The generic term cerebral perfusion refers to tissue-level blood flow in the brain. This principle is used to generate time-attenuation curves for an arterial ROI, a venous ROI, and each pixel (▶Fig. 7.6). This flow can be described using a variety of parameters, which primarily include CBF, CBV, and MTT (▶Fig. 7.7). CBV is defined as the total volume of blood in a given unit of volume of the brain, including blood in the tissues, as well as the blood in the large capacitance vessels, such as arteries, arterioles, capillaries, venules, and veins. CBF is defined as the volume of blood moving through a given unit volume of brain per unit time. MTT is defined as the average of the transit time of blood though a given brain region. The transit time of blood varies depending on the distance traveled between arterial inflow and venous outflow. MTT is related to both CBV and CBF accord- ing to the central volume principle, which states MTT=CBV/ CVF.11,12

Magnetic Resonance Perfusion

Perfusion MRI techniques include dynamic susceptibility contrast (DSC) and arterial spin labeling (ASL). Because of the lack of radiation exposure, these techniques are usually pre- ferred in the evaluation of neurodegenerative diseases. In DSC, images are acquired dynamically before, during, and after the injection of a bolus of a gadolinium. These images are used to track the bolus and the blood in which it is dissolved as it passes through the microvasculature of the brain. In higher con- centrations, gadolinium ions confined within a cerebral blood vessel create a magnetic susceptibility effect that results in sub- stantial loss of signal on T2*-weighted images, a signal loss that extends over a distance comparable in magnitude to the diame- ter of the blood vessel.13 This allows for signal changes affecting

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Fig. 7.5 Contrast-enhanced neck magnetic resonance angiography shows normal course and caliber of common carotid arteries, carotid bifurcation, and internal and external carotid arteries.

shows slowly moving blood. PC MRA can be obtained after intravenous gadolinium because PC MRA does not rely on T1 values to generate the MRA image. A major disadvantage of PC MRA is that it is a much longer sequence to acquire and there- fore more susceptible to motion artifact.

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Fig. 7.6 Time-density curves (TDCs) generated from this artery (A) and vein (V) show the arrival, peak, and passage of the contrast bolus over time. These TDCs serve as the arterial input function and the venous output for the subsequent deconvolution step to formulate color-coded computed tomography perfusion maps.

Fig. 7.7 Computed tomography perfusion colored maps calculated using deconvolution techniques show normal cerebral blood flow (a) and cerebral blood volume (b).

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all of the spins in an image voxel. The technique requires a pulse sequence capable of repeatedly acquiring T2*-weighted images rapidly enough that the concentration of gadolinium within each tissue voxel can be sampled with sufficient tempo- ral resolution, preferably every 1.5 seconds or less. The sequence also needs to be multislice to cover most of the brain tissue. This is accomplished through a fast imaging technique, usually echo-planar imaging (EPI), with which interleaved images of many tissue slices can be obtained within a single TR. Both EPI spin-echo and EPI gradient-echo sequences have been used successfully for PWI.14,15 DSC provides higher spatial resolution, requires shorter scanning time, and can measure CBF, CBV, MTT, and TTP (▶ Fig. 7.8). One of the limitations of the DSC MRI perfusion is use of gadolinium contrast, which can

have potential increased risk in patients with impaired renal function.

In ASL, a preimaging RF pulse is used to magnetically label the hydrogen nuclei within water molecules in arterial blood before they flow into the imaged portion of the brain. Com- pared with a baseline image acquired without labeling, the labeling pulse attenuates the signal arising from each voxel in the brain to a degree dependent on the rate at which the labeled spins flow into the voxel.16,17 This allows for the mea- surement of regional CBF (▶ Fig. 7.9). Major advantages of ALS include lower cost and lack of adverse reactions to contrast material. One of the major limitations is that it allows only measurement of regional CBF and maps produced by ASL are noisier.

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Role of Noninvasive Angiogram and Perfusion in Evaluation

Fig. 7.8 Colored maps generated from the magnetic resonance dynamic susceptibility contrast perfusion show normal cerebral blood flow (a), cerebral blood volume (b), and mean transit time (c).

Fig. 7.9 Normal arterial spin labeling. Multisection cerebral blood flow color maps representing units of mL/100 g tissue per minute.

7.2.2 Alzheimer’s Disease

Neuropathological studies suggest that evidence of AD may be present in the brain years or even decades before onset of clini- cal symptoms. Research to identify these changes is ongoing. One of the parameters under investigation to understand these neuropathological changes is by evaluation of the blood circula- tion. Fluorodeoxyglucose PET, which measures glucose metabo- lism, and HMPAO SPECT, which measures CBF, have been widely used in the evaluation of cerebral metabolism or blood flow. Today, CT or CE MRP can calculate the relative CBF, CBV, MTT, and TTP (time to peak). These techniques, which are predomi- nantly used in stroke and tumor imaging, are gaining early application in the evaluation of dementia, especially AD and vascular dementia. Although the SPECT and PET literature is more robust compared with CTP and MRP, the results are com- parable. ASL, as described above under magnetic perfusion, is another noninvasive MR technique used in evaluation of AD, MCI, and vascular dementia.

7.2 Application in Neurodegenerative Imaging

7.2.1 Changes in Perfusion Parameters in the Normal Aging Brain

In clinical practice, perfusion imaging is rarely performed to understand normal aging; however, brain and vascular changes may be appreciated while scanning the patient for other neuro- logic disorders, such as acute stroke, using CT or MRP. Various SPECT studies using technetium 99m-radiolabeled hexamethyl- propyleneamine oxime (HMPAO) have shown age-related decreases in the regional CBF compared with normal subjects.18 These changes are predominantly seen in the cingulate gyrus, frontal lobe, parietal lobe, and temporal lobe. On CT and MRP, these changes are difficult to appreciate by “eyeballing” and require a quantitative analysis, which is more of a research interest than of clinical significance (▶ Fig. 7.10).

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Fig. 7.10 Quantitative analysis of computed tomography angiography. Cerebral blood flow map from computed tomography perfusion study shows quantitative analysis of the cerebral blood flow using multiple regions of interest in the white and gray matter.

Fig. 7.11 Magnetic resonance perfusion (MRP) in vascular dementia. (a) Cerebral blood flow (CBF) map of MRP shows diffuse decrease in the CBF in the supratentorial white matter in a 60-year-old man with subcortical dementia. (b) MRP perfusion in a 43-year-old man with frontal dementia after a head injury shows an increase in the mean transit time in the bilateral frontal lobes.

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Perfusion defects largely depend on the stage of the AD.19,20 In mild to moderate AD, the greatest hypoperfusion or decrease in the CBF is seen in the parietal lobes and cingulate gyri; a smaller effect may be seen in the frontal lobes. In the early stages of the disease, these deficits are asymmetric. In the later stages, characteristic hypoperfusion and hypometabolism, located mainly in the temporal, parietal, and posterior cingulate cortices, are mostly bilateral and symmetrical.21,22 It is pre- sumed that the localized hypoperfusion in the posterior cingu- late gyrus is due to hypoactivity of the posterior cingulate gyrus, caused by neuronal damage, whereas in the medial tem- poral lobe structures, it is due to loss of tight neuronal connec- tions. On MRP, decreased CBV is most pronounced in the tem- poroparietal region.23 Primary sensorimotor and primary visual cortices, as well as the striatum, thalamus, and cerebellum, are spared in AD patients. CBV decline in the frontal and parietal lobes was primarily in white matter, which was well correlated with structural damage as seen on DTI. It is documented by SPECT studies that frontal lobe deficit (at baseline) was more predictive of future cognitive decline than was that in the tem- poroparietal regions. The MTTs and TTPs of the aforementioned ROIs were significantly lower in the healthy control group than in the AD group.

The results further demonstrate that the pathological basis of AD is neuron injury due to impaired microcirculation. As the changes in hemodynamic parameters indirectly reflect physio- logic and metabolic conditions of brain tissue, the CTP scan can provide evidence for the early diagnosis of AD. Although the major pathological change of AD is cerebral neurodegeneration, there is evidence of vascular risk factors and CVD in patients with AD, which indicates that vascular-related risk factors may play important roles in the development and progression of AD.

7.2.3 Vascular Parkinsonism

The imaging modality of choice for patients with parkinsonian syndromes continues to be MRI. Imaging is primarily done to differentiate between PD and secondary parkinsonism, which also includes vascular parkinsonism (VP). Signal changes in the nigral and subcortical regions are nonspecific and non- discriminatory. Putaminal hypointensity seen on MRI is regarded as due to postsynaptic striatal dysfunction. Frontal lobe atrophy is related to the L-dopa-nonresponsive, predomi- nantly axial parkinsonian syndrome

Patients with VP may show vascular impairment in more than one vascular territory, such as periventricular and sub- cortical white matter, the basal ganglia, and the brainstem. These may be in the form of lacunar or territorial infarcts. It is well documented that dementia occurs more commonly in patients with VP than in those with PD. Although MRI is quite sensitive in detecting these abnormalities, perfusion studies, along with MRA, may be useful in identifying associated changes in the focal or generalized vascular abnormalities.

7.2.4 Vascular Dementia

Both PET and SPECT have limited roles in the evaluation of vas- cular dementia compared with degenerative dementia because vascular dementia is diagnosed with MRI in most cases. CT or MRP may show a typical perfusion defect in the cortical and subcortical structures and cerebellum, depending on the local- ization of the ischemic change. On the PET/SPECT, AD can be differentiated from the pattern of defect seen. In AD, blood flow reduction tends to be posterior predominant, whereas in vascu- lar dementia, it shows anterior predominance (▶Fig. 7.11).24

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Although this defect is seen mostly in the white matter, the overlapping cortex also shows the CBF reduction secondary to disconnection between the deep cerebrum and the cortex. Sometimes these defects may be in the remote region away from the site of infarction and are believed to be caused by functional cortical-subcortical disconnections.

7.2.5 Sickle Cell Disease

vasodilation.30,31 In the nonstroke groups, CBF abnormalities were more prevalent than transcranial Doppler velocity.

7.2.6 Perfusion Changes in Normal

Pressure Hydrocephalus

Diagnosis of normal pressure hydrocephalus (NPH) is clinical, supplemented with various imaging techniques. The imaging tests fail to answer the most important question in patients with NPH, which is, which patients will benefit from ventricular shunting. Vascular imaging plays a limited role in the diagnosis of NPH. However, perfusion analysis in NPH patients using PET has shown decreased regional CBV and regional CBF.32 These parameters showed significant improvement after shunt- ing, suggesting vascular compromise within the brain paren- chyma, possibly resulting from a mass effect and raised intracranial pressure. It is also demonstrated that MRP can improve the prediction of the outcome after shunt placement in patients with NPH.

References

[1] O’Brien JT, Erkinjuntti T, Reisberg B et al. Vascular cognitive impairment. Lancet Neurol 2003; 2: 89–98

[2] Kopka L, Funke M, Fischer U, Vosshenrich R, Oestmann JW, Grabbe E. Paren- chymal liver enhancement with bolus-triggered helical CT: preliminary clinical results. Radiology 1995; 195: 282–284

[3] Puskás Z, Schuierer G. [Determination of blood circulation time for optimiz- ing contrast medium administration in CT angiography] [in German] Radiol- oge 1996; 36: 750–757

[4] Bae KT, Heiken JP, Brink JA. Aortic and hepatic contrast medium enhance- ment at CT. Part II. Effect of reduced cardiac output in a porcine model. Radi- ology 1998; 207: 657–662

[5] Napel S, Marks MP, Rubin GD et al. CT angiography with spiral CT and maxi- mum intensity projection. Radiology 1992; 185: 607–610

[6] Rubin GD, Dake MD, Napel S et al. Spiral CT of renal artery stenosis: compari- son of three-dimensional rendering techniques. Radiology 1994; 190: 181– 189

[7] Vieco PT, Morin EE, III, Gross CE. CT angiography in the examination of patients with aneurysm clips. AJNR Am J Neuroradiol 1996; 17: 455–457
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Reson Imaging Clin N Am 2003; 11: 585–597, vivi

Cognitive decline and dementia symptoms are not uncommon in sickle cell disease (SCD). These changes are mostly related to damage to the brain parenchyma from the various vascular insults, which include vascular endothelial damage and occlu- sion, cerebral ischemia, silent strokes, white matter (sub- cortical) changes, and intracranial hemorrhage.25 A prevalence of overt stroke in SCD is 250 times higher than in the general population.26 Overt stroke produces focal neurologic deficits that are easily diagnosed by MRI. Silent strokes occur in approx- imately 22% of children with SCD and can herald subsequent overt stroke.27 Mechanisms responsible for cerebral ischemia in SCD are complex and seem related to impaired blood flow. Blood flow abnormalities can be caused by narrowing or occlu- sion of cerebral vessels, increased viscosity, adherence of red blood cells to the vascular endothelium, and exhaustion of autoregulatory vasodilation. Detection of abnormalities of blood flow before clinical progression to stroke could be impor- tant information in helping or halting progression.

Transcranial Doppler scanning is commonly used in assess- ment of this abnormality in the CBF. Measurement of the mid- dle cerebral artery or the terminal portion of the internal carotid artery velocity is used as one of the major Doppler crite- ria. A velocity greater than 200cm/s in these vessels is more prone to overt stroke, which can be prevented by using periodic red blood cell transfusion.28 Unfortunately, Doppler has its own limitations. This technique is operator dependent, and overt strokes are seen with a velocity less than 200 cm/s.29 MRP has been used to evaluate the cerebral vascular dynamics, such as CBF and CBV, in SCD patients (▶ Fig. 7.12). MRP showed asym- metrical, elevated baseline CBF in SCD patients related to large cerebral artery stenosis, low hematocrit levels, and resultant

Role of Noninvasive Angiogram and Perfusion in Evaluation

Fig.7.12 A21-year-oldmanwithsicklecelldiseasewithearlysignsofsubcorticaldementiashows(a)adiffusedecreaseinthecerebralbloodvolume(CBV) and (b) an increase in the mean transit time in the cerebral white matter bilaterally, left more than right. (c) Intracranial time-of-flight magnetic resonance imaging shows severe narrowing of the distal internal carotid and middle cerebral arteries bilaterally, findings suggestive of moyamoya.

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. [17]  Edelman RR, Siewert B, Darby DG et al. Qualitative mapping of cerebral blood
flow and functional localization with echo-planar MR imaging and signal tar-
geting with alternating radio frequency. Radiology 1994; 192: 513–520

. [18]  Takahashi K, Yamaguchi S, Kobayashi S, Yamamoto Y. Effects of aging on regional cerebral blood flow assessed by using technetium Tc 99 m hexame- thylpropyleneamine oxime single-photon emission tomography with 3D stereotactic surface projection analysis. AJNR Am J Neuroradiol 2005; 26:
2005–2009

. [19]  Jagust WJ. Neuroimaging in dementia. Neurol Clin 2000; 18: 885–902

. [20]  Petrella JR, Coleman RE, Doraiswamy PM. Neuroimaging and early diagnosis
of Alzheimer’s disease: a look to the future. Radiology 2003; 226: 315–336

. [21]  Bradley KM, O’Sullivan VT, Soper ND et al. Cerebral perfusion SPET correlated with Braak pathological stage in Alzheimer’s disease. Brain 2002;
125: 1772–1781

[22] Lee YC, Liu RS, Liao YC et al. Statistical parametric mapping of brain SPECT perfusion abnormalities in patients with Alzheimer’s disease. Eur Neurol 2003; 49: 142–145

[23] Yoshiura T, Hiwatashi A, Noguchi T et al. Arterial spin labelling at 3-T MR imaging for detection of individuals with Alzheimer’s disease. Eur Radiol 2009; 19: 2819–2825

[24] Yoshikawa T, Murase K, Oku N et al. Heterogeneity of cerebral blood flow in Alzheimer’s disease and vascular dementia. AJNR Am J Neuroradiol 2003; 24: 1341–1347

[25] Behpour AM, Shah PS, Mikulis DJ, Kassner A. Cerebral blood flow abnormali- ties in children with sickle cell disease: a systematic review. Pediatr Neurol 2013; 48: 188–199

[26] Earley CJ, Kittner SJ, Feeser BR et al. Stroke in children and sickle-cell disease: Baltimore-Washington Cooperative Young Stroke Study. Neurology 1998; 51: 169–176

[27] Pegelow CH, Macklin EA, Moser FG et al. Longitudinal changes in brain magnetic resonance imaging findings in children with sickle cell disease. Blood 2002; 99: 3014–3018

[28] Adams RJ, McKie VC, Brambilla D et al. Stroke prevention trial in sickle cell anemia. Control Clin Trials 1998; 19: 110–129

[29] Adams RJ, Brambilla DJ, Granger S et al. STOP Study. Stroke and conversion to high risk in children screened with transcranial Doppler ultrasound during the STOP study. Blood 2004; 103: 3689–3694

[30] Stockman JA, Nigro MA, Mishkin MM, Oski FA. Occlusion of large cerebral vessels in sickle-cell anemia. N Engl J Med 1972; 287: 846–849

[31] Gerald B, Sebes JI, Langston JW. Cerebral infarction secondary to sickle cell disease: arteriographic findings. AJR Am J Roentgenol 1980; 134: 1209–1212 [32] Walter C, Hertel F, Naumann E, Mörsdorf M. Alteration of cerebral perfusion

in patients with idiopathic normal pressure hydrocephalus measured by 3D perfusion weighted magnetic resonance imaging. J Neurol 2005; 252: 1465–1471

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Part III Normal Aging

. 8  Imaging of the Normal Aging Brain 70

. 9  Iron Accumulation and Iron Imaging in
the Human Brain 80

III

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Normal Aging

8 Imaging of the Normal Aging Brain

Ruth A. Wood, Ludovico Minati, and Dennis Chan

Elderly adults represent a significant and rapidly expanding proportion of the population. Some estimates state that by 2030 there will be 72 million individuals over the age of 65 years in the United States of America, constituting 19% of the population.1 Given the high prevalence of brain disorders in this population, attaining a greater understanding of the changes that occur in normal brain aging is of critical importance. Furthermore, an appreciation of such changes is a prerequisite for the identification of abnormalities associated with underly- ing pathology.

As the prevalence of many neurological diseases increases with age, it can be difficult to differentiate between the effects of aging and those of prodromal age-related disease. In addi- tion, several operational challenges exist when establishing the effects of aging on the brain. First, there is a potential ascertainment bias in the selection of individuals considered to represent “normal aging”; without exhaustive screening and follow-up, there is a risk that cohorts contain individuals with clinically silent disease. Second, most studies on normal aging are cross-sectional, with limited longitudinal data on the pro- gression of age-related changes. Third, the brain changes that occur during aging reflect a complex interaction between alter- ations of disparate physiological variables, most notably struc- ture, function, perfusion, and metabolism. Because each of these variables is evaluated using different imaging modalities, careful implementation of multimodal imaging is required to obtain a comprehensive view of aging of the brain, whereas most studies published to date have described changes as observed through single techniques applied in isolation.

This chapter provides an overview of the main brain changes associated with normal aging and the imaging modalities cur- rently used to study them. It does not intend to be a meta- analysis; the focus is on conceptual comprehensiveness rather than exhaustive comparison of published studies.

8.1 Brain Structure

8.1.1 Gray Matter Volume

Autopsy studies consistently demonstrate an age-related reduc- tion in brain weight and volume, with concomitant enlarge- ment of ventricular cerebrospinal fluid (CSF) spaces.2 These gross pathological changes correspond on a histopathological level to neuronal loss in the neocortex, hippocampus, and cere- bellum and neuronal shrinkage and loss of myelinated fibers, particularly in subcortical regions.2 Brain-volume loss observed at autopsy correlates with atrophy as determined using in vivo structural brain imaging; in addition to the use of cross- sectional imaging to identify changes at a single time point, lon- gitudinal imaging studies permit determination of change in rates of atrophy. Most of these studies use magnetic resonance imaging (MRI) techniques for their superior resolution and gray matter (GM):white matter (WM) contrast compared with com- puted tomography (CT).

The changes in brain volume over the human life span do not follow a linear trajectory. Brain volume increases in early life, followed by a plateau during which the CSF:brain volume ratio remains approximately constant.3 Brain volume then declines after the fifth decade, with progressive ventriculomegaly, sulcal expansion, and enlargement of pericerebellar subarachnoid spaces (▶ Fig. 8.1).3

Several trends emerge on more detailed scrutiny, although there is a degree of interstudy variability regarding the precise time course and spatial distribution of age-related volume changes. First, the rate of brain atrophy accelerates with age.4 Second, WM and GM are affected differently (▶ Fig. 8.2). Loss of GM occurs at an early age, and both linear and nonlinear pat- terns of correlation with age have been described (▶ Fig. 8.3),5 whereas WM volume peaks in the fifth decade before declining in older age.5

With regard to the anatomical distribution of volume loss, the regions of the cortex are affected in a non-uniform manner. Overall, there is an anteroposterior gradient of volume loss, with the earliest age-related atrophy occurring in the prefrontal cortex.6 Age-associated atrophy has also been demonstrated in the hippocampus, amygdala, striatum, and cerebellum (▶Fig. 8.4).2 Conflicting reports exist regarding age-related atrophy within the thalamus, and some regions within the basal ganglia and brainstem appear relatively unaffected.7 Although these observations are useful for understanding the aging process, it is important to keep in mind that they are the result of analyses conducted over large groups; at the single- case level, there is substantial individual variability in the extent and localization of atrophy, ranging from near absence of atrophy in comparison to a typical young brain, to moderate atrophy without any accompanying cognitive deficit.

Detailed robust understanding of the regional distribution of volume loss in normal aging is of particular relevance in view of the patterns of atrophy associated with different neuro- degenerative disorders. For example, atrophy of the medial temporal lobe is a typical early feature of Alzheimer’s disease, but this region does not exhibit significant age-related atrophy in the normal aging population.8 In frontotemporal dementia, there is asymmetrical atrophy predominantly affecting the frontal and temporal lobes, which also contrasts with the sym- metrical pattern of atrophy in normal aging.9 However, in any comparison of change between aging and neurodegenerative diseases, it is crucial to bear in mind the fact that atrophy can be absent or minimal in the preclinical and early clinical phases of most neurodegenerative disorders, making differentiation of early-stage disease from normal aging at the individual level quite difficult.

Although the association between the integrity of specific cognitive functions and regional brain atrophy in disease is widely recognized, weaker and less consistent correlations are observed between cognition and age-related atrophy.10,11 For example, reduced GM volume in the medial temporal lobe, prefrontal cortex, and posterior parietal cortex appears to be associated with lower scores on the Mini-Mental State Examination11; furthermore, there is evidence that reduced

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Imaging of the Normal Aging Brain

Fig. 8.1 Axial and coronal T1-weighted magnetic resonance imaging of two representative
young (a,b) and elderly (c,d) healthy brains demonstrating global volume loss and sulcal enlargement.

Fig. 8.2 Scatterplots demonstrating the complex effect of age on white matter, gray matter, and cerebrospinal fluid volumes (CSF). (Reproduced with permission from Sowell ER, Peterson BS, Thompson PM, et al. Mapping cortical change across the human life span. Nat Neurosci. 2003;6:309–15. Copyright Nature Publishing Group, Inc.)

1.0% have been reported for most brain regions.12 In line with measurements of regional brain volume, the most prominent age-related reduction in cortical thickness is observed in the prefrontal cortex.13 Cortical thinning with age is also consis- tently found in parietal and insular regions, and in these regions, thickness measures appear more sensitive than volume measurements to age-related changes.13 Changes in cortical thickness occur in different brain regions at different times dur- ing the life span; between young adulthood and middle age, cortical thinning occurs mainly in the prefrontal cortex and at the parietal-temporal-occipital junction, whereas in the oldest

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hippocampal volume is associated with poor performance on tests of episodic memory.10

Cortical Thickness

The development of automated techniques to obtain quantita- tive MRI measures has permitted the study of the effects of aging on structural parameters, such as cortical thickness (▶ Fig. 8.5), with regional changes in cortical thickness detect- able over time intervals as short as 1 year. In the normal aging population, annual reductions in cortical thickness of 0.5 to

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Normal Aging

Fig. 8.3 Peak age maps showing the nonlinear effect of age on gray matter (GM). Color maps show the mean age at which peak GM density is reached for each point on the lateral, medial, and top surfaces of the brain. Shown in black are regions where the partial correlation coefficient for the nonlinear age effect did not reach statistical significance; age effects in these regions tended to decrease linearly with age rather than quadratically. (Reproduced with permission from Sowell ER, Peterson BS, Thompson PM, et al. Mapping cortical change across the human life span. Nat Neurosci 2003;6:309–315. Copyright Nature Publishing Group, Inc.)

Fig. 8.4 Coronal T1-weighted magnetic reso- nance imaging of two representative young (a) and elderly (b) healthy brains, demonstrating hippocampal atrophy, ventricular enlargement, and increased prominence of the sylvian fissures.

Fig. 8.5 Illustration of the cortical thickness measurement principle. (a) Original magnetic resonance imaging scan; (b) extraction of the cortical gray matter (GM) boundaries (yellow: white matter to GM boundary, red: cerebrospinal fluid to GM boundary); (c) thickness measurement (blue segment).

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elderly, changes are most prominent in the primary sensory and motor cortices.14

The relationship between cognitive function and cortical thickness is less well established than that with cortical volumes, although some data suggest that cortical thickness correlates with performance on tests of reaction time, verbal working memory, and episodic memory.15

8.1.2 Iron Deposition in the

Extrapyramidal Nuclei

Iron accumulates in substantial quantities in the brain with normal aging, and its magnetic properties are readily detectable using MRI. MRI signal intensity changes in GM nuclei, particu- larly on gradient-echo sequences, are well described in associa- tion with normal aging and neurodegenerative disease and correlate with non-heme iron deposition as determined at autopsy (▶ Fig. 8.6).16 Iron deposits are not present at birth, and in children under the age of 10 years, the basal ganglia are hyperintense on MRI, becoming hypointense by approximately 25 years of age.16 In healthy adults, the highest iron concentra- tions are found in the globus pallidus, red nucleus, and pars reticularis of the substantia nigra.17 Deposition also occurs, albeit at a slower rate, in the cerebellum, dentate nucleus, and neostriatum.17

8.1.3 White Matter Macrostructural Lesions

Changes in WM are a common finding in cognitively intact eld- erly individuals and on T2-weighted MRI scans are visualized as hyperintense areas. These WM hyperintensities (WMHs) are classified as periventricular or deep, depending on the lesion’s proximity to the lateral ventricle (▶ Fig. 8.7). The prevalence of WMHs increases with age, and by the fifth decade, they are detectable in nearly all healthy individuals.18

Postmortem studies have identified distinct histopathological correlates for the various WMH subtypes. Periventricular WMHs are subdivided into “caps” surrounding the frontal horns of the lateral ventricles, pencil-thin lining, and halos. Pencil-thin lining and halos represent areas of demyelination

8.8).19,20
caps are associated with myelin pallor, arteriosclerosis, and

astrogliosis.20 Deep WMHs can be punctate, early confluent, or confluent. Confluent and early confluent WMHs represent a continuum of ischemic lesions; histology shows incomplete parenchymal destruction with axonal loss and astrogliosis.19 By comparison, punctate WMHs are considered more benign and correlate with regions of reduced myelination and widen- ing of Virchow-Robin perivascular spaces (▶ Fig. 8.9).19

The relationship between WMH density and cognition in normal aging remains unclear. One meta-analysis has uncov- ered limited evidence that WMHs in healthy individuals are

and subependymal

gliosis

(▶ Fig.

Periventricular

Imaging of the Normal Aging Brain

Fig. 8.6 Axial gradient-echo MRI demonstrating hypointensity of the putamina (as indicated by white arrows) in an elderly brain due to iron accumulation.

Fig. 8.7 Axial T2-weighted (a) and coronal fluid-attenuated inversion recovery (FLAIR)
(b) magnetic resonance imaging demonstrating, respectively, confluent and periventricular white matter hyperintensities, as sometimes observed in the brains of elderly patients.

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Normal Aging

Fig. 8.8 Apologies, the legend is correct but does in fact refer to a different MR image which is attached. Axial T2-weighted image showing an example of nonspecific gliosis in the left frontal WM as indicated by the yellow arrow.

Fig. 8.9 Axial T2-weighted MRI demonstrating diffusely enlarged Virchow-Robin perivascular spaces in subcortical WM. An example of an enlarged Virchow Robin space is indicated by the white arrow.

74

associated with poor performance on tasks assessing declara- tive memory and executive function,21 whereas more recent work suggests that periventricular WMHs correlate most strongly with deficits in multiple cognitive domains.22

Microstructure

Diffusion-based MRI techniques, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion spectrum imaging (DSI), measure aspects of the diffusion of water molecules within brain tissue to probe tissue microarchi- tecture. The diffusion properties can be quantified using vari- ous parameters. Mean diffusivity (MD) is a measure of the rate at which water molecules diffuse: high MD indicates that biological barriers are sparse and indicates low tissue density. Fractional anisotropy (FA) measures the directional coherency with which diffusion occurs. In normal WM, water molecules tend to diffuse in parallel to the myelinated axon bundles, and disruption of these myelinated bundles allows water molecules to diffuse in other planes more readily, measured as a reduction in FA.

Diffusion studies can identify WM changes before the devel- opment of WMHs. In normal aging, the most consistent find- ings are reduced FA and increased MD; axonal degeneration, myelin breakdown, and glial scarring appear to be the main histopathological correlates of these changes.23 Brain regions that exhibit reduced FA with aging include the internal capsule, corpus callosum, and centrum semiovale.23 According to some

investigations, there is evidence of an anteroposterior gradient of reduced FA with aging, paralleling the changes observed using GM volumetry.24 There is limited additional evidence of a superoinferior gradient, with more marked age-related reduction of FA in dorsal fiber tracts, such as the superior longi- tudinal fasciculus.25 Aging also correlates with MD changes, although the amplitude of these changes is smaller in compari- son to changes in FA. The most consistent findings, namely, of an anteroposterior gradient of MD and of increased MD in the corpus callosum, parallel the changes observed in FA.23

There is evidence to indicate that, within the normal aging population, DTI measures are associated with cognitive func- tion. For example, FA in the body of the corpus callosum corre- lates with performance in motor skill tests, and diffusivity mea- sures in frontal regions correlate with performance on tests of verbal fluency.23

8.1.4 Cerebrovascular Changes Microbleeds

Cerebral microbleeds (CMBs) appear as small, round, homoge- neous foci of signal hypointensity on gradient-echo T2*- weighted MRI (▶ Fig. 8.10). Histopathology reveals focal depos- its of perivascular hemosiderin, suggesting that these regions represent previous microhemorrhages.26 In normal aging, CMBs do not have a specific distribution pattern and can be found in cortical and subcortical regions.27

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Imaging of the Normal Aging Brain

this is not supported by the results of studies combining quan- titative perfusion with detailed structural neuroimaging, indi- cating that regional CBF reduction is not always coupled to regional brain atrophy.31,32

Cerebrovascular Reactivity

During the aging process, there is loss of elasticity, progressive fibrosis, and atherosclerosis within the cerebral vasculature.33 Macroscopic cerebrovascular reactivity (CVR), a measure of elasticity in large blood vessels, can be measured noninvasively using transcranial Doppler whereas CT, PET, and MRI-based techniques are used to assess microvascular reactivity. As age advances, macrovascular CVR declines; however, it is uncertain whether this represents a truly physiological component of aging or a reflection of underlying neurodegenerative and cere- brovascular disease.34

A reduction in microvascular CVR has also been demon- strated in association with aging in a range of studies using car- bon dioxide, breath holding, and acetazolamide challenges to assess the vasodilatory capacity of small cerebral vessels.35 A progressive attenuation of both vasodilator and vasoconstrictor responses is seen with advancing age, indicating impaired ves- sel elasticity. This reduction in CVR is more pronounced in the presence of risk factors for cardiovascular disease, for example, diabetes, smoking, and hypertension.35

8.2 Metabolism

Positron emission tomography detects gamma radiation emitted by a positron-emitting radionuclide, or tracer, linked to a biologically active molecule. Molecules like fluorodeoxy- glucose (FDG), a glucose analog, and oxygen can therefore be used to estimate differing aspects of brain metabolic activity.

Positron emission tomography studies using labeled oxygen have demonstrated an age-related decline in the global cerebral metabolic rate of oxygen (CMRO2), with one study finding a decrease in CMRO2 of 0.5% per year in certain brain regions.36 Some key trends are apparent. First, age effects on the CMRO2 are more prominent in GM than in WM.37 Second, the most marked age effects are seen in supratentorial regions, specifi- cally in the frontal, temporosylvian, and parieto-occipital corti- ces.37 In view of the correspondence with the pattern of atrophy observed in aging, it has been suggested that these PET findings occur secondary to volume loss and do not reflect true hypome- tabolism.38 Although a variety of techniques aiming to correct for the presence of atrophy now exist, many PET studies of aging predate the development of such techniques, and there- fore this issue remains contentious.38

FDG-PET studies of the normal aging population have pro- duced somewhat contradictory results.39 Although some research suggests that the aging brain becomes globally hypo- metabolic, this finding is not replicated in all studies.39,40 With regard to the cortical distribution of such changes, FDG hypometabolism is predominantly observed in the frontal lobe, particularly after the age of 60 years (▶ Fig. 8.11).41,42 Hypome- tabolism has also been reported in the temporal, parietal, and somatosensory cortices, but changes are small in magnitude compared with those observed in frontal regions.39,42

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Fig. 8.10 Axial gradient-echo T2-weighted MRI showing a case of multiple cerebral microbleeds. One such microbleed is indicated by the white arrow.

It is uncertain whether CMBs are a feature of normal aging or a marker of small-vessel disease; in one study, 6.4% of healthy participants from an elderly population had at least one CMB.28 The prevalence of CMBs increases with advancing age; however, CMBs are also associated with risk factors for cerebrovascular disease, such as diabetes.27 In addition, there are correlations between the presence of CMBs and lacunar infarcts and conflu- ent WMHs, pointing to an association with vascular disease.28

The presence of CMBs correlates with cognitive impairment in patients with cerebrovascular disease, but few studies have explored this relationship in healthy elderly adults. A single study, however, has reported an association between CMBs and subjective memory impairment.29

Perfusion

Single-photon emission computed tomography (SPECT), MRI, and positron emission tomography (PET) studies have all shown that increasing age is associated with a decline in global cerebral blood flow (CBF).30,31,32 However, changes in CBF are not uniform across the brain; the most marked age effects are found in the prefrontal cortex, although decreased perfusion is also detectable in the parietal lobe, inferior temporal regions, motor cortex, and basal ganglia.30,31 Areas with relatively pre- served CBF include the occipital lobe and the posterior superior temporal lobe.30,31

The cause of age-related CBF reduction remains uncertain. The pattern of change is reminiscent of the anteroposterior gra- dient of atrophy and as a consequence led to the proposal that the reduction in CBF occurs secondary to volume loss, although

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Normal Aging

Fig. 8.11 Significant areas of negative correlation between age and glucose metabolism as determined by FDG-PET in a group of males. Significant areas (p < 0.05) are overlaid on a T1-weighted MRI image. Areas with significant negative correlation in this study included (1) left superior temporal gyrus, (2) right superior temporal gyrus, (3) medial frontal gyrus, and (4) caudate/left subcallosal gyrus. The color scale denotes t value. Reproduced with permission from Shen X, Liu H, Hu Z, Hu H, Shi P. The relationship between cerebral glucose metabolism and age: report of a large brain PET data set. PLoS One. 2012; 7:e51517. Copyright Shen et al.

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Imaging of the Normal Aging Brain

8.3 Brain Function

Imaging techniques used to investigate brain function at rest or in response to stimuli include PET, functional MRI (fMRI), and SPECT. In healthy adults, a transient increase in blood flow occurs locally in engaged brain regions after activation in response to task performance or internal processes. This causes an increase in the oxyhemoglobin/deoxyhemoglobin ratio and, since these two forms of hemoglobin have differing magnetic properties, a blood oxygen-level dependent (BOLD) fMRI signal is generated.43

Task-related fMRI, during which changes in the BOLD signal, as an indirect measure of brain activation, are measured during engagement in specific tasks, can be used to investigate struc- ture-function relationships. With advancing age, task-related activations tend to become weaker and more diffuse, regardless of which task is performed.23 More specifically, a reduction in functional hemispheric lateralization is seen in the prefrontal

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cortex during tests of perception, episodic memory, and inhibi- tory control,23 although the explanation for this altered lateral- ization remains unclear. In some studies, reduced functional hemispheric lateralization was associated with superior per- formance on episodic, semantic, and working memory tasks, indicating a potential compensatory mechanism.44 Reduced functional hemispheric lateralization may also represent age- related loss of cortical inhibition.44

In contrast to task-related fMRI, task-free or resting-state fMRI provides information on intrinsic brain connectivity in the absence of engagement in explicit tasks.45 In normal adults, a specific network of brain regions, termed the default-mode net- work, can be prominently identified on task-free fMRI, and this encompasses the precuneus, posterior cingulate cortex, medial prefrontal cortex, and medial temporal lobe.45 In the healthy elderly population, a consistent finding is decreased functional connectivity across this network, independent of changes in brain volume (▶ Fig. 8.12).23

Fig. 8.12 Whole-brain analyses of functional connectivity using resting-state fMRI representing coherent activity in the default-mode network, comprising correlations between the posterior cingulate cortex/retrosplenial cortex and both the medial prefrontal cortex and the bilateral lateral parietal cortex, and associated decline in old age. The z value refers to the coordinate of the MRI scan along the ventral-dorsal axis according to the Talairach atlas. Reproduced with permission from Andrews-Hanna JR, Snyder AZ, Vincent JL et al. Disruption of large-scale brain systems in advanced aging. Neuron. 2007; 565:924-935. Copyright Elsevier, Inc.

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Normal Aging

Table 8.1 Take-home points of the signs and symptoms generally associated with advancing age

Reduced total brain weight and volume and enlargement of CSF spaces

Reduced brain weight and volume2

Increased prevalence of white matter hyperintensities18

Reduced fractional anisotropy and increased mean diffusivity in the white matter23

Darkening of basal ganglia on T2* magnetic resonance imaging due to iron deposition23

Increased prevalence of cerebral microbleeds27

Reduced cerebral blood flow, globally but particularly in the prefrontal cortex30,31,32

Declining rate of cerebral oxygen and glucose metabolism, especially in frontal regions41,42

Decreased hemispheric lateralization of activation during performance of active tasks23

Reduced integrity of the default-mode network at rest23

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8.4 Conclusions

The physiological processes associated with normal aging result in multidimensional alterations in brain structure and function, ranging from directly observable atrophy to disruption of func- tional connectivity as measured using complex analytical meth- ods (▶Table 8.1). The regional age-associated volume loss is distinct from that observed in neurodegenerative dementias. The development of lesions like WMHs and CMBs reflects a progressive accumulation of focal pathology, whereas hypoper- fusion and hypometabolism are indicative of more widespread degenerative processes.

As brain imaging techniques and associated methods of anal- ysis become increasingly sophisticated, their continued applica- tion to the study of normal aging will be a critical first step toward improved understanding of the imaging changes that accompany the diseases of later life.

8.5 Acknowledgments

The authors are grateful to Ludovico D’Incerti, MD (Neuro- radiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy), Paolo Vitali, MD (Scientific Department, Fondazione IRCCS Istituto Neurologico Mondino, Pavia, Italy), Kuven Moodley, MRCP (Brighton & Sussex Medical School, Falmer, UK), and Daniela Perani, MD (Department of Clinical Neuroscience, Università Vita-Salute e Ospedale San Raffaele, Milan, Italy), for insightful advice on previous drafts and provi- sion of part of the figures.

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[20] Chimowitz MI, Estes ML, Furlan AJ, Awad IA. Further observations on the pathology of subcortical lesions identified on magnetic resonance imaging. Arch Neurol 1992; 49: 747–752

[21] Gunning-Dixon FM, Raz N. The cognitive correlates of white matter abnor- malities in normal aging: a quantitative review. Neuropsychology 2000; 14: 224–232

[22] Bolandzadeh N, Davis JC, Tam R, Handy TC, Liu-Ambrose T. The association between cognitive function and white matter lesion location in older adults: a systematic review. BMC Neurol 2012; 12: 126

[23] Minati L, Grisoli M, Bruzzone MG. MR spectroscopy, functional MRI, and dif- fusion-tensor imaging in the aging brain: a conceptual review. J Geriatr Psy- chiatry Neurol 2007; 20: 3–21

[24] O’Sullivan M, Jones DK, Summers PE, Morris RG, Williams SC, Markus HS. Evi- dence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology 2001; 57: 632–638

[25] Sullivan EV, Rohlfing T, Pfefferbaum A. Longitudinal study of callosal micro- structure in the normal adult aging brain using quantitative DTI fiber track- ing. Dev Neuropsychol 2010; 35: 233–256

[26] Fazekas F, Kleinert R, Roob G et al. Histopathologic analysis of foci of signal loss on gradient-echo T2*-weighted MR images in patients with spontaneous intracerebral hemorrhage: evidence of microangiopathy-related microbleeds. AJNR Am J Neuroradiol 1999; 20: 637–642

[27] Loitfelder M, Seiler S, Schwingenschuh P, Schmidt R. Cerebral microbleeds: a review. Panminerva Med 2012; 54: 149–160

[28] Roob G, Schmidt R, Kapeller P, Lechner A, Hartung HP, Fazekas F. MRI evi- dence of past cerebral microbleeds in a healthy elderly population. Neurology 1999; 52: 991–994

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. [29]  van Norden AG, van Uden IW, de Laat KF et al. Cerebral microbleeds are related to subjective cognitive failures: the RUN DMC study. Neurobiol Aging 2013; 34: 2225–2230

. [30]  Meyer JS, Terayama Y, Takashima S. Cerebral circulation in the elderly. Cere- brovasc Brain Metab Rev 1993; 5: 122–146

. [31]  Chen JJ, Rosas HD, Salat DH. Age-associated reductions in cerebral blood flow are independent from regional atrophy. Neuroimage 2011; 55: 468–478

. [32]  van Es AC, van der Grond J, ten Dam VH et al. PROSPER Study Group. Associa-
tions between total cerebral blood flow and age related changes of the brain.
PLoS ONE 2010; 5: e9825

. [33]  Hegedüs K, Molnár P. Age-related changes in reticulin fibers and other con-
nective tissue elements in the intima of the major intracranial arteries Clin
Neuropathol 1989; 8: 92–97

. [34]  Keage HA, Churches OF, Kohler M et al. Cerebrovascular function in aging and
dementia: a systematic review of transcranial Doppler studies. Dement Ger-
iatr Cogn Dis Extra 2012; 2: 258–270

. [35]  Naritomi H, Meyer JS, Sakai F, Yamaguchi F, Shaw T. Effects of advancing
age on regional cerebral blood flow: studies in normal subjects and subjects with risk factors for atherothrombotic stroke. Arch Neurol 1979; 36: 410–416

. [36]  Leenders KL, Perani D, Lammertsma AA et al. Cerebral blood flow, blood vol- ume and oxygen utilization: normal values and effect of age. Brain 1990; 113: 27–47

[37] Pantano P, Baron JC, Lebrun-Grandié P, Duquesnoy N, Bousser MG, Comar D. Regional cerebral blood flow and oxygen consumption in human aging. Stroke 1984; 15: 635–641

[38] Fazio F, Perani D. Importance of partial-volume correction in brain PET stud- ies. J Nucl Med 2000; 41: 1849–1850

[39] Meltzer CC, Becker JT, Price JC, Moses-Kolko E. Positron emission tomography imaging of the aging brain. Neuroimaging Clin N Am 2003; 13: 759–767
[40] Kuhl DE, Metter EJ, Riege WH, Phelps ME. Effects of human aging on patterns

of local cerebral glucose utilization determined by the [18F]fluorodeoxyglu-

cose method. J Cereb Blood Flow Metab 1982; 2: 163–171
[41] Kalpouzos G, Chételat G, Baron JC et al. Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging. Neurobiol

Aging 2009; 30: 112–124
[42] Herholz K, Salmon E, Perani D et al. Discrimination between Alzheimer

dementia and controls by automated analysis of multicenter FDG PET. Neuro-

image 2002; 17: 302–316
[43] Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with

contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A 1990; 87:

9868–9872
[44] Cabeza R. Hemispheric asymmetry reduction in older adults: the HAROLD

model. Psychol Aging 2002; 17: 85–100
[45] Rosazza C, Minati L. Resting-state brain networks: literature review and

clinical applications. Neurol Sci 2011; 32: 773–785

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Imaging of the Normal Aging Brain

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9 Iron Accumulation and Iron Imaging in the Human Brain

Stefan Ropele and Christian Langkammer

9.1 Iron Accumulation in the

Normal Aging Brain

Iron is an abundant trace element that is essential to the human body and has manifold functions, including blood oxygenation, conversion of blood sugar to energy, and myelin production.1 Although more than 60% of the total body iron is bound to hemoglobin, the most frequently found iron compartment in the brain is (intracellular) ferritin. Ferritin is a storage protein that keeps iron available in a nontoxic and soluble form. Each ferritin shell can host up to 5,000 iron ions. Early work using histologic Perls’ staining demonstrated that iron is not equally distributed across different brain structures; highest concentra- tions are found in deep gray matter nuclei.2 Iron accumulation in the brain is a nonlinear process. As revealed by a chemical brain analysis by Hallgren and Sourander in 1958,3 iron accu- mulates in the first four decades of life and plateaus afterward; no iron is present at birth (▶ Fig. 9.1). The reason for the accu- mulates is unclear, but it seems that iron transfer to the brain is largely one-way traffic. Throughout the brain, the highest iron concentrations can be found in the globus pallidus, red nucleus, substantia nigra, putamen, dentate nucleus, caudate nucleus, and thalamus (descending from 250 to 50 mg/kg). In contrast, cortical areas and white matter have significantly lower iron concentrations.3,4,5 Remarkably, in human brain tissue, there seems to be a baseline of iron levels of 30 mg/kg, which under- lines an essential, multifaceted role of iron and seems to reflect a minimum requirement for normal brain metabolism. Given these rather large differences of concentrations, surprisingly lit- tle is known about why iron accumulates preferably in the basal ganglia structures. Moreover, the exact mechanism of iron transfer between neurons and glia is poorly understood. Loading of intracellular ferritin may involve mitochondrial catabolism, whereas the export of ferritin from the cells to oligodendrocytes is thought to act through the mediation of ferritin receptors.

9.2 Abnormal Iron Accumulation

Although iron is an essential cofactor for many proteins and functions in the brain, iron overload is assumed to exert toxic effects as free or unbound iron ions serve as pro-oxidants.6 Ferric iron (Fe3 + ) reacts with superoxide and generates Fe2 + (Haber-Weiss reaction); ferrous iron (Fe2 + ) triggers the conver- sion of reactive oxygen species to hydroxyl radicals (Fenton reaction). Hydroxyl radicals are highly reactive oxygen species that induce oxidative stress, which may interfere with cellular signaling and lead to neuronal damage. Therefore, iron is often discussed in the context of triggering or mediating an inflam- matory or neurodegenerative cascade in many neurologic dis- eases. Increased iron levels in deep gray matter are a frequent but unspecific finding in several neurologic disorders and are frequently observed in a variety of neurodegenerative and inflammatory diseases, including Alzheimer’s disease (AD), Par- kinson’s disease (PD), Huntington’s disease, multiple sclerosis,

and amyotrophic lateral sclerosis (ALS).7,8,9,10 Besides deep gray nuclei, histologic studies in AD have found abnormally increased levels of iron in the proximity of neuritic plaques and neurofibrillary tangles.11,12 In deep gray matter and neuritic plaques, the role of iron in this context is not entirely clear, but abnormally high concentrations of iron can yield oxidative stress and induce neuronal vulnerability.13 Consequently, iron accumulation might additionally increase the toxicity of exoge- nous or endogenous toxins.

9.3 In Vivo Iron Assessment by Quantitative Magnetic Resonance Imaging

Single iron ions can be barely detected by magnetic resonance imaging (MRI), despite its high sensitivity for the underlying magnetic properties of tissues. However, the iron core of ferritin represents a highly organized structure similar to the mineral ferrihydrite (5Fe2O3 · 9H2O) and exhibits a strong paramagnetic effect that makes it detectable by MRI. This has opened a new window for the in vivo assessment of iron levels and for study- ing the pathological role of iron in the brain.

At the very beginning of clinical MRI, it was observed that the basal ganglia of healthy subjects often appeared hypoin- tense on T2-weighted spin-echo sequences (▶ Fig. 9.2). The sus- ceptibility affects scale with field strengths that make the detection of iron at high and ultrahigh field strengths more sen- sitive (▶ Fig. 9.3). Using iron staining in a histologic correlation study, it could be confirmed that the presence of iron was

Fig. 9.1 The dynamics of iron accumulation in different structures of the brain according to the study of Hallgren and Sourander. The globus pallidus shows the highest iron content and the highest rate of accumulation. The only region that does not show a plateauing effect is the thalamus, where the iron content decreases after the fourth decade of life (not shown). (From Hallgren B, Sourander P. The effect of age on the non-haemin iron in the human brain. J Neurochem 1958; 3 (1): 41–51.)

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responsible for this observation.14 Iron detection in these early studies was done using Perl’s staining, which has a moderate sensitivity for iron in white matter. More sophisticated approaches are based on diaminobenzidine-enhanced staining (▶ Fig. 9.4). Subsequent studies with visual rating of iron depo- sition in the basal ganglia followed but were limited by the intrinsically low sensitivity and a rater bias of this approach. Nowadays, quantitative MRI techniques provide sensitive mea- sures of iron content on a continuous scale, and these measure- ments are highly reproducible and comparable among subjects and scanners. The following sections of this chapter present a brief overview of proposed MR methods for iron mapping along

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Iron Accumulation and Iron Imaging in the Human Brain

Fig. 9.2 Spectrum of hypointensities that can be typically observed on T2-weighted sequences in normal aging subjects as a consequence of
iron accumulation. Appearance of the dendtate nucleus (arrow, a), red nucleus and substantia nigra (arrow, b), globu spallidus, putamen, and caudate nucleus (arrow, c) in a 68-year-old healthy woman on T2-weighted spin-echo

(top rows) and corresponding fluid-attenuated inversion recovery (FLAIR, bottom rows) sequences.

with their advantages and limitations; a summary is provided in ▶ Table 9.1.

The longitudinal relaxation time T1 (also often represented by its inverse R1 = 1/T1) is only moderately affected by brain iron,15 which can be explained by a weak dipolar interaction. In contrast, the magnetic field perturbations introduced by the iron accelerate spin dephasing and, therefore, loss of transversal magnetization. In the case of a spin-echo sequence, where these field inhomogeneities are compensated by refocusing radiofre- quency pulses, some irreversible dephasing effects remain because of the stochastic nature of diffusion. This effect can be considered a series of arbitrary oriented jumps through these

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Fig. 9.3 Ultrahigh-field imaging allows better depiction of iron-loaded gray matter structures because of its higher sensitivity for variation of the magnetic susceptibility and its higher spatial resolution. Appear- ance of the dentate nucleus (arrows) in a formalin-fixed brain sample at 3 T (left) and 7 T (right). Images were acquired with a spoiled T2*- weighted gradient-echo sequence.

Fig. 9.4 Total nonheme iron (ferric and ferrous) can be stained with diaminobenzidine (DAB)-enhanced Perl’s (Turnbull) blue staining.14 Whereas cortical iron content is comparable to the iron content in white matter (see also ▶ Fig. 9.1), substantial variations can be observed in subcortical regions.

Table 9.1 Most relevant magnetic resonance (MR) imaging techniques proposed for the assessment of brain iron

MR method Advantages Limitations

T1 relaxometry

Robust against susceptibility artifacts

Low sensitivity for iron Time consuming

R2 relaxometry

Sequence is readily available on clinical systems Moderate sensitivity for iron
Robust against susceptibility artifacts

Moderate acquisition speed
In multislice acquisition or with fast spin-echo read- out, the observed R2 may also be affected by magnetization transfer effects

R2* relaxometry

Sequence is readily available on clinical systems
Fast 3-dimensional whole-brain acquisition (< 10 min) High sensitivity for iron

Calcifications cannot be separated from clustered iron deposits
Sensitive to macroscopic susceptibility artifacts

Phase imaging

Sequence is readily available on clinical systems High sensitivity for iron
Calcifications can be distinguished from iron deposits

Phase unwrapping and filtering needed
Not a linear measure for iron (does not reflect only local susceptibility)

Susceptibility-weighted imaging

Good sensitivity for iron
Calcifications can be distinguished from iron deposits Enhanced tissue contrast

Same as for phase imaging
Not quantitative (depends on postprocessing parameters)

Quantitative susceptibility mapping

High sensitivity for iron
Linear measure for iron
Calcifications can be distinguished from iron deposits

Extensive image postprocessing

82

field variations as a consequence of Brownian motion. Conse- quently, iron accelerates signal loss in spin-echo (T2) and gradi- ent-echo sequences (T2*), which in turn result in increased transverse relaxation rates R2 and R2* (R2 = 1/T2 and R2* = 1/ R2*), respectively. Both R2 and R2* relaxation rates have been proven to be sensitive and linear measures for brain iron and can be assessed using conventional MR sequences readily avail- able on clinical scanners.16,17 R2 can be measured using a spin- echo sequence and R2* using a gradient-echo sequence, both with a minimum of two acquired echoes. R2* has a higher sensi- tivity for iron than R2, allows faster acquisition of the entire brain, and can also be acquired rapidly at ultrahigh field strengths (7 tesla [T]), where specific absorption rate restric- tions are an issue. Given these advantages, R2* should be the preferred measure for brain iron in a clinical setup.5 Neverthe-

less, R2* is more prone to artifacts in the proximity of the sinus and cavities, which in turn renders R2 an interesting measure at these specific locations.18 Another related measure is R2′, which separates reversible contributions (associated with micro- structure) from total signal dephasing (R2* = R2 + R2′). R2’ is also sensitive for iron but requires a dedicated MRI sequence or, alternatively, the acquisition of both a gradient echo and a spin echo, which is time consuming in a clinical setup. Other studies have demonstrated field-dependent R2 rate increase as a means to measure brain iron, but the application of this technique in a clinical environment is complicated because it requires scan- ning of the subjects at a minimum of two different field strengths.19 Another interesting approach for iron detection is MR phase imaging.20 Phase values represent shifts in the MR frequency induced by iron (as well as other paramagnetic and

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severity and progression. So far, findings have been related mostly to deep gray matter because iron mapping in the cortex suffers from the previously mentioned limitations.

Visual rating of T2-weighted images demonstrated lower sig- nal intensities (as indicative for higher iron concentration) in the putamen and red nucleus of AD patients than in healthy controls.35 In addition, R2 and R2* relaxometry demonstrated increased iron in the hippocampus,36,37 the temporal cortex,38 and the pulvinar nucleus.39 An R2* map showing increased iron deposition in the basal ganglia of an AD patient, and how this is related to a healthy control is depicted in ▶ Fig. 9.5. Extending these findings, MR phase imaging revealed higher iron con- centrations in the hippocampus, parietal cortex, putamen, cau- date nucleus, and dentate nucleus.40,41 Further evidence of increased iron levels in deep gray matter comes from magnetic field dependency studies, where iron levels in the caudate nucleus, putamen, and globus pallidus were elevated in AD patients.42,43

The affinity of iron to amyloid plaques was used to study plaque load and evolution of plaques in postmortem brains, as well as in transgenic animal models. It has been speculated that iron might be involved in the formation of amyloid because of the formation of reactive oxygen species; alterna- tively, iron might be secondarily involved in the removal of amyloid.12,44 Although the idea of depicting amyloid plaques with methods other than positron emission tomography is intriguing, MRI of these plaques is challenging because pla- que size is below the resolution of clinical MRI. Nonetheless, the plaque-attached iron causes perturbations of the mag- netic field that are strong enough to affect bulk tissue suscep- tibility and MR relaxation times.36 So far, single amyloid pla- ques have been detected by T2*-weighted imaging in human brain tissue only ex vivo.45 Unfortunately, the required sig- nal-to-noise ratio and the spatial resolution (40 μm isotropic) cannot be achieved in vivo within a clinically feasible scan time, although much hope was put on ultrahigh field scan- ners (7T and greater), which intrinsically provide a higher signal and a greater sensitivity for susceptibility changes. Consequently, a better strategy might be to use a histogram- based technique for the detection of macroscopic susceptibil- ity, changes that may reduce the need for detecting neuritic plaques on an individual basis.

9.5 Iron Mapping in Animal

Models of Alzheimer’s Disease

Small-bore systems usually operate at ultrahigh field strengths, allowing noninvasive study of the development of pathologic features in animal models at extremely high image resolution. Whereas animal models allow histologic or histo- chemical validation of MRI findings at any time point, valida- tion studies in patients with AD are obviously limited to the time point of death. This limitation hinders investigation of the disease at an early stage and also the investigation of individual therapeutic interventions. Transgenic animal mod- els have been used mostly to refine single-plaque imaging for in vivo applications.46,47

So far, only a single serial MRI study on the dynamics of plaque formation and development has been performed in a

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diamagnetic compounds) and have been shown to scale with iron content.21 Owing to the fact that the MR signal represents the complex transverse magnetization, the raw-phase data obtained have 2π ambiguities, which subsequently have to be eliminated by so-called phase unwrapping algorithms.20 Spatial varying low-frequency field components can be removed by high-pass filtering.22 However, high-pass filtering substantially reduces the sensitivity for iron in a nonlinear manner, and this is why recent studies suggest that the phase-based iron content can be compared in specific brain structures only.23

Susceptibility-weighted imaging (SWI) is a related imaging modality that combines filtered phase and magnitude images to obtain images whose contrast provides a high sensitivity for iron, veins, and other paramagnetic inclusions.22,24 Because of its sensitivity for even small veins, SWI can provide valuable infor- mation for the radiologic assessment; however, SWI is not quan- titative and remains a nonlinear measure for iron content.23

The latest technique to overcome this issue is quantitative susceptibility mapping (QSM). QSM provides absolute values of the magnetic susceptibility, an intrinsic physical property of matter, rendering its results comparable when different scan- ners and field strengths are used. Although the clinical impact of QSM has not yet been fully explored, it is evident that it is a proportional and highly sensitive measure for iron.25,26,27 QSM is mathematically and computationally challenging, and the development of fast algorithms is the focus of current research.28 For practical reasons, the impact of other approaches for imaging brain iron, such as magnetic-field correlation and direct saturation imaging, remains unclear and the subject of further investigations.29,30

Although iron mapping in gray matter can be accomplished reliably, iron mapping in white matter remains challenging because myelin content and neuronal fiber orientation signifi- cantly affect the bulk susceptibility. Diamagnetic myelin coun- teracts the observed susceptibility of paramagnetic iron; in con- trast, it additively impacts relaxation rates.25,31,32 Current research focuses on disentangling these effects and providing precise individual assessment of iron and myelin content. The orientation of white matter fiber bundles with respect to the main magnetic field of the scanner also impacts the effective transverse relaxation rate R2*, gradient-echo phase, and mag- netic susceptibility.33,34 This circumstance can significantly limit comparative studies that are based on regional assessment of iron in white matter.

So far, only a limited number of MRI studies have focused on the in vivo measurement of cortical iron. Because of the thinness of the cortical layer and its relatively low iron concentration, a quite sensitive sequence is required. Unfortunately, especially MR images of the cortex are affected by artifacts of the tissue- liquor interface, which makes the application of postprocessing and correction methods essential. Therefore, current in vivo MRI studies focusing on cortical pathology are based mainly on histo- gram techniques or on an atlas-based group analyses.

9.4 Iron Mapping in Patients with

Alzheimer’s Disease

In vivo MRI studies have measured iron accumulation in the gray matter of patients with AD and related its extent to disease

Iron Accumulation and Iron Imaging in the Human Brain

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Fig. 9.5 Iron mapping with R2* relaxometry is a linear, sensitive, and quantitative approach in the sense that it is reproducible and comparable among subjects. Top row: Fluid-attenuated inversion recovery (FLAIR) image (left) and corresponding R2* map (right) of a 62-year-old Alzheimer’s patient. In the R2* map, a higher signal corresponds to a higher iron concentration. Lower row: FLAIR image and R2* map of a 58-year-old healthy control. Note that the windowing in both maps is identical.

transgenic mouse model at 12, 14, 16, and 18 months.48 Disease progression was reflected by an increase in the number and size of plaques. This progression was also paralleled by an increase of the R2 relaxation rate in the hippocampus and cortex of AD mice, whereas R2 in control mice remained unchanged. The association between plaque development and diffuse iron accu- mulation in gray matter needs further investigation but might offer a new way to assess plaque load and development indirectly.

In line with this finding, a study revealed elevated iron levels in the cortex in a presenilin amyloid precursor protein mouse model at an early stage of 24 weeks.49 In contrast to histo- chemical studies, this study did not find elevated iron levels in neuritic plaques using X-ray fluorescence microscopy. In this context, only neuritic plaques with iron attached seem to be detectable by MRI in a mutant APP mouse model, whereas iron- negative plaques remain invisible for T2*-weighed MRI, which highlights the pathologic role of iron in at least a certain per- centage of neuritic plaques.50

9.6 Iron Mapping in Patients with

Parkinson’s Disease

The concentration of iron in the substantia nigra (SN) is among the highest in all anatomical structures.3 This feature can be used to precisely locate the SN and adjacent regions for deep brain stimulation.51 Postmortem studies in patients with PD and related disorders, such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), consistently demonstrated elevated levels of iron in the SN.52,53,54 Although these studies additionally revealed higher iron levels in the basal ganglia of patients with PSP and MSA, the iron concentration was con- versely found to be lower in PD. In contrast, other studies sug- gested higher iron concentrations also in the basal ganglia of PD patients. Histologic investigation revealed higher iron levels in the putamen, and levels were less pronounced in the SN and the caudate nucleus55; related postmortem work found increased iron levels in the lateral segment of the globus pallidus.56

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Iron Accumulation and Iron Imaging in the Human Brain

Fig. 9.6 In Parkinson’s disease, the substantia nigra is the structure with the highest disease-related iron accumulation. Although this is not obvious in conventional magnetic resonance imaging, susceptibility- weighted techniques allow a better depiction of iron-accumulating structures. (a) Fluid-attenuated inversion recovery (FLAIR) image.
(b) Corresponding R2* map.

Fig. 9.7 Tract-based spatial statistics demonstrates significant reduc- tion in fractional anisotropy (FA) (left column) and significantly increased R2* (right column) in amyotrophic lateral sclerosis patients compared with age- and sex-matched controls (significant voxels at
p < 0.05 are shown in red). The regions with decreased FA and those with increased R2* in the mesencephalic part of the corticospinal tract are closely localized. (Used with permission from Langkammer C, Enzinger C, Quasthoff S, et al. Mapping of iron deposition in conjunction with assessment of nerve fiber tract integrity in amyotrophic lateral sclerosis. J Magn Reson Imaging 2010;31(6): 1339–1345.)

In vivo MRI studies found higher R2* levels in the SN57,58,59,60 but also in the basal ganglia.61 ▶ Fig. 9.6 shows an example of the appearance of the SN on a R2* map from a patient with PD. An increase in the SN was also found using SWI62 or phase mapping.63 Another study found higher R2* rates in the lateral SN pars compacta but additionally found a correlation between the lateralized motor score from the clinically more affected side and the contralateral R2* rate in the SN.64 Additionally, increased iron levels were shown in the SN, even in patients with early untreated PD.65

Patients who developed PD along with an existing dementia showed more iron in the SN than patients with AD alone, a finding that might be relevant for differential diagnosis.66 Furthermore, MSA and PD were differentiated by using phase values from the inner region of the putamen and the pulvinar thalamus.67 Higher R2’ rates were found in the basal ganglia in patients with PSP compared with those with PD, and stepwise discriminant analysis allowed patients with PSP to be distin- guished from patients with PD and healthy controls.68

In a recent longitudinal study, patients with PD showed an increase in R2* in the SN and the putamen during a 3-year period,69 whereas these regions did not shown any change in controls. Additionally, the variation in R2* was correlated with worsening of motor symptoms of PD. These results suggest lon- gitudinal iron mapping as a tool for assessing neurodegenera- tion and monitoring treatments effects in PD.

9.7 Iron Mapping in Patients with

Motor Neuron Diseases

In amyotrophic lateral sclerosis (ALS), the focus of MRI research is mainly on the pyramidal tract of the central nervous system, starting from the spinal cord, to the corticospinal tract (CST), and extending to the motor cortex. Early work revealed a high prevalence of T2-shortening (R2 rate increase) in the precentral cortex in ALS patients, whereas this observation is rarely made in normal subjects.70 Consequently, studies on iron deposition

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in ALS patients focusing on the precentral gyrus confirmed this pattern of cortical hypointensities on T2 and T2*-weighted MRI.71,72 Besides this cortical hallmark, degeneration of the CST is consistently found in ALS patients.73 Patients with ALS showed a trend for increased R2* rates along the mesencephalic CST and in the caudate nucleus compared with controls.74 Com- plementary diffusion tensor imaging revealed lower fractional anisotropy closely localized in the region of the CST, where R2* was also increased, suggesting iron mapping as a potential bio- marker paralleling neurodegeneration (▶ Fig. 9.7).

The source of T2 and T2* changes in ALS is yet not clear. An interesting study compared T2 and T2*-weighted MRI and found that the cortical hypointensities in ALS are present more often on T2- than on T2*-weighted images, which argues against elevated iron levels and suggests that other factors are more dominant.75 The observation that the occurrence of hypo- intensities in the precentral gyrus is related to normal aging also argues for microstructural tissue changes.76 On the other hand, a study that combined in vivo and postmortem MRI with subsequent histology demonstrated that cortical T2* hypoin- tensites are due to abnormally high iron deposition in deeper layers of the motor cortex in ALS.8 Additionally, histologic stain- ing of iron revealed its accumulation in microglial cells. Although the source of the signal variations has not been fully resolved, it seems that the extent and dynamics of the signal change are closely related to the disease state and progression.

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A longitudinal analysis of T2*-weighted images found more pronounced hypointensities in the precentral gyrus at the 6-month follow-up examination after the first evaluation,71 with the extent of the hypointensities correlated with disability as determined by the ALS functional rating scale.

9.8 Conclusion and Outlook

Iron, the most prevalent trace metal, accumulates in the human brain in the process of normal aging. However, abnormally increased iron levels in the basal ganglia are consistently found in pathogenesis, sharing a neurodegenerative and probably also an inflammatory component. Nevertheless, current knowledge about pathologically relevant iron deposition still comes mainly from histologic studies, but it is anticipated that MRI can aid in investigation of the role of iron (i.e., to clarify whether iron accumulation is secondary and reflects accumulated neurode- generation or can also trigger, or at least mediate, the neuro- degenerative cascade).

Iron has a strong paramagnetic effect in perturbing the mag- netic fields, thus rendering it detectable by MRI. Several MRI techniques and novel developments allow not only detection but also quantitative assessment of iron concentrations in the brain in vivo. Owing to the noninvasive nature of MRI, quantita- tive MRI techniques are especially promising in longitudinal studies for monitoring disease progression and possible treat- ment effects. New insight may be expected from the application of novel quantitative MR techniques and from moving to ultra- high-field-strength MRI systems.

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[20] Rauscher A, Barth M, Reichenbach JR, Stollberger R, Moser E. Automated unwrapping of MR phase images applied to BOLD MR-venography at 3 Tesla. J Magn Reson Imaging 2003; 18: 175–180

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[22] Reichenbach JR, Haacke EM. High-resolution BOLD venographic imaging: a window into brain function. NMR Biomed 2001; 14: 453–467

[23] Walsh AJ, Wilman AH. Susceptibility phase imaging with comparison to R2 mapping of iron-rich deep grey matter. Neuroimage 2011; 57: 452–461
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(SWI). Magn Reson Med 2004; 52: 612–618
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intrinsic magnetic tissue properties using MRI signal phase: an approach to

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(QSM) as a means to measure brain iron? A post mortem validation study.

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reconstruction of quantitative susceptibility maps: superfast dipole inversion.

Magn Reson Med 2013; 69: 1582–1594
[29] Smith SA, Bulte JW, van Zijl PC. Direct saturation MRI: theory and application

to imaging brain iron. Magn Reson Med 2009; 62: 384–393
[30] Jensen JH, Chandra R, Ramani A et al. Magnetic field correlation imaging.

Magn Reson Med 2006; 55: 1350–1361
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ceptibility-weighted contrasts in high-field MRI of the brain. Neuroimage

2012; 59: 3967–3975
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matter MRI contrast in the human brain. Neuroimage 2012; 59: 1413–1419 [33] Denk C, Hernandez Torres E, MacKay A, Rauscher A. The influence of white matter fibre orientation on MR signal phase and decay. NMR Biomed 2011;

24: 246–252
[34] Li X, Vikram DS, Lim IAL, Jones CK, Farrell JA, van Zijl PC. Mapping magnetic

susceptibility anisotropies of white matter in vivo in the human brain at 7 T.

Neuroimage 2012; 62: 314–330
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heimer’s disease. Psychiatry Res 1998; 82: 181–185
[36] Schenck JF, Zimmerman EA. High-field magnetic resonance imaging of brain

iron: birth of a biomarker? NMR Biomed 2004; 17: 433–445
[37] Antharam V, Collingwood JF, Bullivant J-P et al. High field magnetic resonance microscopy of the human hippocampus in Alzheimer’s disease: quantitative

imaging and correlation with iron. Neuroimage 2012; 59: 1249–1260
[38] House MJ, St Pierre TG, Foster JK, Martins RN, Clarnette R. Quantitative MR imaging R2 relaxometry in elderly participants reporting memory loss. AJNR

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recovery hypointensity of the pulvinar nucleus of patients with Alzheimer’s disease: its possible association with iron accumulation as evidenced by the T2 map. Korean J Radiol 2012; 13: 674–683

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[46] Jack CR, Jr, Garwood M, Wengenack TM et al. In vivo visualization of Alz-
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. [50]  Vanhoutte G, Dewachter I, Borghgraef P, Van Leuven F, Van der Linden A. Non- invasive in vivo MRI detection of neuritic plaques associated with iron in APP [V717I] transgenic mice, a model for Alzheimer’s disease. Magn Reson Med 2005; 53: 607–613

. [51]  Deistung A, Schäfer A, Schweser F, Biedermann U, Turner R, Reichenbach JR. Toward in vivo histology: a comparison of quantitative susceptibility map- ping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high mag- netic field strength. Neuroimage 2013; 65: 299–314

. [52]  Dexter DT, Wells FR, Lees AJ et al. Increased nigral iron content and altera- tions in other metal ions occurring in brain in Parkinson’s disease. J Neuro- chem 1989; 52: 1830–1836

. [53]  Dexter DT, Carayon A, Javoy-Agid F et al. Alterations in the levels of iron, ferri- tin and other trace metals in Parkinson’s disease and other neurodegenerative diseases affecting the basal ganglia. Brain 1991; 114: 1953–1975

. [54]  Berg D, Hochstrasser H. Iron metabolism in Parkinsonian syndromes. Mov Disord 2006; 21: 1299–1310

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. [56]  Griffiths PD, Crossman AR. Distribution of iron in the basal ganglia and neo- cortex in postmortem tissue in Parkinson’s disease and Alzheimer’s disease. Dementia 1993; 4: 61–65

. [57]  Péran P, Cherubini A, Assogna F et al. Magnetic resonance imaging markers of Parkinson’s disease nigrostriatal signature. Brain 2010; 133: 3423–3433

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Increased iron-related MRI contrast in the substantia nigra in Parkinson’s dis- ease. Neurology 1995; 45: 1138–1143

[65] Martin WRW. Quantitative estimation of regional brain iron with magnetic resonance imaging. Parkinsonism Relat Disord 2009; 15 Suppl 3: S215–S218

[66] Brar S, Henderson D, Schenck J, Zimmerman EA. Iron accumulation in the substantia nigra of patients with Alzheimer’s disease and parkinsonism. Arch Neurol 2009; 66: 371–374

[67] Wang Y, Butros SR, Shuai X et al. Different iron-deposition patterns of multiple system atrophy with predominant parkinsonism and idiopathetic Parkinson’s diseases demonstrated by phase-corrected susceptibility- weighted imaging. AJNR Am J Neuroradiol 2012; 33: 266–273

[68] Boelmans K, Holst B, Hackius M et al. Brain iron deposition fingerprints in Parkinson’s disease and progressive supranuclear palsy. Mov Disord 2012; 27: 421–427

[69] Ulla M, Bonny JM, Ouchchane L, Rieu I, Claise B, Durif F. Is R2* a new MRI bio- marker for the progression of Parkinson’s disease? A longitudinal follow-up. PLoS ONE 2013; 8: e57904

[70] Oba H, Araki T, Ohtomo K et al. Amyotrophic lateral sclerosis: T2 shortening in motor cortex at MR imaging. Radiology 1993; 189: 843–846

[71] Ignjatović A, Stević Z, Lavrnić S, Daković M, Bačić G. Brain iron MRI: a bio- marker for amyotrophic lateral sclerosis. J Magn Reson Imaging 2013; 38: 1472–1479

[72] Imon Y, Yamaguchi S, Yamamura Y et al. Low intensity areas observed on T2- weighted magnetic resonance imaging of the cerebral cortex in various neu- rological diseases. J Neurol Sci 1995; 134 Suppl: 27–32

[73] Ellis CM, Suckling J, Amaro E, Jr et al. Volumetric analysis reveals corticospinal tract degeneration and extramotor involvement in ALS. Neurology 2001; 57: 1571–1578

[74] Langkammer C, Enzinger C, Quasthoff S et al. Mapping of iron deposition in conjunction with assessment of nerve fiber tract integrity in amyotrophic lateral sclerosis. J Magn Reson Imaging 2010; 31: 1339–1345

[75] Hecht MJ, Fellner C, Schmid A, Neundörfer B, Fellner FA. Cortical T2 signal shortening in amyotrophic lateral sclerosis is not due to iron deposits. Neuro- radiology 2005; 47: 805–808

[76] Ngai S, Tang YM, Du L, Stuckey S. Hyperintensity of the precentral gyral sub- cortical white matter and hypointensity of the precentral gyrus on fluid- attenuated inversion recovery: variation with age and implications for the diagnosis of amyotrophic lateral sclerosis. AJNR Am J Neuroradiol 2007; 28: 250–254

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[59] Baudrexel S, Nürnberger L, Rüb U et al. Quantitative mapping of T1 and T2* discloses nigral and brainstem pathology in early Parkinson’s disease. Neuro- image 2010; 51: 512–520

[60] Du G, Lewis MM, Styner M et al. Combined R2* and diffusion tensor imaging changes in the substantia nigra in Parkinson’s disease. Mov Disord 2011; 26: 1627–1632

[61] Ye FQ, Allen PS, Martin WR. Basal ganglia iron content in Parkinson’s disease measured with magnetic resonance. Mov Disord 1996; 11: 243–249

[62] Zhang J, Zhang Y, Wang J et al. Characterizing iron deposition in Parkinson’s disease using susceptibility-weighted imaging: an in vivo MR study. Brain Res 2010; 1330: 124–130

[63] Jin L, Wang J, Zhao L et al. Decreased serum ceruloplasmin levels characteris- tically aggravate nigral iron deposition in Parkinson’s disease. Brain 2011; 134: 50–58

[64] Martin WRW, Wieler M, Gee M. Midbrain iron content in early Parkinson’s disease: a potential biomarker of disease status. Neurology 2008; 70: 1411–

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Part IV Alzheimer’s Disease

. 10  Mild Cognitive Impairment 90

. 11  Overview of Alzheimer’s Disease 113

. 12  Genetics, Neuropathology, and
Biomarkers in Alzheimer’s Disease 119

. 13  Imaging of Alzheimer’s Disease: Part 1 124

. 14  Imaging of Alzheimer’s Disease: Part 2 133

. 15  Magnetic Resonance Imaging and Histopathological Correlation in
Alzheimer’s Disease 139

IV

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Alzheimer’s Disease
10 Mild Cognitive Impairment

Kei Yamada and Koji Sakai

10.1 Outline of Mild Cognitive

Impairment

Mild cognitive impairment (MCI), by definition, is a state of cognitive decline in which cognitive deficits are noted but not significant enough to meet the diagnostic criteria for dementia; MCI is recognized as an intermediate state between normal cognition and dementia (▶Fig. 10.1). The original concept of MCI was proposed by Petersen et al,1 who emphasized mainly memory impairment and the status of MCI as a precursor for Alzheimer’s disease (AD). After several years, MCI was recog- nized as a concept of heterogeneous clinical presentation, cause, and prevalence2,3,4 and was expanded to be adapted to other cognitive domains, thereby extending the early detection of other dementias in their prodromal stages.5,6,7

10.2 Diagnostic Concept

and Its Evolution

The conceptual topics of MCI and MCI-related staging systems are listed in ▶ Table 10.1. As an early concept, in 1837, Prichard identified four stages of dementia: (1) impairment of recent memory with intact remote memories, (2) loss of reason, (3) incomprehension, and (4) loss of instinctive actions.8 Later, in 1962, Kral described an entity that distinguished relatively unimpaired and impaired by the terms benign senescent forget- fulness and malignant senescent forgetfulness.9 By the early 1980s, several staging systems for progressive aging and dementia associated with AD were published: Limited Cognitive Disturbance,10 Clinical Dementia Rating (CDR)11 0.5 (“questionable dementia”), and Global Deterioration Scale for

Assessment of Primary Degenerative Dementia (GDS).12 The third stage of GDS was initially termed mild cognitive decline and sub- sequently was retermed mild cognitive impairment by Reisberg and colleagues.13,14 After the late 1980s, several diagnostic crite- ria to describe cognitive decline by aging and as a precursor of dementia were proposed: age-associated memory impairment,15, aging-associated cognitive decline,16 mild cognitive disorder,17 and mild neurocognitive disorder.18 Later, in 1992, Zaudig pro- posed his own concept and definition for MCI.19 More detailed information regarding the history of MCI evolution can be found in the writings of Reisberg and colleagues.20

The current MCI concept, which has been generally accepted and referred to, was proposed by Petersen and colleagues.5,21 This concept divides MCI into four subtypes21: amnestic single- domain MCI, amnestic multidomain MCI, nonamnestic single- domain MCI, and nonamnestic multidomain MCI.

Although concerns over ambiguity and difficulty in establish- ing diagnosis remain in the current diagnostic guidelines provided by the National Institute on Aging—Alzheimer’s Asso- ciation workgroups,22,23 the term mild cognitive impairment has already been recognized as the expression of a clinical stage between normal cognitive decline and dementia.

10.3 Epidemiology
10.3.1 Mild Cognitive Impairment

Findings from epidemiologic studies have yet to provide unified information regarding the clinical aspects of MCI. Although several findings of MCI have been reported, because of differ- ing diagnostic criteria, measuring instruments, definitions of severity, and different samples based on study population

Fig. 10.1 Mild cognitive impairment as an intermediate stage in the longitudinal course of Alzheimer’s disease. (Reprinted with permission from Smith GE, Bondi MW, Mild Cognitive Impairment and Dementia, Definitions, Diagnosis, and Treatment, New York: Oxford University Press; 2013:6. Original: Petersen, 2004.)

90

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Mild Cognitive Impairment

Table 10.1 Topics in mild cognitive impairment (MCI): concepts and criteria.

Year Study Topic

1837

Prichard8

Four stages of cognitive impairment

1962

Kral9

Benign/malignant senescent forgetfulness

1982

Gurland et al10

Limited cognitive disturbance

1982

Hughes et al11

Clinical Dementia Rating (CDR)

1982

Reisberg et al12

Global Deterioration Scale for Assessment of Primary Degenerative Dementia (GDS), (GDS 3 = mild cognitive impairment)

1986

Crook et al15

Age-associated Memory impairment (AAMI)

1992

Zaudig1,9

Zaudig’s MCI based on Diagnostic and Statistical Manual IIIR/ International Classification of Disease-10

1994

American Psychiatric Association174

Mild neurocognitive disorder (MND)

1995

Levy16

Aging-associated cognitive decline (AACD)

1995

Christensen et al17

Mild cognitive disorder (MCD)

1995

Petersen174

Petersen’s MCI (CDR = 0.5)

1995

Ebly et al173

Cognitive impairment no dementia (CIND)

2004

Petersen7

Four MCI categories

2011

Albert et al21

New criteria for MCI and biomarkers

and clinical reports, we have not established complete epide- miologic findings for MCI. Currently, several nationwide epide- miologic studies associated with the Alzheimer’s Disease Neuroimaging Initiative (ADNI)24 have been ongoing in countr- ies around the world. However, as of today, their findings, par- ticularly regarding the clinical aspects of MCI, have not been summarized to be shared with physicians worldwide. In the fol- lowing sections of this chapter, findings from epidemiologic studies are summarized.

In general, epidemiology serves a triple role in public health: descriptive, analytical, and interventional. The relationships between these roles and MCI are as follows25:

●  Descriptiveepidemiology:ThemonitoringofMCIprevalence and incidence across time

●  Analyticalepidemiology:Thedeterminationofriskfactors and their patterns of interaction, permitting the construction of hypothetical etiologic models of the disease process

●  Interventionalepidemiology:Thedesignationofpotential intervention points for the reduction of morbidity and mortality, which may guide more targeted clinical research.
10.3.2 Descriptive Epidemiology
How widespread are both explicit and implicit MCI in the gen- eral population? The reported incidence rates for MCI vary in the literature. A variety of population-based cohort studies have reported incidence rates within their elderly populations (i.e., 65 to 75 years) to be between 14 and 111 per 1,000 patient- years26,27,28,29,30,31,32; amnestic MCI appears to occur more com- monly than nonamnestic MCI.31
10.3.3 Analytical Epidemiology
Various studies had reported that gender, race, and lower edu- cation are inconsistently associated with various MCIs.31,33,34,55, 36,37 In a community-based study (participants between 70 and 89 years),31,33 MCI was more common in men (odds ratio [OR]=1.5). In addition, elevated blood pressure, diabetes with or without symptomatic cerebrovascular disease, obesity,34,35,36, 37,38,39,40,41 cardiac disease,42 and apolipoprotein E epsilon 4 genotype43,44 were all found to be associated with higher risk of MCI or certain subtypes of MCI. Compared with normal sub- jects, MCI groups were generally seen to manifest left medial temporal lobe atrophy and smaller medical temporal lobe vol- umes.45,46 Artero and colleagues have suggested that white mat- ter lesions, particularly in periventricular areas, are associated with MCI.47 Tervo and colleagues48 examined a range of demo- graphic, vascular, and genetic factors and found the most signifi- cant risk factors to be age (OR 1.08), apolipoprotein E4 (Apo-E4) allele (OR 2.04), and medicated hypertension (OR 1.86).
▶Fig. 10.2 shows the theoretical pathways to MCI,25 which incorporate most of the known risk factors for dementia. There are, however, insufficient population data at present to permit either a statistical calculation of transition probabilities in relation to individual risk factors or a maximum likelihood

Fig. 10.2 Hypothetical etiologic model of mild cognitive impairment (black) and possible treatments (blue). (Reprinted with permission from Fig. 2 in Ritchie K. Mild cognitive impair- ment: an epidemiological perspective, Dialogues Clin Neurosci 2004;6(4): 401–408. © Les Laboratoires Servier)

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91

Alzheimer’s Disease
calculation to assess the overall predictive value of possible

competing hypothetical general models of MCI.

10.3.4 Interventional Epidemiology

Currently, there is no clearly specified treatment for MCI. Nevertheless, it may be possible to reduce overall risk by many kinds of simple risk factor management,49 for instance, man- agement of cardiovascular and cerebrovascular risk factors, such as high blood pressure, from early adult life onward to reduce the risk of infarcts and white matter lesion accumula- tion; depression control; and the provision of adequate learning opportunities from a younger age.

10.4 Clinical Features 10.4.1 Symptoms

Patients with MCI, especially the amnestic subtypes, are known for their memory complaints; this represents a change over baseline. Subjective memory complaints have been demon- strated to predict cognitive decline, even when patients appear to be unimpaired on testing.50,51

Mood and behavioral symptoms are more common in patients with MCI than in normal subjects with intact cogni- tion.52,53,54,55 A population-based study found that apathy, agitation, anxiety, irritability, depression, and delusions were significantly more common in patients with MCI compared with those with normal cognition.54

The correlation between depression and cognitive impair- ment is complicated. Cognitive impairment may be an initial symptom of depression, so called pseudodementia. A number of population-based studies have found an association between various measures of depression and the presence of MCI.52,56,57 However, follow-up studies have yielded mixed results.52,56,61 Overall, depression is more likely to be an early manifestation of cognitive decline rather than an independent risk factor for MCI, although some studies have found disparate results.62,63

10.4.2 Subtypes

The four subtypes of MCI are based on the presence of memory impairment and the number of cognitively impaired domains: (1) amnestic MCI, single domain; (2) amnestic MCI, multidomain; (3) nonamnestic MCI, single domain; and (4) nonamnestic MCI multidomain.21 Within MCI are several types of progression to degenerative dementia other than AD, including vascular demen- tia and dementia with mental and physical causes; also, some MCI patients retain their MCI state for several years and then return to healthy condition.7 Amnestic MCI is often thought of as a precursor to AD.46 Autopsy studies of brains from MCI patients64,65 have not revealed any consistent findings regarding the neuropathological and clinical features of MCI. Therefore, it is important that MCI be recognized as “a group of patients without dementia that exhibits cognitive decline at an abnormal age, but has no difficulty during daily life.”

10.4.3 Underlying Diseases

Diseases that affect cognitive functions, such as intracranial disease, mental disorder, systematic internal disease, and medical poisoning, may all be possible underlying diseases of MCI; known disorders or conditions that can be fitted into such a category include AD, limbic neurofibrillary tangle dementia (LNTD), dementia with Lewy bodies (DLB), frontotemporal lobar degeneration (FTLD), depression, and others (▶ Table 10.2).

10.4.4 Differential Diagnosis

After excluding physiologic factors (organic factors), other pos- sible diagnoses should be considered, including depression, as well as other psychosocial factors (e.g., forfeiture of social role, loss of spouse or family member, illness). Aged patients suffer- ing from depression may display decreased cognitive perform- ance and lowered physical activity as a result of decreased attention and slower psychomotor activity, thus appearing to have dementia (hence the term pseudodementia).66 These

Table 10.2 Underlying diseases of mild cognitive impairment

Dementia Underlying disease and conditions

Dementia with degenerative disease

Frontotemporal lobar degeneration (frontotemporal dementia, semantic dementia), dementia with Lewy body, limbic system neurofibril degenerative dementia, progressive supranuclear palsy, corticobasal degeneration

Dementia with cerebrovascular disease

Cerebral infarction, bleeding cerebrally, multiple infarction dementia, Binswanger’s disease

Dementia with endocrine disease

Hypothyroidism, hypoparathyroidism, reiterate hypoglycemic attack

Dementia with trophopathy and metabolic disorder

Wernicke encephalopathy, vitamin B12 deficiency, chronic metabolic disorder (liver failure, kidney failure), hyponatremia

Dementia with hypoxic encephalopathy

Heart/lung disease, carbon monoxide poisoning

Dementia with tumor

Brain tumor (primary, metastasis), meningitis carcinomatosa, remote effect of cancer

Dementia with infectious disease

Meningitis, encephalitis, brain tumor, neurosyphilis, progressive multifocal leukoencephalopathy, AIDS

Dementia with abnormal metal metabolism

Aluminum (dialysis encephalopathy), Copper (Wilson disease)

Dementia with medical poisoning

Antineoplastic drug, antipsychotic drug, sleeping drug, anticholinergic drug, L-DOPA, cimetidine, β-blocking agents, digitalis and preparations, steroid hormone, antituberculosis drug, hypoglycemic drug, alcohol, etc.

Others

Normal pressure hydrocephalus, chronic subdural hematoma, cerebral contusion, melancholy epilepsy (hippocampal sclerosis), etc.

92

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symptoms should be viewed separately because they may be relieved by antidepressants. On the other hand, if patients with MCI begin to develop depression, their risk of progressing to AD will be 2.6 times greater than those without depression.67 For those cases, long-term observation is important. Apart from depression, other mental disorders, such as delirium, epilepsy, and chemical-induced forgetfulness (e.g., benzodiazepine- derived medicine), should also be differentiated from MCI.

10.5 Neuropathology

Neuropathological studies suggest that MCI represents an early clinical expression of age-related neurodegenerative disease. Several autopsy studies have found that MCI patients have AD pathology that is intermediate in severity between normal and more advanced AD.44,64,68,69,70,71,72,73,74 Some stud- ies also found that pathologies consistent with other dementing processes (DLB, cerebrovascular disease) are overrepresented in MCI patients.44,46,65,75,76 Therefore, having a broad understand- ing of the knowledge and up-to-date information on AD pathol- ogy, as well as other dementing processes, is crucial to a further understanding of MCI.

A large autopsy study conducted by Schneider et al76 found that, of 134 subjects who died having a final antemortem diag- nosis of MCI, slightly more than half met the pathological crite- ria for AD. The subjects who met the pathologic criteria for “def- inite” AD were roughly equally divided between amnestic and nonamnestic MCI subtypes, along with another 20% with mixed pathologies (▶Table 10.3). Statistics indicate that MCI is a pathologically heterogeneous disorder; whether MCI was diag- nosed in its amnestic or nonamnestic form, many subjects exhibit mixed pathologies. These neuropathological findings suggest that MCI is more than a state of uncertainty for clini-

cians: the clinical syndrome of MCI may also reflect a transi- tional neuropathological process.

10.5.1 Risk Factors

The maximum risk factors for AD are recognized as aging and Apo-E4. Some studies report that most MCI patients were Apo E4 positive.77 The risk factors for angiopathy were also signifi- cant in MCI patients.78 Cholesterolemia might be associated with MCI and AD, and the latter has received much attention lately.79 Steenland et al reported that late-life depression was also strong risk factor for normal subjects progressing to MCI.80

10.5.2 Biomarkers

The annual incidence rate of AD among MCI patients is quite high, roughly estimated at around 12%,81 and a certain number of autopsies of MCI patients have shown pathological features similar to those of AD.81 Therefore, it is of pivotal importance to diagnostically predict whether or not MCI patients will proceed to AD; as a matter of fact, biomarkers aimed to distinguish pos- sible AD patients among MCI patients are still being explored continuously to this day.

In 1998, the Ronald and Nancy Reagan Research Institute of the Alzheimer’s Association and National Institute on Aging Working Group proposed a guideline for biomarkers of AD82 as follows: (1) biomarkers are able to detect a fundamental feature of Alzheimer’s neuropathology; (2) biomarkers should be validated in neuropathologically confirmed AD cases; (3) bio- markers should have preciseness (i.e., able to detect AD early in its course and distinguish it from other dementias); (4) bio- markers should be reliable; (5) biomarkers should be non- invasive; (6) biomarkers should be simple to perform; and (7) biomarkers should be inexpensive. At the moment, no bio- marker that has met all these criteria is clinically available. As diagnostic biomarkers for AD, Aβ42, Aβ40 in cerebrospinal fluid (CSF), and total tau and phosphate tau have all been recognized with clinical evidence,83 and it is expected that these biomarkers will soon be applied to MCI.

10.5.3 Current Diagnostic Guidelines

The current diagnostic guidelines for MCI, proposed by the National Institute on Aging—Alzheimer’s Association task force22 are as follows:

●  Concernregardingachangeincognitionreportedbypatient
or informant or observed by clinician

●  Objectiveevidenceofimpairmentinoneormorecognitive
domain, typically including memory

●  Preservationofindependenceinfunctionalabilities

●  Notdemented
However, the pathology of AD is a continuum of brain change, which began a long time before MCI symptoms appear; there- fore, the clear criteria that divide MCI and AD are unnatural. Nevertheless, effective treatment in the early stage of MCI requires effective criteria to divide MCI from AD. Because the current criteria for MCI are still ambiguous in the categorical distinction between MCI and dementia, overlap between MCI and mild AD can cause confusion among clinicians.23

Mild Cognitive Impairment

Table 10.3 Number and percentage of amnestic or nonamnestic MCI patients with no pathology, one type of pathology, or mixed pathology at autopsy

Presence of pathology Amnestic MCI Nonamnestic MCI (n=75) (n=59)

One pathology

41 (54.7%)

32 (54.2%)

AD diagnosis

27 (36.0%)

20 (33.9%)

NIA: high

6 (8%)

4 (6.8%)

NIA: intermediate

21 (28.0%)

16 (27.1%)

Infarcts

10 (13.3%)

11 (18.6%)

Lewy bodies

4 (5.3%)

1 (1.7%)

Mixed pathology

17 (22.7%)

9 (15.3%)

AD + infarcts

15 (20.0%)

8 (13.6%)

AD + Lewy bodies

2 (2.7%)

1 (1.7%)

AD + infarcts + Lewy bodies

0

0

Infarcts + Lewy bodies

0

0

No AD, infarcts, or Lewy bodies

17 (22.7%)

18 (30.5%)

Abbreviations: AD, Alzheimer’s disease; MCI, mild cognitive impair- ment; NIA, National Institute on Aging.
Source: Table 3 in Schneider J, Arvanitakis Z, Leurgans S, et al. The neuropathology of probable Alzheimer’s disease and mild cognitive impairment. Ann Neurol 2009;66:200–208.

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Alzheimer’s Disease

Fig. 10.3 An example of a diagnostic algorithm for mild cognitive impairment. AD, Alzheimer’s disease; CT, computed tomography; MRI, magnetic resonance imaging; PET, positron emission tomography; SPECT, single-photon emission computed tomography. (Reprinted with permission from Mizukami K, How do we deal with mild cognitive impairment [in Japanese]. Seishin Shinkeigaku Zasshi. 2009;111(1):26-30.)

10.5.4 Diagnostic Algorithm

An example of an MCI diagnostic algorithm is as follows (▶ Fig. 10.3)84:

●  Report(background,medicalorsurgicalhistory,family
history, medication) by the patient or a knowledgeable informant or observation by the clinician (refer to rating measures such as the CDR11 and Functional Assessment Staging of Alzheimer’s Disease [FAST]85

●  Neuropsychiatrictest(referfortestsfordementia)

●  Physicalandneurologiccheckup

●  Bloodandurinetests

●  Neuroimaging(computedtomography[CT],magneticreso-
nance imaging [MRI], positron emission tomography [PET]/
single-photon emission computed tomography [SPECT])

●  Electroencephalography and other tests
In addition, the following should be excluded: (1) mental disor- ders, such as depression, schizophrenia, and delusional dis- order; (2) the causes of symptomatic psychosis; and (3) side effects of medicinal treatment. The ADNI items81 are helpful for tangible inspection.
10.6 Neuroimaging 10.6.1 Outline
Although the role of neuroimaging in the evaluation of MCI is yet to be clearly defined, the modalities have provided valuable

information about both healthy elderly and AD patients. There- fore, these neuroimaging studies are anticipated to provide val- uable information for clarifying MCI pathology. The main neuroimaging modalities are SPECT, PET, and MRI. The main role of neuroimaging is to undertake risk validations for MCI, as well as to rule out other types of neurodegenerative dementia. The methods of neuroimaging used to validate MCI originated from those used to validate dementia. In the following subsec- tions, validation methods and neuroimaging studies regarding MCI are summarized.

10.6.2 Nuclear Medical Imaging: Single-Photon Emission Computed Tomography and Positron Emission Tomography

Both PET and SPECT provide mapping of the accumulation of radiopharmaceutical agents at certain regions of the body. These modalities can validate blood flow and brain metabolism. The spatial resolutions, however, of PET and SPECT (3 to 6 mm) are relatively lower than those of CT and MRI (CT, 0.5 to 1.5 mm). On the other hand, the main function of CT and MRI is to depict the structure of the brain. Therefore, these neuro- imaging modalities have been combined to depict the anatomy and function simultaneously. This subsection outlines neuro- imaging studies on MCI using both single modality and mixed modalities of nuclear medicine.

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has been suggested that MCI exists on a continuum between normal elderly people and patients with AD. However, it is diffi- cult to detect the hypometabolic patterns on FDG PET images by visual inspection. Therefore, statistically analyzed images have frequently been used. As shown in ▶Fig. 10.8, bilateral parietal and posterior cingulate metabolism is decreased in patients with MCI compared with healthy elderly subjects.98

In keeping with the distribution of early neurofibrillary pathology of AD, the decline in glucose metabolism involves the limbic and paralimbic cortex, as well as the temporal and parie- tal association cortex, in MCI. Longitudinal studies99 have indi- cated that FDG PET may predict the progression of MCI patients toward AD (▶ Fig. 10.9).

Regional reductions in glucose metabolism included foci in the bilateral paracampal/hippocampal cortex, right inferior pre- frontal cortex, left anterior insular cortex, left middle temporal cortex, bilateral inferior parietal cortex, and posterior cingulate cortex in a group of MCI patients who developed AD (▶Fig. 10.9a). MCI patients who did not develop AD showed only subtle abnormalities in the bilateral inferior frontal gyrus and bilateral temporal gyrus (▶ Fig. 10.9b). Compared with sta- ble MCI patients, significantly lower metabolism of the bilateral posterior cingulate cortex and right precuneus was found in the converter MCI group (▶ Fig. 10.9c).

Amyloid Imaging

A certain amount of cerebral β-amyloid (Aβ) burden has been recognized to be the primary cause of brain deterioration and cognitive decline in AD pathology.100 Therefore, amyloid imaging has rapidly become accepted as one of the central biomarkers in the study of AD progression. Among the amyloid ligands, carbon-11-labeled Pittsburgh Compound B (11C-PIB) is the most commonly studied and used tracer to date, and it appears to bind to brain fibrillar Aβ deposits with high sensitivity.101,102 As shown in ▶Fig. 10.10a, 11C-PIB PET imaging shows a clear difference in 11C-PIB uptake among non- converters to AD.103

The MCI patients showed intermediate uptake and retention between AD and nonconverters and a similar topographic

Mild Cognitive Impairment

Validation of Perfusion by Single-Photon Emission Computed Tomography

In AD pathology, from normal elderly to AD, MCI is thought to represent an intermediate stage of the decline in blood flow. Representative features of SPECT of MCI are the decline of blood flow around the association area of the posterior cerebral cor- tex (posterior cingulate gyrus, precuneus, parietotemporal lobe), and hippocampus compared with healthy elderly subjects (▶ Fig. 10.4).86

Because of the pathological variability of MCI, the patterns of blood flow are also diverse. In general, in the converter group, which shows relatively short-term progress to AD, the decline of blood flow at the region from posterior cingulate gyrus to the parietotemporal lobe is prominent compared with that seen in nonconverter groups (▶ Fig. 10.5).87,88,89,90

Furthermore, the converter group shows a significant decline of blood flow at the hippocampus.91,92 (▶ Fig. 10.6). These hypo- perfusions are noted with neurobiological knowledge of AD, specifically, that the entorhinal cortex is the earliest site to be compromised, even at a preclinical stage.93

A longitudinal follow-up study94 showed perfusion decline in an MCI group of patients in a small region of the middle and posterior cingulate and the frontal, temporal, and parietal regions. In contrast to the MCI group, the AD group showed a decline in perfusion in all cerebral lobes (▶ Fig. 10.7).

Evaluation of SPECT scans using quantitative (voxel-based statistical) analysis can potentially differentiate MCI likely to progress to AD from stable MCI with an accuracy of approxi- mately 73% before the appearance of clinical signs of significant cognitive impairment.95 Furthermore, perfusion SPECT can dif- ferentiate MCI of the AD type from other types of dementia with a sensitivity of 84% and a specificity of 89%.92,96,97 MCI patients who converted to AD showed hypoperfusion in the parahippocampal and inferior temporal gyri bilaterally.

Basal Metabolism

Based on findings and experiences with fluorodeoxyglucose (FDG) PET, which provides a measure glucose metabolism, it

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Fig. 10.4 Mild cognitive impairment versus healthy control on blood flow by single-photon emission computed tomography. (Caffarra P, Ghetti C, Concari L, Venneri A, Differential patterns of hypoperfusion in subtypes of mild cognitive impairment. The Open Neuroimaging Journal 2008;2: 20–28.)

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Alzheimer’s Disease

Fig. 10.5 Mild cognitive impairment (MCI) in patients who converted to Alzheimer disease compared with nonconverted MCI. Rendered brain regions indicating hypoperfusion examined by single-photon emission computed tomography. (Reprinted with permission from Park KW, Yoon HJ, Kang DY, Kim BC, Kim SY, Kim JW, Regional cerebral blood flow differences in patients with mild cogni- tive impairment between those who did and did not develop Alzheimer’s disease. Psychiatry Res Neuro- imag 2012;203:201–206.)

Fig. 10.6 Mild cognitive impairment (MCI) in patients who converted to Alzheimer disease compared with nonconverted MCI. Significant decline of blood flow at hippocampus examined by single-photon emission computed tomogra- phy. (Reprinted with permission from Habert MO, Horna JF, Sarazin M, et al. Brain perfusion SPECT with an automated quantitative tool can identify prodromal Alzheimer’s disease among patients with mild cognitive impairment. Neurobiol Aging 2011;32:15–23.)

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Mild Cognitive Impairment

Fig. 10.7 Regional changes in brain perfusion between baseline and 2-year follow-up in mild cognitive impairment (a) and Alzheimer’s disease (b) groups. (Reprinted with permission from

Fig. 2 in Alegret M, Cuberas-Borrós G, Vinyes- Junqué G, et al. A two-year follow-up of cognitive deficits and brain perfusion in mild cognitive impairment and mild Alzheimer’s disease.

J Alzheimer Dis 2012;30(1):109–120. doi:10.3233/JAD-2012-111850.)

Fig. 10.8 Mild cognitive impairment versus healthy control in glucose metabolism by fluorodeoxyglucose (FDG)-positron emission tomography. (Reprinted with permission from Ishi K, PET Approaches for Diagnosis of Dementia, AJNR Am J Neuroradiol 2014;http://dx.doi.org/ 10.3174/ajnr.A3695Au: Please update with volume and page numbers.)

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Alzheimer’s Disease

Fig. 10.9 Regional changes in brain metabolism between baseline and 1-year follow-up in Alzheimer’s disease (a), and healthy volunteers (b), and MCI groups (c). (Reprinted with permission from Fig. 1, Drzezga A, Lautenschlager N, Siebner H, Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer’s disease: a PET follow-up study. Eur J Nucl Med Mol Imaging 2003;30(8):1104–1113.)

Fig. 10.10 (a) Differences in 18F-florbetaben- fluorodeoxyencephalography (18F-FDG) and carbon-11-labeled Pittsburgh Compound B (11C- PIB) in a normal subject (upper row), a subject with mild cognitive impairment (MCI) (middle row) and a subject with dementia due to Alzheimer’s disease (bottom row). (b) Amyloid imaging with 18F-florbetaben in a healthy control, a participant classified as MCI, one subject with Alzheimer’s disease (AD) and one with fronto- temporal lobar degeneration (FTLD).

(Reprinted with permission from Jimenez Bonilla JF, Carril Carril JM. Molecular neuroimaging in degenerative dementias. Rev Esp Med Nucl Imag Mol 2013;32(5):301–309.) (Reprinted with per- mission from Villemagne VL, Rowe CG. Amyloid imaging. Int Psychogeriatrics 2011;23(Suppl 2): S41–S49.)

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lessened the enthusiasm for cerebral Aβ (e.g., amyloid imaging) to become a stand-alone biomarker of AD.

10.6.3 Magnetic Resonance Imaging

Several MRI techniques and methods have been implemented in the diagnosis and research of MCI, including MR volumetry, structural analysis, H1 (proton) magnetic resonance spectros- copy (MRS) for metabolite evaluation, diffusion-weighted MRI for the evaluation of structure and constitution, and functional MRI (fMRI) for assessing activated brain regions in MCI patients. The following subsections summarize the validation methods based on MRI methods and MRI-based studies on MCI.

MRI Volumetry

Magnetic resonance imaging volumetry is defined as the accu- mulation of voxels or subvoxels within the region of interest (ROI) on MRI. When voxels are used for assessing morphomet- ric change of ROI, this procedure is called voxel-based morphometry (VBM). By using volumetry and VBM, volume losses in MCI patients can be observed, including regions at the hippocampus, entorhinal cortex, and amygdala. Among VBM studies, Chételat et al109 reported that significant volume loss

pattern in the posterior cingulate gyrus, anterior cingulate, and frontal cortex. Although 11C-PIB shows usability for AD diagno- sis, the radioactive decay half-life of 11C is relatively short (approximately 20 minutes) and limits its facility. To overcome this limitation, 18F-labeled Aβ imaging tracers such as 18F-flor- betaben have also been studied (18F has approximately 110 minutes of radioactive decay half-life (▶ Fig. 10.10b).104 Cortical retention of 18F-florbetaben in the frontal, posterior cingulate/precuneus, and lateral temporal areas was noted, with relative sparing of occipital and sensorimotor cortex in MCI and AD subjects. In contrast, no cortical 18F-florbetaben retention was seen in healthy controls or FTLD subjects.

As individuals progress to MCI and dementia, clinical decline and neurodegeneration accelerate and appear to proceed inde- pendently of amyloid accumulation.105 This supposition largely concurs with the model of dynamic changes proposed by Jack et al106 wherein AD biomarkers are most informative in the pre- clinical period (▶ Fig. 10.11).

In addition, findings indicate that a substantial proportion of cognitively intact elderly patients also have a significant level of Aβ plaque burden,107 further suggesting that Aβ may be necessary, but not sufficient, for AD progression.108 The lack of specificity of Aβ to predict cognitive decline, as well as its weak association with clinical symptoms and disease severity, has

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Mild Cognitive Impairment

Fig. 10.11 (a) Dynamic biomarkers of the Alzheimer’s pathological cascade. (b) Positron emission tomography amyloid imaging with Pittsburgh compound B in (a) normal without atrophy on magnetic resonance imaging (MRI), (b) normal with atrophy on MRI, and (c) Alzheimer’s disease patient. (Reprinted with permission from Figs. 1 and 2 in Jack C Jr, Knopman D, Jagust W, et al, Hypothetical model of dynamic biomarkers of the Alzheimer patho- logical cascade, Lancet Neurol 2010;9(1):119— 128.)

Alzheimer’s Disease

Fig. 10.12 Atrophy on (a) mild cognitive impairment (MCI) compared with healthy control and (b) Alzheimer’s disease compared with MCI. (Reprinted with permission from Karas GB, Scheltens P, Rombouts SA, et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage 2004;23:708–716.)

Table 10.4 Gray matter difference in anatomical regions of normal controls versus patients with mild cognitive impairment and Alzheimer’s disease

Label Mean percentage difference

NCLR vs. MCI

MCI vs. AD

L

R

L

R

Lobes

Frontal

4.5

3.1

11.1

9.4

Temporal

1.7

0.8

10.9

11.2

Parietal

6.3

7.2

13.1

12.4

Occipital

0.5

-0.2

12.9

11.2

Medial temporal lobe, basal ganglia, and insula

Amygdala

3.3

4.1

10.7

7.6

Hippocampus

4.9

5.9

7.9

5.5

Thalamus

13.4

12.4

14.1

14.2

Caudate head

4.6

4.1

10.5

10.6

Insula

4.6

3.2

6.9

8.2

Superior temporal cortex

7.2

6.4

8.5

10.7

Cortical association areas and cingulate

Parietal association

2.3

3.0

18.7

16

Retrosplenial cingulate

3.1

3.5

7.3

5.9

Anterior cingulate

–0.2

1.2

9.2

8.1

Abbreviations: AD, Alzheimer’s disease; L, left; R, right; MCI, mild cognitive impairment; NCLR, normal controls.
Source: Table 4 in Karas KB, Scheltens P, Rombouts SA, et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage 2004;23:708–716.

100

was observed in amnestic MCI patients compared with healthy elderly subjects; these losses were seen in the hippocampus, posterior cingulate gyrus, and subcallosal area; on the other hand, compared with AD patients, the volume of gray matter at the posterior association region of the cerebellum was spared. Karas et al110 also reported that, compared with healthy elderly subjects, amnestic MCI patients exhibited significant volume loss at regions of the interior temporal lobe, insula, and thala- mus; compared with AD patients, volumes of the parietal lobe, as well as the anterior and posterior cingular gyrus, were spared (▶Fig. 10.12 and ▶Table 10.4). With these results, it was thought that amnestic MCI patients might exhibit signifi- cant brain atrophy at certain regions, although not as widely as in AD patients, especially at the interior temporal lobe region, including the hippocampus.109,110,111 Furthermore, Bell- McGinty et al112 revealed that the brain atrophy patterns differ in amnestic MCI and multiple cognitive domain MCI (▶ Fig. 10.13).

In the amnestic group, significant volume losses were noted at the left entorhinal cortex and inferior parietal lobe; in multi- ple cognitive domain MCI subjects, on the other hand, signifi- cant volume losses were noted at the right inferior frontal gyrus, right middle temporal gyrus, and bilateral superior tem- poral gyrus. These results suggest that amnestic MCI, which exhibits memory impairment as the primary feature, may be associated with medial temporal lobe atrophy, and multiple cognitive domain MCI may be associated with extensive lesions within the cerebral cortex.

Longitudinal observations also have provided valuable infor- mation and knowledge about the development of MCI. Whit- well et al113 observed prospectively 33 amnestic MCI patients

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for several years until they progressed to AD, and then the investigators analyzed the patients’ longitudinal structural MRI data by VBM (▶ Fig. 10.14). At the time point of 3 years before onset of AD, significant—but not severe—volume loss was observed at the left-side–dominant amygdala, head of hippo- campi, entorhinal cortex, and fusiform gyrus; by the time point of 1 year before onset of AD, significant volume loss developed at the bilateral whole hippocampi, as well as in regions from

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the posterior temporal lobe to the parietal lobe. By the time AD was diagnosed, severe volume losses involving the entire region of the temporal lobe, as well as wide-area volume loss from the temporoparietal lobe to the frontal lobe, were observed.

In these studies, compared with AD patients, wide-area atro- phy in the cerebral cortex was not observed among MCI patients. However, significant atrophy was noted in the hippo- campal region compared with healthy subjects. Furthermore,

Mild Cognitive Impairment

Fig. 10.13 Different atrophy patterns in amnestic mild cognitive impairment and multiple cognitive domain mild cognitive impairment. (Reprinted with permission from Bell-McGinty S, Lopez OL, Meltzer C, et al, Differential cortical atrophy in subgroups of mild cognitive impairment. Arch Neurol 2005;62:1393–1399.)

101

102

Alzheimer’s Disease

Fig. 10.14 Regional changes in brain atrophy between baseline and 9 to 18 months’ follow-up in patients with amnestic mild cognitive impair- ment. L, left; R, right. (Reprinted with permission from Whitwell JL, Przybelski SA, Weigand SD,
et al. 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer’s disease. Brain 2007;130:1777–1786.)

atrophy at the hippocampal region had already begun at the early stages of MCI, and those with severe atrophy frequently progressed to AD within a short time.

Blood Flow Analysis by Arterial Spin Labeling

Neuronal activity is tightly coupled with cerebral blood flow (CBF). Therefore, one of the most reliable approaches to assess

the disease progression of MCI or AD is to measure CBF. MR- based CBF measurements have been developed to investigate hemodynamic alterations in MCI or AD, including arterial spin labeling (ASL)114 and dynamic contrast techniques.115 ASL is a noninvasive MRI technique that allows measurement of CBF without using any contrast agents. Several researchers have reported the usefulness of ASL for revealing CBF abnormalities in patients with MCI.

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Mild Cognitive Impairment

Fig. 10.15 Regional perfusion abnormalities between patients with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) patients and healthy elderly controls (HC) using pulsed arterial spin labeling (PASL). L, left; R, right. (Reprinted with permission from Fig. 1 in Alexopoulos P, Sorg C, Förschler A, et al. Perfusion abnormalities in mild cognitive impairment and mild dementia in Alzheimer’s disease measured by pulsed arterial spin labeling MRI. Eur Arch Psychiatry Clin Neurosci. 2012;262(1):69–77.)

Fig. 10.16 Regional perfusion differences between (a) control, (b) mild cognitive impairment patient, and (c) Alzheimer’s disease patient on cerebral blood flow. (Reprinted with permission from Zhang Q, Stafford RB, Wang Z, et al. Microvascular perfusion based on arterial spin labeled perfusion MRI as a measure of vascular risk in Alzheimer’s disease, J Alzheimers Dis 2012;32(3):677–687. doi:10.3233/JAD-2012- 120964.)

Comparison studies of the regional perfusion abnormalities between MCI or AD patients and healthy elderly controls using pulsed ASL (PASL) have been performed116,117 and showed sig- nificant differences between healthy elderly controls and MCI and AD patients (▶ Fig. 10.15, ▶ Fig. 10.16). Lower CBF in

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patients with MCI compared with healthy controls was found in the right and left superior parietal gyrus, right and left angular gyrus, left inferior parietal gyrus, left and right middle temporal gyrus, and middle occipital gyrus. Patients with AD showed lower CBF than healthy controls in the right angular gyrus, left

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and right superior parietal gyrus, left and right inferior parietal lobe, right middle occipital gyrus, left precuneus, and caudate. They concluded that PASL may be a valuable instrument for investigating perfusion changes in the transition from normal aging to dementia. Furthermore, the cross-validation studies of ASL and PET for assessing CBF on AD pathology have shown good agreement among these two modalities.118,119 Although clinical evidence of ASL on AD pathology has not been established, ASL is expected to become one of the primary measurement techniques for AD pathology because of its noninvasiveness.

1H Magnetic Resonance Spectroscopy

Proton MR spectroscopy (1H MRS) is an analytical imaging tech- nique that is sensitive to the changes in the chemical environ- ment in the brain at the cellular level. With 1H MRS, major pro- ton-containing metabolites in the brain, including N-acetyl aspartate (NAA), myo-inositol (MI), choline (Cho), and creatine (Cr), are quantitatively measured during a common data acqui- sition period as the mean values in the certain size of voxel of interest. Therefore, 1H MRS may have an important role in the clinical evaluation and monitoring of dementia in early stages of AD pathology.120

Several investigations have aimed to distinguish the behavior of brain metabolites in AD pathology from that in normal con- trols. The NAA metabolite is a marker for neuronal integrity, and it decreases in a variety of neurologic disorders, including MCI and AD.121,122 The MI spectrographic peak consists of glial metabolites that are responsible for osmoregulation.123 Elevated MI levels correlate with glial proliferation in inflammatory cen- tral nervous system demyelination124 and are higher in the 1H MRS spectra of patients with MCI and AD than in cognitively normal elderly.121 The greatest amount of Cho in the brain is bound in membrane phospholipids that are precursors of Cho and acetylcholine synthesis. It has been postulated that eleva-

tion of the Cho peak is the consequence of membrane phospha- tidylcholine catabolism to provide free Cho for the chronically deficient acetylcholine production in AD. The result is decreased NAA, whereas MI and Cho are increased in MCI rela- tive to normal values (▶Fig. 10.17).121 Furthermore, Kantarci et al125 showed that NAA:MI and hippocampal volume:total intracranial volume ratios showed an independent effect and found that low levels of the neuronal integrity marker NAA and high levels of the glial metabolite MI increased the risk for MCI. Therefore, the joint effects of these two independent parame- ters can be predictors of MCI in cognitively normal older adults.

From these mentioned studies, the observation of brain metabolites in MCI patients by using 1H MRS may provide a new differential diagnosis method based on the biochemical activity of brain. However, the values found using this method can be easily affected by the quantification method used and by the physical and chemical environment of the brain, such as temperature and hydrogen ion content (pH), and no fundamen- tal solutions have been found. Therefore, further investigation and evaluation are required for stable application in MCI and AD pathologies.

Diffusion Tensor Magnetic Resonance Imaging

Diffusion tensor imaging (DTI) is an MRI technique that allows for investigation of the microstructural integrity of white matter.126 Based on changes in translational diffusion (mean diffusivity [MD], apparent diffusion coefficient [ADC]), and directional diffusion (fractional anisotropy [FA]), structural changes in the white matter can be assessed. Furthermore, the combination of increased MD and ADC and decreased FA shows damage to white matter.

Measures of MD/ADC and FA from DTI can quantify the alter- ations in water diffusivity resulting from microscopic structural

Fig. 10.17 Brain metabolites in controls (C), mild cognitive impairment (MCI), and Alz- heimer’s disease (AD) patients. Cr, creatine; MI, myo-inositol; NAA, N-acetyl aspartate. (Reprinted with permission from Figs. 3 and 5 in Kantarci K, Jack C Jr, Xu Y, et al. Regional metabolic patterns in mild cognitive impairment and Alzheimer’s disease: a 1H MRS study, Neurology 2000;55:210–217.)

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Mild Cognitive Impairment

guished from normal elderly subjects using noninvasive imag- ing techniques.132

Functional Magnetic Resonance Imaging

Functional MRI (fMRI) is a noninvasive technique used to inves- tigate the neural underpinnings of higher cognitive functions by measuring regional hemodynamic changes. These hemo- dynamic changes are thought to be linked to underlying cellular activity.133,134 The blood-oxygen-level-dependent (BOLD) signal changes detected by fMRI are thought to represent integrated synaptic activity by measuring changes in blood flow, blood volume, and the blood oxyhemoglobin:deoxyhemoglobin ratio underlying such synaptic activity.135

The pattern of activated and deactivated brain regions mod- eled in block or event-related design paradigms allows for the identification of brain regions and networks whose activity is modulated by the experimental task. By creating contrasts between disparate behavioral or cognitive conditions (e.g., fixa- tion versus memory encoding), the dynamic nature of the BOLD signal, coupled with the relatively static hemodynamic response to activity, allows for inference of which regions are selectively activated or inactivated by a task (i.e, task-positive or task-negative brain regions).136

Enriching our understanding of task-positive and task- negative brain networks are so-called task-free or resting state or functional connectivity MRI (fcMRI) studies, in which statis- tical correlations in BOLD signal dynamics while the brain is at rest have enabled identification of several large-scale neural networks composed of widely anatomically separated brain regions.137

In the following subsections, task-based and task-free fMRI studies are summarized.

Task-Activated Studies

Most studies in task-based fMRI studies have focused on inter- rogating memory functions, given the early occurrence of known AD pathologic changes in the medial temporal lobes and the reliance on these structures for learning and memory. Nevertheless, it should be noted that the specific abnormality found in a task-based fMRI study of any patient group depends greatly on the task used in the study.138 By using task- based fMRI, greater medial temporal lobe activation in MCI patients compared with controls has been demonstrated (▶ Fig. 10.20).139

As mentioned, this method, which requires cognitive tasking, allows us to observe the current status of brain functions and its relation to the function of associated regions. Therefore, if the task is successfully conducted, the results bring useful infor- mation about brain function and CBF.

Task-Free Studies

Resting-state functional MRI (rs-fMRI) is an imaging method that reflects synaptic activity through changes in blood flow and the oxyhemoglobin:deoxyhemoglobin ratio.140 By measur- ing functional connectivity between spatially distinct brain regions, rs-fMRI can be used to evaluate brain function.141,142 Several networks encompassing brain regions that display func- tional connectivity during the resting state, so-called resting

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Fig. 10.18 Mean fractional anisotropy (FA) and mean diffusivity (MD) values for the genu and splenium in normal control (NC) participants and mild cognitive impairment (MCI) patients. ADC, apparent diffusion coefficient. (Reprinted with permission from Fig. 1 in Delano-Wood L, Bondi MW, Jak AJ, et al, Stroke risk modifies regional white matter differences in mild cognitive impairment. Neurobiol Aging 2010;31 (10):1721–1731.)

changes. Several researchers have revealed the difference between normal controls and MCI patients using these water diffusion–derived measures. The changes in the white matter of study participants (normal controls and MCI patients) by FA showed significant regional reductions in participants with MCI.127,128,129,130 Delano-Wood et al found that the FA value of the splenium was significantly lower in MCI patients than in normal controls, despite finding no differences in gross mor- phometry or hippocampal volumes (▶Fig. 10.18).128 Further- more, they found that MCI patients demonstrated considerably diminished white matter integrity in the posterior cingulum (PC) (▶Fig. 10.19).129 Stebbins et al131 summarized in their review that the brain lobe where MD increased and FA decreased was identified in MCI patients and that the pattern of white matter integrity disruption tends to follow an anterior to posterior gradient, with greater damage noted in posterior regions in AD and MCI patients.

As mentioned, DTI-derived measures, such as FA and ADC, have already shown their usefulness, and advances in their application should provide new insights into AD pathologies. In addition, more evolutionary techniques may bring highly accurate early identification methods for MCI patients as distin-

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Alzheimer’s Disease

Fig. 10.19 (a,b) Posterior cingulum (PC) fractional anisotropy hemispheric differences
in normal controls (NC) and mild cognitive impairment (MCI) group. (Reprinted with per- mission from Figs. 1 and 2 in Delano-Wood L, Stricker N, Sorg S, et al. Posterior cingulum white matter disruption and its associations with verbal memory and stroke risk in mild cognitive impairment. J Alzheimer Dis 2012;29:589–603.)

Fig. 10.20 A phase of compensatory hyperactivation appears to occur in the medial temporal lobe (MTL) in very mild cognitive impairment (MCI) preceding Alzheimer’s disease (AD) dementia. (Reprinted with permission from Dickerson BC, Salat DH, Greve DN, Chua EF, Rand-Giovannetti E, Rentz DM, et al. (2005). Increased hippocampal activation in mild cognitive impairment compared with normal aging and AD. Neurology 2005;65:404–411.)

106

state networks.143,144,145 One network, referred to as the default

mode network (DMN), consists of the bilateral parietal cortex,

precuneus, posterior cingulate cortex, anterior cingulate

cortex, medial prefrontal cortex, hippocampus, and thalamus.

The network is active during episodic and autobiographical

memory retrieval but shows decreased activity during per-

formance of cognitive tasks that demand attention to external stimuli.136,146,147

The use of resting-state analyses to identify alterations in functional connectivity that distinguish normal aging from MCI and AD has gained momentum in recent years, although relatively few studies have thus far been completed

(▶Fig. 10.21).148 Several groups have reported finding decreased connectivity within the posterior DMN (especially the posterior cingulate cortex) in subjects with amnestic MCI compared with controls (▶ Table 10.5).149,150,151,152

As mentioned, this method, which does not required cogni- tive tasks, allows us to apply a wide variety of studies for observing default network connectivity in the brain. This possi- bility was reinforced by the recent National Institute on Aging— Alzheimer’s Association Workgroup definition of preclinical AD by Sperling et al,153 who offered the possibility that fMRI mea- surement of default network connectivity holds promise as a possible preclinical marker of AD. Further studies may reveal

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Mild Cognitive Impairment

Fig. 10.21 Mean z-values from default-mode network (DMN) z-maps reveals alterations in functional connectivity in continuous MCI (cMCI) and Alzheimer’s disease (AD). HC, healthy controls. (Reprinted with permission from Fig. 4 in Binnewijzend MAA, Schoonheim MM, Sanz- Arigita E, et al. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment, Neurobiol Aging 2012;33:2018–2028.)

the differences in default network connectivity among normal, MCI, and AD subjects.

10.6.4 Image Analysis

Information and knowledge about MCI and AD have accumu- lated through the use of neuroimaging modalities like PET, SPECT, and MRI. Amid this accumulation of knowledge, image analysis techniques have played many important roles.

Traditionally, visual assessment has been used on a daily clinical

basis, but this method cannot produce quantitative evaluations

of pathological conditions. Therefore, manually placed ROIs

and statistics within the ROI have been applied to quantitative

analysis. However, manual ROI placement is greatly labor

intense because it requires considerable time for placing the

ROIs along the slices. Routine assessment by this manual ROI

placement method is not feasible; therefore, several semiauto-

mated and fully automated ROI analysis methods have been devised.154,155,156

In general, medical image analysis techniques can be roughly divided into morphometric analysis (morphometry) and photo- metric analysis (photometry). Recently, whole-brain image registration techniques, which provide precise anatomical compensation by linear or nonlinear transformation to the standardized template brain image, have become commonly available.154,155,156 Furthermore, automated segmentation of gray matter, white matter, and cerebrospinal fluid from the whole brain is also now possible by using software like Statisti- cal Parametric Mapping (SPM). Furthermore, MRIstudio can provide registration among variable subjects and percolated brain template for automated brain regional analysis in both morphometry and photometry.157,158 By using such software, we can obtain the volume changes and morphologic changes in subjects quantitatively. These procedures are classified as mor- phometry.159 In addition, software programs that can provide computational analysis for not only general ROI averaging but also automated statistical analysis after transformation to stan- dardized brain template (MRIstudio) are also available. Several research methods have already used such software programs to reveal regionally specific DTI in MCI and AD160 and neuro- psychiatric symptoms in MCI and AD.161 These procedures are classified as photometry. Both morphometry and photometry are based on voxel-based analysis (VBA), which is strongly affected by registration inaccuracy, moving artifacts, and imag- ing artifacts. These errors cause drawbacks in VBA and may lead to difficulty in comparing the results from different image scan- ners. To compensate for these drawbacks, large ROIs, including a large numbers of voxels, may be one solution to maintaining statisticalpower.

Table 10.5 Comparison of major findings in studies of resting-state default mode network activity in aMCI groups compared with normal controls

Subjects Present study Sorg et al1,54 Bai149 Qi et al151,6

Posterior cingulate cortex/precuneus

–B

–L

–B

–B

Inferior parietal lobe

+L

+R

–R, + l

Medial temporal lobe (hippocampus, entorhinal cortex, perirhinal cortex, parahippocampal gyrus)

–L

*

Fusiform gyrus

–L

+R

–L

Lateral perifrontal cortex

–B

–R

+L

Medial prefrontal cortex

+B

+B

Middle cingulate cortex

+B

Medial temporal gyrus

–L

+L

Angular gyrus

–R

Putamen

+B

Abbreviations: –, decreased activities in aMCI; + , increased activities in aMCI, amnestic mild cognitive impairment; B, bilateral; L, left; R, right Source: Table 4 in Jin M, Pelak VS, Cordes D. Aberrant default mode network in subjects with amnestic mild cognitive impairment using resting-state functional MRI. Magn Reson Imaging 2012;30(1):48–61.

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Alzheimer’s Disease

Image analysis methods are under development, and many software programs are available both commercially and freely. Nevertheless, database communization for obtaining strong sta- tistical evidence by image analysis has not been realized, and differences among the different centers and different image scanners remain unresolved.

10.7 Clinical Trials

10.7.1 Medication for Mild Cognitive

Impairment

compensatory strategy, using external aids, also showed pre- liminary evidence that it improved amnestic MCI.170 Physical activity interventions are also being explored as a way to mini- mize cognitive decline in MCI. Recently, combination training with memory compensation, decision making, physical fitness, talking with others, and educational programs have been carried out, and the evaluated data showed a positive impact on patient functional outcomes. Multiple aspects of nonphar- macologic follow-up might be of benefit for MCI patients.

10.7.3 Prophylactic Follow-Up

Several prophylactic follow-ups have reported benefits in MCI prevention. One follow-up reported that individuals with habits involving intellectual activities, such as reading newspapers or magazines, playing games, doing puzzles, or visiting museums, showed a 33% decrease in the risk for dementia.171 Another follow-up reported an eightfold difference in the incidence rate between people who are single and meet other people less than once a week compared with those who live with family members and have interactions with others more than once a week.172

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At present, no medicines or nonpharmacologic therapeutic methods are available for MCI. Therefore, one of the most pro- spective therapeutic methods might be existing medicines. Sev- eral clinical trials that use cholinesterase inhibitors, such as donepezil hydrochloride, galanthamine, and rivastigmine, have been reported. In 2004, Salloway et al made the first report regarding treatment outcomes with donepezil hydrochloride for MCI.162 A significant difference was found between the med- icine and placebo; the improvement ratio was 32.6 and 24.3%, respectively, with no serious side effects reported. In 2005, Petersen et al163 also reported on the effect of donepezil hydro- chloride for treatment of MCI. This article concluded that within a year, this medicine can prevent progression to demen- tia, and after a second year, no additional positive effects to control progression are noted. On the other hand, a clinical trial for MCI with galantamine treatment reported that this agent demonstrated significant effects in preventing MCI from pro- gressing to dementia.164

10.7.2 Follow-Up Pharmacologic Follow-Up

Some authorities believe that current AD medications, namely, cholinesterase inhibitors (ChEIs), could impact the outcome for MCI patients, especially those with the amnestic subtype. On the other hand, Raschetti et al concluded from their review that “the use of cholinesterase inhibitors in MCI was not associated with any delay in the onset of AD or dementia.” Moreover, the safety profile from this review showed that risks associated with ChEls are not negligible.165 Furthermore, the British Asso- ciation for Psychopharmacology166 concluded that the medica- tions approved for AD do not demonstrate efficacy in delaying or preventing dementia in MCI patients.

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Matsuda H. Role of neuroimaging in Alzheimer’s disease, with emphasis on brain perfusion SPECT. J Nucl Med 2007; 48: 1289–1300

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Villemagne VL, Rowe CC. Amyloid imaging. Int Psychogeriatr 2011; 23 Suppl 2: S41–S49
Rabinovici GD, Jagust WJ. Amyloid imaging in aging and dementia: testing the amyloid hypothesis in vivo. Behav Neurol 2009; 21: 117–128

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Karas GB, Scheltens P, Rombouts SA et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage 2004; 23: 708–716
Pennanen C, Testa C, Laakso MP et al. A voxel based morphometry study on mild cognitive impairment. J Neurol Neurosurg Psychiatry 2005; 76: 11–14 Bell-McGinty S, Lopez OL, Meltzer CC et al. Differential cortical atrophy in subgroups of mild cognitive impairment. Arch Neurol 2005; 62: 1393– 1397

Whitwell JL, Przybelski SA, Weigand SD et al. 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer’s disease. Brain 2007; 130: 1777–1786
Detre JA, Leigh JS, Williams DS, Koretsky AP. Perfusion imaging. Magn Reson Med 1992; 23: 37–45

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Zhang Q, Stafford RB, Wang Z, Arnold SE, Wolk DA, Detre JA. Micro- vascular perfusion based on arterial spin labeled perfusion MRI as a measure of vascular risk in Alzheimer’s disease. J Alzheimers Dis 2012; 32: 677–687

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Kantarci K, Weigand SD, Przybelski SA et al. MRI and MRS predictors of mild cognitive impairment in a population-based sample. Neurology 2013; 81: 126–133

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Medina D, DeToledo-Morrell L, Urresta F et al. White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol Aging 2006; 27: 663–672

Delano-Wood L, Bondi MW, Jak AJ et al. Stroke risk modifies regional white matter differences in mild cognitive impairment. Neurobiol Aging 2010; 31: 1721–1731
Delano-Wood L, Stricker NH, Sorg SF et al. Posterior cingulum white matter disruption and its associations with verbal memory and stroke risk in mild cognitive impairment. J Alzheimers Dis 2012; 29: 589–603

Bosch B, Arenaza-Urquijo EM, Rami L et al. Multiple DTI index analysis in normal aging, amnestic MCI and AD: relationship with neuropsychological performance. Neurobiol Aging 2012; 33: 61–74
Stebbins GT, Murphy CM. Diffusion tensor imaging in Alzheimer’s disease and mild cognitive impairment. Behav Neurol 2009; 21: 39–49

O’Dwyer L, Lamberton F, Bokde ALW et al. Using support vector machines with multiple indices of diffusion for automated classification of mild cogni- tive impairment. PLoS ONE 2012; 7: e32441
Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neuro- physiological investigation of the basis of the fMRI signal. Nature 2001; 412: 150–157

Shmuel A, Augath M, Oeltermann A, Logothetis NK. Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1. Nat Neurosci 2006; 9: 569–577
Gusnard DA, Raichle ME, Raichle ME. Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci 2001; 2: 685–694 Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A 2003; 100: 253–258

Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated func- tional networks. Proc Natl Acad Sci U S A 2005; 102: 9673–9678
Dickerson BC, Sperling RA. Functional abnormalities of the medial temporal lobe memory system in mild cognitive impairment and Alzheimer’s disease: insights from functional MRI studies. Neuropsychologia 2008; 46: 1624– 1635

Dickerson BC, Salat DH, Greve DN et al. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD. Neurology 2005; 65: 404–411
Schölvinck ML, Maier A, Ye FQ, Duyn JH, Leopold DA. Neural basis of global resting-state fMRI activity. Proc Natl Acad Sci U S A 2010; 107: 10238– 10243

Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995; 34: 537–541
Cordes D, Haughton VM, Arfanakis K et al. Frequencies contributing to func- tional connectivity in the cerebral cortex in “resting-state” data. AJNR Am J Neuroradiol 2001; 22: 1326–1333

Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 2005; 360: 1001–1013
Damoiseaux JS, Beckmann CF, Arigita EJ et al. Reduced resting-state brain activity in the “default network” in normal aging. Cereb Cortex 2008; 18: 1856–1864

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Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from func- tional MRI. Proc Natl Acad Sci U S A 2004; 101: 4637–4642

Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001; 98: 676–682
Binnewijzend MAA, Schoonheim MM, Sanz-Arigita E et al. Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 2012; 33: 2018–2028

Sorg C, Riedl V, Mühlau M et al. Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proc Natl Acad Sci U S A 2007; 104: 18760–18765
Bai F, Zhang Z, Yu H et al. Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: a combined structural and resting-state functional MRI study. Neurosci Lett 2008; 438: 111–115

Jin M, Pelak VS, Cordes D. Aberrant default mode network in subjects with amnestic mild cognitive impairment using resting-state functional MRI. Magn Reson Imaging 2012; 30: 48–61
Qi Z, Wu X, Wang Z et al. Impairment and compensation coexist in amnestic MCI default mode network. Neuroimage 2010; 50: 48–55

Sperling RA, Aisen P, Beckett L et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Insti- tute on Aging—Alzheimer’s Association Research Roundtable workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7: 280–292

Mapping SP. Wellcome Trust Centre for Neuroimaging. 2013. Available at: http://www.fil.ion.ucl.ac.uk/spm
Studio MRI. An Image Processing Program. 17 May 2007. Available at: https:// http://www.mristudio.org

FSL. FMRIB Software Library v5.0. Analysis Group, FMRIB, Oxford UK. 2014. Available at: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki
Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 1999; 45: 265–269

Jiang H, van Zijl PCM, Kim J, Pearlson GD, Mori S. DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed 2006; 81: 106–116
Miller MI, Priebe CE, Qiu A et al. Morphometry BIRN. Collaborative computa- tional anatomy: an MRI morphometry study of the human brain via diffeo- morphic metric mapping. Hum Brain Mapp 2009; 30: 2132–2141

Mielke MM, Kozauer NA, Chan KCG et al. Regionally-specific diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease. Neuroimage 2009; 46: 47–55
Tighe SK, Oishi K, Mori S et al. Diffusion tensor imaging of neuropsychiatric symptoms in mild cognitive impairment and Alzheimer’s dementia. J Neuro- psychiatry Clin Neurosci 2012; 24: 484–488

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Petersen RC, Thomas RG, Grundman M et al. Alzheimer’s Disease Cooperative Study Group. Vitamin E and donepezil for the treatment of mild cognitive impairment. N Engl J Med 2005; 352: 2379–2388

Petersen RC. Mild cognitive impairment clinical trials. Nat Rev Drug Discov 2003; 2: 646–653
Raschetti R, Albanese E, Vanacore N, Maggini M. Cholinesterase inhibitors in mild cognitive impairment: a systematic review of randomised trials. PLoS Med 2007; 4: e338

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Talassi E, Guerreschi M, Feriani M, Fedi V, Bianchetti A, Trabucchi M. Effec- tiveness of a cognitive rehabilitation program in mild dementia (MD) and mild cognitive impairment (MCI): a case control study. Arch Gerontol Geriatr 2007; 44 Suppl 1: 391–399

Rozzini L, Costardi D, Chilovi BV, Franzoni S, Trabucchi M, Padovani A. Efficacy of cognitive rehabilitation in patients with mild cognitive impairment treated with cholinesterase inhibitors. Int J Geriatr Psychiatry 2007; 22: 356–360

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Greenaway M, Smith G, Lepore S et al. Compensating for memory loss in amnestic mild cognitive impairment. Alzheimers Dementia 2006; 2 Suppl 1: S571
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such as medial temporal regions (hippocampal formations, parahippocampal gyrus, and entorhinal cortex); consequently, episodic memory deficit is the initial symptom for most of AD patients. As the condition progresses, deficits occur in instru- mental functions (language, praxis, visuospatial abilities), which are consistent with the extension of lesions into the neo- cortical associative areas (Braak stage V).

11.2 Genetics

In most cases, AD is considered a disease with multiple causes and results from the interactions between genetic and environ- mental factors.12 The role of genetic factors in the incidence and pathogenesis of AD is complex. AD can be divided into two types according to genetic factors13: (1) familial AD, with mendelian transmission, usually early onset (before age 60 years); and (2) sporadic AD, usually with onset in older age (over 60 years), without an autosomal dominant pattern of transmission. This dichotomy should be taken with caution, however, because cases of early onset AD without evidence of autosomal dominant transmission do occur13; conversely, the importance of genetic factors in sporadic forms of the disease has been established14 by the involvement of sortilin-related receptor (SORL1).15,16,17

Familial AD with autosomal dominant transmission is rare and accounts for less than 5% of AD cases.17 Genetic studies indicate that these forms are related to three possible muta- tions17,18: (1) the gene for APP, located on chromosome 21; (2) the gene for presenilin 1 (PSEN1), located on chromosome 14; and (3) the gene for presenilin 2 (PSEN2), located on chro- mosome 1. These changes have virtually 100% penetrance by the age of 60 years. PSEN1 gene mutations are the most fre- quent (75% of cases among all cases of familial AD).17,18 By con- trast, in a whole-genome sequencing study of Icelandic people, a recent work identified a coding variant in APP that protects against AD and cognitive decline. The mutation leads to reduced production of Aβ by BACE1.19

The genetics of sporadic forms of the disease is much more complex because in most cases there is no obvious familial aggregation.18 The risk of developing AD increases from 4 to 10 times in normal subjects with a first-degree relative affected by the disease compared with patients without a family history.20

The SORL1 gene has been noted to be involved in sporadic forms of the disease.15,16,17 The SORL1 gene is located on chro- mosome 11 and is involved in intracellular trafficking of the amyloid precursor. Deletion of this gene increases the produc- tion of toxic β-amyloid peptide.15,21 However, this mutation is not present in all cases of sporadic AD.16

Among the genetic factors modulating susceptibility to AD, the polymorphism of apolipoprotein E (ApoE) is the most important. ApoE has an essential role in the regulation of lipid metabolism and is implicated in the transport, distribu- tion, endocytosis, and catabolism of lipid particles.22 It also has a role in the mechanisms of neuronal plasticity by partici- pating in synaptogenesis and the stability of synaptic con- nections.22 The mechanisms by which ApoE is involved in the

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Since Alois Alzheimer’s first description of Alzheimer’s disease (AD) in 1906, the disease that was later named after him has became a major public health concern. To date, it is estimated that 24 million people worldwide have dementia, most of whom are thought to have AD.

The main risk factor for developing AD is age.1 Epidemiologic data show that the percentage of patients with AD doubles every 5 years from age 65 onward.1 Thus, the percentage of patients with AD would be of 1% for the population aged 60 years, 5% for the population aged 65 years, and about 30% for the population aged 85 years.1,2 It should be noted, however, that although AD mainly affects the elderly, it also affects an important group of young patients.2

Considering that the incidence of AD and other dementias increases with age, the prevalence of dementias is estimated to grow in the following decades worldwide as a result of the increasing longevity of populations.3 The number of people with dementia worldwide is predicted to double every 20 years, to more than 65 million in 2030 and more than 115 million in 2050.4 Besides its dramatic impact on patients and their fami- lies, AD represents an economic strain for health care systems and communities worldwide, and it is expected that the cost of caring for people with dementia in United States will grow to almost $190 billion by 2015.3

11.1 Neuropathology

Alzheimer’s disease is associated with different neuro- pathological findings, such as neuronal death and a decreased number of synapses. Its main pathological hallmarks, however, are the presence of senile plaques and neurofibrillary tangles, which reflect amyloid and tau pathology, respectively.

Extracellular amyloid plaques are formed by β-amyloid protein peptides (Aβ), which are fragments formed by the cleavage of the amyloid precursor protein (APP).5 APP is a transmembrane glycoprotein that can be processed by α- and γ-secretases, generating a nonamyloidogenic product, or by β- and γ-secretases, generating Aβ peptides, which are amyloidogenic and are prone to form plaques. However, there is no direct correlation between the number and topography of cortical senile plaques and the cognitive defi- cits in AD patients. The amount of senile plaques is not correlated with the severity of the disease, and amyloid dep- osition seems to remain stable during progression of the disease.6,7,8 Recent longitudinal imaging studies indicate that cerebral Aβ deposition precedes the clinical symptoms of AD by a decade or longer.9

Intracellular neurofibrillary tangles are formed as a result of hyperphosphorylation and oligomerization of tau, a micro- tubule-associated protein that is present mainly in the axons of neurons. The progression of tau pathology in the brain is closely correlated to clinical symptoms and to the severity of the disease10 as established by Braak and Braak.11 In the early stages of AD (Braak stages I, II, and III), neurofibrillary degener- ation can be identified in areas critical for episodic memory,

Overview of Alzheimer’s Disease

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pathophysiology of AD are not entirely clear, but ApoE seems to have an important role in the metabolism and the accumulation of Aβ.22,23

The ApoE gene is located on chromosome 19 and has three alleles (ε2, ε3, and ε4). The ε3 allele is present in 50 to 90% of individuals, the ε4 allele in 5 to 35%, and the ε2 allele in 1 to 5%.23 The risk of developing AD associated with ApoE ε4 is dose dependent: individuals carrying one ε4 allele have three times more risk of developing the disease, whereas ε4 homozygotes have 12 times more risk of developing AD than do those with ε3 homozygotes.22,23 On the other hand, the ε2 allele is associated with a reduced risk of developing AD.22,23,24,25

Recent publications showed that, in addition to autosomal dominant frontotemporal lobar degeneration, mutations in the progranulin gene may be a risk factor for AD clinical pheno- types and neuropathology.26

11.3 Clinical Features
11.3.1 Episodic Memory Deficits

The most prominent feature of AD is the decline in cognitive function, with an early impairment of anterograde episodic memory.17 The initial amnestic deficits with progressive wor- sening that remains predominant during the course of the dis- ease is the most frequent phenotype of AD.2 Amnestic symp- toms are characterized by memory impairment of recent events, unusual repeated omissions, and difficulty learning new information. Initially, amnestic symptoms are not associated with a loss of autonomy, and the patient remains independent for activities of daily living.

The investigation of amnestic symptoms requires formal neu- ropsychological testing to quantify and qualify the nature of the memory deficit. In fact, memory disorders are commonly observed in patients with neuropsychiatric disorders other than AD, such as as Parkinson’s disease, vascular dementia, depres- sion, or even iatrogenic conditions. Moreover, subjective memory complaints are also common in the elderly. The appropriate neu- ropsychological evaluation can distinguish genuine memory impairment (e.g., failure of information storage and new mem- ory formation) from attention or retrieval disorders (such as nor- mal aging or depression). More particularly, neuropsychological tests that provide encoding specificity are of great interest and improve the accuracy of the AD diagnosis. The Free and Cued Selective Reminding Test (FCSRT) is a neuropsychological tool in which target materials are encoded along with semantic cues. These cues are used to control for effective encoding and subse- quently are presented to optimize retrieval.27

The FCSRT can identify the so-called amnestic syndrome of the medial temporal type (or of the hippocampal type), which is defined by (1) poor free recall (as in any memory disorder) and (2) decreased total recall resulting from an insufficient effect of cueing. The low performance of total recall despite retrieval facilitation indicates poor storage of information and seems specific of a hippocampal memory disorder. The amnes- tic syndrome of the medial temporal type differs from func- tional and subcorticofrontal memory disorders, characterized by a low free recall performance with normalization (or a quasi-normalization) of the performance in total recall because of good efficacy of cueing.28 This subcortical-frontal profile of

memory impairment can be observed in depression,29 vascular dementia,30 and subcortical dementia.28

The identification of an amnestic syndrome of the medial temporal type by the FCSRT can successfully differentiate patients with AD from healthy controls, even when the disease is in an initial stage. Moreover, the FCSRT, by isolating an amnestic syndrome of the hippocampal type in subjects with mild cognitive impairment (MCI), is able to distinguish patients at an early stage of AD from those with “nonconverter” MCI with a high sensitivity (80%) and specificity (90%).31 Perform- ance of the FCSRT has been well correlated with the left medial temporal lobe volume assessed both by voxel based morphom- etry analysis and automatic volumetric method in a series of AD patients.32 These findings support considering the measure of episodic memory by the FCSRT as a useful clinical marker of medial temporal damage.

A multicenter German study comprising 185 MCI patients investigated whether the performance of the FCSRT predicts Alzheimer’s pathology.33 In this study, three different memory tests (the FCSRT, the Word List Learning Task from the Consor- tium to Establish a Registry for AD, and the Logical Memory Paragraph Recall Test from the Wechsler Memory Scale— Revised) were compared for their ability to predict a cerebro- spinal fluid (CSF) biomarker profile indicative of AD, defined by Aβ42/tau ratio.34 Their results showed that among the three memory tests, the cued recall deficits identified by the FCSRT were by far more predictive of a CSF biomarker profile indica- tive of AD pathophysiology. It should be noted, however, that the amnestic syndrome of hippocampal type may also be observed in other conditions, such as hippocampal sclerosis or behavioral variant frontotemporal dementia.35

11.3.2 Other Cognitive Deficits

Besides episodic memory deficits, temporal-spatial dis- orientation (disorientation in nonfamiliar places and difficulty in establishing a chronological order to recent events) is also present at the initial stages of AD. Patients often show difficulty orienting themselves in familiar places during intermediate stages and progress to severe disorientation in their personal residence as the disease progresses.

The progression of cognitive deficits follows extension of the underlying pathological lesions (more specifically of tau pathol- ogy) through the neocortical associative areas. Patients may develop language disorders, visuospatial and recognition defi- cits, and difficulties in executing the more complex tasks of daily living, leading to loss of autonomy and dementia.17 Patients progress from loss of higher-level activities of daily liv- ing, such as financial transactions and the use of public trans- portation, to abnormalities in the more basic activities of daily living. At severe stages of the disease, patients require contin- ued assistance for basic activities of daily living.

Aphasia may appear as the condition progresses, character- ized by decreased verbal comprehension and naming difficulty. As AD advances, all aspects of language (oral production, com- prehension, reading, and writing), can be impaired, resulting in mutism or incomprehensible language in severe cases. Gestural apraxia refers to an inability to perform learned skilled move- ments, which cannot be attributed to an alteration of judgment or to sensitive motor deficits. It is usually measured by asking

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

the patient to perform pantomimes of tool uses (e.g., asking the patient to imitate how to use a hammer) or symbolic gestures (asking the patient to perform a military salute) or to imitate meaningless gestures. Difficulty using objects, as well as dress- ing apraxia, is observed during moderate to severe AD.36 Patients with AD commonly show visuospatial dysfunction in the moderate stages of AD. Deficits arise first during complex tasks, which require perceptual analysis and spatial planning. Impairment in constructional ability can be easily tested by drawing and copying tasks. In typical AD, a visuoconstructive deficit predicts the development of severe dependency.37 Visual agnosia and complex visual processing dysfunction are observed in advanced stages of the disease. Patients may show impaired recognition of objects or faces.

11.3.3 Severity of the Disease

Different stages of severity are described in AD, from mild to moderate and severe (dementia) stages. According to the recently proposed AD criteria,38 the terms prodromal AD or MCI due to AD refer to early stage of the disease, which precedes the appearance of dementia.39,40 In the prodromal or MCI stage, the patient can live alone. In mild stages of AD, patients require lim- ited home care. In moderate stages, patients need supervision and regular assistance in most activities. In severe stages, resi- dential health care may be required.

The Mini-Mental State Examination (MMSE) assesses global cognitive efficiency and is generally used to evaluate dementia severity. Although the MMSE is not a specific neuro- psychological test for AD diagnosis, it is easy and quick to administer and can track the overall progression of cognitive decline. Longitudinal studies have shown that the mean annual rate of progression of cognitive impairment using MMSE is approximately 2 to 6 points. The Clinical Dementia Rating Scale, based on an overall evaluation of the patient’s condition, offers incremental stages of severity.41 Functional decline increases with disease progression.

11.3.4 Atypical Clinical Presentation

Neuropathological studies have long recognized that AD can manifest as atypical or variant syndromes without predomi- nant amnestic features.42,43,44,45 The most common variant AD phenotypes are posterior cortical atrophy (PCA), logopenic-var- iant primary progressive aphasia (lgPPA), and frontal variant AD. The new criteria for AD grouped these focal variants into atypical AD. Atypical AD is more frequent in early onset of AD.

In PCA, visuospatial deficit is the initial symptom, and then patients develop features of Bálint syndrome (ocular apraxia, optic ataxia, and simultanagnosia), Gerstmann syndrome (acal- culia, agraphia, finger agnosia, and left-right disorientation), visual agnosia, and transcortical sensory aphasia, whereas epi- sodic memory is preserved or only mildly impaired.46 Magnetic resonance imaging (MRI) and functional imaging have shown parieto-occipital localization of atrophy and hypometabolism.47

In lgPPA, language deficit is the initial symptom, character- ized by frequent pauses that disrupt the flow of conversation and the generation of phonologic errors, associated with deficit in sentence repetition. In contrast with other forms of PPA, in lgPPA, patients lack motor speech disorders or show agram-

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Overview of Alzheimer’s Disease

matism as in nonfluent PPA and have less severe semantic impairments than do those with semantic variant PPA.48 Neuro- imaging showed asymmetric involvement of the temporoparie- tal junction, which is more severe in the dominant hemisphere.

The frontal variant of AD is defined by a predominant dysex- ecutive syndrome, which is frequently associated with frontal behavioral symptoms. These clinical features can lead to mis- diagnosis of frontotemporal dementia.49

11.3.5 Neuropsychiatric Features

Behavioral and neuropsychiatric symptoms associated with AD include depressive mood, apathy, agitation, and aggressivity, as well as psychotic symptoms, such as delusions and hallucina- tions.50 These manifestations tend to fluctuate over time, resulting in individual differences. The prevalence of psychosis and behavioral disturbance increases as the disease progresses and may indicate a poor prognosis.37,51

The most frequent behavioral disorder in AD is apathy, which has been found in 25 to 75% of cases.50,52 The prevalence of apa- thy also increases with the severity of dementia. It is notewor- thy that apathy should not be confounded with depression, and apathy may be present without concomitant depression.

Delusions are observed more often than hallucinations, and their frequency is estimated at 20 to 70%.52 Paranoid delusions are probably the most common type, but misidentification phe- nomena and Capgras delusion may also be observed. Hallucina- tions, commonly visual, are rare in the early stages, but they become more prevalent as the disease progresses.52 Symptoms of psychosis or agitation are associated with distress for the patient, an increased burden on caregivers, more rapid cognitive decline, and an increased likelihood of institutionalization.53

11.4 Alzheimer’s Disease Diagnosis

Until recently, the diagnosis of AD has been based on the National Institute of Neurological and Communicative Disor- ders and Stroke (NINCDS)—Alzheimer’s Disease and Related Disorders Association (ADRDA) criteria, which referred to a clin- ical dementia entity that typically manifests with a characteris- tic progressive amnestic disorder with subsequent appearance of other cognitive and neuropsychiatric changes that impair social function and activities of daily living.54 In the NINCDS– ADRDA criteria, biological investigation (blood and CSF) and neuroimaging examination (computed tomography scan or MRI) were proposed to exclude other causes of the dementia syndrome (vascular lesions, tumor, infectious or inflammatory processes).

Advances in establishing the biomarkers of AD, which pro- vide in vivo information about the pathophysiologic process associated with AD, have stimulated the proposal of new diag- nostic criteria.38,39,55,56 According to these frameworks, the diagnosis of AD is based on core clinical criteria, with the sup- port of biomarkers based on imaging and CSF measures. This combined clinical and biological approach may improve accu- racy of the diagnosis. These new diagnostic frameworks permit an earlier diagnosis of AD, before the development of dementia. Following this new perspective of a clinical-biological diagnosis approach, a consideration of preclinical stages of AD was pro- posed, according to which the pathophysiological process of the

115

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disease precedes the clinical manifestations.57 Whatever the stage of the disease, the new AD criteria integrated in the clini- cal approach the use of biomarkers, such as neuroimaging and biological tools.

11.4.1 Structural Brain Imaging

Brain MRI has an important role in the investigation of patients with suspected AD, that is, to detect the treatable causes of cog- nitive dysfunction, such as normal pressure hydrocephalus and subdural hematoma. Moreover, it has also been increasingly recognized that brain MRI is important in identifying the spe- cific patterns of anatomic damage associated with AD.

Atrophy of medial temporal regions, mainly the entorhinal cortex and hippocampus, is observed in early AD. There is a progression of hippocampal atrophy in AD through its different stages: hippocampal volumes of AD patients are 10 to 12% smaller than those of age-matched controls in early (prodro- mal) stages, 15 to 30% reduced in mild stages, and 30 to 40% smaller in moderate stages of the disease.58 Measurement of medial temporal lobe atrophy can distinguish AD from age- matched controls with an overall accuracy greater than 85%.59 Similarly, hippocampal measures provide sensitivity and speci- ficity of approximately 75 to 80% to predict conversion to AD in patients with MCI.58,59,60 Qualitative visual scales or volumetric measurements with specific software can be used to assess hip- pocampal atrophy.61,62 Whereas quantitative methods are restricted mainly to research centers, visual rating scales, which assess medial temporal atrophy on coronal T1-weighted MRI, are of value in clinical settings.63

In summary, hippocampal volume and medial temporal atro- phy by volumetric measures or visual rating are the best vali- dated markers of early AD. Accordingly, in the new AD criteria, loss of hippocampal volume is considered a marker of neuronal injury indirectly caused by the tau pathology.

It should be emphasized, however, that medial temporal atro- phy is not specific for AD and may be observed in other clinical situations, such as frontotemporal dementia and even depression and normal aging.58,64 On the other hand, the rate of hippocam- pal atrophy may be a better indicator of AD pathology, as the progression of hippocampal loss is approximately two to four times faster in AD patients than in healthy controls.65,66

11.4.2 Cerebrospinal Fluid Biomarkers

Biomarkers can be defined as “an objective measure of a biolog- ical or pathogenic process that can be used to evaluate disease risk or prognosis, to guide clinical diagnosis or to monitor ther- apeutic interventions.”67 In the context of AD, the development of biomarkers, especially the CSF biomarkers, opened the possi- bility of identifying in vivo a specific underlying patho- physiologic mechanism and thus leading to a redefinition of clinical diagnosis of the disease.68

The main biomarkers used in diagnosis of AD are β-amyloid peptide (Aβ42), total tau, and the isoforms of phosphorylated tau (P-tau181 and P-tau231). A series of clinicopathological studies demonstrated that these biomarkers reflect the core pathological hallmarks of AD, with CSF levels of Aβ42 reflecting the extracellular deposits of Aβ peptide and CSF levels of tau and P-tau being correlated with the amount neurofibrillary

tangles.69,70,71 CSF biomarkers may thus be considered surro- gate markers of the pathophysiologic process of AD.67

Patients with AD typically exhibit a decrease in CSF Aβ42 and an increase in CSF tau and P-tau compared with healthy con- trols.67,72,73 Each of these biomarkers differentiates AD patients from age-matched controls with 80 to 90% sensitivity and spec- ificity, but accuracy is increased by using the combined analysis of two or more of the three main AD CSF markers (total tau, P-tau, and Aβ42).

The CSF markers can also identify with high accuracy an AD pathophysiology among patients with MCI,67,74 which may be referred to as prodromal AD39 or as MCI due to AD.40 The CSF biomarkers have also been increasingly used in the differential diagnosis between AD and other dementias.75,76,77,78 The com- bined analysis of CSF biomarkers can differentiate AD from behavioral or semantic manifestations of frontotemporal lobe degenerations with high accuracy and may also identify an AD underlying mechanism in patients with atypical presentations, such as lgPPA or PCA.77,79

11.4.3 Single-Photon Emission Computed Tomography and Fluorodeoxyglucose-Positron Emission Tomography Imaging

Functional neuroimaging techniques include the measure of blood f low (technetium 99m [99mTc]-hexamethylpropylene- amine [HMPAO] or 133Xe) with single-photon emission CT (SPECT) and positron emission tomography (PET) with fluoro- deoxyglucose ([18F]-FDG). In AD, abnormalities in SPECT or [18F]-FDG-PET reflect general damage to neurons and synapses, mainly resulting from tau pathology.

The 99mTc-HMPAO SPECT is a useful neuroimaging technique for distinguishing AD from frontotemporal dementia, but a sys- tematic review reported a clinical accuracy for patients with AD versus controls of only 74%.80 On the other hand, in a group with amnestic MCI, an automated quantitative tool for brain perfusion SPECT images using the mean activity in right and left parietal cortex and hippocampus was able to distinguish patients at an early stage of AD from patients with stable MCI (sensitivity, specificity, and accuracy of 82, 90, and 89%, respectively).81

Positron emission tomography with measures of glucose metabolism has shown good accuracy in distinguishing AD patients from both normal controls and patients with non-AD dementias. A reduction in glucose metabolism in bilateral tem- poroparietal regions and in the posterior cingulate cortex is the most common finding in AD.38,40,82 However, one study showed that within different imaging markers, the largest variability of likelihood ratio for AD diagnosis was of [18F]-FDG-PET.83

Amyloid Imaging

The development of amyloid markers in molecular neuro- imaging enabled the in vivo assessment of amyloid load, a key feature in the pathophysiology of AD. The most extensively studied amyloid marker is the carbon-11-labeled Pittsburgh compound B (11C-PiB), for which a high level of correlation has been demonstrated between in vivo 11C-PIB uptake and post- mortem measures of insoluble (fibrillar) Aβ peptide deposits and plaque load.84

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memory deficit in prodromal Alzheimer’s disease. Neurology 2012; 78: 379– [2] Cummings JL. Alzheimer’s disease. N Engl J Med 2004; 351: 56–67 386

. [3]  Middleton LE, Yaffe K. Promising strategies for the prevention of dementia. Arch Neurol 2009; 66: 1210–1215

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. [7]  Cruz de Souza L, Corlier F, Habert MO et al. Similar amyloid-β burden in pos-
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. [8]  Villemagne VL, Pike KE, Chételat G et al. Longitudinal assessment of Aβ and
cognition in aging and Alzheimer’s disease. Ann Neurol 2011; 69: 181–192

[34] Visser PJ, Verhey F, Knol DL et al. Prevalence and prognostic value of CSF markers of Alzheimer’s disease pathology in patients with subjective cognitive impairment or mild cognitive impairment in the DESCRIPA study: a prospective cohort study. Lancet Neurol 2009; 8: 619–627

[35] Bertoux M, Cruz de Souza L, Corlier F et al. Two distinct amnesic profiles in behavioral variant frontotemporal dementia. Biol Psychiatry 2014; 75: 582–588 [36] Thomas-Anterion C, Laurent B. [Neuropsychological markers for the diagnosis

of Alzheimer’s disease] [in French] Rev Neurol (Paris) 2006; 162: 913–920 [37] Sarazin M, Stern Y, Berr C et al. Neuropsychological predictors of dependency

in patients with Alzheimer’s disease. Neurology 2005; 64: 1027–1031
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2007; 6: 734–746

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Overview of Alzheimer’s Disease

A multitude of studies during the last decade showed that AD patients typically have 50 to 90% greater 11C-PIB cortical reten- tion than age-matched controls and thus discriminate AD from aged-matched controls with 80 to 100% sensitivity.85 The speci- ficity varies according to the age of the population. For instance, a high cortical 11C-PIB retention may be observed in less than 10% of asymptomatic subjects younger than 70 years but is found in up to 40% of asymptomatic subjects at the age of 80 years.86,87 A high 11C-PIB cortical uptake may also be found in cerebral amyloid angiopathy and in dementia with Lewy bod- ies.86,87Amyloid imaging by PIB-PET can also identify AD pathol- ogy in atypical clinical presentation without initial amnesia, such as PCA and lgPPA.7,88

Molecular amyloid imaging is restricted mainly to research centers, but progress in the field will likely increase the availa- bility of amyloid markers for clinical practice in the following years, especially in light of new amyloid markers that have been studied, such as 18F-florbetapir (18FAV-45) and florbetaben (18F- BAY94–9172).89

11.5 Conclusion

New proposals for the diagnosis of AD have incorporated bio- logical markers for identifying an underlying pathophysiologi- cal process. This approach allows establishment of a clinical diagnosis of AD before the dementia stage, in contrast to previ- ous diagnostic criteria published in 1984.54 According to these new frameworks, diagnosis of the disease is possible at an early stage, when the cognitive symptoms are still mild and the autonomy is preserved.

The core clinical criteria remain the main landmark of the diagnosis of AD in clinical practice, but evidence provided by biomarkers such as neuroimaging, CSF markers, and amyloid imaging is expected to increase the specificity of the diagnosis. A recent meta-analysis showed that diagnostic accuracy of imaging AD biomarkers is at least as dependent on how the bio- marker is measured as on the biomarker itself.83 Extensive work on biomarker standardization is needed before widespread adoption of these recommendations at any stage of the disease. Despite these limitations, biomarkers to improve the accuracy of the clinical diagnosis will be an essential requisite for new disease-modifying treatments that will tap into specific patho- physiologic targets.

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Genetics, Neuropathology, and Biomarkers in Alzheimer’s Disease 12 Genetics, Neuropathology, and Biomarkers in

Alzheimer’s Disease Maria Martinez-Lage Alvarez and Rashmi Tondon

Alzheimer’s disease (AD) is an adult-onset, slowly progressive neurodegenerative disorder that initially affects memory and later involves other cognitive and basic neurologic functions. AD is the most common form of dementia, particularly in eld- erly adults. It is estimated that 5.2 million Americans have AD in 2014, including approximately 200,000 individuals younger than age 65.1 The pathological hallmarks of the disease found in the brain are extracellular senile plaques, composed of β- amyloid (Aβ), and intracellular neurofibrillary tangles (NFTs), composed of phosphorylated tau protein, which can also be seen in the form of neuropil threads and neurites. AD can be clinically considered as either late or early onset, depending on whether it manifests before or after age 65 years. This chapter describes the genetic aspects, neuropathological findings, and nonimaging biomarkers of AD.

12.1 Genetics of Alzheimer’s Disease

12.1.1 Late-Onset Alzheimer’s Disease

More than 95% of patients with AD have onset after the age of 65, and it is well established that the risk of developing the disease increases exponentially with age: 11% of the population age 65 years and older suffer from AD, and the prevalence is 32% for those age 85 years and older.1 The causes of AD in this sporadic (as opposed to inherited) population is undoubtedly multifactorial, likely resulting from factors like cerebrovascular disease, type 2 diabetes, or obesity. Certain genetic susceptibil- ity loci have been known for more than two decades, whereas numerous new candidates for genetic risk factors have been discovered in more recent years thanks to improved technology and increased access to genomic studies of well-selected samples.

12.1.2 Apolipoprotein E

The association of the apolipoprotein E (ApoE) ε4 allele with late-onset AD in non-Hispanic whites of European ancestry has been well known for more than a decade. ApoE is a plasma pro- tein involved in the transport of cholesterol that exists as three isoforms determined by three alleles (ε2, ε3, and ε4). A single ApoE ε4 allele conveys a twofold to threefold increase in risk of developing AD, whereas having two copies is associated with a fivefold increase, demonstrating an additive risk association.2 The ε2 allele is considered protective, also in an additive manner, so that a homozygous ε2/ε2 genotype confers a lower risk than just one ε2 allele. Not only a higher risk of developing the disease, but also an earlier age of onset, has been associated with the ε4 allele; however, the presence of ε4 is neither suffi- cient nor necessary to develop AD.

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Sortilin-Related Receptor

Encoding a protein that participates in vesicle trafficking between the cell surface and Golgi apparatus, the sortilin- related receptor (SORL1) gene was chosen as a potential candi- date for AD susceptibility in an association study.3 Despite initial contradicting results from replication cohorts, sufficient evidence now supports the association of specific variants in this gene with a higher risk of developing AD, at least in the white population.4

Additional Genes Discovered in Genome-Wide Association Studies

With the advance of genome-wide technologies, it has been possible to move away from candidate gene approaches and toward unbiased assessments of the whole genome, eliminating the need to preselect candidates and opening the possibilities of detecting either novel genes or pathways not suspected to participate in a particular disorder. The effort has been tremen- dous in AD in the last several years, with collaborative studies analyzing more than 10,000 patients and more than 10,000 controls.2 Several genes have been identified with this method and replicated in diverse cohorts, making it worthwhile to mention them here. Of note, none of these other genes is simi- lar in effect to ApoE, which still remains the major genetic risk factor for late-onset AD. The most salient genes identified in these studies are listed in ▶ Table 12.1, along with their relative odds ratio values.2 Of note, these genes and their related encoded proteins can be clustered in a few functional and metabolic pathways, including lipid metabolism, innate and adaptive immunity, cell adhesion, and endocytosis, all of which are likely involved in the development of the neuropathologic substrates of AD.

12.1.3 Early-Onset Alzheimer’s Disease

The identification of families with autosomal dominant inheri- tance patterns contributed to the discovery in the late 1980s and early 1990s of three genes responsible for most early-onset AD (approximately 1% of all cases of AD). Amyloid precursor protein (APP) and presenilins 1 and 2 (PSEN1 and PSEN2 ) are involved in the processing of the Aβ molecule.5 Most mutations in these genes are autosomal dominant, albeit not always fully penetrant. These are considered causative genes because indi- viduals carrying mutations will inevitably develop the disease (except with incomplete penetrance), and detection of the mutation in an individual is diagnostic of AD.

Amyloid Precursor Protein

Located on chromosome 21q21.3, APP encodes for the amyloid precursor protein, a 110- to 130-kDa ubiquitously expressed

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Table 12.1 Molecular genetic classification of Alzheimer’s disease

Gene symbol Gene name Chromosomal Key information location

Definitive disease-causing genes (causative mutations of early onset AD)

APP

Amyloid precursor protein

21q21.3

Autosomal dominant, 25 pathogenic mutations, 16% of early-onset AD

PSEN1

Presenilin-1

14q24

Autosomal dominant, 185 pathogenic mutations, 66% of early-onset AD

PSEN2

Presenilin-2

1q31

Autosomal dominant,12 pathogenic mutations

Genes with increased susceptibility (risk variants for late-onset AD)

ApoE

Apolipoprotein E

19q13.32

ε2q13.32rot–15 ε5q13.32rotein –3

SORL1

Sortilin-related receptor

11q24.1

OR 1.08

ABCA7

Adenosing triphosphate-binding cassette, subfamily A, member 7

19p13.3

OR 1.2

BIN1

Bridging integrator 1

2q14.3

OR 1.2

CD33 (SIGLEC6)

CD33 antigen/sialic-acid binding immunoglobulin-like lectin 6

19q13.41

OR 0.9

CD2AP

CD2-associated protein

6p12.3

OR 1.1

CLU

Clusterin

8p21.1

OR 0.8

CR1

Complement component receptor 1

1q32.2

OR 1.2

EPHA1

Ephrin receptor EphA1

7q34-q35

OR 0.9

MS4A4E/MS4A6A

Membrane-spanning 4-domains, subfamily A, members 6E, 4A

11q12.2

OR 0.9

PICALM

Phosphatidylinositol-binding clathrin assembly protein

11q14.2

OR 0.8

Abbreviations: OR, odds ratio (note that an OR > 1 implies higher relative risk of disease, whereas an OR < 1 implies lower relative risk for the disease; the greater the number, the largest the size effect for that genotype).
Source: OMIM (http://omim.org/entry/104300), Schellenberg GD, Montine TJ. The genetics and neuropathology of Alzheimer’s disease. Acta Neuropathologica 2012;124(3):305–323.

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transmembrane protein that contains an internal 39–43 amino acid sequence coding for Aβ peptides. Cleavage by β- and γ-secretases results in the formation of peptides Aβ1-40 and Aβ1-42, the major component of the hallmark senile plaques of AD.6 Up to 25 point mutations have been identified as pathogenic to date7 (see http://www.molgen.vib-ua.be/ADMu- tations). All point mutations are clustered in a 54 amino acid segment that lies within or adjacent to the sequence encoding for Aβ peptides.2 In addition to being responsible for approxi- mately 16% of cases of early-onset AD,8 mutations in the APP gene can cause autosomal dominant cerebral amyloid angiop- athy and syndromes in which the two overlap. The London mutation (V717I) is the most common APP mutation and results in increased levels of Aβ1–42 by interfering with the activity of γ-secretase. The Swedish mutation involves two different codons (K670M and N671K) and increases total levels of Aβ production. The excess of Aβ is therefore considered sufficient to cause the disease, and this has been largely supported by the observation of a high prevalence of AD neuropathological changes and increased incidence of dementia in patients with trisomy 21 (Down syndrome), who carry an extra copy of the APP gene.2 Furthermore, several gene duplication events not associated with trisomy have been recognized as pathogenic events in AD.7 Lastly, the Arctic mutation, E693G, rather than altering the total amount of Aβ or interfering with γ-secretase activity, creates a mutant peptide that is more prone to aggre- gation than is wild-type Aβ.9

Presenilins 1 and 2

Mutations in presenilin 1 (PSEN1), located in chromosome 14q24.3, are responsible for the highest percentage of auto- somal dominant early-onset AD, accounting for up to 66% of all cases.8 At least 185 pathogenic mutations have been identified to date7 (see http://www.molgen.vib-ua.be/AD mutations), all with complete penetrance by age 60 to 65 years. As is the case for APP, there is significant heterogeneity in the phenotypic characteristics of individuals with mutations in PSEN1 in terms of age of onset (as early as the late 20s), rate of progression, and severity of the disease.10 PSEN1 is the catalytic component of γ-secretase, a protein complex responsible for the cleavage of a number of membrane proteins, including APP. Normal γ-secretase activity yields mainly Aβ1–40, with smaller amounts of Aβ1–42. PSEN1 mutations alter the secretase activity,11 leading to increased ratio of Aβ1–42 to Aβ1–40, thus facilitating the depo- sition of amyloidogenic species. Presenilin 2 (PSEN2) is a highly homologous protein located in 1q31-q42, which also partici- pates in the γ-secretase complex as the catalytic domain in the absence of PSEN1. PSEN2 mutations are less common than PSEN1 variants, with only 13 pathogenic mutations known to date.7 Compared with those with PSEN1, patients with PSEN2 mutations tend to have a higher age of onset (accounting for the small number of late-onset AD cases caused by an inherited causative mutation), longer course of the disease, and more var- iable penetrance.2

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Genetics, Neuropathology, and Biomarkers in Alzheimer’s Disease

12.2 Neuropathology of

Alzheimer’s Disease

The hallmark pathological features of AD include the accumula- tion of the protein fragment Aβ in extracellular “senile” plaques and other deposit and the intracellular buildup of phosphoryl- ated protein tau in the form of NFTs and neuritic threads. Other changes that are characteristic of AD include cerebral amyloid angiopathy (CAA) caused by deposition of Aβ in and around vessels, loss of synapses, neuron loss, glial activation, and ulti- mately brain atrophy. ▶Fig. 12.1 illustrates these pathological features of AD.

Amyloid precursor protein, the product of the APP gene, can be processed in two divergent pathways. When the full- length protein is cleaved by α- and γ-secretases, it results in a C-terminal fragment that is nonamyloidogenic. Conversely, when cleaved via the β- and γ-secretases, several species of Aβ fragments can occur, with Aβ1–40 the most common and Aβ1–42 being prone to aggregation (amyloidogenic). Aβ1–42 molecules form toxic oligomers, which then aggregate as extracellular insoluble fibrils with β-pleated sheet conformation, giving rise to the typical amyloid senile plaques. Aβ deposits are morpho- logically variable, ranging from the so-called neuritic plaques in which they are at the center of a cluster of tau-positive dys- trophic neurites, to diffuse (non-neuritic) plaques and diffuse

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deposits known as amyloid lakes. The “amyloid cascade hypoth- esis” suggests that the accumulation of Aβ fibrils in plaques is the primary pathological event in the disease and that it leads to the formation of the other pathological features, such as NFTs, synaptic loss, neuronal degeneration, and death.12

Tau protein is associated with microtubules and is thought to participate in regulating their stability in neuronal axons. For reasons not entirely understood, tau protein becomes aber- rantly hyperphosphorylated, dissociates from microtubules, and aggregates into paired helical filaments, insoluble fibrils that then are deposited as the characteristic NFTs and neuropil threads with β-pleated sheet conformation. Studies have dem- onstrated that in at least some individuals, tau pathology appears well before β-amyloid deposits are seen, which cannot be explained in the light of the amyloid cascade hypothesis and suggests that tau pathology can be an initiating event in the disease.

The progression of AD pathology, including amyloid plaques and NFTs, follows a specific spatial and anatomical pattern, starting in the limbic cortex (entorhinal cortex and hippocam- pus) and extending toward the neocortical surface, some sub- cortical nuclei, and in some cases the brainstem. With respect to NFTs, the staging system described by Braak and Braak13 is still recommended because it reflects this pattern of progres- sion with robust reliability. It proposes six stages, but there is

Fig. 12.1 Histopathological hallmarks of Alzheimer’s disease (AD). Photomicrographs of immunohistochemical stains demonstrate the typical findings of AD and comorbid pathologies. β-amyloid immunohistochemistry highlights abundant senile plaques in the hippocampus
(a, 500x) and frontal cortex c, 100x) in this case of advanced AD. Phosphorylated tau immuno- histochemistry demonstrates neurofibrillary tangles as well as neuropil threads and neurites in the hippocampus (b, 500x; d, 200x) in the same case. Note that phosphorylated tau also labels a number of neuritic senile plaques as there are tau-positive neurites in the center of these amyloid plaques. Coexisting pathology was present in this case, as is commonly seen in AD. Lewy bodies and Lewy neurites are seen with α-synuclein immunostaining in the amygdala
(e, 200x); whereas an antibody for phosphory- lated TDP-43 also demonstrates inclusions in the same region (e, 200x).

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Alzheimer’s Disease

increased inter-rater agreement when reduced to four stages14: (1) no NFT; (2) in Braak stages I/II, NFTs are predominantly in the entorhinal cortex and closely related areas; (3) in Braak stages III/IV, NFTs are more abundant in the hippocampus and amygdala and there is slight involvement of association cortex; (4) in Braak stages V/VI, NFTs are widely distributed throughout the neocortical areas, with eventual involvement of the primary motor and sensory areas.

In addition, many but not all cases of AD will demonstrate additional coexisting non-AD-type pathology in the brain, such as Lewy body disease (LBD), vascular brain injury, hippocampal sclerosis, and TDP-43 pathology.14 Lewy bodies, composed largely of α-synuclein and characteristic of Parkinson’s disease and LBD, are commonly seen in brains with moderate to severe AD changes, not only in sporadic cases but also in some patients with PSEN1 and PSEN2 mutations.15 Cerebrovascular disease and vascular brain injury, together with CAA, are commonly identified in brains with AD changes and should be acknowl- edged. TDP-43 is the major protein present in the pathological inclusions of frontotemporal lobar degeneration not caused by tau pathology and in amyotrophic lateral sclerosis,16 and it is increasingly recognized as present in the limbic structures of brains with AD pathology, with or without coexisting hippo- campal sclerosis.

Currently, a definitive diagnosis of AD still relies on postmor- tem examination of the brain to detect the typical senile pla- ques and NFTs, which are known to correlate with the presence of clinical symptoms of AD. The guidelines for neuropathologic evaluation and assessment of AD were reviewed in a seminal consensus paper from the National Institute on Aging and the Alzheimer’s Association in 2012, 25 years after the prior con- sensus.14 The main change to the diagnostic criteria was the recognition of AD as a dynamic entity, with a prodromal asymptomatic phase during which pathology has started to accumulate but has not caused major symptoms, thus allowing for the diagnosis of AD neuropathologic changes in the absence of a clinical history of dementia and bringing to the neuro- pathology arena the concept of early and preclinical AD. The guidelines recommend documentation of the AD pathologic features as stated herein, as well as documentation of the comorbidities in the autopsy report, including LBD, vascular pathology, and TDP-43 pathology. The “ABC” staging protocol is recommended, based on the data-driven documentation of the relative amounts of each of the three morphologic characteris- tics of AD: A, Aβ/amyloid plaque score (based on Thal phases17: A0 to A3); B, NFT score (based on Braak stage13: B0, B1=I/II, B2 = III/IV, B3 = V/VI); and C, neuritic plaque score (based on Consortium to Establish a Registry for Alzheimer’s Disease [CERAD] criteria18: C0 to C3). The combination of A, B, and C scores provides, for each case, a descriptor of “not,” “low,” “intermediate,” and “high” for AD neuropathologic change (entirely independent of clinical symptoms).14

At autopsy, patients with disease-causing mutations (APP, PSEN1, and PSEN2 ) tend to have greater amounts of neocortical senile plaques than patients who had “sporadic” AD, with no dif- ference in the amounts of tau pathology. Some mutations in these genes also result in differences in the morphology of the amyloid pathology compared with sporadic cases, such as large dense pla- ques in the APP Flemish mutation,19 ringlike plaques in the APP Arctic mutation,20 and cotton-wool plaques in PSEN1 mutations.2

12.3 Nonimaging Biomarkers in the

Diagnosis of Alzheimer’s Disease

Revised clinical criteria and guidelines for diagnosing AD were proposed and published in 201121 and recommended the con- sideration of AD as a slowly progressive disease that begins well before clinical symptoms emerge. In addition to imaging bio- markers largely discussed elsewhere, enormous efforts have been dedicated in the last two decades to the discovery of other biological biomarkers for the diagnosis of AD. As discussed earlier, a definitive diagnosis has classically been considered attainable only postmortem with the examination of the affected brain. This, however, is insufficient when we take into account the need to establish a degree of certainty both at the research level (e.g., to identify and monitor possible disease- modifying agents), as well as at the clinical level when approaching an individual patient.

12.3.1 Plasma

Plasma biomarker discovery started off with the idea of detect- ing β-amyloid in plasma. With the hypothesis that there is always an equilibrium state of Aβ production and deposition in the brain and that there is correlation with plasma levels, stud- ies were able to determine that there is an increase in plasma levels of Aβ, at least in patients with familial AD.22 Plasma level results in the general population in sporadic AD are, however, controversial and limited by complicated technological and methods problems with this test.

12.3.2 Cerebrospinal Fluid

Cerebrospinal fluid (CSF) has the potential to reflect reliably the state of chemical and cellular homeostasis in the brain, given its direct contact with the cerebral extracellular space. As such, CSF biomarkers have been incorporated into the revised research diagnostic criteria for AD since 2007,23 although they are still not largely available in community clinical practice. Levels of Aβ1–42, total tau, and phospho-tau can be used to aid in the diagnosis of AD.24 The increase in the total concentrations of tau in CSF is directly related to axonal degeneration in the cortex, whereas levels of phospho-tau are associated with NFTs. In this setting, total tau levels can increase in any process that involves cortical degeneration, such as stroke, trauma, and other neurodegenerative diseases,25 but phospho-tau is a more precise measurement associated with the underlying pathology of AD. In addition to confirmation of diagnosis in patients mani- festing full-blown symptoms, the importance of CSF AD bio- markers lies in their ability to contribute to early diagnosis, identify patients in prodromal phases (including mild cognitive impairment) that will go on to develop the disease, and select and monitor subjects for clinical trials. Because of analytical issues with the technology used (enzyme-linked immuno- sorbent assay and other immunoassay methods) across institu- tions and geographic locations, definite standardization is under way to establish homogeneity of results and improve the yield and quality of data obtained from these assays.25 Of note, CSF biomarkers are to be used in conjunction with clinical, genetic, and neuroimaging data to provide the most accurate diagnostic information.

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References

. [1]  Alzheimer’s Association. 2014 Alzheimer’s disease facts and figures. Alzheimers Dement 2014; 10: e47–92

. [2]  Schellenberg GD, Montine TJ. The genetics and neuropathology of Alzheimer’s disease. Acta Neuropathol 2012; 124: 305–323

. [3]  Rogaeva E, Meng Y, Lee JH et al. The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer’s disease. Nat Genet 2007; 39: 168–177

. [4]  Reitz C, Cheng R, Rogaeva E et al. Genetic and Environmental Risk in Alzheimer Disease 1 Consortium. Meta-analysis of the association between variants in SORL1 and Alzheimer’s disease. Arch Neurol 2011; 68: 99–106

. [5]  Reitz C, Mayeux R. Alzheimer’s disease: epidemiology, diagnostic criteria, risk factors and biomarkers. Biochem Pharmacol 2014; 88: 640–651

. [6]  O’Brien RJ, Wong PC. Amyloid precursor protein processing and Alzheimer’s disease. Annu Rev Neurosci 2011; 34: 185–204

. [7]  Cruts M, Theuns J, Van Broeckhoven C. Locus-specific mutation databases for neurodegenerative brain diseases. Hum Mutat 2012; 33: 1340–1344

. [8]  Raux G, Guyant-Maréchal L, Martin C et al. Molecular diagnosis of autosomal dominant early onset Alzheimer’s disease: an update. J Med Genet 2005; 42: 793–795

. [9]  Nilsberth C, Westlind-Danielsson A, Eckman CB et al. The ‘Arctic’ APP mutation (E693G) causes Alzheimer’s disease by enhanced Abeta protofibril formation. Nat Neurosci 2001; 4: 887–893

. [10]  Ridge PG, Ebbert MT, Kauwe JS. Genetics of Alzheimer’s disease. Biomed Res Int 2013; 2013: 254954

. [11]  Chau D-M, Crump CJ, Villa JC, Scheinberg DA, Li Y-M. Familial Alzheimer’s dis- ease presenilin-1 mutations alter the active site conformation of γ-secretase. J Biol Chem 2012; 287: 17288–17296

. [12]  Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002; 297: 353–356

. [13]  Braak H, Braak E. Neuropathological staging of Alzheimer-related changes.
Acta Neuropathol 1991; 82: 239–259

. [14]  Hyman BT, Phelps CH, Beach TG et al. National Institute on Aging-Alzheimer’s
Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement 2012; 8: 1–13

. [15]  Leverenz JB, Fishel MA, Peskind ER et al. Lewy body pathology in familial Alzheimer disease: evidence for disease- and mutation-specific pathologic phenotype. Arch Neurol 2006; 63: 370–376

. [16]  Neumann M, Sampathu DM, Kwong LK et al. Ubiquitinated TDP-43 in fronto- temporal lobar degeneration and amyotrophic lateral sclerosis. Science 2006; 314: 130–133

. [17]  Thal DR, Rüb U, Orantes M, Braak H. Phases of Aβ-deposition in the human brain and its relevance for the development of AD. Neurology 2002; 58: 1791–1800

. [18]  Mirra SS, Heyman A, McKeel D et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuro- pathologic assessment of Alzheimer’s disease. Neurology 1991; 41: 479–486

. [19]  Kumar-Singh S, Cras P, Wang R et al. Dense-core senile plaques in the Flemish variant of Alzheimer’s disease are vasocentric. Am J Pathol 2002; 161: 507–520

. [20]  Basun H, Bogdanovic N, Ingelsson M et al. Clinical and neuropathological fea- tures of the arctic APP gene mutation causing early-onset alzheimer disease. Arch Neurol 2008; 65: 499–505

. [21]  McKhann GM, Knopman DS, Chertkow H et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alz- heimer’s disease. Alzheimers Dement 2011; 7: 263–269

. [22]  Scheuner D, Eckman C, Jensen M et al. Secreted amyloid β-protein similar to that in the senile plaques of Alzheimer’s disease is increased in vivo by the presenilin 1 and 2 and APP mutations linked to familial Alzheimer’s disease. Nat Med 1996; 2: 864–870

. [23]  Dubois B, Feldman HH, Jacova C et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 2007; 6: 734–746

. [24]  Hansson O, Zetterberg H, Buchhave P et al. Prediction of Alzheimer’s disease using the CSF Abeta42/Abeta40 ratio in patients with mild cognitive impair- ment. Dement Geriatr Cogn Disord 2007; 23: 316–320

. [25]  Kang JH, Korecka M, Toledo JB, Trojanowski JQ, Shaw LM. Clinical utility and analytical challenges in measurement of cerebrospinal fluid amyloid-β (1–42) and τ proteins as Alzheimer’s disease biomarkers. Clin Chem 2013; 59: 903–916

Genetics, Neuropathology, and Biomarkers in Alzheimer’s Disease

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13 Imaging of Alzheimer’s Disease: Part 1

Donald G. McLaren, Guofan Xu, and Vivek Prabhakaran

Alzheimer’s disease (AD), the most common dementia, is a pro- gressive, devastating, nonreversible, and ultimately fatal neuro- degenerative disorder that leads to loss of memory and of the ability to function independently.1 It is expected that by 2050 more than 16 million Americans and 135 million people world- wide will suffer from AD. These numbers, coupled with the fact that current treatments only slow the clinical progression of AD, necessitate the utilization and improvement of bio- markers for understanding the disease processes, improving the diagnostic accuracy, and improving treatment outcomes. Although the spatial progression of AD pathology has been understood for a number of years,2,3 the development of bio- markers to measure the spatial progression has only partially mirrored the histologic work. In 2011, the National Institute on Aging/Alzheimer’s Association Workgroup released revised criteria for the diagnosis of AD,4 which called for research on the use of abnormal levels of biomarkers in future diagnostic criteria. The workgroup concluded that advancements in bio- markers would enhance the pathophysiologic specificity of the diagnosis. Thus, this chapter focuses on structural (i.e., magnetic resonance imaging [MRI]) and metabolic or molecular imaging of AD (i.e., single photon emission tomography [SPECT] and positron emission tomography [PET]), and Chapter 14 focuses on functional neuroimaging and brain connectivity in AD (e.g., perfusion, functional MRI, and diffusion tensor imaging). It is important keep in mind that use of neuroimaging and neuroimaging biomarkers is not a replacement for neuro- psychological or neurologic assessment; rather, it complements them.

13.1 Magnetic Resonance Imaging

Magnetic resonance imaging is a noninvasive imaging tech- nique that can measure brain structure and function (see

Chapter 14). The image is typically formed by detecting the radiofrequency signal emitted by hydrogen atoms after applying a radiofrequency pulse. Different types of images are acquired by modifying the time and strength of the pulse as well as the time before detecting the emitted signal. The most common types of brain MRI are T1-weighted images, T2-weighted images, and T2*-weighted images. Some MRI scans have specific clinical uses. For example, susceptibility-weighted imaging (SWI) can be used to detect cerebral microhemorrhages.

13.1.1 T1-Weighted Imaging

T1-weighted scans provide a clear picture of the gray and white matter in the brain, which appear as gray and white on the image, respectively (▶ Fig. 13.1). T1-weighted imaging is com- monly used to assess whether a patient has normal or abnormal brain structure. In cases of cognitive impairment, it can be used to rule out strokes and tumors as well as to identify areas of atrophy in the brain. Atrophy, or brain volume loss, in the hip- pocampus, medial and lateral temporal lobes, lateral parietal lobes, and precuneus is typical in AD patients (▶ Fig. 13.2).5 A similar pattern, albeit to a lesser degree and more limited to the temporal lobes, is found in patients with mild cognitive impair- ment (MCI).5

Recently, researchers have focused on individuals with pre- clinical AD. In 2011, the National Institute on Aging and Alz- heimer’s Association Workgroup came up with three stages characterizing preclinical AD.6 Preclinical stage 1, asymptomatic amyloidosis, includes patients with high PET amyloid tracer retention or low cerebrospinal fluid (CSF) β-amyloid (Aβ)1–42. Preclinical stage 2, asymptomatic amyloidosis plus neurodegen- eration, includes patients who meet the definition for stage 1 but also have neuronal dysfunction on fluorodeoxyglucose (FDG)-PET, high CSF tau/p-tau, or cortical thinning or hippo-

Fig. 13.1 Axial, coronal, and sagittal slices of a T1-weighted magnetic resonance imaging scan showing gray matter (gray areas) and white matter (white areas). Blue crosshairs are located in the right hippocampus.

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campal atrophy on structural MRI. Preclinical stage 3, asympto- matic amyloidosis plus neurodegeneration plus subtle cognitive decline, includes patients who met the definition for stage 2 but also show subtle cognitive decline from baseline or poor performance on challenging cognitive tests and did not meet the criteria for MCI. Researchers have reported the prevalence of each of these stages in cognitively normal older adults7,8: 16% had preclinical AD stage 1, 12% had preclinical AD stage 2, and 2% had preclinical AD stage 3. Interestingly, an additional 23% of the sample had evidence of abnormal hippocampal volume or hypometabolism as measured by FDG-PET. These patients were labeled as having suspected non-AD pathology, indicating that there are other pathways to hypoperfusion and hippocam- pal volume loss. This research indicates that more research is needed into the earliest structural changes in AD.

Because of the low cost of MRI scans, large-scale research studies (e.g., Alzheimer’s Disease Neuroimaging Initiative, or ADNI) routinely collect high-resolution T1-weighted images to conduct voxel-based morphometry (VBM) and cortical thick- ness studies.9 VBM studies10 are conducted to investigate how gray matter volume changes with clinical status as well as with cognition. For example, researchers found significant correla- tions between gray matter volume in the left supramarginal gyrus, anterior cerebellum, and left superior temporal gyrus and the Free and Cued Selective Reminding Test.11 Cortical thickness studies have also investigated the relationship of cor- tical thinning to clinical status5,12,13 (▶ Fig. 13.2) and the rela- tionship between cortical thickness and cognition.14,15 In the research setting, cortical thickness can be estimated using Free- surfer (http://freesurfer.net)16; clinicians may prefer the com- mercial and Food and Drug Administration (FDA)-approved Neuroquant package from CorTech Labs (http://www.cortechs. net) for diagnostic imaging.

Large longitudinal data sets, such as ADNI, have also enabled more advanced analysis approaches of atrophy across the AD spectrum. One recent study investigated the covariation of atro- phy across brain regions in patients with MCI (▶Fig. 13.3).17

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Imaging of Alzheimer’s Disease: Part 1

Fig. 13.2 Graphical representation of the cortical signature of Alzheimer’s disease (AD < controls p < 0.0001). Nodes with significant cortical thinning observed between individuals with AD and healthy controls. Data shown are on the pial surface of the FreeSurfer average brain. (Used, with permission, from Gross AL, Manly JJ, Pa J, et al; Alzheimer’s Disease Neuroimaging Initiative. Cortical signatures of cognition and their relationship to Alzheimer’s disease. Brain Imaging Behav 2012;6(4):584–598.)

This study found two patterns of atrophy related to AD bio- markers. The first pattern revealed coordinated atrophy across posterior nodes of the default-mode network, and the second pattern largely represented atrophy in the medial temporal lobe. The research indicates that there are likely to be indepen- dent, yet simultaneous disease processes causing atrophy in patients with MCI. Future longitudinal studies will likely inves- tigate the pathophysiological basis of these distinct patterns of atrophy.

13.1.2 White Matter Hyperintensities

White matter hyperintensities (WMHs) are lesions in deep white matter that are thought to reflect small-vessel disease (▶ Fig. 13.4). Current thinking is that lesions result from chronic hypoperfusion and disrupted blood-brain barrier integrity.18 WMHs appear bright on T2-weighted fluid attenuated inversion recovery (FLAIR) scans and can now be quantified with imaging software. The association between WMH and AD has been mixed, with some studies concluding that there is a relation- ship19,20 and other studies concluding that there is not a rela- tionship.21 The differences between studies likely reflect the analysis performed. In the aforementioned study showing a relationship, individuals with AD were compared with healthy controls, whereas the other study investigated whether WMHs were predictive of future AD. Yet another study revealed that individuals with high cognitive reserve may be able to cope with a greater WMH burden than those with low cognitive reserve. The implication is that cognitive reserve may be able to delay the onset of AD symptoms.22 These studies indicate that more research is needed in tracking the progression of WMHs during normal aging as well as throughout the AD pathophysio- logical process. Compared with the relationship between WMH and AD, the relationship between WMH and cognition is more established. A number of studies have reported that increased WMH burden is associated with lower cognitive perform- ance.23,24,25 Although the progression of WMHs in the course of

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Fig. 13.3 Visual depiction of the coevolution of atrophy factors related to Alzheimer’s disease (AD) biomarkers. Top row: Factor 1: Covariation of atrophy in the posterior default-mode network and hippocampus. Bottom row: Factor 3: Covariation of atrophy in medial temporal cortices. Note: Factors not related to AD biomarkers are not shown. (Modified, with permission, from Carmichael O, McLaren DG, Tommet D, Mungas D, Jones RN; for the Alzheimer’s Disease Neuroimaging Initiative. Coevolution of brain structures in amnestic mild cognitive impairment. Neuroimage 2012;66C:449–456.)

Fig. 13.4 T2 fluid-attenuated inversion recovery (FLAIR) images provide evaluation for white matter disease. White matter hyperintensities are lesions in the deep white matter that are thought to reflect small-vessel disease and indicate areas of gliosis.

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the development of AD is unknown, the risk factors for WMH are modifiable. Thus, there could be potential value in screening for WMHs clinically. Future research will likely investigate the potential value of routine WMH scans.

13.1.3 Amyloid-Related Imaging

Abnormalities

Although the assessment of amyloid-related imaging abnormal- ities (ARIA) is not useful in the diagnosis of dementia, it is critical in the development of anti-amyloid therapeutics and is potentially important in patients’ prescribed anti-amyloid therapies. ARIAs have arisen through the advent of AD thera- peutics aimed at lowering Aβ burden. Specifically, clinical trials of amyloid-lowering therapeutics revealed MRI signal changes that represent ARIA-edema or effusions (ARIA-E) and ARIA- hemosiderin deposition (ARIA-H). The observance of these abnormalities has led to new recommendations on MRI in clini- cal trials of anti-amyloid therapeutics.26

The finding of ARIA-E was an unexpected transient MRI sig- nal abnormality in several individuals undergoing anti-amyloid therapy (the highest bapineuzumab dose group [5 mg/kg], 3 of 10 patients developed ARIA-E) and was initially labeled vaso- genic edema based on MRI characteristics. Because of the scar- city of histopathological evidence that the MRI signal changes were in fact vasogenic edema, the MRI signal changes are now referred to as ARIA-E. ARIA-E is most often characterized as increased MRI signal on T2-weighted FLAIR scans in the paren- chyma, leptomeninges, or both. A low incidence of spontaneous ARIA-E has also been noted.

ARIA-H is a MRI signal abnormality that is thought to repre- sent hemosiderin deposits, including microhemorrhages and superficial siderosis. Microhemorrhages are round, focal, low- intensity lesions in the parenchyma that are detected by T2* GRE. SWI, which is essentially a T2*-gradient-echo (GRE) sequence with added susceptibility weighting, is more sensitive at detecting microhemorrhages. Not surprisingly, the size and number of microhemorrhages detected are related to the sequence resolution, sensitivity, and scanner strength. Thus, criteria for determining abnormal microhemorrhages need to be adjusted accordingly. Superficial siderois is characterized as curvilinear low intensities adjacent to the brain surface. The prevalence of microhemorrhages increases with age. The preva- lence of microhemorrhages in persons over age 80has been reported to be greater than 35% and is higher in those with hypertension and AD.26 Patients with microhemorrhages at baseline are more likely to have them during the clinical trials of anti-amyloid therapeutics.26 Work is ongoing to understand the natural progression in the increase in microhemorrhages with age and clinical status.

An Alzheimer’s Association Research Roundtable Workgroup recommends T2* GRE (due to its availability) with a slice thick- ness of 5mm or less, echo time of at least 20ms on at least a 1.5T scanner to identify ARIA-H, and a T2-weighted FLAIR sequence to identify ARIA-E.26 The following are additional rec- ommendations: more frequent scanning in phase I and early phase II clinical trials to ascertain the rates of abnormalities; considering the pharmacodynamics effects in determining the postdose time of scanning; and having short rescan intervals in individuals who develop ARIA during treatment. ARIA-E should

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Imaging of Alzheimer’s Disease: Part 1

be interpreted for the severity and relevance of symptoms. Indi- viduals with more than four microhemorrhages using the afore- mentioned sequences should be excluded from clinical trials of anti-amyloid therapies. The workgroup also recommended dis- continuation from the study of individuals whose incident ARIA-H is related to significant clinical symptoms. Ongoing and future work is aimed at standardizing and reporting ARIA.27

13.2 Single-Photon Emission Computed Tomography/Positron Emission Tomography Imaging

Nuclear medicine techniques, such SPECT and PET, for imaging the central nervous system are largely different from the ana- tomical imaging methods like computed tomography (CT) and MRI. Nuclear medicine imaging relies on radiotracers that pro- vide specific molecular information about pathophysiological brain processes. Nuclear medicine imaging of dementia can be divided into two major approaches: SPECT and PET. Radio- tracers have been developed for measuring regional cerebral blood flow (rCBF), regional cerebral glucose metabolism, cere- bral amyloid deposition, neurofibrillary tangles, dopamine transporter density, and many more.

13.3 Single-Photon Emission

Computed Tomography

Brain imaging with SPECT uses lipophilic radiopharmaceuticals that cross the blood-brain barrier to measure brain perfusion. The most commonly used radiotracers include technetium- 99m-hexamethylpropyleneamine oxime [HMPAO] and 99mTc- ethylene l-cysteinate dimer [ECD]. Both these agents are injected intravenously using doses of 10 to 20 mCi (370 to 740 MBq) and are retained in brain tissue with a rapid first-pass uptake. Uptake of these radiotracers is localized in active brain tissue and reflects rCBF. SPECT images are obtained 15 to 20minutes after the tracer injection. The resolution of the SPECT perfusion image is about 1 cm. Although high-resolution imaging obtained with dedicated multidetector cameras could provide greater anatomical details, the primary purpose of SPECT imaging is to evaluate relative rCBF rather than the speci- ficity of anatomical structures.

The brain is a well perfused and regulated organ based on tight neural vascular coupling mechanisms. The rCBF reflects underlying normal physiological or pathophysiologic processes. External sensory stimuli, such as touch, sound, smell, and vision, as well as patient’s motion and cognitive activities, could all affect rCBF. In dementia patients, focal pathological pro- cesses can result in substantial neuronal loss leading to perfu- sion deficits. Specific patterns of deficits in rCBF can help diag- nosis and differentiate dementias.

As the normal distribution of perfusion agents is proportional to regional blood flow, there is approximately fourfold greater uptake in the cortical gray matter compared with white matter. Normal brain perfusion is symmetric and greater in the strip of cortex along the convexity of the frontal, parietal, temporal, and occipital lobes. Activity, and consequently uptake, is also high in the regions corresponding to subcortical gray matter, including

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the basal ganglia and the thalamus. The cortical white matter

has significantly lower activity and correspondingly low uptake. The border between the white matter and ventricles may be indistinct.

Visual interpretation of the cerebral perfusion images usually is performed by comparing both hemispheres for symmetry or by scrutinizing the continuity in the rims of cortical gray matter. The local perfusion is measured as increased, similar, or decreased relative to the perfusion in the identical area in the contralateral hemisphere. In AD patients, the most common finding using SPECT brain perfusion imaging is symmetric bilat- eral posterior temporal and parietal perfusion defects. This pat- tern of decreases has a positive predictive value of greater than 80%.28 Although the pattern has a high positive predictive value, the pattern is not pathognomonic for AD. Other pathophysio- logical processes can alter focal brain perfusion to produce a similar pattern of changes in perfusion. The pattern of deficits observed in AD has also been reported in vascular dementia, Parkinson’s disease, and various encephalopathies. Further- more, about 30% of AD patients manifest with asymmetric decreased cortical perfusion, depending on the stage of their dementia. In those cases, unilateral temporal or parietal hypo- perfusion could be seen. Frontal lobe hypoperfusion has also been seen, but with less predictive value. The negative predic- tive value of a normal SPECT perfusion scan is high for mid-to- late stage of disease.28

Clinical use of SPECT perfusion in the diagnosis of dementia is limited by its relatively low resolution, lack of anatomical specificity, and nonspecific perfusion deficit pattern among milder AD patients. However, it is a great neuroimaging research tool for dementia imaging because of its lower cost and ease of access relative to PET.

Numerous research studies have been using SPECT brain per- fusion to characterize various dementias, as well as the normal aging process and the relationship of perfusion to cognitive change.29 These research projects are usually facilitated by com- puter-aided fusion of SPECT images with corresponding CT or MRI images. A control cohort is then compiled based on high-quality “normal” brains. Finally, various advanced imaging analysis techniques, such as voxel-based analyses, three-dimen- sional stereotactic surface–based projection, and tomographic z-score mapping, greatly enhance both the sensitivity and spec- ificity of SPECT perfusion imaging in dementia characterization. Additionally, partial volume correction based on MRI anatomi- cal imaging has been reported to further improve the specificity and sensitivity in dementia characterization.29

13.4 Positron Emission

Tomography

Positron emission tomography uses positron-emitting radio- pharmaceuticals to provide spatially specific information about brain metabolism or specific molecular targets (e.g., amyloid). 2-Deoxy-2-(18F)fluoro-D-glucose, or FDG, a glucose analog that reflects glucose metabolism, is currently the most widely used PET tracer for dementia imaging in the clinic. However, there are many other emerging tracers, such as amyloid tracers, car- bon-11-labeled Pittsburgh compound B (PIB), or 18F-labeled Aβ- PET radiopharmaceuticals, to enable in vivo detection of human

brain amyloid deposition, and tau tracers, 18F-labeled T807, and 18F-labeled T808 to enable in vivo detection of hyperphosh- porylated tau proteins. These newer tracers show great poten- tial for dementia characterization. Because amyloid deposition occurs decades before symptoms, amyloid tracers could poten- tially be used for early diagnoses and to guide potential therapy for amyloid-related dementia.30

13.5 Positron Emission

Tomography: Fluorodeoxyglucose

The normal distribution of FDG in the brain is similar to those SPECT perfusion agents that have the highest uptake in cortical gray matter, basal ganglia, and thalami. This normal metabolic imaging pattern changes with aging and shows significant intersubject variations. Relatively decreased uptake has been reported to be associated with normal aging. However, the uptake within the thalamus, basal ganglia, occipital cortex, and cerebellum is usually unchanged with normal aging. The poste- rior cingulate cortex, lateral temporal lobe, posterior parietal lobes, and anterior frontal lobes generally have higher uptake related to their high resting metabolism. The regions collect- ively make up the default-mode network.31

Metabolic imaging by FDG-PET has shown usefulness in cer- tain discrete clinical settings to evaluate the cause of dementia, including AD, frontotemporal dementia, dementia with Lewy bodies, Parkinson’s disease, multi-infarct dementia, and Hun- tington disease (▶ Fig. 13.5). AD patients have reduction in both glucose metabolism and CBF in the parietotemporal association cortex. The parietotemporal involvement is usually bilateral, although asymmetry of perfusion or metabolism reduction is commonly seen. These deficits then spread to the frontal lobes as disease progresses. The primary motor, sensory, and visual cortices are typically spared until very late stage of dementia. These findings have been widely recognized as a diagnostic pat- tern for AD (▶Fig. 13.5a). As with SPECT, the FDG diagnostic pattern typical in AD is not pathognomic, although it is highly predictive.32

13.5.1 PositronEmissionTomography:

Amyloid and Tau Imaging

Many other non-FDG PET tracers show great success in charac- terizing AD by in vivo imaging of amyloid deposition. Among many of these emerging tracers, N-methyl [11C]2-(4 methylami- nophenyl)-6-hydroxy-benzothiasole (Pittsburgh compound B), named 11C-PIB, is the most successful amyloid tracer in the field of dementia neuroimaging research.33,34 It has revealed high in vivo retention that correlates with cerebral pathological changes of Alzheimer’s patients (▶ Fig. 13.6). Despite its great success of in vivo amyloid imaging, the clinical use of 11C-PIB is limited by its relatively short half-life and the limited availabil- ity of the tracer.

Another promising agent, 18F-florbetapir, also known as AV- 45 or Amyvid, has shown similar capability of in vivo mapping of beta amyloid density in the brain.35 Amyvid was recently approved by the FDA for clinical use and was selected as the amyloid imaging tracer in the anti-amyloid treatment in asymptomatic AD (A4) trial for its wide availability.

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Although amyloid plaques are one of the defining pathologi- cal features of AD, normal elderly people without dementia and other patients with clinical syndromes other than dementia could have elevated levels of amyloid deposition in the brain.36 Whereas a positive amyloid scan indicates a significant amyloid burden, a negative scan carries no prognosis of future amyloid burden. Therefore, the clinical utility of amyloid PET imaging requires careful consideration to ensure its role in the proper clinical context. It is particularly important with the considera- tion of cost-effective use of limited health care resources. The current diagnostic guideline from the Amyloid Imaging Task Force does not advocate the use of such neuroimaging bio- marker tests for routine diagnostic purposes.37,38 There are sev- eral reasons for the limitation, including current clinical core criteria, which provide good diagnostic accuracy and utility in most patients; more work needs to ensure the appropriate criteria of biomarker use, limited standardization of biomarkers, and limited access to the biomarkers. Presently, the use of these advanced amyloid imaging markers may be useful only in the

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Imaging of Alzheimer’s Disease: Part 1

Fig. 13.5 (a) Alzheimer’s disease (AD). A 58-year-old woman with complaints of forgetfulness and a family history of AD. Fluorodeoxyglucose (FDG) positron emission tomography (PET) shows significant hypometabolism in the bilateral parietotemporal association cortices, as well as the bilateral frontal lobes. Of note, the motor and visual cortices are spared. (b) Dementia with Lewy bodies. A 58-year-old woman with progressive cognitive decline over 2 years. FDG-PET shows significant hypometabolism in the bilateral parietotemporal association cortices, right greater than left, with metabolism deficits in the bilateral visual cortices. Of note, the bilateral frontal lobes are spared. (c) Frontotemporal dementia. A 66-year-old woman with progressive cognitive decline and memory loss for 2 years with speech difficulty. Significant FDG hypometabolism in the bilateral frontal lobes, left greater than the right. Mild hypometabolism is also seen within the bilateral temporal lobes. There is sparing of the parietal lobes and posterior cingulate cortices.

following circumstances: investigational studies, clinical trials, and as optional clinical tools where available and when deter- mined appropriate by the clinician (e.g., differentiate fronto- temporal dementia from AD).

Another investigational PET tracer, fluoroethyl methyl amino-2 napthyl ethylidene malononitrile (18F-FDDNP) appears to bind to senile plaques and neurofibrillary tangles. Thus, for imaging amyloid, the other aforementioned tracers are preferred.

Recently, novel tracers (e.g. F18-T807 and F18-T808) have been developed that are thought to bind to hyperphosphory- lated tau proteins (PHF-tau), such as neurofibrillary tangles.39,40 The tau specificity is based on co-localization of tracer uptake with immunoreactive PHF-tau pathology, but not amyloid pathology.41 Recent research in humans has shown increased tracer uptake in patients with AD.39,40 Researchers are now investigating whether this tracer can map the temporal pro- gression of tau from the entorhinal cortex to other cortical areas, as well as the clinical significance of hyperphosphory- lated tau burden.

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Fig. 13.6 Amyloid image with [11C] Pittsburgh Compound B PET scan. (a,b) Unambiguous positive amyloid binding in the cortex relative to white matter and the cerebellum. (c,d) An indeterminate classification where gray matter binding was present in the cortex of at least three lobes resembling an Alzheimer’s disease (AD) pattern but less intense and convincing than an overtly positive scan. (e,f) No cortical amyloid burden or only nonspecific white matter uptake; nonsignificant patchy or diffuse cortical gray matter binding not resembling an AD pattern (significant uptake in the basal ganglia was common and not considered in the visual rating). (Images courtesy of Dr. Sterling Johnson.)

13.6 Early Diagnosis of

Alzheimer’s Disease

Given their great sensitivity for pathophysiological abnormality in dementia patients, both SPECT perfusion and FDG-PET have been used in early AD patients or people at high risk for devel- oping AD. It is crucial to recognize their brain perfusion and metabolic abnormalities to facilitate possible intervention or disease-modifying therapy. Individuals with amnestic MCI, patients who have memory problems but do not meet the crite- ria for AD, are most likely to convert to AD in the future. Diag- nosis typically includes the following criteria: patient has mem- ory concerns, objective memory impairment for age, normal general cognitive function, capability of normal daily activity,

and not demented. Many studies have demonstrated that amnestic MCI patients have a reduction in both glucose metab- olism and CBF in the posterior cingulate cortex and precuneus. Because these brain regions have a high level of perfusion as well as metabolism in normal patients, it is quite difficult to visualize these subtle decreases in the very early stage of dis- ease onset. However, statistical analysis reveals lower metabo- lism in amnestic MCI patients relative to controls in these areas. Furthermore, reduction in metabolism and perfusion in these areas could predict a cognitive decline in asymptomatic patients (i.e., preclinical AD stage 2). The glucose hypometabo- lism seen on PET reflects projections from dysfunctional neu- rons in other brain regions, such as the hippocampus within the mesial temporal lobe. Amnestic MCI patients could poten- tially benefit more from early intervention and disease-modify-

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ing therapies than could mild AD patients. The idea that AD therapies may work best early in the disease process is the premise of the A4 clinical trial. Researchers will give solanezu- mab, a monoclonal antibody-targeting amyloid, or placebo to elderly adults with amyloid (e.g., preclinical AD). The hope is that earlier treatment will be disease modifying.

13.7 Conclusions

Structural, metabolic, and molecular imaging research over the past several decades has advanced our understanding of AD pathophysiological processes, yet imaging biomarkers should not replace clinical neurologic assessments. These advances have led to an influential conceptual model for the progression of AD (biomarker) pathology.42 However, the earliest neuro- imaging biomarkers are costly, and their initiation is unknown, making it difficult to identify the earliest rise in AD risk. Future multimodal imaging research, coupled with more sensitive markers of AD pathology, will aid in identifying those at an increased risk earlier in the pathophysiological cascade. Key to this identification will be the ability to separate brain changes of normal aging from those attributable to AD-related pathol- ogy. To differentiate AD from normal or exaggerated aging, numerous studies have investigated the relationship between aging and neuroimaging findings. For example, in FDG-PET studies, the reductions most common with age were observed in the dorsolateral and medial frontal areas and the perisylvian insular cortices rather than in the default-mode network.29

The growing neuroimaging literature is focusing on investi- gating brain changes in regions affected by AD. In one study, thinner cortices in areas of atrophy in AD were associated with increased risk of conversion from normal to AD.43 Other studies have gone further to create criteria for defining abnormal bio- markers to help identify individuals in the various stages of pre- clinical AD.7,8,9 Future studies will use the preclinical AD stages to look for other subtle changes related to AD pathology. Finally, studies that investigate networks of atrophy and cortical thin- ning may have more potential to differentiate AD-related brain changes from those associated with normal aging based on the co-occurring change in spatially disparate regions.17

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Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Imaging of Alzheimer’s Disease: Part 1

[8] Knopman DS, Jack CR, Jr, Wiste HJ et al. Short-term clinical outcomes for stages of NIA-AA preclinical Alzheimer’s disease. Neurology 2012; 78: 1576– 1582

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[10] Ashburner J, Friston KJ. Why voxel-based morphometry should be used. Neuroimage 2001; 14: 1238–1243

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[13] Im K, Lee JM, Seo SW et al. Variations in cortical thickness with dementia severity in Alzheimer’s disease. Neurosci Lett 2008; 436: 227–231

[14] Gross AL, Manly JJ, Pa J et al. Alzheimer’s Disease Neuroimaging Initiative. Cortical signatures of cognition and their relationship to Alzheimer’s disease. Brain Imaging Behav 2012; 6: 584–598

[15] Ahn H-J, Seo SW, Chin J et al. The cortical neuroanatomy of neuro- psychological deficits in mild cognitive impairment and Alzheimer’s disease: a surface-based morphometric analysis. Neuropsychologia 2011; 49: 3931– 3945

[16] Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 2000; 97: 11050–11055 [17] Carmichael O, McLaren DG, Tommet D, Mungas D, Jones RN for the Alz-

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Initiative. White matter hyperintensities and cerebral amyloidosis: necessary and sufficient for clinical expression of Alzheimer’s disease? JAMA Neurol 2013; 70: 455–461

[21] Weinstein G, Beiser AS, Decarli C, Au R, Wolf PA, Seshadri S. Brain imaging and cognitive predictors of stroke and Alzheimer’s disease in the Framing- ham Heart Study. Stroke 2013; 44: 2787–2794

[22] Brickman AM, Siedlecki KL, Muraskin J, et al. White matter hyperintensities and cognition: Testing the reserve hypothesis. NBA. 2009:1–11

[23] Brickman AM, Honig LS, Scarmeas N et al. Measuring cerebral atrophy and white matter hyperintensity burden to predict the rate of cognitive decline in Alzheimer’s disease. Arch Neurol 2008; 65: 1202–1208

[24] Hedden T, Mormino EC, Amariglio RE et al. Cognitive profile of amyloid bur- den and white matter hyperintensities in cognitively normal older adults. J Neurosci 2012; 32: 16233–16242

[25] Oosterman JM, Sergeant JA, Weinstein HC, Scherder EJA. Timed executive functions and white matter in aging with and without cardiovascular risk factors. Rev Neurosci 2004; 15: 439–462

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[27] Barkhof F, Daams M, Scheltens P et al. An MRI rating scale for amyloid-related imaging abnormalities with edema or effusion. AJNR Am J Neuroradiol 2013; 34: 1550–1555

[28] Mettler F, Guiberteau M. Essentials of nuclear medicine imaging. Essentials of nuclear medicine imaging. 6th ed. Philadelphia: Elsevier/Saunders; 2012:71– 97

[29] Matsuda H. Role of neuroimaging in Alzheimer’s disease, with emphasis on brain perfusion SPECT. J Nucl Med 2007; 48: 1289–1300

[30] Rowe CC, Villemagne VL. Brain amyloid imaging. J Nucl Med Technol 2013; 41: 11–18

[31] Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001; 98: 676–682

[32] Murray AD. Imaging approaches for dementia. AJNR Am J Neuroradiol 2012; 33: 1836–1844

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. [35]  Wong DF, Rosenberg PB, Zhou Y et al. In vivo imaging of amyloid deposition in Alzheimer’s disease using the radioligand 18F-AV-45 (florbetapir [cor- rected] F 18). J Nucl Med 2010; 51: 913–920

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of Nuclear Medicine and Molecular Imaging. Amyloid Imaging Task Force. Appropriate use criteria for amyloid PET: A report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer’s Association. Alzheimers Dement 2013; 9: e-1–e-16

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[42] Jack CR, Jr, Knopman DS, Jagust WJ et al. Tracking pathophysiological pro- cesses in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 2013; 12: 207–216

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Imaging of Alzheimer’s Disease: Part 2 14 Imaging of Alzheimer’s Disease: Part 2

Christian La, Wolfgang Gaggl, and Vivek Prabhakaran

As an ongoing effort toward the identification of various bio- markers and early detection of Alzheimer’s disease (AD), the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was con- ceived to help researchers and clinicians develop new treat- ments and increase the safety and efficacy of drug development. With a primary focus on structural magnetic resonance imaging (MRI) of the brain, data generated from ADNI-1 have improved the understanding of relationships between imaging and chem- ical biomarkers of AD with the acquisition of a three-dimen- sional T1-weighted magnetization-prepared rapid acquisition with gradient echo (MP-RAGE) and a dual fast spin-echo (pro- ton density/T2-weighted) sequence. As a continuation to the initiative, ADNI-GO and ADNI-2 expanded on the ADNI imaging core protocol with inclusion of resting-state functional MRI (fMRI), T2 fluid-attenuated inversion recovery (FLAIR), diffusion tensor imaging (DTI), and arterial spin-label perfusion imaging. In this chapter, we provide an overview of the research involv- ing perfusion imaging, FLAIR MRI, DTI, and magnetic resonance spectroscopy (MRS) in the AD population (▶ Table 14.1).

14.1 Perfusion-Weighted Imaging

Perfusion-weighted imaging (PWI) is an MRI sequence that is sensitive to the flow of blood in the capillaries and capillary beds, a technique that is getting more attention in the investiga- tion of AD and other neurodegenerative diseases. For the past two decades, single-photon emission computed tomography (SPECT) and positron emission tomography (PET) have served as the mainstream imaging for perfusion and metabolism and remain highly effective. Although the risk of radioactivity is rather minimal, preparation of the necessary isotopes and radioactive tracer remains a potential challenge in these nuclear medicine techniques. Only a few large-scale hospitals and research institutes have the resources to support such a

system. In contrast, PWI provides a survey of perfusion that is free of such radioactive isotopes. This method is comparatively easy to implement and is available for most commercial scan- ners used at hospitals and medical centers.

Perfusion-weighted MRI can be categorized into two general classes, depending on the method of obtaining contrast. Also called bolus-tracking MRI, dynamic-susceptibility contrast (DSC) is currently the most widely used approach. With the tracking of a bolus injection of paramagnetic, gadolinium-based contrast (GBC) agents, relative measures of regional cerebral blood flow (rCBF), regional cerebral blood volume (rCBV), mean transit time (MTT), and time-to-peak (TTP) can be assessed and recorded. However, administration of GBC agents has been associated with nephrogenic systemic fibrosis in patients with significant renal insufficiency, limiting its current application. Arterial spin labeling (ASL) technique, on the other hand, makes use of endogenous arterial blood as the tracer for the quantifi- cation of blood flow. In contrast to DSC MR perfusion, which provides relative perfusion measurements, ASL MRI allows absolute quantification of perfusion as expressed in terms of milliliters per 100g per minute. The quantitative values obtained from ASL MRI offer a reliable whole-brain CBF mea- surement that is comparable to the traditional 15O-water PET perfusion imaging method,1 without the radioactivity. Its ease of acquisition, noninvasive nature, and high reproducibility over time also make it an attractive and potentially cost-effective alternative to PET.

Perfusion imaging methods have been successful in the detection of AD-related perfusion deficiencies. The pathology of AD is that of a slowly progressing neurodegenerative disorder commonly characterized by decreased rCBF. It has been previ- ously reported that patients with AD symptoms consistently show patterns of cerebral hypoperfusion, and although in such individuals a global decrease in blood flow is regularly demonstrated compared with healthy controls, CBF reduction may be more pronounced in certain regions than in others. Indeed, individuals with AD have shown a more pronounced reduction of CBF in the following regions: the precuneus, the posterior cingulate, and the lateral parietal cortices, with such findings demonstrated by DSC perfusion2 and ASL perfusion (▶Fig. 14.1).3 These perfusion abnormalities have been recorded as early as in patients with mild cognitive impair- ments (MCI) and in patients in the early preclinical phases of AD, with effects persisting well into the later stages of the dis- ease. During the preclinical stages of asymptomatic individuals, the apolipoprotein (Apo)E4 allele and family history are well- recognized risk factors for the onset of AD. Carriers of ApoE4 allele and the presence of family history, in particular maternal family history, increase the likelihood of AD-related hypoperfu- sion as tested in the asymptomatic population. Previously, the gender of the AD-affected parent has been suggested to influ- ence the risk of disease progression.4

Along with CBF deficiencies, patients even in early stages of the disease often exhibit changes in their cortical structure. Brain-volume losses are particularly prominent in the meso- temporal structures.5 The extent of atrophy in MCI patients is

Table 14.1 Magnetic resonance imaging (MRI) acquisition protocol for ADNI-1 and ADNI-GO/2

ADNI-1: (1.5-Tesla scanner) ADNI-GO/2 (3-Tesla scanner)

● Localizer

● Localizer

● MP-RAGE

● Sagittal MP-RAGE/IR-SPGR

● MP-RAGE (repeat)

● Accelerated sagittal MP-RAGE/IR-SPGR

● B1 calibration: head coil

● Resting-state fMRI (Philips Systems only): eyes open

● B1 calibration: body coil

● Axial T2-FLAIR

● T2 dual echo

● Axial T2

● Axial ASL perfusion (Siemens systems only) – eyes open

● Axial DTI scan (GE systems)

Abbreviations: ADNI, Alzheimer’s Disease Neuroimaging Initiative; ASL, arterial spin labeling; DTI, diffusion tensor imaging; FLAIR, fluid- attenuated inversion recovery; fMRI, functional magnetic resonance imaging; MP-RAGE, magnetization-prepared rapid acquisition with gradient echo.

Source: (http://adni.loni.usc.edu/methods/mri-analysis/mri-acquisition/).

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Fig. 14.1 Cerebral perfusion reduction in Alzheimer’s disease (AD) patients compared with healthy elderly controls. Statistical t-map of cerebral blood flow difference in AD patients and healthy elderly controls by way of arterial spin labeling perfusion magnetic resonance imaging, with activation representing areas of reduced cerebral perfusion in patients at p < 0.005 un- corrected. (Image courtesy of Ozioma C. Okonwo and the Wisconsin Alzheimer’s Disease Research Center.)

more limited. Patients with amnestic MCI who eventually prog- ress to AD have demonstrated greater susceptibility to gray matter loss in the medial and inferior temporal lobes, temporo- parietal, posterior cingulate, precuneus, anterior cingulate, and some regions of the frontal lobes compared with clinically sta- ble MCI patients.6 Despite this fact, after adjusting for brain atrophy and gray matter volume, the previously described effects of regional hypoperfusion in the posterior cingulate, the precuneus, the inferior parietal, and the lateral prefrontal corti- ces persist and cannot be explained solely by brain atrophy.3

Such hypoperfusion patterns are in line with numerous fluo- rodeoxyglucose (FDG) PET studies.7 CBF and cerebral metabo- lism are generally believed to be tightly coupled. A study com- bining FDG-PET and ASL-MR perfusion imaging in the same patients demonstrated a high degree of overlap between the two modalities of perfusion MRI and PET.8 In that study, imple- mentation of concurrent FDG-PET and ASL-MRI demonstrated not only similar regional abnormalities in AD between the two modalities, but the two modalities also provided comparable sensitivity and specificity for the detection of AD as reviewed by expert readers.8 The agreement in hypoperfusion and hypo- metabolism patterns suggests a possible sensitivity of ASL CBF toward the neurometabolic alterations among individuals with risk factors for AD.3,8

Nonetheless, despite the strong correlation between the two measures of cerebral perfusion and cerebral metabolism, some regions exhibited differential observations. Whereas the reduc- tion of CBF is somewhat consistent in the population of AD, some studies have also reported opposite findings. Such signs of hyperperfusion are discordant with the hypometabolism reported for the same regions by a number of FDG-PET studies.9 Although the explanation for this decoupling remains unclear,

it has been suggested that this increase of perfusion might be the direct or indirect result of local inflammatory response or compensatory activity in the face of neurodegeneration.10

14.2 Functional Magnetic

Resonance Imaging

A different modality that has been successfully used in the AD population, and that has been added to ADNI-2, is fMRI. Given the impairments in memory associated with progression of AD, numerous fMRI studies have focused on the functional changes in such processes. Memory issues are one of the first observable symptoms of AD, but not all memory systems are equally impaired. Episodic memory is generally the first and most affected of the memory systems. Not surprisingly, areas of the medial temporal lobe that are critical to episodic memory have been reported to sustain heavy neuronal loss, as previously stated.5 In AD, multiple regions pertaining to functional net- works subserving episodic memory sustain alterations in corti- cal activation patterns compared with age-matched healthy controls. Decreased activity from episodic memory task-fMRI has been reported in the hippocampal formation in AD patients, such as during picture encoding11 and verbal retrieval.12 Con- versely, lateral prefrontal activity has been shown to increase during verbal retrieval, suggesting the notion of a compensa- tory mechanism.12

Despite those promising results, investigations using task- evoked fMRI in the AD population are heavily confounded by individual differences and in their abilities to perform the task. Alternatively, resting-state fMRI (rs-fMRI), also called task- negative or task-free fMRI, provides an investigation of the

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Imaging of Alzheimer’s Disease: Part 2

brain network free of the confounding factors associated with a task. In this context, rest refers to a constant condition without imposed stimuli. By surveying the existing spontaneous low- frequency fluctuation (SLFF) of blood-oxygen-level–dependent (BOLD) signal during rest as originally illustrated by Biswal et al,13 valuable information can be extracted. A wide array of consistent, segregated functional networks, or rs networks (RSNs), can be assessed using such a technique. Functional connectivity analysis provides an assessment of functional networks and allows for an appraisal of network integrity of specific RSNs in clinical populations compared with healthy normal persons. A survey of rs intrinsic activity might be as sig- nificant as, if not more significant than, evoked activity in terms of overall brain function.

Of particular interest is the default-mode network (DMN). Comprising primarily the precuneus/posterior cingulate, inferolateral parietal, and medial prefrontal cortices, this net- work is thought to have a role in introspection and self- reflective thinking. In normal aging, mild cognitive impair- ment, AD, and various other neurologic disorders, the DMN experiences disruption, especially in terms of functional con- nectivity (▶Fig. 14.2).14,15 Greicius et al14 demonstrated abnormality within the DMN in AD patients, with a decrease in DMN coactivity in the posterior cingulate and hippocam- pus from a study of 13 mild AD patients. These findings of reduced DMN connectivity have been replicated on multiple occasions in AD patients,16,17 in MCI patients,15 as well as in cognitively healthy older controls harboring amyloid pla- ques18,19 and in healthy older carriers of the ApoE4 allele.19 The study from Sorg et al15 additionally revealed that func- tional connectivity between both hippocampi in the medial temporal lobes and the posterior cingulate of the DMN was present in healthy controls but absent in patients, represent-

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ing the effects of ongoing early neurodegeneration, possibly reflecting the later reduced integrity of the communicating fiber tract.20

Analyses of rs-fMRI have yielded consistent findings, primar- ily with a loss of intranetwork connectivity in large-scale networks in AD and MCI, including the DMN, dorsal attention network, salience network, and sensorimotor network.21 Within the DMN, patients suffer from disruption of functional connectivity spanning from the posterior to the anterior por- tions of the network.22 Although with age the anterior DMN shows increases in frontal lobe connectivity and the posterior DMN shows declines in connectivity throughout, with onset of AD pathology hastening these patterns of age-associated changes, particularly in the posterior regions.16 Furthermore, functional connectivity between regions separated by greater physical distance was markedly attenuated with increasing dis- ease severity, with such loss associated with less efficient global and nodal network topology.23

In addition to intranetwork connectivity deficiency, internet- work connectivity was also consistently disrupted.21 Studies have found decreased DMN connectivity to be associated with increased prefrontal connectivity24 and increased salience net- work connectivity,17 suggesting that the pathology of AD is associated with an alteration in large-scale functional brain net- works, which extends well beyond the DMN. Several task-fMRI studies have demonstrated decreased ability to deactivate regions irrelevant for task performance in AD and MCI popula- tions compared with the normal elderly population.25 There- fore, the ability to decrease brain activity and disengage the DMN during executive tasks is also associated with brain health. Together, these findings suggest that AD pathology is associated with widespread disruption of both intranetwork and internet- work correlations.

Fig. 14.2 Resting-state functional connectivity reduction in Alzheimer’s disease (AD) patients compared with healthy elderly controls. Statistical t-map of functional connectivity difference in AD patients and healthy elderly controls by way of seed-based approach with a seed placed in the posterior cingulate cortex (PCC) MNI [2–54 26] overlaid on an averaged anatomical brain image. Activation clusters represent areas of reduced functional connectivity with the PCC, a principal component of the default-mode network at

p < 0.005, uncorrected. (Image courtesy of Sterling C. Johnson and the Wisconsin Alzheimer’s Disease Research Center.)

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A family history of the presence of the ApoE4 allele and amy- loid aggregation, two well-accepted risk factors for AD, have been studied using such methods. Fleisher et al26 demonstrated that the high-risk population exhibited distinct characteristics compared with low-risk population. In that study, groups were defined by family history of dementia and whether or not par- ticipants were carriers of the ApoE4 allele. The two risk groups were distinguished by their activity over nine regions, including regions of the prefrontal, orbital frontal, temporal, and parietal lobes.26 In another study of cognitively normal individuals, a family history of late-onset AD was associated with reduced resting state functional connectivity between particular nodes of the DMN, namely, the posterior cingulate and medial tempo- ral cortex,27 where amyloid deposition has been previously observed in individuals with a family history of AD, but not in those without such a history.28 As suggested by Wang et al,27 it is possible that the decreased functional connectivity may be related to amyloid deposition in an age-dependent fashion in individuals with a family history of AD.

Although much work has been dedicated toward solving the increasingly complex puzzle that constitutes AD, the relation- ship between functional-structural connectivity and metabolic measures remains to be better understood. Previously, amyloid deposition and aerobic glycolysis were demonstrated to be cor- related for both individuals with AD and for cognitively normal amyloid-positive participants, suggesting a possible association between regional aerobic glycolysis and the later development of AD pathology.29 Additionally, regions of normally high aero- bic glycolysis in healthy individuals coincides with regions of the DMN, where decreased metabolic activity, concurrent with increased amyloid deposition, is found to have occurred in AD patients. Together, this distinct pattern offers the suggestion of a particular susceptibility of these regions the pathophysiology of AD.29

From rs-fMRI, it has been demonstrated that those regions are also associated with a reduction of functional connectivity with onset of the disease. Moreover, several studies have provided evidence of decrease connectivity within the DMN in cognitively normal elderly individuals with elevated brain amy- loid.18 This discovery in cognitively normal elderly individuals supports the concept that rs-fMRI may have the ability to detect early manifestations of amyloid (Aβ) toxicity, before any appearance of clinical symptoms.

14.3 Diffusion Tensor Imaging in

Alzheimer’s Disease

The changes demonstrated in volumetric studies, fMRI and rs-fMRI, as described previously, are attributed to gray matter loss and neuronal degeneration and with it the altered connec- tivity of the functional network of the brain. More recently, white matter changes have been investigated in subjects with AD and MCI, including decreased myelin density and myelin basic protein, as well as oligodendrocyte loss. Additionally, wal- lerian degeneration is a mechanism causing axonal loss following neuronal degeneration.

A promising MRI modality for studying structural changes in white matter is DTI. Changes in diffusivity in the direction of the fiber bundles (longitudinal or axial diffusivity) are attrib-

uted primarily to axonal loss through wallerian degeneration, whereas changes in diffusivity in the direction orthogonal to the fiber bundles (transverse or radial diffusivity) have been associated with damage of cell walls and myelin sheaths. Loss of white matter fibers has been reported by Wang et al,30 who showed a decreased fractional anisotropy (FA) and increased mean diffusivity (MD) in multiple areas of the brain correlated to functional impairments as assessed using the Mini-Mental State Examination and the AD Assessment Scale. Li et al31 com- bined volumetric analysis with DTI by comparing mild AD patients with normal aging controls and demonstrated hippo- campal atrophy at the mild AD stage. A study by Solodkin et al32 suggests that DTI in the parahippocampus could be used as a biomarker of disease progression in the white matter pathology of AD. They classified MCI and AD cases using dis- criminant analysis and noted that the MCI cases identified as AD in their analysis either met the diagnostic criteria for AD or showed significant cognitive decline 1 year later. With this evidence demonstrating white matter disruption to be an important part in the pathogenesis of AD, Shu et al33 performed a systematic study of white matter changes and compared DTI at 7T and histologic images in a APP/PS1 mouse model com- pared with wild-type controls. Abnormalities of FA or axial diffusivity agreed with ultrastructural findings demonstrating histopathological changes of AD.

Methods that are investigating DTI as a marker of AD use either region-of-interest (ROI) or voxel-wise approaches. ROI approaches can rely either on manually drawn ROIs that have the advantage of being anatomically accurate in the individual subject or on template warping, which allows efficient process- ing of many subjects in group studies. Tract-based analysis poses an efficient alternative to manual ROI drawing for the individual subject. Voxel-wise approaches always rely on warp- ing of the subject anatomy to a specified template, but because of individual brain differences and imperfections of nonlinear mapping algorithms, this method is typically used for large group studies. Whether it is more effective to use whole-brain assessments or to focus on specific brain areas that have been shown to be altered by AD remains an open question. A concern regarding methods is the effect of anatomical normalization during template mapping and its effects on the scalar measures of DTI, but this has been found to be much smaller than averag- ing or blurring in ROI studies.

Several studies have documented the changes of DTI markers with AD in the hippocampus,34 the medial temporal lobe,34 the parahippocampal white matter and perforant path,35 entorhinal cortex,36 and posterior cingulum.37 Degeneration generally seems to follow the pattern of retrogenesis, where areas of late myelination during development are affected early by the dis- ease, and areas of early myelination are affected later in the progression of the disease. Compared with healthy aging, DTI changes to posterior brain structures are generally found earlier in the disease than are changes to the frontal areas.38

The DTI studies that have investigated the loss of memory, the most prevalent symptom defining AD, have focused on the area around the hippocampus and parahippocampal white matter,35 with the perforant pathway transmitting inputs into the entorhinal cortex to the hippocampus. Multiple studies found DTI changes in the perforant pathway for both AD and MCI39 compared with healthy aging, with changes in AD

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generally more pronounced than in MCI. Solodkin et al32 dem- onstrated the use of DTI for in vivo assessment of parahippo- campal white matter to identify patients with MCI at risk of converting to AD.32

Furthermore, a study by Bendlin et al4 suggested that changes to white matter structure can be detected many years before detection of cognitive changes associated with AD by looking at asymptomatic patients with a family history of AD. They found that a parental family history of AD is correlated with a lowered FA value in brain areas that have been identified to be affected by the disease, including the hippocampus, corpus callosum, cingulum, uncinate fasciculus, tapetum, and neighboring white matter structures.

Although reports disagree about the structures affected by AD and MCI compared with normal aging, such as the frontal lobes and parietal lobes, most studies found consistent struc- tural changes in several brain areas using DTI metrics (typically FA and MD), reflecting the development of pathological changes with AD (▶Fig. 14.3). Differences between studies may be caused by different sensitivities of the analysis method that is used (ROI, tract, or voxel based) and the DTI sequence parame- ters chosen, such as image resolution, motion robustness, eddy current compensation, and the use of parallel imaging methods as well as technological advances in scanner hardware and DTI pulse sequences.40

14.4 Proton Magnetic Resonance

Spectroscopy

Klunk et al demonstrated that, compared to control subjects, a decrease in NAA in postmortem brain samples of AD patients correlated with the presence of plaques and neurofibrillary tangles.41

Several studies have shown MRS to be able to distinguish between AD patients and healthy controls. Whereas Klunk et al41 showed a decrease in NAA in AD patients, several investi- gators have demonstrated that NAA levels can improve in AD patients after acetylcholinesterase treatment.42 NAA has also been shown to correlate with psychiatric components of AD, where AD patients with psychosis had significantly reduced NAA levels compared with healthy controls.43

Not only does MRS hold promise of providing insight into the availability of selected metabolites as disease biomarkers, but it also provides a wide chemical spectrum of metabolites that can be used as a chemical fingerprint of the patient’s disease state.44 Together with other metrics, such as measuring the hippocam- pal volume42 and modalities like amyloid PET imaging,45 MRS can be used to provide complementary data in clinical diagno- sis. There is evidence that amyloid plaques start accumulating before behavioral symptoms of neurodegeneration can be seen: in cognitively normal older adults, the Cho:Cr and mI:Cr ratios correlated with amyloid PET imaging.45 Additionally, MRS can provide a measure of glial activity by elevation of mI in AD.46

Although there is ample evidence that adding MRS to the diagnosis and treatment of AD provides a more complete pic- ture and allows better disease monitoring and a more tailored treatment regimen, the technique has not been widely adopted for routine clinical care of AD patients. Graff-Radford and Kant- arci44 state that the primary reasons are both the lack of stan- dardization and normative data across sites and an insufficient understanding of the pathological basis of the changes observed using MRS. A practical approach would be to combine MRS with other clinical imaging techniques (e.g., volumetric MRI, DTI, functional connectivity MRI, FDG-PET).

References

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Magnetic resonance spectroscopy (MRS) allows quantification of biochemical metabolites by means of MRI. Changes from nor- mative levels provide biomarkers, for disease processes, com- plementing other diagnostic imaging methods. Metabolites that are generally quantified include N-acetyl aspartate (NAA), cho- line (Cho), creatine (Cr), and myo-inositol (mI). Creatine can be used as an internal control because it is generally unchanged in AD. The American Academy of Neurology does not recommend using MRS for routine clinical imaging in AD diagnosis because of lack of evidence, but as a research tool, MRS can provide val- uable information for studying the disease and its progression.

Imaging of Alzheimer’s Disease: Part 2

Fig. 14.3 Patient diagnosed with Alzheimer’s disease (right image) compared with age-matched normal subject (left image).
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[41] Klunk WE, Panchalingam K, Moossy J, McClure RJ, Pettegrew JW. N-Acetyl-L- aspartate and other amino acid metabolites in Alzheimer’s disease brain: a preliminary proton nuclear magnetic resonance study. Neurology 1992; 42: 1578–1585

[42] Krishnan KR, Charles HC, Doraiswamy PM et al. Randomized, placebo- controlled trial of the effects of donepezil on neuronal markers and hippocampal volumes in Alzheimer’s disease. Am J Psychiatry 2003; 160: 2003–2011

[43] Sweet RA, Panchalingam K, Pettegrew JW et al. Psychosis in Alzheimer disease: postmortem magnetic resonance spectroscopy evidence of excess neuronal and membrane phospholipid pathology. Neurobiol Aging 2002; 23: 547–553

[44] Graff-Radford J, Kantarci K. Magnetic resonance spectroscopy in Alzheimer’s disease. Neuropsychiatr Dis Treat 2013; 9: 687–696

[45] Kantarci K, Lowe V, Przybelski SA et al. Magnetic resonance spectroscopy, β-amyloid load, and cognition in a population-based sample of cognitively normal older adults. Neurology 2011; 77: 951–958

[46] Hattori N, Abe K, Sakoda S, Sawada T. Proton MR spectroscopic study at 3 Tesla on glutamate/glutamine in Alzheimer’s disease. Neuroreport 2002; 13: 183–186

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Magnetic Resonance Imaging and Histopathological Correlation

15 Magnetic Resonance Imaging and Histopathological Correlation in Alzheimer’s Disease

Mark D. Meadowcroft and Qing X. Yang

Magnetic resonance imaging (MRI) presents a unique opportu- nity to noninvasively image neurodegenerative diseases and their progression. Although MRI has been extremely valuable in clinical diagnosis and treatment, currently, how MRI parameters relate to the specific pathological changes in the Alzheimer’s disease (AD) brain have not been established and validated. A gap exists between MRI contrast/metrics and the alterations in micro- and macrostructural histologic patterns of disease pathology, which results in a fundamental concern when using MRI findings in the clinical interpretation of disease processes without direct knowledge of the relationship between image contrast and disease pathology. Whereas many in vivo studies have ostensibly reported correlations of MRI contrast and metrics to AD stage, the exact anatomic-pathologic underpinnings and correlates of MRI findings remain unclear.

Advances in MRI have allowed researchers to push the boundaries of resolution constraints by using microscopic magnetic resonance imaging (μMRI). Numerous microimaging studies have been performed within the literature base, with most techniques comprising the placement of whole-tissue samples within volume or under surface radiofrequency coils, which presents difficulty in the coregistration of MR slice selec- tion with actual tissue sections cut on a cryostat or vibratome. This obstacle can be overcome by direct imaging of tissue sample slices followed by histologic staining of the tissue sec- tions, resulting in a one-to-one comparison between MRI and microscope images.1,2

The formation of beta-amyloid (Aβ) plaques remains a major neuropathological hallmark and cardinal feature of Alzheimer’s pathology. The ability to distinguish Aβ plaques with MRI has been demonstrated ex vivo with human AD tissue samples and in vivo with transgenic mice that produce amyloid plaques. These data have shown Aβ plaques as hypointensities on T2- and, to a more pronounced degree, T2*- weighted images.

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The signal dropout in T2-/T2*-weighted MRIs has been attrib- uted to the iron deposition associated with amyloid plaques in human tissue samples (▶ Fig. 15.1). Homeostatic misregulation of iron is known to occur in the AD brain. The increased con- centration of iron in the brain tissue of AD patients has been well demonstrated,3,4,5 and a close association of iron with amy- loid plaques in AD tissue has been established.6,7 Aβ amyloid fibrils have a high affinity for iron, on the order of eight magni- tudes greater than transferrin for iron.8 The association with iron aids in the formation of Aβ plaque masses as the incorpora- tion of the Aβ fibrils into plaque assemblies is accelerated in an iron-enriched environment.9,10 Focal iron deposition in the form of hemosiderin, derived from ferritin protein breakdown or cerebral microbleeds, and diffuse iron are found throughout the Alzheimer’s brain parenchyma.

Microscopic MRI of tissue samples from late-stage AD tissue (Braak VI) demonstrates focal hypointensities within the gray matter in gradient-echo (GRE) images (▶ Fig. 15.2). Costaining of the same MRI tissues samples for fibrillar amyloid, with thio- flavin-S, and iron, with Perls’ stain, demonstrates that these hypointensities correspond to amyloid plaques and/or focal iron deposition. Amyloid plaques that are high in iron content exhibit a greater signal dropout on the GRE images than Aβ pla- ques that have minimal iron association (▶Fig. 15.3). Larger plaques exhibit a greater signal dropout than smaller plaques. It is apparent that the amount of signal dropout on MRI is associ- ated with the quantity of iron present at that location and the morphology of the plaques.

A similar trend in transverse relaxation is found when imaging amyloid plaques in a transgenic mouse model that harbors human mutations in amyloid precursor protein (APP) and presenilin-1 (PS1). The mice produce plaques throughout the brain in response to increased production of Aβ at approximately 9 months of age. GRE images of animal

Fig. 15.1 Iron associated with β-amyloid plaques in Alzheimer’s disease at 400x magnification. The amyloid protein is a metalloprotein with a high affinity for iron. Iron is found throughout the diffuse plaque regions as well as the highly fibrillar core. Iron within microglial cells can be viewed around the periphery of amyloid plaque mass. (a) Perls’ stain; (b) thioflavin-S amyloid stain.

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Fig. 15.2 Gradient-echo microscopic magnetic resonance images 60-μm-thick in Alzheimer’s (a) and age-matched control (b) tissue sections from the entorhinal cortex (Brodmann area 28/ 34). Cortical gray matter and subcortical white matter are clearly visible on the images at the same resolution. Alzheimer’s tissue exhibits punctate hypointensities within the cortical gray matter, which are not found within the gray matter of control tissue.

plaques demonstrate the same relationship of hypointensities with the plaques (▶Fig. 15.4). When staining is done for fibrillar amyloid and iron, a negligible amount of ferric iron is observed in plaque mass in the mouse tissue. Transverse MR relaxation measurements from regions of interest con- sisting of individual plaques and surrounding tissue demon- strate that plaques in both AD and APP/PS1 tissue have faster relaxation rates than the surrounding tissue. In comparison of plaques in AD with plaques in APP/PS1 tissue, the human plaques have shorter relaxation times (T2*) and increased relaxation rates (R2*) (▶Fig. 15.5). Although relaxation does not differ between control tissue and regions without pla- ques in AD and APP tissue, there is a significant relaxation difference between AD and APP/PS1 Aβ plaques. Histologic staining of the same tissue sections show that less iron is associated with the amyloid plaques in the APP/PS1 mouse model (▶Fig. 15.6). The decrease in iron within the trans- genic mouse plaques is congruent with the reduced R2 in pla- ques compared with AD plaques. The difference in the R2 between the AD and APP/PS1 plaques of approximately 22% is hypothesized to be due to the synergistic role that both Aβ plaques and iron have in transverse relaxation. The increased rate of relaxation in the AD plaques is predominantly a result of the summation in relaxation attributable to higher iron content in, and morphology of, the plaques. Whereas both human and transgenic mouse plaques are composed of aggregation of Aβ protein fibrils, the morphology of the transgenic plaques is quite dissimilar to that found in Alz- heimer’s tissue. APP/PS1 plaques are larger, globular shaped, and have a greater core density, with smaller extent in the surrounding diffuse halo region. Conversely, Alzheimer’s pla- ques are generally smaller, with a smaller core and a larger diffuse region. The size of the plaques plays a role in the ability to visualize them on MRI, with larger plaques more easily distinguishable. The minimal size of discernable trans- genic and AD plaques is approximately 40μm in diameter. Of note, though, plaque diameter alone does not confer the ability to visualize Aβ plaques. Alzheimer’s plaques of simi- lar size without iron are marginally discernable on MRI, whereas APP/PS1 plaques of the same size without iron are visible as hypointense spots.

The composition and morphology of the Aβ plaques are important for the interpretation of the associated changes in image contrast and parametric measurements. Immunohisto- logic staining reveals that the core of Alzheimer’s plaques is composed primarily of the 42 amino acid Aβ variant (Aβ42), whereas the coronal region is composed of Aβ40. Transgenic mouse plaques stain solely for Aβ40 in both the core and coronal regions (▶Fig. 15.7). The composition of the Aβ protein con- tains numerous hydrophobic amino-acid residues, for which Aβ42 contains two additional hydrophobic amino-acid residues compared with Aβ40.11 The increased hydrophobic nature of the Aβ42 protein is hypothesized to result in its increased amyloido- genic properties. In general, proteins centralize hydrophobic side-chains in the middle of the protein during thermal folding. The central core composition of the Alzheimer’s Aβ plaques is congruent with this notion. Conversely, the transgenic plaque cores and coronal regions are composed of less hydrophobic Aβ40.

The hypointense image contrast of transgenic mouse pla- ques is generated by the reduced mobile water content due to the aggregation of hydrophobic Aβ protein in the plaques. In addition to mobile proton (water) content, image contrast of Alzheimer’s plaques is also dependent on the amount of iron colocalized with the plaques as a result of magnetic sus- ceptibility inhomogeneities induced by iron within the pla- ques. To tease apart the synergistic effect of contrast enhancement due to iron content and plaque morphology, AD tissue samples were subjected to iron chelation with deferoxamine mesylate salt (DFO) overnight to reduce Aβ pla- que iron load. The binding affinity of DFO for Fe3 + is greater than that of Aβ, with a binding constant specific for Fe3 + (not Fe2 + ) on the order of 1030. Thioflavin-S and Perls’ staining for plaques and iron (respectively) indicate that DFO chelation reduces the amount of iron associated with the Aβ plaques (▶ Fig. 15.8). MRI of the AD tissue samples treated with DFO demonstrates that AD plaques can be discerned without iron load in the plaques (▶Fig. 15.9), congruent with the trans- genic plaques’ case, which have low iron load as well. It is also noted that MRI signal decrease in AD plaques treated with DFO is visibly less than that in untreated ones. The cor- responding R2* rate is higher in the plaques with iron than in

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Magnetic Resonance Imaging and Histopathological Correlation

Fig. 15.3 T2* -weighted gradient-echo magnetic resonance imaging (MRI) (a) and histologic images of thioflavin-S staining for β-amyloid plaques (b) and Perls’ iron stain (c) of the same 60-μm-thick tissue sample from the entorhinal cortex of an Alzheimer’s disease patient. Selected hypointensities in the MRI correspond to the location of amyloid plaques (red arrows) and/or focal regions of high iron (blue arrows). The figure illustrates that the size of the β-amyloid plaque and the amount of focal iron associated with the amyloid mass are responsible for the hypointensities on the T2*-weighted images. Large plaques are more readily visible on the images, as are plaques containing a high amount of iron. β-amyloid plaques of smaller diameter and those with minimal associated iron are still visible to a reduced degree.

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Fig. 15.4 T2*-weighted image (a) and histologic thioflavin-S β-amyloid (b) and Perls’ iron (c) stains of the same 60-μm-thick slice from amyloid precursor protein(APP)/ presenilin-1 (PS1) mouse brain at –2.92 mm Bregma. Select hypointensities on the magnetic resonance imaging (MRI), which correspond to the location of β-amyloid plaques, are highlighted with red arrows. The figure illustrates that the hypointensities seen in the T2*- weighted image are in the same region as large β-amyloid plaques approximately 50 to 60 μm in diameter. Unlike the Alzheimer’s tissue, iron deposition is not present at the plaque locations. Similar to Alzheimer’s tissue, plaque diameter is an important consideration for visibility on MRI visibility, as larger plaques are more readily visible on T2*-weighted images.

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Magnetic Resonance Imaging and Histopathological Correlation

support the hypothesis that plaque size and morphology play a dominant role in their imaging, as most of the relaxation remains after iron chelation. It is interesting to note that the R2* rate in the plaques stripped of iron is still quite high compared with that of the surrounding gray matter, similar again to the transgenic mouse plaques. The plaques in both chelated and unchelated conditions have R2* values that are significantly greater than those in surrounding gray matter. The percentage of reduction in R2* after iron chelation was 14.6% for Aβ pla- ques, 17.4% for white matter, and 2.0% for gray matter. White matter is known to have large amounts of iron associated with oligodendrocytes, and iron is required for myelination. The sim- ilar reduction in R2* for white matter and Aβ plaques is indica- tive of the similar iron decrease in these tissue types. Gray mat- ter tissues have less iron, and chelation resulted in only a minor change in R2* rate in these regions.

The mechanism for T2 relaxation in Aβ plaques is multifac- eted, with iron loading in the plaques accounting for a portion of the apparent transverse relaxation. Iron is well known to perturb the local magnetic field, causing the MR signal from rapid diffusing water molecules in and near the plaques to dephase during each echo time.14 However, the morphology and composition of the plaques themselves are also synergisti- cally involved as major contributors. Several plausible mecha- nisms can lead to increased T2 relaxation rate in the transgenic mouse and human plaques without appreciable iron content. The Aβ plaque morphology revealed by the transmission elec- tron microscopy (TEM) in ▶ Fig. 15.10 provides evidence for a plausible relaxation mechanism. The transgenic mouse plaques are densely packed globular aggregates, whereas Alzheimer’s plaques appear as loosely connected patches with numerous infiltrated gaps or channels within the plaque mass. The highly compacted plaques behave similarly to a polymer-like solid. In such cases, water molecules are either repelled from the hydro- phobic moieties and/or bound to hydrophilic regions of the pla- ques. Hydrogen bonding of water molecules to the hydrophilic regions would result in a first-order cross-relaxation via pro- ton-proton magnetization exchange, leading to rapid T2 relaxa- tion. Such an effect, termed plaque dehydration, could be a sig- nificant contributor to the hypointense T2 contrast in the pla- ques. In addition, the magnetic susceptibility differences between the highly compact Aβ protein mass and surrounding tissue could induce static magnetic field inhomogeneity in con- cert with iron but to a lesser degree. The gaps and channels in the human AD plaques allow water molecules to diffuse in and out the plaques, leading to increased water molecule interac- tion with the macromolecular environment, which, in turn, would increase proton T2 relaxation. Our data suggest that pla- que dehydration appears to be a dominant factor over iron loading in the shortening T2 relaxation in the Aβ plaques. The mechanism of water dehydration can be validated by using magnetization transfer (MT) contrast imaging. When applied to AD patients, the magnetization transfer ratio (MTR) (i.e. ratio of image contrast with and without RF frequency offset) has been reported to be decreased in the whole brain.15 Regional mea- sures of MTR are reduced in the hippocampus, amygdala, and

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Fig. 15.5 Transverse relaxation R2* rates from regions of interest (ROIs) with plaques and without plaques and in control tissue in human (a) and mouse (b). The R2* rate of plaque ROIs in the Alzheimer’s disease (AD) tissue is significantly greater than both regions without plaques and control tissue sections. A similar trend is found in the mouse data. The increased R2* rate for the plaque ROIs in the AD compared with the amyloid precursor protein (APP)/presenilin-1 (PS1) mouse is hypothesized to be due to higher iron in the AD plaques.

plaques without iron, similar to the comparison of AD with transgenic plaques in ▶ Fig. 15.5.

The ability to discern Aβ plaques on MRI has generally been attributed to the iron within the plaques.12,13 Microscopic MRI has provided evidence that iron is not the only cause of Aβ plaque-associated hypointensities. Our data have shown that there is a synergistic dual relaxation mechanism in play between the amount of iron integrated within the plaques and the size or morphology of the plaques. The relaxation data

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Fig.15.6 Alzheimer’s(left)andamyloidprecursor protein (APP)/presenilin-1 (PS1) transgenic (right) thioflavin-S (a,b) and Perls’ iron (c,d) stains of β-amyloid plaques at 100x magnification. The thioflavin-S stain and Perls’ stain illustrate a close relationship between β-amyloid plaques and focal iron deposition in Alzheimer’s disease (AD). The relationship between plaques and iron is not seen in the APP/PS1 animal. Differences in plaque morphology between the AD and APP/PS1 plaques are evident. The human AD plaques have a dense core of fibrillar amyloid protein with a halo of amyloid protein. APP/PS1 plaques exhibit a larger and denser thioflavin-S-positive core with a smaller halo region around them. Compared with the human AD plaques, the APP/PS1 images show a reduction in focal iron within the plaques that is diffusely found throughout the plaque.

temporal lobe of AD and mild cognitive impairment (MCI) patients compared with controls.16 A longitudinal decline in global brain MTR in AD patients over a period of 6 and 12 months has also been reported.17 The basis for the decrease in MTR has been speculated as due to microstructural changes of the gray and white matter. Specifically, it has been hypothe- sized that neurodegeneration, inflammation, gliosis, and increased interstitial fluid reduce the MT ratio. When transi- tioned to the preclinical space, it is interesting to note that stud- ies using the amyloid-generating transgenic mouse model pres- ent data that are contradictory to the MTRs seen in AD. A longi- tudinal increase in global and regional brain MTR within the APP/PS1 model compared with controls has been reported.18,19 The mechanism for the increased MTR in the APP/PSI animal models has been hypothesized to be associated with Aβ-plaque load. To better understand the cause of the paradoxical human and animal model data, it is necessary to understand the physi- cal mechanism of the MT technique. MT imaging relays infor- mation on the exchange between free and bound water mole- cules.20 The transverse relaxation for protons (water) in the direct vicinity of macromolecules or cellular structures is very short (> 1 ms) compared with free diffused water, as they are rotationally (or irrotationally) bound to the macromolecules via hydrogen bonds. As a result, through dipolar coupling or chemi- cal exchange mechanisms, the macromolecular pool is able to influence the relaxation of the free protons. Saturation of the protein-bound macromolecular pool via a radiofrequency

pulse with given frequency offset causes the net magnetization of the free water pool to decrease, resulting in an increase in the MTR.

To understand proton magnetization transfer in the direct vicinity of Aβ plaques, experiments using off-resonant satura- tion of amyloid-bound protons in 60-μm slices of Alzheimer’s tissue were undertaken. An optimal predetermined off- resonant pulse at 15 kHz (50 parts per million [ppm]) was used to saturate the amyloid-bound proton pool. Amyloid plaques are again visible with thioflavin stains and correspond to the location of hypointensities on GRE images. When plaque location is overlaid on the MTR data set, it is apparent that vox- els containing amyloid plaques have increased MTR compared with the surrounding gray matter (▶ Fig. 15.11). The increased MTR in white matter verifies the MTR calculation, as white matter is known to have an increased MTR compared to that of gray matter. Regions of interest in the plaques have significantly higher MTR than in surrounding gray matter. Following known trends on MTR values in gray and white matter, the white mat- ter has a significantly greater MTR than gray matter in the data set.

The decrease in MTR in AD plaques follows the same trend as previously published amyloid-generating mouse model data. These transgenic animals produce Aβ plaques with a moderate gliotic inflammatory response. Data from individual plaque measurements support the hypothesis that the increase in MTR for the transgenic animals is in fact due to amyloid load. The

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Magnetic Resonance Imaging and Histopathological Correlation

Fig. 15.7 β-Amyloids Aβ40 and Aβ42 immunohis- tologic and thioflavin-S (Thio-S) stains of amyloid plaques at 200x magnification in Alzheimer’s (top) and amyloid precursor protein (APP)/ presenilin-1 (PS1) (bottom) tissue samples. Alzheimer’s plaques contain both 40 and 42 amino-acid β-amyloid fragments. APP/PS1 transgenic plaques contain only the 40-amino-acid variant while staining negatively for Aβ42. The figure illustrates the morphologic and compositional differences between Alzheimer’s and transgenic plaques.

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Fig. 15.8 Alzheimer’s tissue samples untreated (left) and treated (right) with deferoxamine (DFO) stained with thioflavin-S (top) and Perls’ iron (bottom) stains at 200x magnification.

The β-amyloid plaques in the DFO iron-chelated samples are stripped of iron compared with untreated plaques.

Fig. 15.9 T2*-weighted (a,d) and histologic stains for Perls’ iron (b,e) and thioflavin-S (c,f) in deferoxaminemesylate salt (DFO) untreated (left) and treated (right) Alzheimer’s entorhinal cortex tissue samples. Large plaques and those with high iron content are readily visible on magnetic resonance images of the DFO-untreated samples. Amyloid plaques in DFO-treated samples with little to no associated iron retain their discernibility on the gradient echo data sets. Chelated Alzheimer’s plaques without iron generate hypointensities on T2*-weighted images similar to amyloid precursor protein (APP)/presenilin-1 (PS1) plaques without iron. The data illustrate the ability to visualize plaques without iron and support the synergistic hypothesis that plaques are able to induce transverse proton relaxation as a result of both their association with iron and their morphology.

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plaques in the AD tissue also have an increased MTR; however, in vivo, this is hypothesized to be overshadowed by the domi- nant partial-volume decrease in MTR resulting from an increase in regional interstitial fluid.

Magnetic resonance imaging of AD and corresponding histo- logic analysis of individual Aβ plaques from the same tissue

sample have allowed the establishment of specific relationships between image metrics and disease pathology. Such relation- ships will provide a foundation for clinical interpretation of MRI findings in AD and other neurodegenerative diseases, that is, the ability of MRI to determine Aβ plaque load to aid in the diagnostic determination of AD severity.

Magnetic Resonance Imaging and Histopathological Correlation

Fig. 15.10 Transmission electron microscope im- ages of an Alzheimer’s disease (AD, left) and APP/ presenilin-1 (PS1) transgenic (right) plaque at 4,600x (top) and 22,500x (bottom) magnification. The ultrastructural composition of AD and APP/ PS1 plaques are evident in the images. The Alzheimer’s plaques exhibit a reduced density compared with the transgenic plaques, even in the condensed core of the amyloid mass (bottom). The reduced density of the Alzheimer’s plaques allows the infiltration of water (protons) into the core, which is hypothesized to aid in the increased transverse relaxation associated with Alzheimer’s plaques.

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Fig. 15.11 T2*-weighted (a), Perls’ iron stain (b), thioflavin-S stain (c), and magnetization transfer ratio (MTR) images of the same Alzheimer’s entorhinal cortex tissue sample. Hypointensities on the gradient-echo magnetic resonance image correspond to thioflavin and iron-positive amyloid plaques (arrows). Selection of these plaques on the magnetization transfer image shows that these plaques cause an increase in magnetization transfer, notwithstanding the noise present in the image. This is hypothesized to be due to the macromolecular interaction of protons in the vicinity of the amyloid mass. In addition, the known increase in MTR associated with white matter tracts can be seen on the image set.

References

. [1]  Meadowcroft MD, Connor JR, Smith MB, Yang QX. MRI and histological analy- sis of beta-amyloid plaques in both human Alzheimer’s disease and APP/PS1 transgenic mice. J Magn Reson Imaging 2009; 29: 997–1007

. [2]  Meadowcroft MD, Zhang S, Liu W et al. Direct magnetic resonance imaging of histological tissue samples at 3.0 T. Magn Reson Med 2007; 57: 835–841

. [3]  Connor JR, Snyder BS, Beard JL, Fine RE, Mufson EJ. Regional distribution of
iron and iron-regulatory proteins in the brain in aging and Alzheimer’s dis-
ease. J Neurosci Res 1992; 31: 327–335

. [4]  Connor JR, Menzies SL, St Martin SM, Mufson EJ. A histochemical study of
iron, transferrin, and ferritin in Alzheimer’s diseased brains. J Neurosci Res
1992; 31: 75–83

. [5]  Lovell MA, Robertson JD, Teesdale WJ, Campbell JL, Markesbery WR. Copper, iron
and zinc in Alzheimer’s disease senile plaques. J Neurol Sci 1998; 158: 47–52

. [6]  Collingwood J, Dobson J. Mapping and characterization of iron compounds in
Alzheimer’s tissue. J Alzheimers Dis 2006; 10: 215–222

. [7]  Collingwood JF, Chong RK, Kasama T et al. Three-dimensional tomographic
imaging and characterization of iron compounds within Alzheimer’s plaque
core material. J Alzheimers Dis 2008; 14: 235–245

. [8]  Jiang D, Li X, Williams R et al. Ternary complexes of iron, amyloid-beta, and
nitrilotriacetic acid: binding affinities, redox properties, and relevance to iron-induced oxidative stress in Alzheimer’s disease. Biochemistry 2009; 48: 7939–7947

. [9]  Collingwood JF, Mikhaylova A, Davidson M et al. In situ characterization and mapping of iron compounds in Alzheimer’s disease tissue. J Alzheimers Dis 2005; 7: 267–272

. [10]  Bush AI. The metallobiology of Alzheimer’s disease. Trends Neurosci 2003; 26: 207–214

. [11]  Yan Y, Liu J, McCallum SA, Yang D, Wang C. Methyl dynamics of the amyloid- beta peptides Abeta40 and Abeta42. Biochem Biophys Res Commun 2007; 362: 410–414

. [12]  Falangola MF, Lee SP, Nixon RA, Duff K, Helpern JA. Histological co-localiza- tion of iron in Abeta plaques of PS/APP transgenic mice. Neurochem Res 2005; 30: 201–205

. [13]  Jack CR, Jr, Garwood M, Wengenack TM et al. In vivo visualization of Alz- heimer’s amyloid plaques by magnetic resonance imaging in transgenic mice without a contrast agent. Magn Reson Med 2004; 52: 1263–1271

. [14]  Chavhan GB, Babyn PS, Thomas B, Shroff MM, Haacke EM. Principles, tech- niques, and applications of T2*-based MR imaging and its special applica- tions. Radiographics 2009; 29: 1433–1449

. [15]  Kabani NJ, Sled JG, Chertkow H. Magnetization transfer ratio in mild cognitive impairment and dementia of Alzheimer’s type. Neuroimage 2002; 15: 604–610

. [16]  Mascalchi M, Ginestroni A, Bessi V et al. Regional analysis of the magnetiza-
tion transfer ratio of the brain in mild Alzheimer’s disease and amnestic mild
cognitive impairment. AJNR Am J Neuroradiol 2013; 34: 2098–2104

. [17]  Ropele S, Schmidt R, Enzinger C, Windisch M, Martinez NP, Fazekas F. Longi- tudinal magnetization transfer imaging in mild to severe Alzheimer’s disease.
AJNR Am J Neuroradiol 2012; 33: 570–575

. [18]  Pérez-Torres CJ, Reynolds JO, Pautler RG. Use of magnetization transfer con-
trast MRI to detect early molecular pathology in Alzheimer’s disease. Magn
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. [19]  Bigot C, Vanhoutte G, Verhoye M, Van der Linden A. Magnetization transfer
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review. NMR Biomed 2001; 14: 57–64

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Part V

Non-Alzheimer’s Cortical Dementia

16 Dementia with Lewy Body Disease 150 17 Frontotemporal Lobar Degeneration 157

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Non-Alzheimer’s Cortical Dementia
16 Dementia with Lewy Body Disease

Aristides A. Capizzano and Toshio Moritani

16.1 History

Dementia with Lewy bodies (DLB) is the most recently recog- nized of the major neurodegenerative dementias. Lewy bodies (LBs) are proteinaceous cytoplasmic neuronal inclusions (▶Fig. 16.1) originally described by Friedrich Lewy in Parkin- son’s disease (PD).1 The two morphologically and molecularly distinct types of LBs are the classic brainstem and cortical LBs, both of which are immunoreactive to the presynaptic protein α-synuclein (▶Fig. 16.2).2 Therefore, from a molecular stand- point, DLB is counted among the α-synucleinopathies, together with PD and multiple-system atrophy (MSA).

A report of two cases from 1961 described two elderly men with progressive dementia and flexion contractures, who on neuropathological examination revealed extensive LBs along the neuraxis as the only pathological alteration.3 More than 30 other clinical dementia cases in which LBs with or without senile plaques and neurofibrillary tangles were the main patho- logical findings were reported by Japanese investigators over the following two decades.4 The overlap between LBs and Alzheimer’s disease (AD)-like neuropathology led to the consid- eration of DLB as a variant of AD.5

By the late 1980s, there was increased recognition of a syndrome affecting as much as 20% of the demented elderly population with confusion, hallucinations, and behavioral dis- turbances in which cortical and subcortical LBs, with variable plaque formation, heralded the pathological picture.6 This syn- drome of “senile dementia of Lewy body type” was then con- sidered within the spectrum of LB diseases, between the polar types of PD and “diffuse Lewy body disease.” Improved neuro- pathological techniques to label LBs, such as anti-ubiquitin immunohistochemistry, have been instrumental in advancing

Fig. 16.2 (a,b) α-Synuclein immunohistochemistry at 1,000x. Cortical Lewy bodies. (Courtesy Dr. Patricia Kirby, University of Iowa.)

Fig. 16.1 (a) Hematoxylin and eosin stain, 600x. Cortical Lewy body (arrow) in patient with Lewy body dementia. (b) Same Lewy body at 1,000x. (Courtesy Dr. Patricia Kirby, University of Iowa.)

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16.3 Genetics

DLB was long considered a sporadic disorder with late onset, and twin investigations of DLB did not support a major genetic cause for this disease.11 However, DLB and core clinical features thereof aggregate in families.12 A systematic review suggested a genetic overlap between familial cases of DLB and PDD.13 Fur- ther support for a genetic predisposition to DLB comes from families with combined features of dementia and parkinsonism inherited in a mendelian manner.14 The first locus for DLB was mapped on chromosome 2q35-q36 in an autosomal dominant family with autopsy-confirmed DLB,15 but in-depth molecular genetic follow-up investigations did not reveal a simple patho- genic or gene dosage mutation that cosegregated with DLB, sug- gesting that the mutation responsible for DLB in this family is complex.16 Because the current understanding of the genetics of DLB is unclear and a major DLB gene has not yet been uncov- ered, it has been suggested that mutations underlying DLB are biologically more complex than expected for monogenic disorders.14

16.4 Neuropathology

Macroscopically, the degree of cortical atrophy is variable in DLB. Given the clinical importance of distinguishing DLB from AD, some studies compared pathological and imaging changes in the hippocampal formation between the two diseases. Medial temporal lobe (including the hippocampus and parahip- pocampal gyrus) area measurements in fixed brains were significantly larger in DLB than in AD or mixed AD/DLB cases.17 Accordingly, neuron counts in the perforant pathway connect- ing the entorhinal cortex with the dentate gyrus are signifi- cantly depleted in AD compared with DLB and controls, although high variability was reported.18 An important macro- scopic feature of DLB brains is pallor of the substantia nigra and locus ceruleus, as in PD, reflecting a loss of neuromelanin. Preliminary results in PD have shown signal loss seen on in vivo heavily T1-weighted MRI in the substantia nigra compared with controls, likely reflecting a loss of paramagnetic neuromela- nin.19

Microscopically, the presence of LBs is the only histo- pathological requirement for the diagnosis of DLB.7,20 Classic brainstem and cortical LBs are best demonstrated by using immunostaining for α-synuclein21 (▶Fig. 16.2), but they con- tain a variety of other molecular components, such as ubiquitin, neurofilaments, parkin, components of the ubiquitin-protea- some system, molecular chaperones, and lipids.2 LBs likely represent a cellular response to the accumulation of abnormal proteins and undergo several phases during their formation.2 Cortical LB progression starts in the amygdala, spreads to the limbic cortex, and finally spreads to the neocortex.22 Apart from LBs, other histopathological features of DLB are Lewy-related neurites, AD-type pathology (plaques and tangles), spongiform changes, and synapse loss.7

In accord with the National Institute on Aging Reagan Criteria for diagnosis of AD,23 DLB diagnosis is related directly to the burden of LB pathology and inversely to AD pathology.8 Sub- types of DLB have been recognized in relationship to the burden

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the understanding of what is now termed dementia with Lewy bodies, which accounts for approximately 15% of cases of late- onset dementia, being the second most prevalent degenerative dementia after AD.

16.2 Clinical Features

The characteristic initial symptoms and signs of DLB have been operationalized in consensus diagnostic criteria origi- nally published in 19967 and revised in 2005.8 The essential central feature is dementia, with memory deficits not neces- sarily occurring in the early stages but common with pro- gression, and particularly prominent deficits in attention, executive function, and visuospatial skills. Core features are fluctuating cognition, recurrent visual hallucinations, and spontaneous parkinsonism, and these distinguish DLB from AD. Suggestive diagnostic features are rapid eye movement sleep behavior disorder, severe neuroleptic sensitivity, and reduced dopamine transporter uptake in the basal ganglia on single-photon emission computed tomography (SPECT) or positron emission tomography (PET) imaging. Probable DLB is diagnosed with two core features or one core and at least one suggestive feature; possible DLB is diagnosed with either one core or one or more suggestive features.8 Supportive features of the diagnosis, which are commonly present but do not have proven diagnostic specificity, are repeated falls, transient unexplained loss of consciousness, severe auto- nomic dysfunction, systematized delusions, depression, nonvisual hallucinations, relative preservation of medial temporal lobe volume on structural imaging, generalized low SPECT/PET uptake with reduced occipital activity, low 123I-metaiodobenzylguanidine (MIBG) myocardial uptake, and prominent slow-wave activity on electroencephalogra- phy with temporal lobe transient sharp waves. A diagnosis of DLB is less likely in the presence of clinical or imaging signs of cerebrovascular disease, of any other illness suffi- cient to account at least in part for the clinical picture, or if parkinsonism appears only at a stage of severe dementia.8

Men are more susceptible than women to DLB. The parkin- sonian signs are commonly bilateral, with rigidity, bradykine- sia, amimia, and slow shuffling gait. Resting tremor is less common.9 These symptoms show modest response to levo- dopa treatment. Visual hallucinations are the best clinical dis- criminator with AD, seen in up to 80% of DLB patients; they are recurrent and vivid and typically involve animals or peo- ple. Depression is commonly associated with DLB. In terms of imaging, supportive features for DLB diagnosis are lack of medial temporal atrophy (as typically seen in AD), low SPECT/ PET perfusion in the occipital lobe, and low MIBG myocardial scintigraphy.

Main differential diagnostic considerations of DLB are PD with dementia (PDD) and AD. Diagnostic criteria indicate that dementia should occur before or concurrently with parkinson- ism to diagnose DLB,8 whereas PDD is diagnosed when parkin- sonism is present for 12 months or longer before the onset of dementia.10 The arbitrariness of the distinction between DLB and PDD strongly suggests that both clinical phenotypes lie along the same pathological continuum.

Dementia with Lewy Body Disease

151

Non-Alzheimer’s Cortical Dementia

of AD-type pathology.24 The “pure” form of DLB contains LBs in only the brainstem and cerebral cortex; LBs associated with senile plaques but a low Braak tangle stage define the “com- mon” form; finally, LBs in conjunction with senile plaques and NFTs sufficient to diagnose AD are seen in the “AD form” of DLB.

16.5 Neuroimaging

16.5.1 Structural Magnetic Resonance

Imaging

In contradistinction to AD, where brain atrophy has been exten- sively reported from neuroimaging studies, atrophic changes are less conspicuous and are distributed differently in imaging studies of DLB (▶Fig. 16.3). Using voxel-based morphometry (VBM), a pattern of volume loss involving the dorsal midbrain, hypothalamus, and substantia innominata with sparing of the hippocampus and temporoparietal cortex has been proposed in DLB (▶ Fig. 16.4, ▶ Fig. 16.5).25 Furthermore, patients with a high probability of DLB on postmortem neuropathological assessment were found to have a low volume of dorsal meso- pontine gray matter with normal hippocampal volumes on antemortem MRI.26 Moreover, a high-resolution study of the medial temporal lobes with submillimiter pixel resolution27

showed that AD patients had a thinner subiculum, smaller CA1 area, and effacement of the hippocampal striation compared with DLB, features that could therefore distinguish between the two disorders. On the other hand, other studies found hippo- campal volume loss in DLB. A shape analysis of hippocampal volumes in DLB showed a differential atrophy pattern involving mostly the anterior aspect of the CA1 sector in DLB compared with AD28; hippocampal volume deficit was between 10 and 20% in DLB vs. controls (▶Fig. 16.6). More recently, using the same hippocampal radial distance technique as in the previ- ously mentioned study, DLB had left-predominant hippocampal atrophy centered at CA1 and the subiculum compared with controls, whereas no significant differences were detected between DLB and AD, although the latter may be secondary to the smaller sample size of DLB subjects.29

White matter T2 signal hyperintensities (WMSHs) are a well- known feature in the elderly and in patients with neuro- degenerative or vascular dementia; they correlate with age and history of hypertension and denote myelin loss, gliosis, and periventricular interstitial fluid accumulation. Comparison of DLB and AD showed that the results of WMSH load were inconsistent even among quantitative studies, probably reflect- ing differences in methods or subject inclusion criteria. Greater WMSHs were seen in AD than in DLB, and the latter showed WMSHs similar to those of controls30; also, similar WMSH loads

Fig. 16.3 A 75-year-old man with executive visuospatial dysfunction and visual hallucinations, rapid eye movement (REM) behavior disorder, and gait instability raising concern for dementia with Lewy bodies. (a,b) Axial T1-weighted images showing bilateral mild frontotemporal volume loss. (c) Sagittal T1-weighted with mild expansion of the sylvian fissure. (d) Axial fluid-attenuated inversion recovery with mild posterior periven- tricular leukoaraiosis.

152

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in AD and DLB have been reported.31 The frequent coexistence of DLB with AD should also be considered (▶ Fig. 16.7).

Apart from the preceding differences between DLB and AD, an important comparison is the differential atrophy pattern between DLB and PDD, the main clinical phenotypes of LB pathology. Using VBM, DLB had gray matter reduction in the right superior frontal, premotor, and inferior frontal regions compared with PDD.32 Furthermore, frontal gray matter deficits in DLB correlated with attentional deficits, whereas right hip- pocampal and amygdala volumes correlated with visual mem- ory performance. In a pathologically confirmed cohort of DLB and PDD, amygdala volumes in MRI in vivo inversely corre- lated with the density of Lewy body neuropathology in the amygdala.33

16.5.2 DiffusionTensorImaging
Using tract-based spatial statistics, a voxel-wise approach to

diffusion tensor imaging (DTI) data in the style of VBM, DLB displayed lower fractional anisotropy (FA) of parieto-occipital white matter voxels compared with controls, with significantly fewer changes in frontal regions. AD subjects, on the other hand, had more diffuse reductions in FA on both sides of the central sulcus. Mean diffusivity (MD) changes were

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Dementia with Lewy Body Disease

Fig. 16.4 Voxel-based morphometry-derived patterns of gray matter loss in Lewy body dementia (DLB) (left side) vs. controls (right side) and Alzheimer’s disease vs. controls (right side) corrected for multiple comparisons, p < 0.05. Gray matter loss in DLB is focused on the substantia innominata, dorsal midbrain, and hypothalamus. A, anterior; P, posterior. (Used with permission from Whitwell JL, Weigand SD, Shiung MM, et al. Focal atrophy in dementia with Lewy bodies on MRI: a distinct pattern from Alzheimer’s disease. Brain 2007;

130(Pt 3):708–719.)

widespread in both conditions. The DTI changes in DLB correlated with episodic memory, letter fluency, and parkinso- nian signs.34 A previous study demonstrated increased MD in the amygdala and reduced FA in the inferior longitudinal fascic- ulus in DLB, which correlated with parkinsonism and visual hallucinations, respectively, with a different pattern of DTI changes in AD patients who had involvement of temporoparie- tal regions and associated white matter tracts.35 Also, DLB (but not AD) patients had reduced FA compared with controls in the bilateral inferior occipitofrontal and left inferior longitudinal fasciculi, encompassing visual association areas, with both demented groups showing lower FA in the uncinate fasciculus bilaterally.36

16.5.3 Dopaminergic Imaging

Dopaminergic function in DLB using either SPECT or PET has become a suggestive feature of the diagnosis under the consen- sus criteria.7,8 DLB and PDD patients display severely reduced dopaminergic uptake in the caudate and putamen compared with controls and AD patients. [123I]N-ω-fluoropropyl-2β- carbomethoxy-3β-(4-iodophenyl)nortropane (FP-CIT) SPECT has 80 to 90% sensitivity and specificity for the diagnosis of DLB and PDD,37 which becomes clinically significant for the

153

154

Non-Alzheimer’s Cortical Dementia

Fig. 16.5 Three-dimensional surface renders showing voxel-based morphometry-derived patterns of gray matter loss in Lewy body dementia (DLB) vs. controls (left) and Alzheimer’s disease (AD) vs. controls (right) corrected for multiple comparisons; p < 0.05. DLB shows much less cortical atrophy than AD. (Used with permission from Whitwell JL, Weigand SD, Shiung MM, et al. Focal atrophy in dementia with Lewy bodies on MRI: a distinct pattern from Alzheimer’s disease. Brain 2007;130(Pt 3):708–719.)

differential diagnosis of DLB and AD. The specificity of FP-CIT scans to distinguish DLB from FTD, however, is significantly less because one-third of FTD cases also have reduced striatal dopa- mine uptake.38 The reduced dopaminergic uptake in the basal ganglia in DLB correlates with neuronal depletion in the sub- stantia nigra but not with the pathological burden of α-synu- clein, tau, or amyloid deposition, suggesting that disruption of the nigrostriatal pathway is responsible for the FP-CIT scan abnormalities in DLB.39

bution of perfusion deficits, with visual hallucinations corre- lated with parieto-occipital hypoperfusion.43 Clinical fluctua- tions, a core characteristic of DLB, correlate with brain perfu- sion changes on hexamethylpropyleneamine (HMPAO) SPECT,44 and reduced hallucinations in response to the acetylcholin- esterase inhibitor donepezil correlate with improved occipital blood flow.45

16.5.5 Management of Lewy Body

Dementia

No disease-modifying treatment is available at this point for patients with DLB. Nonpharmacologic interventions include education, reassurance, orientation and memory prompts, attentional cues, and targeted behavioral interventions.46 Par- kinsonism is treated with the lowest effective dose of levodopa because higher doses are associated with worsened confusion and hallucinations. The percentage of patients with more than 10% motor improvement is lower among DLB patients than for those with PD and PDD.47 Cholinesterase inhibitors are effective and relatively safe for treating neuropsychiatric and cognitive symptoms in DLB.48 The role of these agents in DLB may become important given the high risk of severe sensitivity reactions and cerebrovascular events with neuroleptics in these patients. However, current evidence supporting the efficacy of

16.5.4 Perfusion and Metabolism

Imaging

Occipital hypoperfusion and hypometabolism in DLB have been reported using SPECT and fluorodeoxyglucose (FDG) PET, respectively.40 In distinguishing DLB from AD, however, FP-CIT displayed better diagnostic accuracy than technetium 99m- exametazime SPECT.41 Accordingly, low SPECT/PET uptake in the occipital lobes is considered only among the supportive fea- tures of the diagnosis of DLB.8 FDG PET was shown to be more sensitive than SPECT-iodoamphetamine to the occipital and parietal changes in DLB, which may result from improved spa- tial resolution with PET over SPECT and also from higher meta- bolic than perfusion deficits in DLB.42 Furthermore, psychotic symptom clusters in DLB correlate with the anatomical distri-

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Dementia with Lewy Body Disease

Fig. 16.6 A 71-year old woman with Lewy body dementia (DLB) with parkinsonism, delirium, and orthostatic hypotension. Axial fluid-attenuated inversion recovery (FLAIR) (upper row) and coronal inversion recovery (IR) T1 weighted images (lower row) displaying bilateral temporal and hippocampal atrophy without significant FLAIR hyperintensity. R, right; S, superior. (Courtesy of Dr. Kei Yamada, Kyoto Prefectural University of Medicine, Japan.)

Fig. 16.7 A 74-year-old man with a clinical picture consistent with mixed Lewy body dementia (DLB)/Alzheimer’s disease (AD) dementia. (a) Axial fluid-attenuated inversion recovery showing confluent leukoaraiosis. Oblique coronal T1-weighted (b) and T2- weighted (c,d) images perpendicular to the temporal horn display severe hippocampal atrophy and confluent

leukoaraiosis.

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155

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cholinesterase inhibitors or the N-methyl-D-aspartate receptor

antagonist memantine in DLB remains inconclusive.49

References

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. [21]  Lowe J. Neuropathology of dementia with Lewy bodies. In: Handbook of Clin- ical Neurology: Dementias. Aminoff M, Boller F, Swaab D, eds. 3rd Series. New York: Elsevier; 2008;321–330

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Kantarci K, Ferman TJ, Boeve BF et al. Focal atrophy on MRI and neuro- pathologic classification of dementia with Lewy bodies. Neurology 2012; 79: 553–560

Firbank MJ, Blamire AM, Teodorczuk A et al. High resolution imaging of the medial temporal lobe in Alzheimer’s disease and dementia with Lewy bodies. J Alzheimers Dis 2010; 21: 1129–1140
Sabattoli F, Boccardi M, Galluzzi S, Treves A, Thompson PM, Frisoni GB. Hippo- campal shape differences in dementia with Lewy bodies. Neuroimage 2008; 41: 699–705

Chow N, Aarsland D, Honarpisheh H et al. Comparing hippocampal atrophy in Alzheimer’s dementia and dementia with lewy bodies. Dement Geriatr Cogn Disord 2012; 34: 44–50
Burton EJ, McKeith IG, Burn DJ, Firbank MJ, O’Brien JT. Progression of white matter hyperintensities in Alzheimer’s disease, dementia with lewy bodies, and Parkinson’s disease dementia: a comparison with normal aging. Am J Geriatr Psychiatry 2006; 14: 842–849

Oppedal K, Aarsland D, Firbank MJ et al. White matter hyperintensities in mild lewy body dementia. Dement Geriatr Cogn Dis Extra 2012; 2: 481–495 Sanchez-Castaneda C, Rene R, Ramirez-Ruiz B et al. Correlations between gray matter reductions and cognitive deficits in dementia with Lewy Bodies and Parkinson’s disease with dementia. Mov Disord 2009; 24: 1740–1746 Burton EJ, Mukaetova-Ladinska EB, Perry RH, Jaros E, Barber R, O’Brien JT. Neuropathological correlates of volumetric MRI in autopsy-confirmed Lewy body dementia. Neurobiol Aging 2012; 33: 1228–1236

Watson R, Blamire AM, Colloby SJ et al. Characterizing dementia with Lewy bodies by means of diffusion tensor imaging. Neurology 2012; 79: 906–914 Kantarci K, Avula R, Senjem ML et al. Dementia with Lewy bodies and Alz- heimer’s disease: neurodegenerative patterns characterized by DTI. Neurol- ogy 2010; 74: 1814–1821

Kiuchi K, Morikawa M, Taoka T et al. White matter changes in dementia with Lewy bodies and Alzheimer’s disease: a tractography-based study. J Psychiatr Res 2011; 45: 1095–1100
McKeith I, O’Brien J, Walker Z et al. DLB Study Group. Sensitivity and specificity of dopamine transporter imaging with 123I-FP-CIT SPECT in dementia with Lewy bodies: a phase III, multicentre study. Lancet Neurol 2007; 6: 305–313

Morgan S, Kemp P, Booij J et al. Differentiation of frontotemporal dementia from dementia with Lewy bodies using FP-CIT SPECT. J Neurol Neurosurg Psychiatry 2012; 83: 1063–1070
Colloby SJ, McParland S, O’Brien JT, Attems J. Neuropathological correlates of dopaminergic imaging in Alzheimer’s disease and Lewy body dementias. Brain 2012; 135: 2798–2808

Taylor JP, O’Brien J. Neuroimaging of dementia with Lewy bodies. Neuro- imaging Clin N Am 2012; 22: 67–81, viiiviii
Colloby SJ, Firbank MJ, Pakrasi S et al. A comparison of 99mTc-exametazime and 123I-FP-CIT SPECT imaging in the differential diagnosis of Alzheimer’s disease and dementia with Lewy bodies. Int Psychogeriatr 2008; 20: 1124– 1140

Ishii K, Hosaka K, Mori T, Mori E. Comparison of FDG-PET and IMP-SPECT in patients with dementia with Lewy bodies. Ann Nucl Med 2004; 18: 447–451 Nagahama Y, Okina T, Suzuki N, Matsuda M. Neural correlates of psychotic symptoms in dementia with Lewy bodies. Brain 2010; 133: 557–567

O’Brien JT, Firbank MJ, Mosimann UP, Burn DJ, McKeith IG. Change in perfu- sion, hallucinations and fluctuations in consciousness in dementia with Lewy bodies. Psychiatry Res 2005; 139: 79–88
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Aristides A. Capizzano and Toshio Moritani

Frontotemporal lobar degeneration (FTLD) includes different neurodegenerative disorders clinically characterized by pro- gressive behavioral changes and language disturbances. The average age at clinical onset is 50 to 60 years, and the incidence is roughly equal between men and women. Prevalence under 65 years has been reported at 15 per 100,000 inhabitants1 and is thought to approach that of presenile Alzheimer’s disease (AD). Twenty percent of FTLD patients are older than 65 at onset.2 Survival is shorter than for AD, with functional and cog- nitive decline occurring significantly more rapidly in FTLD than in AD, although there is heterogeneity depending on the partic- ular FTLD syndrome involved.3

17.1 Clinical Features

17.1.1 Frontotemporal Dementia

Syndromes

Frontotemporal dementia (FTD) syndromes encompass three clinical syndromes: behavioral variant frontotemporal dementia (bvFTD), semantic dementia (SD), and progressive nonfluent aphasia (PNFA).4 These differentiate in clinical and anatomical terms but not in pathological substrate; although one syndrome predominates early in the disease process, with progression of brain atrophy, there is increasing clinical overlap.5

Behavioral variant FTD is characterized by profound person- ality change with disinhibition and/or apathy, blunted affect, loss of insight and empathy to others, and pressured speech. These are the characteristics of bvFTD that frequently lead to first consultation at a psychiatric clinic. Anatomically, it may involve dorsomedial or ventromedial and orbitofrontal pre- frontal areas. Cognitive deficits are less severe than the person- ality changes and involve executive tasks, with impairment in working memory, attention, set shift, verbal fluency, response inhibition, and abstract reasoning. Memory complaints are var- iable, and there is preservation of declarative verbal and visual memory in contradistinction with AD.

Semantic dementia or semantic variant of primary progres- sive aphasia (svPPA) is a disorder of progressive loss of knowledge about words and concepts associated with ante- rior temporal atrophy; the clinical manifestation depends on the side of the brain preferentially involved. Left-predomi- nant cases manifest as fluent, anomic aphasia. Patients com- plain of word-finding difficulties and trouble with naming; speech remains fluent with intact syntax and prosody. The loss of knowledge extends beyond language, with a lack of ability to put objects in their proper context. Episodic mem- ory and executive and spatial functions are preserved. Sub- jects with right-predominant temporal lobe atrophy show a behavioral syndrome with flat affect, loss of insight, and alterations of social conduct.

Progressive nonfluent aphasia, or agrammatic variant of pri- mary progressive aphasia (agPPA), is a disorder of expressive language and speech production related to left perisylvian atro- phy. Naming and repetition are impaired, but comprehension of

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Frontotemporal Lobar Degeneration

17 Frontotemporal Lobar Degeneration

single words is preserved. Reading and writing are affected with errors, and speech apraxia is commonly present. PNFA may resemble logopenic aphasia (▶Fig. 17.1), in which brain atrophy involves more posterior brain regions and is thought to be associated with AD.6

Apart from the three classic syndromes outlined in the pre- ceding, there is a strong association between FTLD and amyo- trophic lateral sclerosis (ALS): half of ALS patients have cognitive impairment of the frontal type; 15% meet the criteria for FTD.7 Conversely, in a prospective study, up to 50% of FTD patients had clinical features of ALS, and 14% met the criteria for definite ALS.8 Apart from the clinical overlap, FTLD and ALS share patho- logical and genetic features that suggest that both entities con- stitute manifestations of the same disease process.9 Finally, there is also clinical overlap with corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP), both of which are also tauopathies, covered in Chapter 18 in this volume.

17.1.2 Inclusion and Exclusion Criteria

Consensus criteria for the core FTD syndromes have been widely used in research and clinical practice.4 For bvFTD, five core diagnostic features are required to fulfill the diagnostic criteria: insidious onset and gradual progression, early decline in social interpersonal conduct, early impairment in regulation of personal conduct, early emotional blunting, and early loss of insight.4 However, limitations of these criteria, such as ambigu- ity of some descriptors and arbitrary distinction of core and supportive features, have led to more recently revised guide- lines for diagnosis of bvFTD.10 The International Consensus Diagnostic Criteria for possible bvFTD require three of the fol- lowing symptoms: early behavioral disinhibition; early apathy or inertia; early loss of sympathy or empathy; early persevera- tive, stereotyped, or compulsive behavior; hyperorality; and a neuropsychological profile of executive deficits with sparing of memory and visuospatial functions. Probable bvFTD is diag- nosed when, apart from meeting the criteria for possible bvFTD, the following are present: significant functional decline and frontal and/or anterior temporal atrophy (on magnetic reso- nance imaging [MRI] or computed tomography [CT]) or hypo- metabolism/hypoperfusion (on positron emission tomography [PET] or single-photon emission computed tomography [SPECT]). Exclusionary criteria for bvFTD are that deficits are better accounted for by nondegenerative disorders, that behav- ioral disturbance is better accounted for by a psychiatric diag- nosis, or that biomarkers are strongly indicative of AD or other neurodegenerative process.10

17.2 Genetics

Between 35 and 50% of FTLD patients have a family history of dementia, which supports a strong genetic role in this disease, usually involving autosomal dominant inheritance.11 Approxi- mately 50% of familial cases are associated with mutations in the tau or progranulin (GRN) genes, with less than 5% of muta-

157

158

Non-Alzheimer’s Cortical Dementia

Fig. 17.1 A 73-year-old woman with primary progressive aphasia, logopenic variant. Coronal T2-weighted images perpendicular to the temporal lobe axis (a-d) in rostrocaudal order displaying mild to moderate left temporopolar and left hippocampal atrophy.

tions occurring in the valosin-containing protein, charged mul- tivesicular body protein 2B, transactive response (TAR)-DNA binding protein (TARDP), and fused in sarcoma (FUS) genes, which illustrate the heterogeneity of FTLD.12 More than 40 mutations have been recognized in the tau gene in families with FTD and parkinsonism associated with chromosome 17q (FTDP-17), which correlate with tau neuropathology.13 The GRN gene, also on chromosome 17, like tau, has been involved in more than 60 mutations in familial FTD. GRN encodes pro- granulin, a growth factor abundantly expressed in specific neu- ronal populations. Neuropathologically, GRN mutations result in tau-inegative, ubiquitin- and TDP-43-positive inclusions with characteristic intranuclear neuronal inclusions.14 More recently, the expansion of a noncoding hexanucleotide repeat in the C9ORF72 gene on chromosome 9p was shown to be the most common genetic abnormality in familial FTD (11.7%) and famil- ial ALS (23.5%).15 Different patterns of gray matter atrophy were identified using voxel-based morphometry (VBM) among patients with C9ORF72, tau, and progranulin mutations and sporadic FTD.16

17.3 Neuropathology

Although FTLD is pathologically heterogeneous, the different subtypes share common features. An early recognized feature is gross circumscribed atrophy of the frontal or anterior temporal lobes. The early pattern of atrophy, as depicted by clinical imag- ing, determines the specific clinical syndrome. Thus, prefrontal

atrophy leads to bvFTD, anterior temporal atrophy correlates with SD, and left perisylvian atrophy correlates with PNFA. Because the sequence of atrophy in FTLD is predictable, a stag- ing system has been proposed: Initial atrophy occurs in the orbital and superior medial frontal cortices and hippocampus (stage 1), progressing to involve other anterior frontal regions, temporal cortices, and basal ganglia (stage 2), and then becom- ing diffuse with white matter loss and ventricular dilatation (stage 3) until marked atrophy is observed in all areas, includ- ing marked basal ganglia flattening resulting in concavity of the lateral ventricles (stage 4).17

Based on the types of intracellular inclusions and immuno- histochemistry, three subtypes of FTLD are recognized18: (1) tau-positive pathology with or without inclusions, (2) tau- negative ubiquitin-positive inclusions, and (3) tau-negative, ubiquitin-negative pathology.

17.3.1 Frontotemporal Lobe Degeneration-Tau: Pick’s Disease and Other Tauopathies

Named after Arnold Pick, who in 1892 reported the case of a 71-year-old man with behavioral changes and progressive aphasia with focal left temporal atrophy, Pick disease is charac- terized by Pick bodies: round/oval argyrophilic cytoplasmic neuronal inclusions (▶ Fig. 17.2). These are found in the hippo- campus, amygdala, and frontal and temporal isocortex and are readily detected with tau immunohistochemistry and stain

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Frontotemporal Lobar Degeneration

on clinical CT and MRI studies that correlate with the clinical syndrome.24 In bvFTD, there is an anteroposterior gradient of atrophy involving the frontal and temporal lobes, with sparing of the parietal and occipital lobes. Although commonly bilateral, the volume loss is often asymmetrical (▶ Fig. 17.3, ▶ Fig. 17.4). A recent meta-analysis of VBM studies in bvFTD demonstrated significant gray matter loss in prefrontal regions compared with controls, with the most significant changes in the medial frontal lobes and also volume reductions in the insula and striatum.25 The earliest site of involvement is the orbitofrontal cortex, which shows sulcal widening before the mesiofrontal regions. The dorsolateral prefrontal cortex becomes involved later in the course of the disease (▶ Fig. 17.5). Hippocampal and amygdalar atrophy are also seen with bvFTD.26 Anterior mesiotemporal atrophy involving the amygdala and hippocampal head pre- dominates in FTLD and correlates with temporopolar atrophy. In the early phase of the disease, structural imaging is often normal, but most patients progress to show frontotemporal atrophy later. On 1-year follow-up, limbic and paralimbic regions, particularly the anterior cingulate cortex, exhibit pro- gressing gray matter atrophy in bvFTD.27 Furthermore, baseline determination of the site of predominant brain atrophy predicts functional decline in bvFTD, with frontal and frontotemporal predominant atrophy subtypes having faster decline compared with the temporal dominant and temporofrontal parietal sub- types.28 Behavioral deficits, such as disinhibition and apathy, are associated with right frontotemporal atrophy in patients with dementia.29

On the other hand, it has been recognized that there is a sub- group of bvFTD that does not display brain atrophy on MRI and has a significantly more benign course of the disease.30 Yet another perplexing observation is the occurrence of bvFTD symptoms in patients displaying brain sagging from intracranial hypotension,31 a potentially reversible condition, for which the name of frontotemporal brain sagging syndrome was proposed (▶Fig. 17.6). Very interestingly, the latter two groups of patients are almost exclusively males, suggesting a strong gen- der effect on the vulnerability for the clinical phenotype of bvFTD.

Semantic dementia shows consistent left anterior temporal lobe atrophy, also involving inferior and mesial temporal lobe regions, with a an anteroposterior gradient (predominant atro- phy seen anteriorly) that distinguishes SD from AD.32 The rate of volume loss over time is also more accelerated in FTD (pre- dominantly frontal atrophy) and SD (predominantly temporal atrophy) compared with AD.33

Progressive nonfluent aphasia leads to cortical thinning and atrophy of the left inferior frontal lobe, including the Broca area, superior temporal lobe, and insula (▶Fig. 17.7).6,34 Therefore, the patterns of cortical thinning differ between both variants of PPA, with more frontal and parietal atrophy in PNFA and bilateral temporal cortical atrophy in SD.34 PPA patients who show aphasia of speech, a motor speech disorder characterized by slow speaking rate, abnormal prosody, and distorted sound substitutions, additions, and repetitions have predominant atrophy in premotor and supplementary motor cortices, whereas the anterior perisylvian region correlates with nonfluent aphasia.35

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Fig. 17.2 Tau immunohistochemistry at X600. Pick bodies in the dentate gyrus. (Courtesy Dr. Patricia Kirby, University of Iowa.)

variably for ubiquitin. The predominant tau isoform in Pick’s disease is 3-repeat (3R).19

17.3.2 FTLD with Ubiquitin and Transactive-Response-43 Positive Inclusions

Transactive-response DNA-binding protein of 43k Da (TDP-43) is an RNA and DNA binding protein with neuronal and glial nuclear localization that is normally involved in gene transcrip- tion regulation. TDP-43 is the main component of inclusions seen in ALS/motor neuron disease and FTLD-U, in which the protein is detected in the cytoplasm.20 There is a wide range in the morphology and distribution of ubiquitin and TDP-43- positive inclusions, with significant overlap between FTD and ALS, suggesting that these syndromes are strongly related.21

17.3.3 Dementia Lacking Distinctive

Histology

The term dementia lacking distinctive histology (DLDH) describes dementia cases with evident atrophic brain changes, but with both tau and ubiquitin immunohistochemistry being negative,22 and represents a minority of cases. Apart from FTLD-T, FTLD-U, and DLDH, which together encompass the vast majority of FTLD cases, other rare subtypes have been reported. More importantly, patients with the clinical syndromes of FTLD may present lack of FTLD neuropathology but rather have pathological findings of AD, vascular dementia, dementia with Lewy bodies (DLB), prion disease, or even normal brains on autopsy.23

17.4 Neuroimaging 17.4.1 Structural Imaging

In contradistinction to the subtle structural imaging findings of LBD, FTLD cases demonstrate characteristic atrophic patterns

159

Non-Alzheimer’s Cortical Dementia

Fig. 17.3 A 55-year-old man with apraxia, impaired visual reasoning, acalculia, deficient executive functioning, and defective speeded visual processing. He had less pronounced deficits in the areas of verbal memory, associative fluency, and verbal comprehension of complex instructions. Symptoms progressed over 7 years. (a) Right sagittal T1, (b) axial T2, (c,d) coronal T2-weighted magnetic resonance imaging.

Fig. 17.4 Same patient’s magnetic resonance imaging as ▶ Fig. 17.3. (a) Right sagittal T1 and (b) axial T2 at the corresponding levels of (a) and (b) on ▶ Fig. 17.3, respectively, but 7 years earlier, showing interval marked progression of right-predominant frontotemporal and parietal atrophy.

160

17.4.2 Diffusion Tensor Imaging and

Functional Magnetic Resonance Imaging

Diffusion tensor imaging (DTI) studies in bvFTD showed bilat- eral involvement of white matter tracts connecting the frontal lobes, such as the anterior cingulum, superior longitudinal fas- ciculus, and genu of the corpus callosum.36,37 PPA patients dis- played more focal white matter involvement than bvFTD,

patients with differential involvement in the three clinical sub- types of PPA (nonfluent, semantic, and logopenic variants).37 White matter disorganization in FTLD likely results from axonal degeneration secondary to neuronal body death, as supported by the correlation between white matter changes and cortical atrophy.

Semantic dementia patients displayed abnormal white matter on DTI analysis involving the uncinate and inferior

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weighted specificity of 79.9%.40 A pattern of bilaterally reduced frontal cerebral blood flow in the absence of parietal hypoperfusion is characteristic in pathologically confirmed FTLD.41 However, there is substantial heterogeneity in the reported SPECT results. Reduced frontal and anterior tempo- ral glucose metabolism in FTD compared with controls has been reported using FDG-PET (▶ Fig. 17.8), with involvement also of the medial temporal lobes, striatum, and thalamus.42 The regions of hypometabolism in FTD correspond to those with cortical atrophy as determined with MRI using VBM analysis, whereas less congruent and asymmetric changes are seen in the temporal lobes.43

Using Pittsburgh compound-B (PiB-PET), which labels amy- loid deposits as those present in AD but not FTD neuro- pathology, PET imaging had 89.5% sensitivity and 83% specificity in distinguishing FTD from AD, with an overall simi- lar diagnostic accuracy compared with FDG-PET.44 PNFA shows reduced glucose metabolism in left frontal regions, whereas in SD the left anterior temporal lobe is hypometabolic.45 The two latter subtypes of FTLD also show less amyloid tracer uptake

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longitudinal fasciculi, whereas nonfluent patients had damage of the superior longitudinal fasciculus,38 which corresponds to the topography of cortical atrophy in these disorders. White matter tract degeneration in PNFA involves primarily the left superior longitudinal fasciculus and its subcomponent the arcu- ate fasciculus, which projects to the inferior frontal lobe, with sparing of ventral tracts.38

Resting-state fMRI studies evaluating functional connectivity described reduced connectivity in the salience network (includ- ing the anterior cingulate and frontoinsular regions) in bvFTD, with increased connectivity in the default network, features which may distinguish FTLD from AD.39

17.4.3 Nuclear Medicine

Nuclear medicine clinical imaging studies in FTLD demon- strate abnormal brain perfusion and glucose metabolism using SPECT and PET, respectively. A recent meta-analysis of brain perfusion SPECT studies for differentiating AD from FTD reported pooled weighted sensitivity of 79.7% and pooled

Frontotemporal Lobar Degeneration

Fig. 17.5 A 60-year-old man with severe mixed dementia with 10 years of evolution: Alzheimer’s disease early onset, behavioral variant frontotemporal dementia. (a-d) axial computed tomography (CT), (e,f) coronal CT images with prominent bilateral anterior prefrontal cortical atrophy and marked bilateral mesial temporal atrophy (left > right) and enlargement of the third ventricle.

161

Non-Alzheimer’s Cortical Dementia

Fig. 17.6 Frontotemporal brain sagging syndro- me. A 48-year-old man with clinical diagnosis of behavior variant frontotemporal dementia and severe brain sagging on magnetic resonance imaging (MRI). (a) Sagittal T1- (b-d) axial T2- weighted MRI. Neuropathological evaluation at autopsy was negative for frontotemporal lobar dementia.

Fig. 17.7 An 84-year-old woman with progressive nonfluent aphasia. Axial fluid-attenuated inversion recovery (FLAIR) (upper row) and coronal IR T1- weighted images (lower row) with bilateral (right greater than left) perisylvian and hippocampal atrophy and FLAIR hyperintensity. (Courtesy Dr. Kei Yamada, Kyoto Prefectural University of Medicine, Japan.)

162

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. [6]  Gorno-Tempini ML, Dronkers NF, Rankin KP et al. Cognition and anatomy in three variants of primary progressive aphasia. Ann Neurol 2004; 55: 335– 346

. [7]  Ringholz GM, Appel SH, Bradshaw M, Cooke NA, Mosnik DM, Schulz PE. Prev- alence and patterns of cognitive impairment in sporadic ALS. Neurology 2005; 65: 586–590

. [8]  Lomen-Hoerth C, Anderson T, Miller B. The overlap of amyotrophic lateral sclerosis and frontotemporal dementia. Neurology 2002; 59: 1077– 1079

. [9]  Morris HR, Waite AJ, Williams NM, Neal JW, Blake DJ. Recent advances in the genetics of the ALS-FTLD complex. Curr Neurol Neurosci Rep 2012; 12: 243– 250

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[28] Josephs KA, Jr, Whitwell JL, Weigand SD et al. Predicting functional decline in behavioural variant frontotemporal dementia. Brain 2011; 134: 432–448
[29] Rosen HJ, Allison SC, Schauer GF, Gorno-Tempini ML, Weiner MW, Miller BL.

Neuroanatomical correlates of behavioural disorders in dementia. Brain

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[30] Davies RR, Kipps CM, Mitchell J, Kril JJ, Halliday GM, Hodges JR. Progression in

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netic resonance imaging. Arch Neurol 2006; 63: 1627–1631
[31] Wicklund MR, Mokri B, Drubach DA, Boeve BF, Parisi JE, Josephs KA. Fronto- temporal brain sagging syndrome: an SIH-like presentation mimicking FTD.

Neurology 2011; 76: 1377–1382
[32] Chan D, Fox NC, Scahill RI et al. Patterns of temporal lobe atrophy in semantic

dementia and Alzheimer’s disease. Ann Neurol 2001; 49: 433–442
[33] Krueger CE, Dean DL, Rosen HJ et al. Longitudinal rates of lobar atrophy in frontotemporal dementia, semantic dementia, and Alzheimer’s disease.

Alzheimer Dis Assoc Disord 2010; 24: 43–48
[34] Rohrer JD, Warren JD, Modat M et al. Patterns of cortical thinning in the

language variants of frontotemporal lobar degeneration. Neurology 2009; 72:

1562–1569
[35] Josephs KA, Duffy JR, Strand EA et al. Clinicopathological and imaging corre-

lates of progressive aphasia and apraxia of speech. Brain 2006; 129: 1385– [12] Seelaar H, Rohrer JD, Pijnenburg YAL, Fox NC, van Swieten JC. Clinical, genetic 1398

[36] Whitwell JL, Avula R, Senjem ML et al. Gray and white matter water diffusion in the syndromic variants of frontotemporal dementia. Neurology 2010; 74: 1279–1287

[37] Agosta F, Scola E, Canu E et al. White matter damage in frontotemporal lobar degeneration spectrum. Cereb Cortex 2012; 22: 2705–2714

[38] Galantucci S, Tartaglia MC, Wilson SM et al. White matter damage in primary progressive aphasias: a diffusion tensor tractography study. Brain 2011; 134: 3011–3029

[39] Zhou J, Greicius MD, Gennatas ED et al. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease. Brain 2010; 133: 1352–1367

[40] Yeo JM, Lim X, Khan Z, Pal S. Systematic review of the diagnostic utility of SPECT imaging in dementia. Eur Arch Psychiatry Clin Neurosci 2013; 263: 539–552

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

and pathological heterogeneity of frontotemporal dementia: a review. J Neu-

rol Neurosurg Psychiatry 2011; 82: 476–486

. [13]  Hutton M, Lendon CL, Rizzu P et al. Association of missense and 5’-splice-site
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. [14]  Mackenzie IR, Baker M, Pickering-Brown S et al. The neuropathology of fron-
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9p-linked FTD and ALS. Neuron 2011; 72: 245–256

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temporal dementia genetics: C9ORF72, tau, progranulin and sporadics. Brain 2012; 135: 794–806

Frontotemporal Lobar Degeneration

Fig. 17.8 Sagittal reconstructions of fluorodeoxyglucose (FDG) posi- tron emission tomography with classical findings in frontotemporal lobar dementia of marked frontal hypometabolism with preservation of occipitoparietal glucose uptake. (Courtesy Dr. Yusuf Menda, University of Iowa.)

than the logopenic variant of PPA, which is a recently described variant of AD.

References

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. [2]  Rosso SM, Donker Kaat L, Baks T et al. Frontotemporal dementia in the Neth- erlands: patient characteristics and prevalence estimates from a population- based study. Brain 2003; 126: 2016–2022

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[17] Broe M, Hodges JR, Schofield E, Shepherd CE, Kril JJ, Halliday GM. Staging disease severity in pathologically confirmed cases of frontotemporal dementia. Neurology 2003; 60: 1005–1011

[18] Cairns NJ, Bigio EH, Mackenzie IRA et al. Consortium for Frontotemporal Lobar Degeneration. Neuropathologic diagnostic and nosologic criteria for frontotemporal lobar degeneration: consensus of the Consortium for Fronto- temporal Lobar Degeneration. Acta Neuropathol 2007; 114: 5–22

[19] Muñoz DG, Dickson DW, Bergeron C, Mackenzie IRA, Delacourte A, Zhukareva V. The neuropathology and biochemistry of frontotemporal dementia. Ann Neurol 2003; 54 Suppl 5: S24–S28

[20] Neumann M, Sampathu DM, Kwong LK et al. Ubiquitinated TDP-43 in fronto- temporal lobar degeneration and amyotrophic lateral sclerosis. Science 2006; 314: 130–133

[21] Mackenzie IRA, Feldman HH. Ubiquitin immunohistochemistry suggests classic motor neuron disease, motor neuron disease with dementia, and frontotemporal dementia of the motor neuron disease type represent a clinicopathologic spectrum. J Neuropathol Exp Neurol 2005; 64: 730–739

[22] McKhann GM, Albert MS, Grossman M, Miller B, Dickson D, Trojanowski JQ Work Group on Frontotemporal Dementia and Pick’s Disease. Clinical and pathological diagnosis of frontotemporal dementia: report of the Work Group on Frontotemporal Dementia and Pick’s Disease. Arch Neurol 2001; 58: 1803–1809

[23] Forman MS, Farmer J, Johnson JK et al. Frontotemporal dementia: clinico- pathological correlations. Ann Neurol 2006; 59: 952–962

[24] Lu PH, Mendez MF, Lee GJ et al. Patterns of brain atrophy in clinical variants of frontotemporal lobar degeneration. Dement Geriatr Cogn Disord 2013; 35: 34–50

[25] Pan PL, Song W, Yang J et al. Gray matter atrophy in behavioral variant fronto- temporal dementia: a meta-analysis of voxel-based morphometry studies. Dement Geriatr Cogn Disord 2012; 33: 141–148

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[27] Brambati SM, Renda NC, Rankin KP et al. A tensor based morphometry study of longitudinal gray matter contraction in FTD. Neuroimage 2007; 35: 998–

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. [42]  Ishii K. PET approaches for diagnosis of dementia. AJNR Am J Neuroradiol 2013[epub ahead of print]

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Part VI

Dementia with Extrapyramidal Syndromes

18 Parkinson’s Disease 166 19 Atypical Parkinsonian Syndromes 180 20 Secondary Parkinsonism 186

VI

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166

Dementia with Extrapyramidal Syndromes

18 Parkinson’s Disease
Jennifer G. Goldman, John W. Ebersole, Doug Merkitch, and Glenn T. Stebbins

Parkinson’s disease (PD) is a chronic and progressive neuro- degenerative disease that affects about 1 to 2% of the popula- tion older than 60 years. About four million people over the age of 50 have PD, and rates are expected to double by 2030.1 PD symptoms include its cardinal motor features, including brady- kinesia, resting tremor, rigidity, and gait or postural impair- ment. In addition, nonmotor features are well recognized in cognitive, behavioral, mood, sleep, autonomic, vision, and pain systems. These motor and nonmotor features can occur throughout the stages of PD, extending from premotor or early PD to moderate and advanced PD, and they affect daily func- tion, independence, and the quality of life of patients and care- givers. At present, however, no curative or neuroprotective therapies for PD have been established, but imaging and other biomarkers may play a role in the development of these therapeutics.

The diagnosis of PD has been largely clinically based, but more recently techniques like dopamine transporter imaging, transcranial ultrasound (TCS), diffusion tensor imaging (DTI), and others provide a way to detect brain changes associated with PD or parkinsonian disorders. Structural, functional, meta- bolic, and neurochemical imaging techniques may advance our understanding of the underlying neurochemistry and neuro- pathology of PD. PD is accompanied by neurodegeneration and neurotransmitter changes in the brainstem, striatal, subcortical, and cortical regions, which affect norepinephrine, serotonin, dopamine, acetylcholine, and glutamate, among others. Lewy bodies with α-synuclein staining and depigmentation of sub- stantia nigra neurons are the neuropathological hallmarks of PD. A stepwise staging system of neurodegeneration has been proposed, beginning with Lewy-related changes in the auto- nomic and olfactory systems and subsequently involving the brainstem and cortex.2,3 The field awaits in vivo imaging of α- synuclein in PD patients, although research is ongoing. This chapter discusses neuroimaging in PD as related to the diagno- sis of PD, its motor features and complications, and nonmotor issues that can occur not only in the premotor phase of PD but also in more advanced PD.

18.1 Imaging in the Diagnosis of Parkinson’s Disease

18.1.1 Early Diagnosis

The diagnosis of PD has largely relied on clinical criteria demon- strating the classic motor features of rest tremor, bradykinesia, rigidity, and gait impairment.4 However, evidence suggests that dopaminergic degeneration in the substantia nigra precedes symptom onset, with clinical symptoms not appearing until approximately 80% of striatal and 50% of nigral dopaminergic neurons are lost.5 Further, degeneration of dopaminergic neu- rons progresses most rapidly during the presymptomatic phase and the first years after symptom onset.6,7,8 Thus, early diagno- sis is critical for allowing early intervention and developing possible neuroprotective measures. Imaging techniques that

may aid in the presymptomatic or early diagnosis of PD include molecular imaging of dopamine transport proteins using single-photon emission computed tomography (SPECT) and positron emission tomography (PET), TCS, and magnetic reso- nance imaging (MRI) with techniques such as DTI. SPECT imag- ing can measure the density of transmembrane dopamine transporters (DATs) and thereby reflect presynaptic dopaminer- gic neuron integrity in vivo. [123I]FP-CIT (ioflupane I-123, DaTSCAN) and 123I-labeled-2β-carbomethoxy-3β-(4-iodo- phenyl-nortropane ([123I]-β-CIT), among others, are radiophar- maceuticals used for SPECT brain imaging.9 DAT imaging lig- ands differ in their kinetics and dopamine transporter affinity; [123I]FP-CIT has a rapid time to peak (2 to 3 hours), whereas [123I]-β-CIT has a longer time to peak (8 to 18 hours), although both have a prolonged washout phase. Studies are interpreted based on the signal shape and intensity in the striatal regions; normal studies demonstrate two symmetric, crescent-shaped regions of uptake, with distinct margins relative to surrounding brain tissue (▶Fig. 18.1), whereas abnormal studies reveal either symmetric or asymmetric decreased or absent activity in the putamen greater than caudate, such as in PD.10 SPECT imag- ing has been proposed as a highly sensitive indicator of early PD.11 [123I]-β-CIT SPECT imaging had 92% sensitivity and 100% specificity in diagnosing PD compared with the gold standard of clinical diagnosis by a movement disorder specialist.12 SPECT may have a role in ruling out PD in clinically uncertain cases.12, 13 In cases of suspected PD, DAT binding will be reduced in 90%.14 In several studies, [123I]FP-CIT has differentiated with high sensitivity and specificity, PD from essential tremor (ET), a neurologic condition characterized by postural or action trem- ors in the hands or tremors affecting the head, neck, or voice that can mimic some PD signs.15 In 2011, the United States Food and Drug Administration approved DaTSCAN using the [123I]FP- CIT ligand for use in suspected parkinsonian syndromes, based on two multicenter, phase III studies.13,16 In early parkinsonian patients with or without tremor (designated possible and prob- able PD), compared with patients with non-PD tremor and healthy controls, the DaTSCAN had 79% sensitivity and 97% specificity, whereas clinical diagnosis in early PD had 98% sensi- tivity but 67% specificity.

Positron emission tomography is an imaging modality that has been studied as an in vivo technique to measure dopamin- ergic function, cerebral blood flow, and metabolic changes. Besides dopaminergic function, there is a growing interest in radiotracers for serotonin, acetylcholine, and opioids, as well as for measuring amyloid and microglia activity in PD studies. PET can measure the ability of striatal dopaminergic neurons to take up radiolabeled levodopa or measure dopamine turnover and dopa decarboxylase activity using a variety of 11C or 18F ligands. The typical finding in PET scans in PD is asymmetric decreased uptake of the radiopharmaceutical in the putamen. The order of this decreased uptake on PET in PD occurs from rostral to cau- dal, with relative preservation of the caudate and reduced uptake progressing from the anterior to posterior putamen, in opposite direction from what is seen in normal aging.11 By the time motor symptoms develop, there is a 50% reduction in the

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uptake of 6-[18F]-fluoro-L-dopa (18F-dopa), suggesting that PET may be useful in presymptomatic diagnosis and monitoring progression of PD.17 PET scans, however, have several limita- tions, including the low availability of cyclotrons, regional spec- ificity resulting from limited spatial resolution and partial vol- ume effects, longer scanning times, and radioactive ligands, despite safety monitoring and low doses. To acquire a truly quantitative measure of ligand uptake, monitoring of arterial trace elements is also required.

Transcranial ultrasound permits the visualization of the echogenicity of the substantia nigra at the level of the mes- encephalic brainstem. The typical TCS finding in PD is bilateral increased echogenicity (i.e., hyperechogenicity) of the lateral midbrain, which is present in 90% to 96% of clinically diag- nosed PD cases.18,19,20 The degree of echogenicity, however, does not correlate with severity of PD motor symptoms.18 Echogenicity of the substantia nigra may represent iron depo- sition and an increased susceptibility to PD, although the exact reason is unknown and other iron-rich brain regions do not exhibit hyperechogenicity.21 These findings, coupled with its high sensitivity, low cost, and noninvasive and readily available use, make TCS a promising potential screening tool for early PD. Drawbacks, however, include its dependence on user experience, obtaining an adequate bone window, lack of well-established cutoffs for hyperechogenicity or hypoechoge- nicity, and a fairly high rate of false-positives, particularly with ET, in which up to 16% of patients can have positive findings.22

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Structural MRI in general plays a limited role in the early diagnosis of PD, aside from ruling out alternative diagnoses, such as hydrocephalus or vascular disease. Diffusion-weighted imaging, which measures the ability of water to diffuse freely in brain tissue, is highly sensitive to changes in striatal structure. Experimentation with T2 relaxometry has shown reduced T2 relaxation times in the substantia nigra in PD, possibly indicat- ing tissue destruction detectable on MRI. DTI, which provides a measure of directional diffusion and may be considered a proxy for tissue integrity (▶Fig. 18.2), has demonstrated differences in fractional ansiotropy (FA) in different cortical and subcortical regions (▶ Fig. 18.3). Decreased FA has been found in the sub- stantia nigra in all 14 newly diagnosed PD cases, compared with healthy controls, suggesting high sensitivity and specificity in this study.24 The reduced FA in the substantia nigra regions occurred particularly caudally, consistent with postmortem dopaminergic cell loss location. Magnetic resonance spectros- copy, which detects differences in the neurochemical profiles of brain structures, revealed significantly higher concentrations of γ-aminobutryic acid (GABA) in the pons and putamen of PD patients compared with healthy controls,25 suggesting involve- ment of the lower brainstem structures, similar to Braak PD staging, as well as basal ganglia in early PD.

18.1.2 Differential Diagnosis
Distinguishing idiopathic PD from other parkinsonian syn-

dromes can be difficult, especially early in the disease, when

Parkinson’s Disease

Fig. 18.1 [123I]FP-CIT (Ioflupane I-123, DaTSCAN) single-photon emission computed tomography scan of a patient with parkinsonian features. Regions of increased presynaptic dopamine transporter receptor binding in the caudate and putamen are indicated by increased signal. Note the relatively blurred margins and slightly asymmetric uptake, with caudate regions demonstrating increase uptake compared with the putamen. The color scale indicates the magnitude of ligand uptake, with lowest appearing in dark green/black and the highest in bright orange/white. The right side of the image represents the left side of the brain.

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Fig. 18.2 In diffusion tensor imaging, the application of at least six noncollinear gradients creates a 3 × 3 matrix that can be described by a mathematical construct called a tensor. From the diffusion tensor in each voxel, the three eigenvalues (λ1, λ2, and λ3) define the magnitude of the diffusion system and the three associated eigenvectors that describe the direction of the diffusion system. Based on the ratio of the three eigenvalues, the intravoxel direction of hydrogen diffusion can be determined and is termed fractional anisotropy (FA). Cerebrospinal fluid has extremely low FA values because hydrogen is free to diffuse in any direction. Gray matter has low FA because cellular structures (e.g., cell membrane, organelles) impede the free diffusion of hydrogen, but these structures do not promote organized, directional diffusion. Highly organized white matter tracts have high FA because hydrogen diffusion is directionally constrained by the tract’s cellular organization. In the figure, changes in FA across the life span can be seen as a decrease in the intensity in major white matter pathways.

Fig. 18.3 Decreases in fractional anisotropy (FA) in 20 patients with Parkinson’s disease compared with 20 age- and gender-matched normal control subjects. Significant decreases in FA were found in bilateral frontal forceps, superior longitudinal fasciculi, and the anterior and posterior limb of the internal capsule (regions in black circles). Additional regions of decreased FA were noted in the brainstem (not shown). Differences were analyzed using a two-sample t-test statistic. Significance thresholds were set for p < 0.05, corrected for multiple comparisons. Voxels evidencing significant differences between groups are displayed on representative axial sections on a canonical brain image. The color scale indicates the magnitude of t values, with lowest appearing in dark red and the highest in bright yellow/white. The left side of the images represents the left side of the brain.

168

some symptoms might not yet be present or at their fullest extent. Clinical differentiation can be unclear, leading to mis- diagnosis in up to 24% of cases.26 Other diagnoses may include atypical parkinsonian syndromes (e.g., multisystem atrophy [MSA], progressive supranuclear palsy [PSP], and corticobasal degeneration [CBD]), ET, vascular parkinsonism, and drug- induced parkinsonism. Imaging studies can aid in the differen-

tial diagnosis of these conditions and subsequently directing patient management.

In MRI studies, patients with MSA-parkinsonian type may exhibit putaminal hypointensities and a hyperintense rim along the lateral putamen on T1 images on 1.5 Tesla MRI. MRI T2 images may reveal a “hot cross bun sign” in addition to cerebel- lar atrophy in MSA cerebellar-type patients, which is thought to

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indicate a loss of myelinated transverse pontocerebellar fibers. PSP patients can have midbrain and frontal lobe atrophy, as well as the “hummingbird sign” on sagittal MRI sequences as a result of midbrain atrophy and third ventricle widening.27 CBD patients may exhibit asymmetric atrophy of posterior frontal and parietal lobes on structural MRI. In several studies, appar- ent diffusion coefficient measurements on MRI scans may dif- ferentiate MSA or PSP from PD with high sensitivities and posi- tive predictive values.28,29,30 TCS also can help differentiate PD from atypical parkinsonian syndromes, with 91% sensitivity and 82% specificity and greater hyperechogenicity in atypical parkinsonism compared with PD.31

Dopamine transporter SPECT imaging does not readily distin- guish among parkinsonian syndromes, although more symmet- ric loss of the putamen and caudate may suggest an atypical parkinsonian syndrome. Approximately 10% of patients with clinically diagnosed with early PD, however, will have scans without evidence of dopaminergic deficiency (SWEDDs) on SPECT scan.32 Most SWEDD patients are unlikely to have PD at follow-up and in some cases have ET or dystonia.13 DAT SPECT scans, however, can distinguish PD from other neurologic diag- noses, such as ET, with 95% sensitivity and specificity.33 Vascu- lar parkinsonism, which manifests clinically as a “lower body” parkinsonism with prominent gait disorder and postural instability, can be accompanied by white matter ischemic changes or lacunar lesions in the basal ganglia on structural MRI, but a definitive diagnosis of vascular parkinsonism is only made at autopsy. Mean [123I]FP-CIT uptake in the basal ganglia was significantly decreased in vascular parkinsonism compared with healthy controls, and preservation of symmetri- cal uptake may help discriminate it from PD.34,35 Drug-induced parkinsonism attributable to dopamine-blocking medications (e.g., for nausea or psychiatric reasons) can clinically mimic PD. [123I]FP-CIT SPECT can differentiate these entities by showing integrity of nigrostriatal neurons in drug-induced parkinsonism versus degeneration of these neurons and reduced uptake in PD.36

Parkinson’s Disease 18.2 Imaging and Parkinson’s

Disease Motor Features 18.2.1 Motor Hallmarks

In addition to the clinical examination and rating scales used to assess the classic motor symptoms of PD, imaging techniques provide a complementary approach to understanding the struc- tural, functional, and metabolic alterations in the PD brain and how these changes relate to pathophysiology, motor pheno- type, and disease progression. This section highlights several different imaging techniques used to examine the motor fea- tures of PD (▶ Table 18.1).

Tremor

Rest tremor, one of the cardinal motor features associated with PD, occurs in about 70% of patients. Although nigrostriatal degeneration of dopaminergic neurons is a pathological hallmark of PD, tremor does not correlate with the severity of striatal dopaminergic deficit.37,38 Thus, nondopaminergic mech- anisms and circuitry extending beyond the striatum contribute to PD tremor. Studies suggest that PD tremor is mediated by an interaction of the basal ganglia, cerebellar, and thalamic circuits.39 Using combined surface electromyography and whole-head magnetoencephalography, tremor-related oscilla- tory activity was found within a cerebral network, with abnor- mal coupling in a cerebello-diencephalic-cortical loop and cor- tical motor and sensory areas contralateral to the tremor hand.40 PD patients with tremor, compared with PD patients without tremor and healthy controls, demonstrated increased imagery-related activity in the somatosensory area to a motor imagery task during functional MRI scanning. This increased activity was independent from tremor-related activity identi- fied in the motor cortex, cerebellum, and thalamic ventral inter- mediate nucleus (Vim), which is often a target for deep brain stimulation in tremor patients. In structural MRI studies using

Table 18.1 Imaging and Parkinson’s disease motor severity

Modality/analysis subjects Brain regions correlated with motor rating scales

[18F] F-DOPA PET

32 PD, assessed at baseline and mean 18 + /– 6 months

Reduced putamen > caudate uptake inversely correlated with UPDRS
Putamen with most rapid mean rate of progression (4.7% of normal mean per year)17

[18F] DOPA PET

27 nondemented PD 10 controls

Reduced putamen > caudate uptake inversely correlated with Hoehn and Yahr stage and UPDRS63

[123I] CIT SPECT

12 PD

Reduced putamen uptake inversely correlated with UPDRS, especially bradykinesia subscore145

MRI: FreeSurfer software

142 PD

Decreased cortical thickness in parietotemporal regions inversely correlated with UPDRS48

MRI-VBM gray matter

Meta-analysis of PD studies 498 PD
375 controls

Decreased gray matter volume in the left inferior frontal/ orbitofrontal gyrus inversely correlated with Hoehn and Yahr stage146

MRI-DTI region of interest approach (caudate, putamen, globus pallidus, thalamus, substantia nigra), measuring FA

151 PD
78 controls

Reduced FA in the substantia nigra inversely correlated with Hoehn and Yahr stage147

Abbreviations: CIT, carbomethoxy-iodophenyl-nortropan FA, fractional anisotropy; FDOPA, fluorodopa; MRI, magnetic resonance imaging; PD, Parkinson’s diseases; PET, positron emission tomography; SPECT, single-photon emission computed tomography; UPDRS, Unified PD Rating Scale; VBM, voxel-based morphometry.

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Dementia with Extrapyramidal Syndromes

Fig. 18.4 Voxel-based morphometry (VBM) processing allows for comparison of individual images in a standardized coordinate system. In the process, images are first segmented into gray matter, white matter, cerebrospinal fluid (CSF), and nonbrain compartments. Then the gray matter segment is spatially normalized to a standard gray matter template using a 12-parameter affine normalization and nonlinear adjustments with 7X7X7 basis functions. The transformation parameters obtained from the gray matter normalization are then applied to the whole-brain T1-weighted volumes. Individual normalized whole-brain volumes are then segmented into gray matter, white matter, CSF, and nonbrain partitions. To correct for possible volume changes during normalization, the normalized gray and white matter segments are modulated to maintain the original non-normalized volume per voxel in the normalized gray and white matter segments. In the modulation step, voxel values are multiplied by the Jacobian determinants derived from the normalization of the T1-weighted images. The segmented, normalized, and modulated segments are then smoothed with a gaussian kernel. The smoothing step compensates for interindividual variability and conforms the data more closely to gaussian random field theory, which provides for corrected statistical inference.143,144

voxel-based morphometry (VBM) techniques (▶Fig. 18.4), PD patients with unilateral rest tremor demonstrated increased gray matter in the Vim nucleus contralateral to the side that is most affected, although no comparison group was included.41 Compared with PD patients without rest tremor, PD patients with rest tremor exhibited decreased gray matter in the poste- rior right quadrangular lobe and decline of the cerebellum.42 Using [18F]-fluorodeoxyglucose (FDG) PET, a tremor-related metabolic pattern characterized by increased activity in the cerebellum/dentate nucleus, primary motor cortex, and to some degree the striatum was detected. This pattern correlated significantly with clinical ratings of tremor but not akinesia- rigidity scores. Expression of the tremor-related metabolic pat- tern was suppressed to a greater degree by Vim rather than by subthalamic deep brain stimulation, thereby supporting the selective involvement of cerebellothalamocortical pathways in PD tremor.43 Several imaging studies suggest serotonergic

dysfunction in PD tremor. Serotonin (5HT) receptor 1A binding potential as measured by 11C-WAY 100635 PET, which is a selective antagonist for 5HT1A receptors, in the midbrain raphe was significantly reduced in PD patients compared with healthy controls. In addition, the 5HT1A binding correlated significantly with tremor rating scores but not bradykinesia or rigidity scores.44 A PET study using 11C-3- amino-4-[2-[(di(methyl)amino)methyl]phenyl]sulfanylbenzo- nitrile (11C-DASB), a marker of serotonin transporter binding, revealed reduced tracer uptake in the raphe nuclei, caudate, putamen, thalamus, and motor circuitry regions in tremor- dominant PD patients, compared with akinetic-rigid PD patients and healthy controls, suggesting potential contribu- tions of presynaptic 5HT terminal dysfunction to PD tremor, although because this reduction correlated primarily with action and postural tremor, further study is needed regarding rest tremor.45

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Bradykinesia and Rigidity

Bradykinesia and rigidity have been investigated in several imaging studies, either independently or in combination. Using [18F]FDG-PET, a pattern of glucose metabolism differentiated PD patients from healthy controls and MSA (striatonigral degeneration) patients.46 This PD-related motor pattern, with increased metabolic activity in the globus pallidum, putamen, and thalamus and decreased activity in the lateral frontal, para- central, interior parietal and parieto-occipital areas, signifi- cantly correlated with Unified PD Rating Scale (UPDRS) scores of bradykinesia and rigidity but not tremor ratings. The PD- related motor pattern has undergone further validation using both oxygen-15 water (H2O15) and [18F]FDG-PET scans and demonstrates high within-subject and test-retest reproducibil- ity in early and advanced stage PD patients.47 Distinct metabolic patterns such as these may have use in the diagnosis or thera- peutic monitoring. Structural MRI scans analyzed for cortical thickness revealed correlations between decreased cortical thickness in the parietotemporal sensory association areas and longer PD duration and increased motor deficits on the UPDRS, particularly bradykinesia and axial motor function48; cortical thickness in this PD sample, however, did not correlate with tremor scores. Although additional study is needed, these cortical regions overlap with those exhibiting decreased meta- bolic activity in other studies and suggest cerebral cortical dys- function in advancing PD.

Comparisons of Tremor-Dominant and Akinetic-Rigid Parkinson’s Disease Phenotypes

Some, but not all, imaging studies have identified significant differences in the neurochemistry and neural circuitry underlying tremor-dominant versus postural instability-gait impairment or akinetic-rigid motor phenotypes of PD. Tremor- dominant patients are frequently younger at onset and have a slower rate of disease progression and less cognitive decline. Using [123I]FP-CIT scans, nigrostriatal dopaminergic system impairment, even at early stages of PD, with reduced uptake in putamen and caudate regions is associated more with akinetic- rigid than tremor-dominant symptomology.49 Other [123I]FP- CIT studies, however, failed to find a significant difference in striatal dopamine transporter uptake between tremor-domi- nant and non-tremor-dominant PD subgroups.37 These studies suggest that nondopaminergic mechanisms may contribute to differences in PD motor phenotype and that further refinement of optimal methods for classification (visual morphology, semi- quantitative, other) is needed.

Because structural MRI scans of PD patients with different motor phenotypes have not revealed robust differences, studies have explored differences in blood-oxygen-level- dependent (BOLD) function on functional MRI (fMRI). BOLD activation was reduced in bilateral dorsolateral prefrontal cortex, contralateral lingual gyrus, caudate, globus pallidum interna and externa, and ispilateral thalamus in a nontremor dominant PD group compared with the tremor-dominant PD group. No significant differences were seen in gray or white matter volume between these groups as detected using

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VBM.50 In another study using fMRI with regions of interest examining the striatal-thalamo-cortical and cerebello- thalamo-cortical circuits, akinetic-rigid PD patients showed more activity during a finger-tapping task in multiple corti- cal and subcortical regions, as well as striatal-thalamo- cortical and cerebello-thalamo-cortical circuits, compared with tremor-dominant PD patients; in contrast, tremor- dominant patients had greater activity in the vermis, contra- lateral cerebellar hemisphere, and ipsilateral thalamus.51 Thus, different imaging modalities may differentiate the PD subtypes and identify specific neurobiological substrates for the different motor phenotypes.

Gait Impairment

Gait and balance issues in PD are associated with morbidity, mortality, and disability and in advanced PD may respond poorly to dopaminergic treatments. Falls, start hesitation, and freezing of gait also can occur in advancing PD. Whereas imag- ing during actual gait is often impossible because most scanners require that subjects be immobile and supine, novel methods, such as motor imagery, virtual reality, or foot pedals, have been developed to simulate gait-related brain activity.

Studies using SPECT reveal that although PD patients and healthy controls have similar patterns of gait-related brain acti- vation (e.g., cortical motor regions for foot and trunk, brain- stem, and cerebellum), PD patients show significantly less regional cerebral blood flow activation in the right supplemen- tal motor area, left precuneus, and right cerebellar hemisphere. In addition, PD patients had increased activation in the lateral premotor area when visual cues are added to improve PD gait.52 Using fMRI to measure gait-related activation during mental imagery, the PD group had differences in the mean gait activa- tion pattern (i.e., hypoactivity within the parieto-occipital regions, left hippocampus, midline/lateral cerebellum, and pedunculopontine nucleus locomotor area) compared with healthy controls; activation levels in the right posterior parietal cortex correlated with severity of gait measures.53 Another fMRI study investigating gait-related activation during mental imag- ery and video stimuli of gait initiation, stepping over an obsta- cle, and gait termination found that PD patients had greater activation of visuomotor areas during the latter two mental imagery scenarios compared with healthy controls.54 Other studies suggest that gait disturbances in PD invoke nondopami- nergic and extrastriatal systems, including the cholinergic sys- tem, which is involved in locomotion and cognition. Comparing acetylcholinesterase hydrolysis rates in PD patients with a his- tory of falls to PD patients without a history of falls, thalamic acetylcholinesterase activity was reduced in the “fallers” as measured by [11C]PMP-PET; in contrast, DTZB-PET scans did not reveal significant nigrostriatal dopaminergic differences between the groups.55

Freezing of gait (FOG) is an intriguing, although not well understood, episodic gait phenomenon that occurs in moder- ate-advanced PD and is characterized by the inability to initiate and produce effective stepping or gait patterns.56 Studies have examined metabolic, functional, and structural imaging corre- lates of FOG, comparing PD patients who have FOG (FOG +) with

Parkinson’s Disease

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Dementia with Extrapyramidal Syndromes

those who do not FOG (FOG–). Using [18F]-6-fluoro-levodopa (FDOPA) and [18F]FDG in a small sample of PD patients, trends toward differences in striatal tracer uptake in FOG + compared with FOG– patients were noted. In FOG+patients, caudate uptake of FDOPA and FDG was reduced, whereas FDOPA decrease in the putamen was associated with FDG increases. Computer-based virtual reality paradigms have been developed to simulate FOG in the MRI environment. Processing cognitive and environmental information obtained during bipedal motor activity (i.e., using foot pedals) has been shown to induce FOG-like symptoms in PD patients who are prone to FOG epi- sodes in the “off” state.57 By using this task during fMRI, increased BOLD signal was found in the bilateral dorsolateral prefrontal cortex and posterior parietal cortices, with a concur- rent decrease in BOLD signal in bilateral sensorimotor cortices during the contrasts of the motor arrests and simulated “walk- ing.” In addition, this was also associated with significantly decreased BOLD signal in various basal ganglia and thalamic nuclei during periods of motor arrest compared with “walk- ing.”58 Several studies have examined structural gray and white matter differences associated with FOG in PD. Using VBM, greater gray matter atrophy in frontal and parietal cortices has been found in FOG+patients than in FOG– patients, sug- gesting contributions of executive dysfunction and/or altered perceptual judgment.59 Another VBM study demonstrated a predominantly posterior pattern of gray matter atrophy (i.e., cuneus, precuneus, lingual gyrus, and posterior cingulate cortex), which may implicate visuoperceptive and discrimination dysfunction in FOG.60 Using probabilistic trac- tography DTI analyses to examine the white matter con- nectivity of the pedunculopontine nucleus in a small sample of PD patients, there was decreased connectivity of the pedunculopontine nucleus and cerebellum and increased con- nectivity with the pons in FOG+patients compared to FOG– patients.61

18.3 Imaging and Parkinson’s Disease Nonmotor Features and Complications

18.3.1 Nonmotor Features

In addition to the motor features described above, nonmotor features are now recognized to accompany PD, even from early stages to more advanced disease. Early nonmotor features include decreased or loss of sense of smell (i.e., hyposmia or anosmia), depression, anxiety, constipation, cognitive impair- ment, and sleep disturbances, including dream enactment with loss of normal muscle atonia during rapid eye movement (REM) sleep (i.e., REM behavior disorder, or RBD). In moderate to advanced PD, nonmotor symptoms may include depression, anxiety, fatigue, apathy, sleep disturbances, cognitive impair- ment or dementia, and hallucinations or psychosis. Imaging studies with structural, functional, metabolic, and other tech- niques have been used to identify neurobiological substrates of various nonmotor features and to develop related biomarkers.

This section highlights imaging studies examining select olfac- tion, sleep, cognitive, and behavioral issues.

Premotor Symptoms

In recent years, the concept of a prodromal or premotor phase of PD before the onset of its classic motor features has emerged and is characterized primarily by several nonmotor features that have been associated with increased risk of developing PD.62,63,64,65 These symptoms include hyposmia or anosmia, constipation, depression, anxiety, and RBD and may be due to PD-related neurochemical and neuropathological changes in the olfactory system, gastrointestinal mucosa, and brainstem.2,3

Hyposmia or anosmia has been studied as a possible bio- marker for the development of PD in epidemiologic and imag- ing studies, in some cases in first-degree relatives of PD patients.66,67 In a prospective study of 361 asymptomatic first- degree relatives of PD patients, DaT-SPECT scanning was com- bined with olfactory testing. After 5 years, 5 of 40 hyposmic rel- atives were diagnosed by clinical criteria with PD, and all these individuals had abnormal DaT scans at baseline.67,68 In addition, several studies have focused on olfaction in already clinically diagnosed PD patients. [18F]FDG PET was used in a study of 69 Japanese, nondemented PD patients who were also evaluated for hyposmia. It found olfactory dysfunction was clinically related to cognitive dysfunction, but also to abnormal brain glu- cose metabolism in the piriform cortex and amygdala, regions involved in olfaction, memory, and emotion.69 Interestingly, activation in similar brain regions was abnormal in a small fMRI study of eight PD patients compared with controls, in which subjects rated olfactory stimuli as pleasant or unpleasant dur- ing fMRI. In PD patients, both pleasant and unpleasant smells were associated with decreased activation in the amygdalohip- pocampal complex, whereas in controls pleasant smells were associated with increased activity in the striatum and left inferior frontal gyrus and unpleasant smells with decreased activation of the ventral striatum.70 Neurochemical deficits associated with hyposmia in PD have been examined by PET scans using ligands to measure cholinergic and monoaminergic activity. In a study of nondemented PD patients who under- went [11C]-methyl-4-piperidinyl proprionate acetylcholin- esterase PET and [11C] dihydrotetrabenazine vesicular mono- amine transporter type 2 PET along with olfactory testing, smell identification scores correlated positively with acetylcholines- terease activity in the hippocampus, amygdala, and neocortex and with monoaminergic activity in the striatum.71 Some stud- ies have focused on white matter microstructural integrity of the olfactory structures. In a voxel-wise analysis, increased dif- fusivity was found in the olfactory tracts in PD patients com- pared with controls in one study.72 In another study, using tract-based spatial statistics, reduced FA was found in white matter adjacent to the gyrus rectus or in primary olfactory areas in PD patients with hyposmia or anosmia.73

Rapid eye movement behavior disorder symptoms may pre- cede PD or other synucleinopathies for up to 5 to 50 years before motor parkinsonism is seen.74,75 Similar to the olfaction imaging studies, some studies have focused on identifying brain changes associated with idiopathic RBD or development of PD,

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PD dementia have focused on gray matter atrophy of the mesial temporal lobe and were based largely on manual volumetry, visual rating scales, or semiautomated techniques (whole-brain or region of interest VBM) used in the AD field.89–99 In sum- mary, these studies highlight that hippocampal atrophy occurs in PD dementia (PDD) and, in some studies, in nondemented PD patients; hippocampal or mesial temporal atrophy in PDD, however, was less severe than in AD. Some volumetric MRI studies note greater atrophy of the anterior cingulate gyrus, amygdala, or entorhinal cortex in PDD compared with cogni- tively normal or nondemented PD.93,95,98,100,101,102 Structural MRI VBM studies examining PD-MCI patients have yielded vari- able results, with some finding no gray matter differences between PD-MCI patients and controls103,104,105 but others identifying greater gray matter atrophy in temporal (e.g., hip- pocampal), parietal, and frontal (e.g., prefrontal and orbitofron- tal) lobe regions in PD-MCI patients with impaired verbal mem- ory, decision-making, and reaction time tests.103–109 A small number of studies have evaluated white matter changes, either as hyperintensities on T2-weighted or fluid-attenuated inver- sion recovery sequences using visual rating scales or semiauto- mated segmentation protocols or altered microstructural integ- rity with DTI measuring FA and MD. In some, but not all studies, PDD was associated with increased deep and periventricular white matter hyperintensities compared with cognitively nor- mal or PD-MCI patients.110,111 DTI studies using tract-based spatial statistics found reduced FA in the superior longitudinal, inferior longitudinal, fronto-occipital, and uncinate fasciculi, cingulum, and corpus callosum in patients with PDD compared with those with normal cognition; further studies are needed to determine whether there are differences in PD-MCI.105 Using [18-F]FDG-PET and a VBM modeling approach in two studies, a PD-related cognitive pattern characterized by decreased metab- olism in frontal and relatively, association areas and relative increased metabolism in the cerebellum correlated with per- formance on tests of memory and executive function and dem- onstrated progressively worse metabolic changes across the PD cognitive spectrum (from cognitively normal to single-domain PD-MCI to multiple-domain impairment PD-MCI).112,113 Because some PDD patients have comorbid AD pathology at autopsy, there has been interest in amyloid imaging with the PET tracer Pittsburgh Compound B (PIB). Results of PIB studies in PDD, however, have been somewhat variable, although sev- eral revealed increased PIB uptake or higher amyloid burden, generally to a lesser degree than that found in AD and dementia with Lewy bodies.114,115,116

Mood disorders like depression occur in about 45% of PD patients117 and may be present in premotor or early phases as well as in more advanced PD. Intrinsic neurochemical altera- tions and neurodegenerative changes in the brainstem, frontal/ limbic cortical regions, and subcortical structures likely contrib- ute. Structural MRI studies of depression, in some cases accom- panied by apathy or anxiety, yield mixed results regarding mor- phologic changes. Using VBM, one study found greater gray matter atrophy in the left orbitofrontal cortex, bilateral rectal gyrus, and right superior temporal pole,118 but another did not detect gray matter differences in PD depression.119 White matter hyperintensities as measured on T2-weighted MRI by a

whereas others have examined already diagnosed PD patients who have RBD symptoms. In a structural MRI VBM study com- paring 20 idiopathic RBD patients with controls, those with RBD had significantly decreased gray matter volume in the anterior lobes of the cerebellum bilaterally, tegmental part of the pons, and left parahippocampal gyrus.76 Brainstem white matter changes also have been found in DTI studies of idio- pathic RBD patients compared with healthy controls, with decreased FA in the midbrain tegmentum and rostral pons and increased mean diffusivity (MD) in the pontine reticular forma- tion. Interestingly, this study also detected increased gray mat- ter density in bilateral hippocampi in RBD patients, which requires further study.77 A functional connectivity study of the substantia nigra revealed different correlations using voxel- wise analyses between the left substantia nigra and left puta- men as well as in the right cuneus/precuneus and superior occipital gyrus in the RBD patients compared with PD patients and healthy controls.78 RBD patients who underwent serial dopamine transporter imaging with [123I]FP-CIT demonstrated reduced mean binding in striatal regions at baseline and after 3 years. At 3 years, three patients were diagnosed with PD; these patients also had the lowest dopamine transporter uptake at baseline and about a mean 24 to 33% reduction in striatal uptake at 3 years’ follow-up.79 Using technetium 99m ethylene cysteinate dimer (ECD) SPECT, 20 idiopathic RBD patients were examined at baseline, and 10 of these patients developed PD or dementia with Lewy bodies after 3 years; those who converted to PD or dementia with Lewy bodies had increased regional cerebral blood flow in the hippocampus at baseline compared with those who did not convert.80 In some cases, RBD has been associated with mild cognitive impairment, and perfusion changes may relate to incipient or mild cognitive deficits.81 PET studies using ligands for acetylcholine, serotonin, and mono- amines demonstrate that nondemented PD patients who have RBD symptoms have decreased neocortical, limbic, and tha- lamic cholinergic innervation compared with those without RBD symptoms, but no differences in brainstem or striatal sero- tonin transporter binding were seen.82

Cognitive and Behavioral Symptoms

Cognitive and behavioral symptoms are important contributors to PD patients’ overall function and well-being, quality of life, and outcomes. These symptoms include mild cognitive impair- ment and dementia; depression, anxiety, apathy, or other mood disorders; and hallucinations and delusions. In recent years, imaging techniques have been used to identify specific neuro- biological substrates of these issues and biomarkers suggestive of underlying neuropathology or disease progression.

Cognitive decline and dementia in PD occur in about 80% of patients as the disease progresses.83,84 Cognitive deficits that are mild but do not impair one’s ability to carry out activities of daily living have been termed mild cognitive impairment (PD- MCI).85,86 About 40% of PD patients develop dementia,87 and these patients typically have more advanced disease, older age in general, older age at PD onset, and sometimes greater poste- rior cortical neuropsychological deficits (i.e., impaired semantic fluency, visuospatial abilities).88 Many structural MRI studies of

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visual rating scale were significantly increased in periventricu- lar regions in depressed PD patients compared with nonde- pressed PD patients and healthy controls of similar age, sex, and cerebrovascular risk factors, but they did not show signifi- cant differences in other brain regions.120 In VBM studies, white matter loss in the right frontal lobe, anterior cingulate, and inferior orbitofrontal region have been detected in depressed PD patients.119 In addition, reduced FA in bilateral mediodorsal thalamic regions has been found in DTI studies of depressed PD patients compared with nondepressed PD patients.121 Resting- state fMRI studies suggest abnormal activity in the prefrontal- limbic network in depressed PD patients. In two studies, PD patients with depression, compared with those who did not have depression and with healthy controls, had reduced ampli- tudes of low-frequency fluctuations in the left orbitofrontal areas122 and dorsolateral prefrontal cortex, ventromedial pre- frontal cortex, and rostral anterior cingulate cortex.123 Abnor- malities in the serotonin system are well recognized to be asso- ciated with depression. In a PET study using [18-F]MPPF, a selec- tive 5HT1A receptor antagonist, to investigate the postsynaptic serotonergic system, depressed PD patients had reduced tracer uptake in the left hippocampus, right insula, left superior tem- poral cortex, and orbitofrontal cortex compared with nonde- pressed PD patients, thereby implicating limbic serotonergic dysfunction.124 In a study using a different serotonin ligand, [11C]DASB, depressed PD patients exhibited increased binding in the dorsolateral and prefrontal cortex compared with healthy controls.125

Psychosis in PD ranges from mild illusions to formed halluci- nations to delusions.126,127 Hallucinations occur in about one- third of PD patients treated with chronic dopaminergic therapy and are most often visual. These hallucinations may be due to medications but also may be due to disease-related factors, such as age, akinetic-rigid motor phenotype, cognitive impair- ment or impaired attention, depression, sleep disturbances, and visual problems. Structural MRI studies of visual hallucinations in PD have examined regional and global brain atrophy pat- terns. VBM studies comparing PD hallucinators with nonhallu- cinating PD patients and healthy controls demonstrate gray matter atrophy in the hippocampal, limbic, paralimbic, frontal, and neocortical regions.94,128,129,130 These studies support regional neuroanatomical changes and strong links between hallucinations and cognitive impairment but also pose potential confounds because these brain regions are implicated in cogni- tive impairment or dementia. Other VBM studies suggest tha- lamic or pedunculopontine atrophy in PD hallucinators.128,131 Given the predominant visual hallucinatory phenotype in PD, however, other VBM studies have found greater gray matter atrophy in regions associated with visual processing in PD hal- lucinators compared with nonhallucinators, including the left lingual gyrus and bilateral superior parietal lobes132 and when carefully controlling for effects of cognitive status, bilateral cunei, fusiform, middle occipital, precentral, cingulate gyri, inferior parietal lobules, right lingual gyrus, and left paracentral gyrus.133 fMRI studies in PD hallucinators demonstrate altered cortical activation patterns compared with those of PD nonhal- lucinators. Using visual stimulation fMRI paradigms (i.e., stro- boscopic and kinematic), PD hallucinators had significantly greater frontal and subcortical activation to both visual stimula-

tion paradigms and decreased cerebral activation in occipital, parietal, and temporal-parietal regions compared with nonhal- lucinators,134 thereby suggesting a disruption in normal visual processing mechanisms in the hallucinators (▶Fig. 18.5). Another study using complex visual stimuli (e.g., face recognition task) revealed significant reductions in right pre- frontal areas, including the inferior, superior, and middle frontal gyrus and anterior cingulate gyrus in PD hallucinators to the face stimulus compared with nonhallucinating PD and healthy controls.135 Further evidence for impaired visual processing in hallucinating PD comes from an fMRI study in which several seconds before an image recognition task, the nondemented, hallucinating PD patients showed reduced activation of the lat- eral occipital cortex and extrastriate temporal visual cortices compared with nonhallucinating PD patients and healthy con- trols.136 One issue with these studies is that the actual halluci- natory event is not captured during imaging. Contrasts are developed between individuals who have hallucinations by self-report and those who deny hallucinations. A recent study was able to capture BOLD activation during visual hallucina- tions in a single patient with PD. Increased activation during visual hallucinations was noted in the frontal lobes, insula, cin- gulate, thalamus, and brainstem, whereas decreased activation was found in the fusiform, inferior occipital lobe, superior tem- poral lobe, and middle frontal lobe (▶ Fig. 18.6).137 In a resting- state fMRI study, PD patients with misperceptions had decreased functional connectivity between the ventral and dor- sal attention networks, thereby implicating the role of attention in generating hallucinations.130 Decreased perfusion or glucose metabolism in predominantly posterior brain regions, fre- quently involved in visual processing, has been reported in PD hallucinators by using SPECT or PET and, in some studies, increased frontal perfusion or metabolism. Using technetium 99m-hexamethylpropyleneamine oxime (HMPAO) SPECT, hal- lucinating PD patients had decreased cerebral blood flow to temporal-occipital lobe regions138 and reduced perfusion in bilateral parieto-occipital regions in PD patients with visual hal- lucinations compared with those without hallucinations.139 Another [123I]IMP SPECT study, however, found hypoperfusion in the right fusiform gyrus but also hyperperfusion in the right superior and middle temporal gyri in PD hallucinators when covarying for Mini-Mental State Examination score and PD duration.140 Similarly, decreased metabolism in temporal- occipital-parietal regions and also increased metabolic rates in frontal regions, especially the left superior frontal gyrus, have been identified in PD hallucinators compared with nonhalluci- nators using [18F]FDG-PET.141,142

18.4 Conclusion

Neuroimaging in PD has grown tremendously over the years and has advanced our understanding of the neurobiological substrates of PD-related motor and nonmotor features. Neuroimaging biomarkers will be relevant and important for diagnosing premotor PD and, as classically defined, PD; monitoring disease progression; and measuring the effects of treatments for both PD-related motor and nonmotor symptoms.

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Parkinson’s Disease

Fig. 18.5 Representative regions of significant functional magnetic resonance imaging activation during stroboscopic versus no visual stimulation in nonhallucinating Parkinson’s disease (PD) patients (top panel) and hallucinating PD patients (second panel). Note the decreased occipital lobe activation in the hallucinating patients. The two bottom panels display activation differences during apparent kinematic versus stationary visual stimulation in nonhallucinating PD patients (third panel) and hallucinating PD patients (bottom panel). Note the decreased activation in MT/V5 region and increased frontal lobe activation in the hallucinating patients. Significance thresholds were set for p < 0.001 (uncorrected for multiple comparisons) for both analyses. Voxels evidencing significant activation are displayed on representative axial sections (z = z plane Talairach coordinates) on a canonical brain image. The color scale indicates the magnitude of t values, with the lowest appearing in dark red and the highest in bright yellow/white. The left side of the images represents the left side of the brain.

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Fig. 18.6 This figure displays representative regions of significant functional magnetic resonance imaging activation in a single patient with Parkinson’s disease who experienced visual hallucinations in the scanner. This individual had frequent and brief hallucinations of African tribesmen and chimpanzees. During the scan, the patient reported 16 hallucinations interspersed with periods of no hallucinations. Voxels evidencing significant differences in activation during the hallucinations are displayed on representative sagittal, axial, coronal sections on a canonical brain image. The color scale indicates the magnitude of t values, with the lowest appearing in dark red and the highest in bright yellow/white. The left side of the images represents the left side of the brain.

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Tam CW, Burton EJ, McKeith IG, Burn DJ, O’Brien JT. Temporal lobe atro- phy on MRI in Parkinson’s disease with dementia: a comparison with Alzheimer’s disease and dementia with Lewy bodies. Neurology 2005; 64: 861–865

Beyer MK, Janvin CC, Larsen JP, Aarsland D. A magnetic resonance imaging study of patients with Parkinson’s disease with mild cognitive impairment and dementia using voxel-based morphometry. J Neurol Neurosurg Psychia- try 2007; 78: 254–259

Bouchard TP, Malykhin N, Martin WR et al. Age and dementia-associated atrophy predominates in the hippocampal head and amygdala in Parkinson’s disease. Neurobiol Aging 2008; 29: 1027–1039
Kenny ER, Burton EJ, O’Brien JT. A volumetric magnetic resonance imaging study of entorhinal cortex volume in dementia with lewy bodies. A compari- son with Alzheimer’s disease and Parkinson’s disease with and without dementia. Dement Geriatr Cogn Disord 2008; 26: 218–225

Apostolova LG, Beyer M, Green AE et al. Hippocampal, caudate, and ventricu- lar changes in Parkinson’s disease with and without dementia. Mov Disord 2010; 25: 687–695
Dalaker TO, Zivadinov R, Larsen JP et al. Gray matter correlations of cognition in incident Parkinson’s disease. Mov Disord 2010; 25: 629–633

Hattori T, Orimo S, Aoki S et al. Cognitive status correlates with white matter alteration in Parkinson’s disease. Hum Brain Mapp 2012; 33: 727–739
Brück A, Kurki T, Kaasinen V, Vahlberg T, Rinne JO. Hippocampal and pre- frontal atrophy in patients with early non-demented Parkinson’s disease is related to cognitive impairment. J Neurol Neurosurg Psychiatry 2004; 75: 1467–1469

Melzer TR, Watts R, MacAskill MR et al. Grey matter atrophy in cognitively impaired Parkinson’s disease. J Neurol Neurosurg Psychiatry 2012; 83: 188– 194
Song SK, Lee JE, Park HJ, Sohn YH, Lee JD, Lee PH. The pattern of cortical atrophy in patients with Parkinson’s disease according to cognitive status. Mov Disord 2011; 26: 289–296

Weintraub D, Doshi J, Koka D et al. Neurodegeneration across stages of cogni- tive decline in Parkinson’s disease. Arch Neurol 2011; 68: 1562–1568
Beyer MK, Aarsland D, Greve OJ, Larsen JP. Visual rating of white matter hyperintensities in Parkinson’s disease. Mov Disord 2006; 21: 223–229

Shin J, Choi S, Lee JE, Lee HS, Sohn YH, Lee PH. Subcortical white matter hyperintensities within the cholinergic pathways of Parkinson’s disease patients according to cognitive status. J Neurol Neurosurg Psychiatry 2012; 83: 315–321

Huang C, Mattis P, Perrine K, Brown N, Dhawan V, Eidelberg D. Metabolic abnormalities associated with mild cognitive impairment in Parkinson’s dis- ease. Neurology 2008; 70: 1470–1477
Huang C, Mattis P, Tang C, Perrine K, Carbon M, Eidelberg D. Metabolic brain networks associated with cognitive function in Parkinson’s disease. Neuro- image 2007; 34: 714–723

Gomperts SN, Locascio JJ, Marquie M et al. Brain amyloid and cognition in Lewy body diseases. Mov Disord 2012; 27: 965–973
Edison P, Rowe CC, Rinne JO et al. Amyloid load in Parkinson’s disease demen- tia and Lewy body dementia measured with [11C]PIB positron emission tomography. J Neurol Neurosurg Psychiatry 2008; 79: 1331–1338

Foster ER, Campbell MC, Burack MA et al. Amyloid imaging of Lewy body- associated disorders. Mov Disord 2010; 25: 2516–2523
Cummings JL, Masterman DL. Depression in patients with Parkinson’s disease. Int J Geriatr Psychiatry 1999; 14: 711–718

Feldmann A, Illes Z, Kosztolanyi P et al. Morphometric changes of gray matter in Parkinson’s disease with depression: a voxel-based morphometry study. Mov Disord 2008; 23: 42–46
Kostić VS, Agosta F, Petrović I et al. Regional patterns of brain tissue loss associated with depression in Parkinson’s disease. Neurology 2010; 75: 857–863

Petrovic IN, Stefanova E, Kozic D et al. White matter lesions and depression in patients with Parkinson’s disease. J Neurol Sci 2012; 322: 132–136
Li W, Liu J, Skidmore F, Liu Y, Tian J, Li K. White matter microstructure changes in the thalamus in Parkinson’s disease with depression: a diffusion tensor MR imaging study. AJNR Am J Neuroradiol 2010; 31: 1861–1866

Luo C, Chen Q, Song W et al. Resting-state fMRI study on drug-naive patients with Parkinson’s disease and with depression. J Neurol Neurosurg Psychiatry 2014
Wen X, Wu X, Liu J, Li K, Yao L. Abnormal baseline brain activity in non- depressed Parkinson’s disease and depressed Parkinson’s disease: a resting- state functional magnetic resonance imaging study. PLoS ONE 2013; 8: e63691

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Boileau I, Warsh JJ, Guttman M et al. Elevated serotonin transporter binding in depressed patients with Parkinson’s disease: a preliminary PET study with [11C]DASB. Mov Disord 2008; 23: 1776–1780

Fénelon G, Alves G. Epidemiology of psychosis in Parkinson’s disease. J Neurol Sci 2010; 289: 12–17
Ravina B, Marder K, Fernandez HH et al. Diagnostic criteria for psychosis in Parkinson’s disease: report of an NINDS, NIMH work group. Mov Disord 2007; 22: 1061–1068

Shin S, Lee JE, Hong JY, Sunwoo MK, Sohn YH, Lee PH. Neuroanatomical sub- strates of visual hallucinations in patients with non-demented Parkinson’s disease. J Neurol Neurosurg Psychiatry 2012; 83: 1155–1161
Ibarretxe-Bilbao N, Ramirez-Ruiz B, Junque C et al. Differential progression of brain atrophy in Parkinson’s disease with and without visual hallucinations. J Neurol Neurosurg Psychiatry 2010; 81: 650–657

Shine JM, Halliday GM, Gilat M et al. The role of dysfunctional attentional control networks in visual misperceptions in Parkinson’s disease. Hum Brain Mapp 2014
Janzen J, van ’t Ent D, Lemstra AW, Berendse HW, Barkhof F, Foncke EM. The pedunculopontine nucleus is related to visual hallucinations in Parkinson’s disease: preliminary results of a voxel-based morphometry study. J Neurol 2012; 259: 147–154

Ramírez-Ruiz B, Martí MJ, Tolosa E et al. Cerebral atrophy in Parkinson’s dis- ease patients with visual hallucinations. Eur J Neurol 2007; 14: 750–756 Goldman JG, Stebbins GT, Dinh V et al. Visuoperceptive region atrophy inde- pendent of cognitive status in patients with Parkinson’s disease with halluci- nations. Brain 2014; 137: 849–859

Stebbins GT, Goetz CG, Carrillo MC et al. Altered cortical visual processing in PD with hallucinations: an fMRI study. Neurology 2004; 63: 1409–1416 Ramírez-Ruiz B, Martí MJ, Tolosa E et al. Brain response to complex visual stimuli in Parkinson’s patients with hallucinations: a functional magnetic resonance imaging study. Mov Disord 2008; 23: 2335–2343

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T. Impaired visual processing preceding image recognition in Parkinson’s disease patients with visual hallucinations. Brain 2009; 132: 2980–2993 Goetz CG, Vaughan CL, Goldman JG, Stebbins GT. I finally see what you see: Parkinson’s disease visual hallucinations captured with functional neuro- imaging. Mov Disord 2014; 29: 115–117

Okada K, Suyama N, Oguro H, Yamaguchi S, Kobayashi S. Medication-induced hallucination and cerebral blood flow in Parkinson’s disease. J Neurol 1999; 246: 365–368
Matsui H, Nishinaka K, Oda M et al. Hypoperfusion of the visual pathway in parkinsonian patients with visual hallucinations. Mov Disord 2006; 21: 2140–2144

Oishi N, Udaka F, Kameyama M, Sawamoto N, Hashikawa K, Fukuyama H. Regional cerebral blood flow in Parkinson’s disease with nonpsychotic visual hallucinations. Neurology 2005; 65: 1708–1715
Boecker H, Ceballos-Baumann AO, Volk D, Conrad B, Forstl H, Haussermann P. Metabolic alterations in patients with Parkinson’s disease and visual halluci- nations. Arch Neurol 2007; 64: 984–988

Nagano-Saito A, Washimi Y, Arahata Y et al. Visual hallucination in Parkin- son’s disease with FDG PET. Mov Disord 2004; 19: 801–806
Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001; 14: 21–36

Good CD, Scahill RI, Fox NC et al. Automatic differentiation of anatomical pat- terns in the human brain: validation with studies of degenerative dementias. Neuroimage 2002; 17: 29–46
Shinotoh H, Uchida Y, Ito H, Harrori T. Relationship between striatal [123I] beta-CIT binding and four major clinical signs in Parkinson’s disease. Ann Nucl Med 2000; 14: 199–203

Pan PL, Song W, Shang HF. Voxel-wise meta-analysis of gray matter abnor- malities in idiopathic Parkinson’s disease. Eur J Neurol 2012; 19: 199–206 Chan LL, Rumpel H, Yap K et al. Case control study of diffusion tensor imaging in Parkinson’s disease. J Neurol Neurosurg Psychiatry 2007; 78: 1383–1386

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19 Atypical Parkinsonian Syndromes

Nicola Pavese and David J. Brooks

In this chapter, we discuss the different contributions of struc- tural and functional imaging to the diagnosis and management of atypical parkinsonian disorders. We focus mainly on the most common clinical conditions: multiple-system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD).

19.1 Multiple-System Atrophy

Multiple-system atrophy is a progressive neurodegenerative disorder characterized clinically by varying combinations of parkinsonism, cerebellar dysfunction, autonomic failure, and corticospinal tract dysfunction.1 Based on the predominant symptoms, MSA is classified into two subtypes, MSA with predominant parkinsonism (MSA-P) and MSA with cerebellar features (MSA-C).

The pathological hallmark of the disease is neuronal loss and gliosis in the striatal, nigral, olivo-ponto-cerebellar network, and the lateral columns of the spinal cord, with the presence of intracytoplasmic and intranuclear argyrophilic fibrillary inclu- sions containing α-synuclein in both oligodendrocytes and neurons.2 Despite the different pathology, there is a significant clinical overlap between MSA and Parkinson’s disease (PD), par- ticularly in the early stages of the disease. Three levels of diag- nostic certainty—possible, probable, and definite—have been proposed for MSA, and abnormalities in structural and func- tional imaging are indicated as features supporting (“red flags”) a diagnosis of possible MSA.1

19.1.1 Structural Imaging

Magnetic resonance imaging (MRI) with conventional sequences and, more sensitively, diffusion-weighted (DWI) and diffusion tensor imaging (DTI) have proved to have a role for discriminating MSA from typical PD and other atypical parkin- sonian syndromes.3,4 Atrophy of the putamen, the presence of a “slit” hyperintensity of the lateral margin of the putamen in T2- weighted MRI images (so called “slit sign”), and putaminal hypointesity are specific features of established MSA but are present in only around half of the cases (▶Fig. 19.1). Other

typical features (“red flags”) of MSA include atrophy of several subtentorial structures, such as the pons, the middle cerebellar peduncles, and the cerebellum, with dilatation of the fourth ventricle. In MSA-C, the severe loss of neurons and myelinated fibers at the basis pontis and the gliosis of the middle part of the reticular formation result in a characteristic cruciform hyperintensitity in the pons on T2-weighted MRI sequences, which is known as the “hot cross bun sign” (▶Fig. 19.1). The hot cross bun sign can also be seen in patients with MSA-P. Horimoto and colleagues5 performed a longitudinal MRI study to determine the exact time when the hot cross bun sign and slit sign appeared in a cohort of MSA patients. They graded the development of hot cross bun sign into six progressive stages and the slit sign into four stages. The hot cross bun sign was seen (MRI shows cross, stage IV) earlier in MSA-C than in MSA- P, often before 5 years of symptomatic disease duration. Con- versely, MSA-P showed earlier bilateral putamen changes (stage II) than MSA-C, generally before 3 years of symptoms (stage I).

Despite being highly specific for MSA (specificity > 90%), these abnormalities seen on T2-weighted MRI have not proved sensi- tive enough to be of diagnostic value (sensitivity up to 50 to 60%, with higher values of sensitivity for the basal ganglia abnormalities than for the subtentorial ones).3,4

In contrast, DWI and DTI are more sensitive to changes in putamen structure and are potentially useful for discriminating MSA from idiopathic PD. DWI MRI has been reported to detect raised water-proton apparent diffusion coefficients in the puta- men in up to 100% of patients with clinically probable MSA, whereas apparent diffusion coefficients in the putamen are normal in PD.6,7,8 An altered water diffusion signal in the middle cerebral peduncle has been reported to be useful to discrimi- nate MSA from PSP.8 A possible limitation of these studies is that they have all involved well-established atypical cases, whereas it remains to be established whether DWI MRI is also valuable to discriminate early cases where there is clinical diag- nostic uncertainty.

Voxel-based morphometry (VBM) is an MRI technique that localizes significant changes in gray and white matter density in disease. Compared with controls, MSA patients show signifi- cant reductions in the gray matter of the cerebellum and

Fig. 19.1 Details of T2 magnetic resonance imaging showing the “slit” sign (red arrow) and the “hot cross bun” sign (blue arrow). Both signs are visible on axial images.

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Atypical Parkinsonian Syndromes

cerebral cortex and in the white matter of the cerebellar pedun- cles and brainstem. White matter loss along the corpus callo- sum has also been reported in MSA patients.9

Finally, transcranial sonography of brain parenchyma, which shows atypical lateral midbrain hyperechogenicity in more than 90% of patients with idiopathic PD, is normal in most MSA cases. However, MSA patients can show increased echogenicity of the lentiform nucleus, which is absent in typical PD. It has been reported that a combination of normal midbrain signal combined with lentiform nucleus hyperechogenicity separated atypical from typical PD with a sensitivity of 59% and specificity of 100% and a positive predictive value of 100%.10

19.1.2 Functional Imaging

In vivo functional neuroimaging investigations in MSA have focused on dopaminergic dysfunction in MSA-P and the changes in brain regional glucose metabolism and cerebral blood flow that occur in patients with MSA-P and MSA-C subtypes.

Both presynaptic11,12 and postsynaptic striatal dopamine deficits13,14 have been reported in MSA-P patients. Regional cerebral [18F]flurodopa (18F-dopa) uptake, expressed as an influx constant, Ki, reflects the functional integrity of monoam- inergic terminals.15 In the striatum, where dopamine innerva- tion from the midbrain substantia nigra is the major monoam- inergic component, 18F-dopa uptake reflects the integrity of dopaminergic nigrostriatal terminals and correlates well with striatal dopamine levels and also with nigrostriatal cell counts in postmortem and animal studies.16,17 MSA patients show an asymmetrically reduced striatal uptake on 18F-dopa positron emission tomography (PET) that resembles the pattern observed in patients with idiopathic PD with relatively pre- served head of caudate function. The caudate nucleus can be more severely affected in MSA than in PD, leading to a more homogeneous reduction in tracer uptake within the striatal structures (▶Fig. 19.2).11,13,18 There is, however, considerable overlap of individual levels of putamen 18F-dopa uptake in MSA and PD patients. Therefore, 18F-dopa PET is not useful in clinical practice for discriminating MSA from typical PD.19 Similar

findings have been observed in a number of PET and SPECT studies imaging the dopamine transporter (DAT), another com- monly used marker for nigrostriatal dopaminergic terminals nerve in the striatum. A recent study with 18F-FP-CIT PET reported that MSA patients showed a more prominent and ear- lier DAT loss in the ventral putamen compared with PD patients.20 Briefly, both MSA and PD groups showed similar anteroposterior gradients of putaminal DAT loss. However, the MSA group did not show the typical ventrodorsal gradient of putaminal DAT loss described in PD. In fact, there was a rela- tively even DAT loss from the ventral putamen to the posterior putamen, which could reflect the loss of striatal dopaminergic terminals that precedes nigral involvement in MSA.21 These authors suggested that the assessment of the ventrodorsal gra- dient could be useful for differentiating PD from MSA, even in the early stage.

The availability of postsynaptic D2 receptors can be evaluated using PET and SPECT benzamide tracers such as 11C-raclopride and 123I-IBZM. Both 11C-raclopride and 123I-IBZM binding is reduced in MSA patients compared with that in normal subjects and untreated PD patients, suggesting that degeneration of striatal D2 receptors occurs in this condition.22,23 Unfortunately, this finding is not sensitive enough to be used in clinical prac- tice to differentiate MSA from PD because there is an overlap of their D2 binding ranges.

18F-2-fluoro-2-deoxyglucose (FDG) PET studies of MSA patients have shown significant bilateral hypometabolism in both caudate and putamen nuclei. Further reductions have been reported in the cerebellum and in the frontal cortex.24,25,26 The same areas showed a reduction in regional cerebral blood flow with perfusion SPECT.27,28 Hypometabolism and hypoper- fusion in the cerebellum and pons are particularly prominent in MSA-C patients.29 Eckert and colleagues have reported that FDG-PET has 96% sensitivity and 99% specificity for the diagno- sis of MSA versus PD when computer-assisted methods are applied.30 This finding has been confirmed in subsequent stud- ies.31,32 Finally, network analysis of metabolic changes across the brain by spatial covariance analysis of FDG-PET scans has identified an MSA-related pattern (MSARP) characterized by

Fig. 19.2 18F-dopa positron emission tomography images in a healthy control (HC), a patient with Parkinson’s disease (PD), and a patient with multiple-system atrophy (MSA).

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covarying metabolic reductions in the putamen and the cere- bellum. MSARP values correlate with clinical ratings of motor disability and with disease duration.33 It has been suggested that the MSARP may be a useful biomarker in trials of novel neuroprotective therapies for MSA.

Despite the widespread subcortical neurodegeneration reported in postmortem studies in MSA patients, the involve- ment of extrastriatal monoaminergic and cholinergic pathways has not been extensively investigated with functional neuro- imaging in vivo. Using SPECT with 123I-β-CIT, a tropane deriva- tive with high affinity for all monoamine transporters, Scherfler et al reported decreased uptake in midbrain and pontine regions in patients with MSA-P but not in PD patients.34 In a recent study, 18F-dopa PET was used to explore changes in brain monoaminergic function in both striatal and extrastriatal areas in MSA-P. Findings in MSA-P patients were compared with those seen in idiopathic PD patients matched for disease dura- tion and healthy controls. The results of the study suggest the presence of a more widespread monoaminergic dysfunction in MSA than in PD with similar disease duration. The MSA patients showed significantly decreased 18F-dopa uptake in putamen, caudate nucleus, ventral striatum, globus pallidus externa, and red nucleus compared with that in controls, whereas PD patients showed decreased 18F-dopa uptake only in the puta- men, caudate nucleus, and ventral striatum. Additionally, in contrast to PD, no evidence was seen of early compensatory pallidal increases in regional 18F-dopa uptake in MSA patients. Interestingly, MSA cases with orthostatic hypotension had lower 18F-dopa uptake in the locus coeruleus than patients without this symptom.35

Cholinergic pathways in MSA-P patients have been investi- gated with 11C-PMP PET, a marker of acetylcholinesterase (AChE) activity. Whereas cerebral cortical cholinergic activity was decreased to a similar level in MSA-P, PD, and PSP com- pared with normal controls, thalamic and pontine cholinergic activity was significantly lower in MSA-P and PSP patients than in those with PD. Interestingly, decreased AChE activity in the brainstem and cerebellum of all three disorders correlated with disturbances of balance and gait. The authors suggest that the earlier cholinergic reductions may account for the greater gait disturbances in the early stages of MSA-P and PSP than in PD.36

Cholinesterase activity has also been evaluated with PET in a small group of patients with MSA-C,37 and these cases also showed a reduction of AChE activity in the thalamus and cere- bellum. Taken together, these findings suggest that pharmaco- logic boosting of the cholinergic system could have a role in the treatment of these conditions.

The role of neuroinflammation and microglia activation in the pathogenesis of MSA has been investigated with 11C-(R)- PK11195 PET, a selective in vivo marker of activated microglia.

Activation of microglia in response to acute and chronic brain insults occurs in order to remodel connections and clear dam- aged tissue in the affected areas. However, there is cumulative evidence suggesting that, in conditions characterized by exten- sive chronic microglial activation, cytokines and other neuro- toxic factors are released by these cells, which may promote further neurodegeneration by causing death of surrounding healthy neurons.38 Gerhard and colleagues39 have reported increased 11C-(R)-PK11195 binding in both basal ganglia (puta- men, pallidum) and extrastriatal regions (dorsolateral pre-

frontal cortex, pons, and substantia nigra) in MSA patients com- pared with normal controls, suggesting that neuroinflamma- tory responses by activated microglia occur in MSA and may contribute to the neurodegenerative process.

A prospective 48-week, randomized, double-blind, multi- national clinical trial was recently conducted to investigate the efficacy of the antibiotic minocycline, a suppressant of micro- glial activation, as a drug treatment of MSA-P patients.40 In a small subgroup of patients, 11C-(R)-PK11195-PET was per- formed to assess the effect of minocycline on activated micro- glia. This study failed to show a clinical effect of minocycline on symptom severity as assessed by clinical motor function. In the PET subgroup, however, the three patients treated with minocy- cline showed a 30% reduction in microglial activation compared with the two cases treated with placebo. These findings warrant further investigations.

Finally, MIBG SPECT and 18F-dopamine PET studies have reported that patients with idiopathic PD show a significant loss of adrenergic innervation of the heart. This loss is not seen in patients with MSA as the loss of sympathetic function is pre- synaptic rather than postsynaptic. However, up to 50% of early PD cases (Hoehn and Yahr stage I) still show normal tracer binding,41,42 so cardiac sympathetic imaging is not a sensitive discriminator of MSA from PD.

19.2 Progressive Supranuclear

Palsy

Progressive supranuclear palsy is another cause of parkinson- ism, accounting for around 5% of cases. The disease usually develops after the sixth decade of life and is characterized by a combination of symmetric parkinsonism targeting the trunk and neck, which are held extended rather than flexed, supranu- clear vertical gaze palsy, dementia of subcortical type, and pseudobulbar signs, including dysphagia, dysarthria, and emo- tional incontinence. Bradykinesia, rigidity affecting axial muscles more than the limbs, postural instability, and gait dis- turbances are the most common parkinsonian symptoms.

Pathological changes in PSP consist of decreased pigment in the substantia nigra and locus coeruleus and loss of neurons in the basal ganglia, brainstem and ocular nuclei, cerebellar nuclei, and frontal cortex. Neurofibrillary 4-repeat tau tangles are present in affected structures and frontal and midbrain atro- phy; third ventricular widening are common in advanced cases.

19.2.1 Structural Imaging

Conventional T1- and T2-weighted MRI sequences detect char- acteristic structural changes in established PSP patients.3,4 The most common MRI finding in PSP is atrophy of the midbrain and superior cerebellar peduncle with dilatation of the third ventricle. Other commonly observed findings include atrophy of the basal ganglia, frontal and temporal cortices, and increased T2 signal in the midbrain. The selective atrophy of the midbrain, along with the dilatation of the third ventricle and a relatively preserved pontine profile, creates a peculiar visual effect on midsagittal T2 MRI images, which recall the silhouette of a bird where the head of the bird is represented by the atrophied midbrain and the body by the pons, known as

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with healthy controls and PD patients as a result of degenera- tion of D2 receptors.22,50

Cholinergic function has been investigated in PSP with 11C- MP4A PET, a marker of AChE activity. PSP patients showed a severe reduction of thalamic 11C-MP4A uptake,51 which is likely to reflect reduced input from the degenerating pedunculopon- tine nucleus and other brainstem cholinergic nuclei, which are the main sources of thalamic cholinergic input. The peduncolo- pontine nucleus is involved in posture and gait control, eye movements, and attention. Therefore, its dysfunction may con- tribute to the locomotor and cognitive impairment observed in PSP patients.

Finally, Gerhard and colleagues have reported widespread increases of activated microglia in basal ganglia, midbrain, frontal lobe, and cerebellum of PSP patients.52

19.3 Corticobasal Degeneration

Corticobasal degeneration is a progressive neurodegenerative disease that involves the basal ganglia and cerebral cortex. Clinically, CBD is characterized by a progressive asymmetric akinetic-rigid syndrome with apraxia, limb dystonia, myoclo- nus, and other features indicative of cortical dysfunction, such as cortical sensory loss, alien limb phenomena, and mirror movements. The neuropathological hallmarks of CBD include mild atrophy of the cortical gyri with swollen, achromatic neu- rons scattered throughout the cerebrum, particularly in the posterior frontal and inferior parietal areas, and severe neuro- nal loss within the substantia nigra. Abnormal tau accumulation in both neurons and glial cells is extensive in gray and white matter of the cortex, basal ganglia, diencephalon, and the ros- tral part of the brainstem. Abnormal tau accumulation within astrocytes forms pathognomonic astrocytic plaques.53

19.3.1 Structural Imaging

The most common MRI finding in CBD is asymmetric cortical atrophy, although symmetric atrophy has also been reported. The cortical atrophy typically targets the parietal lobe, the para- central regions, and the frontal lobe (anterior middle and poste- rior inferior frontal lobe). Atrophy of the ipsilateral cerebral peduncle is often present in these patients. A subtle hyperin- tensity of the white matter adjacent to the areas of cortical

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“the penguin” or “the hummingbird” sign and has been reported to be highly specific for PSP (▶ Fig. 19.3).43 On axial T2, the reduction of the anterior–posterior diameter of the mid- brain with selective atrophy of the midbrain tegmentum, along with the thinning of cerebral peduncle, can form the so-called “morning glory” or “Mickey mouse” sign (▶ Fig. 19.3).

Several planimetric measurements of pons, midbrain, middle, and superior cerebellar peduncles have been proposed to differ- entiate PSP from idiopathic PD and MSA. The midbrain-to-pon- tine ratio (m:p ratio) and the more complex MR parkinsonism index (MRPI)44 have been shown to have 80 to 100% diagnostic accuracy when used to differentiate PSP from controls, MSA, and idiopathic PD patients, with MRPI being more accurate to differentiate PSP from MSA-P and the m:p ratio more sensitive to differentiate PSP from PD.45

DWI MRI shows raised water-proton apparent diffusion coef- ficients in the superior rather than the middle cerebellar peduncles, caudate, putamen, globus pallidus, thalamus, pons, prefrontal white matter, and precentral white matter in PSP patients compared with controls and PD patients.7,8

Finally, in PSP patients, VBM has detected pronounced loss in the gray matter of the frontotemporal cortex, including the prefrontal and insular cortices and in the white matter of the central midbrain region and the cerebral peduncles.

19.2.2 Functional Imaging

Perfusion SPECT studies in PSP patients reveals hypoperfusion in the frontal cortex and the midbrain.18,28,46 FDG-PET studies also show areas of reduced glucose metabolism in the frontal cortex, midbrain, and striatum.47,48 Overall, these findings par- allel those observed in MSA. However, when computer-assisted methods are applied, FDG-PET has been reported to have 85% sensitivity and 99% specificity for discriminating PSP from other parkinsonisms.30

18F-dopa PET studies reveal a uniform symmetric reduction of dopamine storage in the caudate and the anterior and poste- rior putamen, in contrast to PD and MSA, where reductions are asymmetric and target putamen.11 Using a voxel-based statisti- cal parametric mapping, Tai and colleagues49 have detected reduced 18F-dopa uptake in the orbitofrontal cortex in patients with familial PSP. Striatal D2 binding measured with 11C- raclopride PET and 123I-IBZM SPECT is reduced in PSP compared

Atypical Parkinsonian Syndromes

Fig. 19.3 Details of T2 magnetic resonance imaging showing “the penguin” sign (red arrow) and the “Mickey mouse” sign (blue arrow). The penguin sign is visible on midsagittal images, whereas the Mickey mouse sign is visible on axial images.

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atrophy is detected on fluid-attenuated inversion recovery (FLAIR) image, likely to reflect demyelination secondary to axo- nal loss or dysfunction. Conversely, basal ganglia structures generally show normal volume and MRI signal in these patients.54

Using VBM, Josephs and colleagues55 analyzed antemortem MRI images of patients with subsequent autopsy-confirmed CBD. All the MRI had been performed at the first neurologic evaluation. CBD patients were divided into two groups: patients with clinically dominant dementia syndrome and pathologi- cally confirmed CBD (D-CBD) and patients with clinically domi- nant extrapyramidal features and pathologically confirmed CBD (E-CBD). They found a characteristic pattern of posterior frontal atrophy in these patients regardless of the clinical syndrome, suggesting that this finding could be a useful biomarker of CBD pathology. The middle corpus callosum and the basal ganglia, in particular the pallidum, were heavily affected, whereas there was no evidence of brainstem atrophy. The E-CBD and D-CBD subgroups differed from each other in terms of the patterns of atrophy. The D-CBD group showed more cortical gray matter atrophy yet practically no white matter atrophy, compared with the E-CBD subgroup, which had both moderate cortical gray matter and white matter atrophy.

Finally, in CBD, DTI has shown an increased water diffusion coefficient in the motor thalamus, the superior mesenteric artery (SMA), and the precentral and postcentral gyri contra- lateral to affected limbs. FA was decreased in the precentral gyrus, SMA, postcentral gyrus, and cingulum.

19.3.2 Functional Imaging

Striatal 18F-dopa uptake is asymmetrically reduced in CBD patients, targeting the caudate and putamen similarly.56 Asym- metrically decreased striatal DAT binding has also been

[3] Seppi K, Poewe W. Brain magnetic resonance imaging techniques in the diag- nosis of parkinsonian syndromes. Neuroimaging Clin N Am 2010; 20: 29–55

[4] Massey LA, Micallef C, Paviour DC et al. Conventional magnetic resonance imaging in confirmed progressive supranuclear palsy and multiple system atrophy. Mov Disord 2012; 27: 1754–1762

[5] Horimoto Y, Aiba I, Yasuda T et al. Longitudinal MRI study of multiple system atrophy – when do the findings appear, and what is the course? J Neurol 2002; 249: 847–854

[6] Schocke MF, Seppi K, Esterhammer R et al. Diffusion-weighted MRI differenti- ates the Parkinson variant of multiple system atrophy from PD. Neurology 2002; 58: 575–580

[7] Seppi K, Schocke MF, Esterhammer R et al. Diffusion-weighted imaging discriminates progressive supranuclear palsy from PD, but not from the parkinson variant of multiple system atrophy. Neurology 2003; 60: 922–927

[8] Nicoletti G, Lodi R, Condino F et al. Apparent diffusion coefficient measure- ments of the middle cerebellar peduncle differentiate the Parkinson variant of MSA from Parkinson’s disease and progressive supranuclear palsy. Brain 2006; 129: 2679–2687

[9] Minnerop M, Lüders E, Specht K et al. Callosal tissue loss in multiple system atrophy—a one-year follow-up study. Mov Disord 2010; 25: 2613–2620

[10] Walter U, Dressler D, Probst T et al. Transcranial brain sonography findings in discriminating between parkinsonism and idiopathic Parkinson’s disease. Arch Neurol 2007; 64: 1635–1640

[11] Brooks DJ, Ibanez V, Sawle GV et al. Differing patterns of striatal 18F-dopa uptake in Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy. Ann Neurol 1990; 28: 547–555

[12] Varrone A, Marek KL, Jennings D, Innis RB, Seibyl JP. [123I]β-CIT SPECT imaging demonstrates reduced density of striatal dopamine transporters in Parkinson’s disease and multiple system atrophy. Mov Disord 2001; 16: 1023–1032

[13] Antonini A, Leenders KL, Vontobel P et al. Complementary PET studies of striatal neuronal function in the differential diagnosis between multiple system atrophy and Parkinson’s disease. Brain 1997; 120: 2187–2195

[14] Schulz JB, Klockgether T, Petersen D et al. Multiple system atrophy: natural history, MRI morphology, and dopamine receptor imaging with 123IBZM- SPECT. J Neurol Neurosurg Psychiatry 1994; 57: 1047–1056

[15] Moore RY, Whone AL, McGowan S, Brooks DJ. Monoamine neuron innerva- tion of the normal human brain: an 18F-DOPA PET study. Brain Res 2003; 982: 137–145

[16] Snow BJ, Tooyama I, McGeer EG et al. Human positron emission tomographic [18F]fluorodopa studies correlate with dopamine cell counts and levels. Ann Neurol 1993; 34: 324–330

[17] Pate BD, Kawamata T, Yamada T et al. Correlation of striatal fluorodopa uptake in the MPTP monkey with dopaminergic indices. Ann Neurol 1993; 34: 331–338

[18] Pirker W, Djamshidian S, Asenbaum S et al. Progression of dopaminergic degeneration in Parkinson’s disease and atypical parkinsonism: a longitudi- nal beta-CIT SPECT study. Mov Disord 2002; 17: 45–53

[19] Burn DJ, Sawle GV, Brooks DJ. Differential diagnosis of Parkinson’s disease, multiple system atrophy, and Steele-Richardson-Olszewski syndrome: dis- criminant analysis of striatal 18F-dopa PET data. J Neurol Neurosurg Psychia- try 1994; 57: 278–284

[20] Oh M, Kim JS, Kim JY et al. Subregional patterns of preferential striatal dopa- mine transporter loss differ in Parkinson’s disease, progressive supranuclear palsy, and multiple-system atrophy. J Nucl Med 2012; 53: 399–406

[21] Goto S, Matsumoto S, Ushio Y, Hirano A. Subregional loss of putaminal effer- ents to the basal ganglia output nuclei may cause parkinsonism in striatonig- ral degeneration. Neurology 1996; 47: 1032–1036

[22] Brooks DJ, Ibanez V, Sawle GV et al. Striatal D2 receptor status in Parkinson’s disease, striatonigral degeneration, and progressive supranuclear palsy, measured with 11C-raclopride and positron emission tomography. Ann Neurol 1992; 31: 184–192

[23] Plotkin M, Amthauer H, Klaffke S et al. Combined 123I-FP-CIT and 123I-IBZM SPECT for the diagnosis of parkinsonian syndromes: study on 72 patients. J Neural Transm 2005; 112: 677–692

[24] Otsuka M, Ichiya Y, Kuwabara Y et al. Glucose metabolism in the cortical and subcortical brain structures in multiple system atrophy and Parkinson’s dis- ease: a positron emission tomographic study. J Neurol Sci 1996; 144: 77–83

[25] Taniwaki T, Nakagawa M, Yamada T et al. Cerebral metabolic changes in early multiple system atrophy: a PET study. J Neurol Sci 2002; 200: 79–84

[26] Juh R, Pae C-U, Lee C-U et al. Voxel based comparison of glucose metabolism in the differential diagnosis of the multiple system atrophy using statistical parametric mapping. Neurosci Res 2005; 52: 211–219

reported in CBD patients. Postsynaptic striatal D ability may be reduced or may be preserved.57

2

receptor avail-

Perfusion SPECT studies have revealed asymmetric hypoper- fusion in the basal ganglia and the frontoparietal cortex. Simi- larly, FDG-PET studies have shown a characteristic pattern of reduced glucose metabolism in striatum, thalamus, and inferior parietal cortex contralateral to the most affected side. FDG-PET has been reported to have 91% sensitivity and 99% specificity for the diagnosis of CBD compared with other parkinsonisms when computer-assisted methodologies are applied.30

Evidence of microglial activation involvement in the patho- genesis of CBD has been reported.52

Finally, an fMRI study has shown decreased activation of the parietal lobe contralateral to the more affected arm in patients with early CBD, when movements, simple or complex, were performed with the hand. This finding suggests that altered higher cortical motor organization is present early in the disease.58

References

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. [2]  Wenning GK, Stefanova N, Jellinger KA, Poewe W, Schlossmacher MG. Multi- ple system atrophy: a primary oligodendrogliopathy. Ann Neurol 2008; 64: 239–246

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[30] Eckert T, Barnes A, Dhawan V et al. FDG PET in the differential diagnosis of 2449

parkinsonian disorders. Neuroimage 2005; 26: 912–921

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and 18F-FDG PET in the differentiation of Parkinsonian-type multiple system
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. [32]  Hellwig S, Amtage F, Kreft A et al. [18F]FDG-PET is superior to [123I]IBZM- SPECT for the differential diagnosis of parkinsonism. Neurology 2012; 79:
1314–1322

. [33]  Poston KL, Tang CC, Eckert T et al. Network correlates of disease severity in
multiple system atrophy. Neurology 2012; 78: 1237–1244

. [34]  Scherfler C, Seppi K, Donnemiller E et al. Voxel-wise analysis of [123I]beta-CIT SPECT differentiates the Parkinson variant of multiple system atrophy from
idiopathic Parkinson’s disease. Brain 2005; 128: 1605–1612

. [35]  Lewis SJ, Pavese N, Rivero-Bosch M et al. Brain monoamine systems in multi- ple system atrophy: a positron emission tomography study. Neurobiol Dis
2012; 46: 130–136

. [36]  Gilman S, Koeppe RA, Nan B et al. Cerebral cortical and subcortical
cholinergic deficits in parkinsonian syndromes. Neurology 2010; 74:
1416–1423

. [37]  Hirano S, Shinotoh H, Arai K et al. PET study of brain acetylcholinesterase in
cerebellar degenerative disorders. Mov Disord 2008; 23: 1154–1160

. [38]  Smith JA, Das A, Ray SK, Banik NL. Role of pro-inflammatory cytokines released from microglia in neurodegenerative diseases. Brain Res Bull 2012;
87: 10–20

. [39]  Gerhard A, Banati RB, Goerres GB et al. [11C](R)-PK11195 PET imaging of
microglial activation in multiple system atrophy. Neurology 2003; 61: 686–
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. [40]  Dodel R, Spottke A, Gerhard A et al. Minocycline 1-year therapy in multiple-
system-atrophy: effect on clinical symptoms and [11C] (R)-PK11195 PET
(MEMSA-trial). Mov Disord 2010; 25: 97–107

. [41]  Goldstein DS, Holmes CS, Dendi R, Bruce SR, Li ST. Orthostatic hypotension
from sympathetic denervation in Parkinson’s disease. Neurology 2002; 58:
1247–1255

. [42]  Takatsu H, Nishida H, Matsuo H et al. Cardiac sympathetic denervation from
the early stage of Parkinson’s disease: clinical and experimental studies with
radiolabeled MIBG. J Nucl Med 2000; 41: 71–77

. [43]  Kato N, Arai K, Hattori T. Study of the rostral midbrain atrophy in progressive
supranuclear palsy. J Neurol Sci 2003; 210: 57–60

[46] Johnson KA, Sperling RA, Holman BL, Nagel JS, Growdon JH. Cerebral perfu- sion in progressive supranuclear palsy. J Nucl Med 1992; 33: 704–709

[47] Karbe H, Grond M, Huber M, Herholz K, Kessler J, Heiss WD. Subcortical dam- age and cortical dysfunction in progressive supranuclear palsy demonstrated by positron emission tomography. J Neurol 1992; 239: 98–102

[48] Piccini P, de Yebenez J, Lees AJ et al. Familial progressive supranuclear palsy: detection of subclinical cases using 18F-dopa and 18fluorodeoxyglucose pos- itron emission tomography. Arch Neurol 2001; 58: 1846–1851

[49] Tai YF, Ahsan RL, de Yébenes JG, Pavese N, Brooks DJ, Piccini P. Characteriza- tion of dopaminergic dysfunction in familial progressive supranuclear palsy: an 18F-dopa PET study. J Neural Transm 2007; 114: 337–340

[50] Schwarz J, Tatsch K, Arnold G et al. 123I-iodobenzamide-SPECT in 83 patients with de novo parkinsonism. Neurology 1993; 43 Suppl 6: S17–S20

[51] Shinotoh H, Namba H, Yamaguchi M et al. Positron emission tomographic measurement of acetylcholinesterase activity reveals differential loss of ascending cholinergic systems in Parkinson’s disease and progressive supra- nuclear palsy. Ann Neurol 1999; 46: 62–69

[52] Gerhard A, Trender-Gerhard I, Turkheimer F, Quinn NP, Bhatia KP, Brooks DJ. In vivo imaging of microglial activation with [11C](R)-PK11195 PET in pro- gressive supranuclear palsy. Mov Disord 2006; 21: 89–93

[53] Kouri N, Whitwell JL, Josephs KA, Rademakers R, Dickson DW. Corticobasal degeneration: a pathologically distinct 4 R tauopathy. Nat Rev Neurol 2011; 7: 263–272

[54] Koyama M, Yagishita A, Nakata Y, Hayashi M, Bandoh M, Mizutani T. Imaging of corticobasal degeneration syndrome. Neuroradiology 2007; 49: 905–912

[55] Josephs KA, Whitwell JL, Dickson DW et al. Voxel-based morphometry in autopsy proven PSP and CBD. Neurobiol Aging 2008; 29: 280–289

[56] Sawle GV, Brooks DJ, Marsden CD, Frackowiak RS. Corticobasal degeneration. A unique pattern of regional cortical oxygen hypometabolism and striatal flu- orodopa uptake demonstrated by positron emission tomography. Brain 1991; 114 Pt 1B: 541–556

[57] Klaffke S, Kuhn AA, Plotkin M et al. Dopamine transporters, D2 receptors, and glucose metabolism in corticobasal degeneration. Mov Disord 2006; 21: 1724–1727

[58] Ukmar M, Moretti R, Torre P, Antonello RM, Longo R, Bava A. Corticobasal degeneration: structural and functional MRI and single-photon emission computed tomography. Neuroradiology 2003; 45: 708–712

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Atypical Parkinsonian Syndromes

. [27]  Cilia R, Marotta G, Benti R, Pezzoli G, Antonini A. Brain SPECT imaging in mul- tiple system atrophy. J Neural Transm 2005; 112: 1635–1645

. [28]  Van Laere K, Casteels C, De Ceuninck L et al. Dual-tracer dopamine trans- porter and perfusion SPECT in differential diagnosis of parkinsonism using template-based discriminant analysis. J Nucl Med 2006; 47: 384–392

. [29]  Shinotoh H. Neuroimaging of PD, PSP, CBD and MSA-PET and SPECT studies. J Neurol 2006; 253 Suppl 3: iii30–iii34

[44] Quattrone A, Nicoletti G, Messina D et al. MR imaging index for differentia- tion of progressive supranuclear palsy from Parkinson’s disease and the Par- kinson variant of multiple system atrophy. Radiology 2008; 246: 214–221

[45] Hussl A, Mahlknecht P, Scherfler C et al. Diagnostic accuracy of the magnetic resonance Parkinsonism index and the midbrain-to-pontine area ratio to differentiate progressive supranuclear palsy from Parkinson’s disease and the Parkinson variant of multiple system atrophy. Mov Disord 2010; 25: 2444–

185

Dementia with Extrapyramidal Syndromes

20 Secondary Parkinsonism

Thyagarajan Subramanian, Kala Venkiteswaran, and Elisabeth Lucassen

In this chapter, we consider a limited set of disorders that mani- fest with clinical parkinsonism and have known secondary causes; the common clinical manifestation is secondary parkin- sonism. One simplistic but useful way to classify secondary par- kinsonism is based on the location of pathology in the central nervous system. This simplistic classification can help organize a large variety of disorders. The first group of conditions com- prises those that cause secondary parkinsonism as a result of lesions primarily at the level of the substantia nigra pars com- pacta (SNpc) neurons in the midbrain (group 1 disorders). Examples of this type of secondary parkinsonism include focal vascular malformations or ischemic infarcts in the midbrain that cause hemiparkinsonism with hemiparesis. Another exam- ple is the hemiparkinsonism-hemiatrophy syndrome, in which a developmental defect appears at the level of the SNpc and its immediate surrounding region.

The second group of conditions (group 2 disorders) manifest as secondary parkinsonism and result from lesions primarily at the level of the striatum (caudate and the putamen) or its con- nections to the remainder of the basal ganglia connectome. Examples in this category include drug-induced parkinsonism, vascular parkinsonism, Wilson’s disease, parkinsonism seen in Huntington’s disease (HD), dentatorubral-pallidoluysian atro- phy (DRPLA), pantothenate kinase–associated neurodegenera- tion (PKAN, Hallervorden-Spatz syndrome), other associated disorders included in the classification of disorders under neu- rodegeneration with brain iron accumulation (NBIA) category, and toxin-induced parkinsonism.

The final group (group 3 disorders) consists of secondary parkinsonisms that potentially involve the SNpc, their striatal targets, other basal ganglia nuclei, and diffuse pathology in the central nervous system (CNS). Examples include postinfectious parkinsonism and frontotemporal dementia with parkinsonism. Because some of these disorders are discussed in detail else- where in this book, this chapter focuses on the underlying com- mon pathology for secondary parkinsonism and more detailed discussion of four key examples: HD, DRPLA, Wilson’s disease, and vascular parkinsonism.

20.1 Pathology of Secondary

Parkinsonism

One of the key pathological mediators of secondary parkinson- ism is the dopaminergic nigrostriatal pathway and its targets in the striatum. To understand this pathology, a brief review of the basal ganglia connectome is necessary. The dopaminergic neu- rons in SNpc are located in the midbrain adjacent to the crux cerebri, the red nucleus, and the cerebral aqueduct. The long axons derived from these cell bodies terminate in the caudate nucleus and the striatum primarily, but they also have minor connections to the globus pallidus internal segment (GPi), globus pallidus external segment (GPe), thalamus, substantia nigra pars reticulata (SNr), and into the subthalamic nucleus (STN). These minor connections represent only 20% of the dopamine synthesized by the nigrostriatal pathway. Although

most of these connections are unilateral, there is evidence for interhemispheric connectivity in the nigrostriatal pathway that may be of considerable importance.1 The vast majority of dopamine secreted by the nigrostriatal pathway is used in the striatum to act on the D1-type and D2-type receptors located on the medium spiny neurons. These medium spiny neurons then send their axonal connections to either the GPi/SNr via the direct pathway or to the GPe via the indirect pathway. The GPe neurons project to the STN, which in turn projects to the GPi and SNr. The output of the GPi and SNr both project to the motor thalamus and from there to the primary motor cortex and the supplementary motor cortex. These direct and indirect pathways not only mediate motor systems, but also modulate various aspects of emotions, eye movements, and cognition and complications associated with various forms of parkinsonism.2

In secondary parkinsonism, lesions at the level of the mid- brain location of the cell bodies of the substantia nigra (group 1 disease, as defined earlier in this chapter) usually cause damage to adjacent pyramidal tracts, most frequently manifesting clini- cally as unilateral parkinsonism. Examples of this type of pathology are cavernomas that bleed, causing focal neuronal injury in the midbrain.3,4,5 Other examples include traumatic injuries and inadvertent injury during neurosurgical manipula- tions of the midbrain.6,7,8,9,10 An uncommon and rare disorder that is considered genetic or developmental in origin is the hemiatrophy-hemiparkinsonism (HA-HP) syndrome.11,12 Here the pathology is associated with developmental atrophy of the contralateral midbrain and in many cases the entire hemi- sphere. Although HA-HP is classically thought to be due to pathology that implicates the SNpc and its immediate sur- roundings, a recent report has suggested putaminal pathology that would place this condition in group 2 disorders (striatal pathology). Injury to the SNpc cell bodies has also been noted in postencephalitic parkinsonism, especially with neurotropic viruses like Japanese B, West Nile, Coxsackie, and polio infection-related encephalitis.13–17 In most of these patients, however, there is more diffuse pathology outside the SNpc. The exclusive involvement of the SNpc is rare, but when it happens, it can be dramatic. The neurotoxin 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine (MPTP) specifically causes degeneration of the SNpc and produces pathology in the nigrostriatal pathway that is similar in many ways to idiopathic PD.18 However, MPTP-induced parkinsonism produces pathology that is quite symmetric and lacks the classic α-synuclein-positive Lewy bod- ies as intracytoplasmic inclusions that are obligate in PD.19,20 Dementia with Lewy bodies is another example where the pathology is primarily presynaptic (i.e., in the SNpc and its axons).21 This entity is discussed in Chapter 16.

Secondary parkinsonism with pathology that afflicts the stri- atum is much more common (group 2 disorders). Perhaps the most common condition that presents itself as symmetric par- kinsonism is drug-induced parkinsonism.22 Here the D1-type and D2-type receptors are variably blocked, resulting in parkin- sonism; pathological studies in such patients are scant.23 In general, pathological studies in such patients do not provide

186

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ogy and genetics is beyond the scope of this chapter, so here we focus primarily on HD pathology.

In addition to striatal pathology, HD is associated with degen- eration of the temporal and the frontal lobes of the cerebral cor- tex, a part of the brain responsible for integrating higher mental functioning, movements, and sensations. The degenerative changes in HD primarily affect the striatal medium-sized spiny neurons that project into the GP and SNr. These spiny neurons secrete γ-aminobutyric acid as the primary neurotransmitter. One theory suggests that selective loss of these specialized cells involved in the “indirect pathway” of the basal ganglia and the relative sparing of the cells involved in the “direct pathway” of the basal ganglia result in decreased inhibition (i.e., increased activity) of the thalamus. Therefore, the thalamus increases its output to regions of the cerebral cortex involved in movement, which may be the cause of the disorganized, excessive (hyper- kinetic) movement patterns of chorea. However, as disease progresses, more and more medium-sized spiny neurons degenerate, and medium spiny neurons involved in both the direct and indirect pathways are equally involved in advanced HD; in such patients, chorea no longer occurs and is replaced by severe secondary parkinsonism. It is reported that even in advanced HD, the SNpc remains relatively spared.

Huntington’s disease is due to a mutation in a gene that is transmitted as an autosomal dominant trait. In addition, although HD usually occurs in certain families, the disorder may sometimes occur as the result of a spontaneous (sporadic) change in the gene for HD. The gene responsible for HD is known as IT15 and is located on chromosome 4. This gene regu- lates, controls, or encodes the production of a protein known as huntingtin. Mutations of the IT15 gene result in abnormally long CAG trinucleotide repeats. These expanded CAG sequences result in the production of abnormal huntingtin protein (poly- glutamine). The length of the expanded CAG repeats is thought to have some relation to the age at symptom onset. For exam- ple, those with a large number of repeats tend to develop symp- toms at an earlier age.32 Patients with CAG repeat lengths that are larger (usually > 60) manifest with symptoms in childhood or adolescence. This form of HD is called juvenile HD (Westphal variant).33

Most individuals with juvenile HD experience an age of onset that is much younger than that of their affected parents. They also often face a much more rapid progression of the disease. This occurrence is described as genetic anticipation, where a disease increases in severity in successive generations. Genetic anticipation occurs in many other genetic disorders and is not unique to HD. The molecular pathology of HD has been the focus of much research. There is clearly some commonality between other degenerative disorders in that misfolding of the huntingtin protein seems crucial for the neurodegeneration to occur in HD.34

Group 3 disorders have more widespread pathology that often involves both the SNpc cell bodies and their striatal tar- gets. Examples include postinfectious parkinsonism reported from West Nile encephalitis. Here diffuse pathology has been noted in patients who exhibited parkinsonism, which often involves multiple nuclei in the basal ganglia.35,36 Postinfectious parkinsonism has also been reported with dengue fever, Japanese B encephalitis, and in an epidemic termed encephalitis lethargica that occurred between 1915 and 1926. The validity

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

any specific clues except for the primary pathology for which the dopamine antagonists were used (e.g., schizophrenia) or that they have vascular lesions that predispose them to parkin- sonism.

The second most common disease that presents with sym- metric parkinsonism is vascular parkinsonism.24 Here the most frequent pathology is related to multiple lacunar infarcts bilaterally involving most frequently the lenticular-striate arteries.25 Frequently, these lacunar infarcts involve the inter- nal capsule in addition to the striatum, resulting in symmetric parkinsonism and pyramidal tract signs. It is not uncommon to find ischemic changes in the subcortical white matter. Underlying poorly treated hypertension, diabetes, and hyper- cholesterolemia-related pathology is frequently seen. Overall, atrophy of the brain and evidence of vascular lesions else- where in the brain, such as the pons and the cerebellum, are also frequent.24,25 Manganese toxicity, NBIA, and Wilson’s dis- ease are characterized by the deposition of metals in the basal ganglia and are seen clinically as mostly symmetric parkinson- ism.26,27,28,29 Manganese accumulates in the striatum and in the globus pallidus in patients environmentally or occupation- ally exposed to large quantities of manganese (e.g., in manga- nese miners). This situation can also occur in the setting of chronic liver failure resulting in a more subtle manganese deposition but causes a parkinsonian syndrome that is quite similar to other secondary parkinsonisms. Accumulation of iron in the globus pallidus is characteristic in NBIA to generate the “eye of the tiger” imaging finding on MRI along with other very characteristic imaging findings.26,27 Wilson’s disease pathology causes copper accumulation in the brain and in the eyes and in many other organs.28 The pathophysiological rea- sons for why heavy metals have a selective affinity to the basal ganglia have not been completely established. It is thought that the relative abundance of enzymes that use heavy metals in the basal ganglia may be a putative reason for the basal gan- glia to be at risk for heavy metal deposition. It is also unclear as to why this leads to parkinsonism. The best worked out molecular pathology is in the case of Wilson’s disease, where there is mutation in the Wilson’s disease protein ATP7B gene. This is an autosomal recessive genetic disorder, and it occurs when the child inherits both copies of the mutation and results in deficiency of ceruloplasmin and release of free cop- per into the serum, resulting in its widespread deposition in the body but particularly in the kidneys, eyes, and in brain. Classic Wilson’s disease pathology shows clear-cut serum deficiency of ceruloplasmin, paradoxically low serum copper (thought to be due to its deposition in tissues), excess secre- tion of copper in the urine, and deposition of copper into the cornea (KF ring) and into the basal ganglia.30

Huntington’s disease and the closely related DRPLA cause unique pathology characterized by progressive degeneration of neurons primarily in the caudate nuclei and putamen of the basal ganglia. Many other diseases can appear as HD-like sec- ondary parkinsonism. These include ataxia with oculomotor apraxia, certain spinocerebellar ataxias, PLA2G6-associated neurodegeneration, Wilson’s disease (as discussed earlier), and PKAN form of NBIA. However, the pathology that causes parkin- sonism in all these conditions appear to be centered at the level of the striatum. Most of these disorders also have associated pathology in other parts of the CNS.31 The review of all pathol-

Secondary Parkinsonism

187

Dementia with Extrapyramidal Syndromes

of this later entity has been recently questioned, and the pathology in these disorders has been quite variable.14,37,38 Therefore, modern examples of postencephalitic parkinsonism that primarily show imaging abnormalities in the midbrain and basal ganglia should be distinguished from encephalitis lethargica.

20.1.1 Specific Examples of Secondary Parkinsonism

Huntington’s Disease

Huntington’s disease is a hereditary progressive neuro- degenerative disorder characterized by the development of emotional, behavioral, and psychiatric abnormalities; loss of previously acquired intellectual or cognitive functioning; and movement abnormalities.39 The classic signs of HD include the development of chorea–or involuntary, rapid, irregular, jerky dancelike movements that simultaneously afflict both proximal and distal muscles. This movement disorder may affect the face, arms, legs, or trunk. Patients also have gradual loss of thought processing and acquired intellectual abilities (dementia). There may be impairment of memory, abstract thinking, and judg- ment; disorientation; increased agitation; and personality changes. Although symptoms typically become evident during the fourth or fifth decades of life, age at onset is variable and ranges from early childhood to late adulthood. HD is transmit- ted as an autosomal dominant trait and is due to gene muta- tions on chromosome 4 (4p16.3). See details of pathology in an earlier section of this book.

The clinical course of HD can last 15 to 20 years. In the early stages, the chorea is focal and segmental but progresses to involve multiple body parts. The chorea typically peaks within 10 years and is gradually replaced by bradykinesia, rigidity, and dystonia. In a very small percentage of cases, HD may present with a parkinsonian syndrome rather than with chorea (West- phal variant).33 The latter cases typically have an early onset (e.g., <20 years). The behavioral and cognitive disturbances characteristic of HD most often account for the brunt of the patient’s disability and most of the hardship to the family. Approximately one-third develop dysthymia or an affective dis- order; one-third an intermittent explosive disorder; and the remaining third substance-abuse problems, sexual dysfunction, antisocial personality traits, or schizophreniform symptoms. Depression with suicidal tendencies is not uncommon. Even the minority who may not manifest behavioral problems ultimately succumb to dementia. Thus, secondary parkinsonism in HD is a late feature in adults patients and is an early feature in juvenile HD. This is a matter of major imaging importance when patients are referred for neuroimaging with a putative diagno- sis of HD.

The diagnosis of HD is confirmed by genetic testing. Imaging abnormalities typically involve early loss of volume in the CNS and atrophy of the caudate head in early disease (▶ Fig. 20.1). As the disease advances, there is further degeneration and atro- phy of the entire striatum and adjacent basal ganglia structures. The midbrain is relatively preserved in HD. In more advanced HD, degenerative changes in the cerebellum and more advanced cortical atrophy, especially in the prefrontal lobes, are noted.

Treatment of HD involves a multidisciplinary team that can provide social, medical, neuropsychiatric, and genetic guidance to patients and families throughout the course of the illness. Although dopamine blockers are moderately effective for cho- rea, they may aggravate bradykinesia and dystonia. Tetrabena- zine, a short-acting agent that can provide relief without high risk of causing parkinsonism, is often used to treat chorea in HD. Treatment of concomitant depression and substance abuse, if any, is critical in HD patients. From an imaging perspective, these matters need to be considered when interpreting imaging findings in HD. See Chapter 40 on advances in the treatment of dementia for details.

Dentatorubral-Pallidoluysian Atrophy

Dentatorubral-pallidoluysian atrophy is a rare subtype of type I autosomal dominant cerebellar ataxia. It is characterized by involuntary movements, ataxia, epilepsy, mental disorders, cog- nitive decline, and prominent genetic anticipation.40 The dis- ease is found most commonly in Japan, where the prevalence is estimated to be 1 in 208,000. Age of onset ranges from 1 to 60 years. The clinical symptoms are variable depending on the age of onset of the disease; myoclonus, epilepsy, and mental retardation are the main symptoms in juvenile onset, whereas cerebellar ataxia, choreoathetosis, and dementia are seen in adult onset, which is quite similar to onset in some adult HD patients. Clinical features are significantly correlated with the size of CAG repeats. Head magnetic resonance imaging (MRI) shows atrophy of cerebellum, brainstem, cerebrum, and high signal in periventricular white matter.41 T1-weighted MRI fre- quently shows cerebral atrophy, predominantly in the fronto- temporal region, with dilatation of the lateral ventricles and atrophy of the cerebellum, pons, and midbrain accompanied by dilatation of the fourth ventricle and the aqueduct (▶ Fig. 20.2).

Fig. 20.1 Coronal magnetic resonance imaging showing atrophy of the caudate and putamen in adult Huntington’s disease. There is also accompanying cortical atrophy. The loss of signals from the caudate head resulting in change of the shape and size of the lateral ventricles is classic for this diagnosis.

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Diffuse high signal intensities throughout the periventricular white matter and centrum semiovale on T2-weighted MRI, mimicking leukoaraiosis or leukodystrophy, seem to be charac- teristic findings in DRPLA. Fluid-attenuated inversion recovery (FLAIR) images are useful for demonstrating the pathological changes in white matter more clearly than conventional T2- weighted images in DRPLA. Axial midbrain images show the signal difference between the red nucleus and the surrounding fasciculi, which has been described as a characteristic of this disease. Neuropathologically, a combined degeneration of the dentatorubral and pallidoluysian systems is a characteristic fea- ture of DRPLA.42 Neuropathological findings in DRPLA include thickening of the skull bone, atrophy of the brain, degeneration of the dentate nucleus and its afferent fibers, degeneration of the GP-STN nucleus system, atrophy of the tegmentum of the brainstem especially in the pons, degeneration of the striatum, degeneration of the superior colliculus, degeneration of the gracile nucleus, degeneration of the pyramidal tract, mild degeneration of the cerebellar cortex, mild degeneration of the cerebral cortex, and degeneration of the cerebral white matter. In juvenile type manifesting with progressive myoclonus epi- lepsy syndromes, degeneration of the GP is more severe than that of the dentate nucleus. In adult patients with cerebellar

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ataxia and choreoathetoid movements without myoclonus or epilepsy, degeneration of the dentate nucleus is more severe than that of the GP.43

One clear distinction of DRPLA from other entities that have similar pathology is the preservation of SNpc in this disease.44 The pathological basis of the diffuse white matter changes seen in the subcortical white matter is unclear. Histopatholog- ical investigation has disclosed diffuse decrease of myelin sheaths and axons without gliosis and without evidence of microvascular pathology. These findings suggest that the pri- mary genetic defect may be the basis for the MRI-detected pathology in the white mater. These prominent white matter changes seen on FLAIR imaging may be quite useful because this is usually not seen in HD patients or in other forms of spi- nocerebellar atrophies that come in the differential diagnosis of DRPLA. An unstable expansion of the trinucleotide (CAG) repeats in the DRPLA gene on the short arm of chromosome 12 (ATN1 gene; 12p13.31) has been identified as causative. DRPLA progresses rather rapidly. The mean disease duration is about 13 years. Recurrent seizures and dysphagia with fre- quent fluid and food aspiration lead to bronchopneumonia and subsequent death. However, some patients can reach 60 years of age or older.

Secondary Parkinsonism

Fig. 20.2 Magnetic resonance images of patient with adult-onset DRPLA. (a–f). The T2-weighted axial images obtained from a 60-year-old patient show high-signal-intensity lesions in the middle and upper pons, midbrain tegmentum, and cerebral white matter, in addition to a left pallidal high- signal-intensity spot resulting from an old lacunar infarction (d). (f) A T1-weighted midsagittal image shows atrophy of the brainstem and cerebellum. (Used with permission from Sunami Y, Koide R, Arai N, Yamada M, Mizutani T, Oyanagi K. Radiologic and neuropathologic findings in patients in a family with dentatorubral-pallidoluysian atrophy. AJNR Am J Neuroradiol. 2011 Jan;32(1):109-14.)

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Wilson’s Disease

Wilson’s disease is a rare genetic disorder of copper metabolism that leads to an excessive accumulation of copper in certain tis- sues and organs, including the liver, brain, kidneys, or corneas of the eyes. Without prompt, appropriate treatment, the disorder may result in progressive liver disease, degenerative changes of the brain, psychiatric abnormalities, and other symptoms. Neuro- logic findings may include resting, action, or postural tremor; somewhat characteristic wing-beating tremor or flapping tremor where the patient has proximal tremor at the shoulders that mimics the beating of wings of a bird; choreoathetosis; sustained muscle contractions (e.g., risus sardonicus, a forced facial gri- mace); dystonia; dysarthria; and dysphagia. Some patients may also experience increasing irritability, anxiety, severe depression, or other psychiatric symptoms. Diagnosis is via genetic testing and blood and urine chemistries to document copper abnormali- ties as detailed in the preceding pathology section. Imaging changes in Wilson disease are well described. MRI often shows T2 and FLAIR hyperintense lesions involving thalami, midbrain, and pons. These lesions are typically hypointense on T1-weighted sequence and showed no evidence of restricted diffusion. Occa- sionally, hyperintense signal on T2/FLAIR images are seen in the striatum. Involvement of the midbrain gives the appearance of the “face of the giant panda” as dorsal pontine signal abnormali- ties resemble the face of a cub panda. Face of the giant panda and her cub constitute the “double panda sign” (▶ Fig. 20.3, ▶ Fig. 20.4), which is characteristic for this disease.45

These findings are attributed to neurodegeneration and the deposition of copper into the striatum and the GP. The “face of the giant panda” sign consists of high signal intensity in the teg- mentum, preservation of signal intensity of the lateral portion of the pars reticulata of the SN and red nucleus, and hypointen- sity of the superior colliculus. The “face of panda cub” is some- times identified in the dorsal pons. “Eyes of the panda” are formed from the relative hypointensity of the central tegmental tracts, in contrast with the hyperintensity of the aqueduct opening into the fourth ventricle (“nose and mouth of the panda”) bounded inferiorly by the superior medullary velum. The panda’s “cheeks” are formed from the superior cerebellar peduncles.46 Prompt initiation of treatment as a child is often curative. The standard treatment of Wilson’s disease has evolved and is discussed in Chapter 40 in greater detail. In selected patients, liver transplantation is needed, and when successful can cure the patient.

Vascular Parkinsonism

Vascular parkinsonism frequently presents as what is classically described as lower body parkinsonism.47 The findings are that patients have disproportionate amount of parkinsonian signs and symptoms in the lower body (below the umbilicus). Symp- tom onset is insidious and can sometimes be noted as mild cog- nitive deficits. Most frequent manifestation is with slowness in walking and difficulty climbing stairs. On examination, these patients have increased tone in the lower extremities and

Fig. 20.3 T2-weighted axial MRI demonstrating the “face of the giant panda” in the midbrain (arrow). (Used with permission from Jacobs DA, Markowitz CE, Liebeskind DS, Galetta SL. The “double panda sign” in Wilson’s disease. Neurology 2003;61(7):969.)

Fig. 20.4 T2-weighted axial MRI reveals the “face of the miniature panda” in the pontine tegmentum (arrow). (Used with permission from Jacobs DA, Markowitz CE, Liebeskind DS, Galetta SL. The “double panda sign” in Wilson’s disease. Neurology 2003;61(7):969.)

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bradykinesia seen with foot tapping and stomping with their heels. Gait is also slow, and patients have difficulty turning; some patients have a positive retropulsion test. Deep tendon jerks may be exaggerated, and a positive Babinski sign is not uncommon. Some patients have parkinsonism in the upper body as well, but this is disproportionately mild. These patients usually have other comorbid diabetes, hypertension, cardiovas- cular disorders or hypercholesterolemia. Other well-known vas- cular risk factors, like family history of strokes or cardiovascular disease, are also common, along with a history of nicotine abuse in some patients. Brain imaging frequently demonstrates lacunar infarcts in the basal ganglia (▶ Fig. 20.5), corona radiata, thalamus, or pons.48

In many patients, these lesions are silent without any known previous symptoms; others have clinical history of transient ischemic attacks or clearly documented strokes with subse- quent near complete recovery. There is also frequent report of brain volume loss and atrophy in this entity. Clinical diagnosis of concomitant Binswanger’s disease is not uncommon. Inter- estingly, imaging studies in a large series of patients diagnosed as having vascular parkinsonism failed to provide specific crite- ria for imaging abnormalities.25 Instead, a large variety of multi- focal small vascular lesions were noted. It is important to note, from an imaging perspective, that such patients are exceedingly unlikely to have imaging findings exclusive to the SNpc which is much more characteristic of idiopathic PD. However, midbrain atrophy can be frequently seen in vascular parkinsonism and is thought to be secondary to wallerian degeneration and atrophy of the crux cerbri.49

Treatment for vascular parkinsonism is primarily sympto- matic. Modest clinical improvement is noted with large doses of L-dopa given multiple times every day. Often the dose needed to get clinical benefit is in the range of 1.5 to 2 g/day of L-dopa.50 Interestingly, such patients generally do not exhibit motor fluctuations that are noted in idiopathic PD patients, and it is rare for such patients to develop drug-induced dyskine- sias.24 This is an important clinical distinction, and from an imaging perspective, the risk of involuntary movements inter- fering with imaging quality is low. Distinction between vascular parkinsonism and idiopathic PD on clinical grounds is usually not difficult. However, on occasion, this distinction can be prob- lematic in certain subtypes of idiopathic PD patients. In such patients, a dopamine transporter single-photon emission

computed tomography (SPECT) scan may be useful.51 The dif- ferential diagnosis of vascular parkinsonism always includes normal pressure hydrocephalus (NPH), and imaging studies can often be helpful, as the distinctive features of NPH, where there is more or less uniform enlargement of the entire ventricular system and accompanying brain cortical atrophy, are lacking in vascular parkinsonism. Instead, in vascular parkinsonism, there is an apparent enlargement of the lateral ventricles resulting from the loss of gray matter volume in the caudate and the putamen secondary to multiple lacunar infarcts. In some cases, however, this distinction is difficult to make on the basis of structural imaging alone. A large-volume lumbar puncture and removal of cerebrospinal fluid to determine whether the patient has tangible improvement in parkinsonism and gait is often performed as a diagnostic test. In itself, however, this test or any improvement in parkinsonism as a result of this test is not definitive.52,53 In such patients, diagnostic uncertainty remains, and often a pragmatic treatment approach that com- bines shunting with pharmacotherapy is required.

References

[1] Lieu CA, Subramanian T. The interhemispheric connections of the striatum: Implications for Parkinson’s disease and drug-induced dyskinesias. Brain Res Bull 2012; 87: 1–9

[2] Lieu CA, Shivkumar V, Gilmour TP, et al. Pathophysiology of drug-induced dyskinesias. In: Parkinson’s Disease Book 3 [Internet], 2011; InTech Publish- ers. http://www.intechopen.com/books/symptoms-of-parkinson-s-disease

[3] Ghaemi K, Krauss JK, Nakamura M. Hemiparkinsonism due to a pontomesen- cephalic cavernoma: improvement after resection: case report. J Neurosurg Pediatr 2009; 4: 143–146

[4] Li ST, Zhong J. Surgery for mesencephalic cavernoma: case report. Surg Neu- rol 2007; 67: 413–417, discussion 417–418

[5] Vhora S, Kobayashi S, Okudera H. Pineal cavernous angioma presenting with Parkinsonism. J Clin Neurosci 2001; 8: 263–266

[6] Matsuda W, Matsumura A, Komatsu Y, Yanaka K, Nose T. Awakenings from persistent vegetative state: report of three cases with parkinsonism and brain stem lesions on MRI. J Neurol Neurosurg Psychiatry 2003; 74: 1571–1573

[7] Pérez Errazquin F, Gomez Heredia MJ. [Levodopa-responsive parkinsonism- dystonia due to a traumatic injury of the substantia nigra] [in Spanish] Neu- rologia 2012; 27: 181–183

[8] Bhatt M, Desai J, Mankodi A, Elias M, Wadia N. Posttraumatic akinetic-rigid syndrome resembling Parkinson’s disease: a report on three patients. Mov Disord 2000; 15: 313–317

[9] Nayernouri T. Posttraumatic parkinsonism. Surg Neurol 1985; 24: 263–264

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Secondary Parkinsonism

Fig. 20.5 Magnetic resonance imaging from a patient showing vascular parkinsonism.
(a) T1-weighted image showing multiple lacunar strokes bilaterally in the putamen.
(b) Corresponding T2-weighted image. (Used with permission from Fujimoto KI. Vascular parkinsonism. J Neurol 2006;253 [Suppl 3]:III/ 16–III/21.)

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. [11]  Silvers DS, Menkes DL. Hemibody mirror movements in hemiparkinsonism- hemiatrophy syndrome. J Neurol Sci 2009; 287: 260–263

. [12]  Wijemanne S, Jankovic J. Hemiparkinsonism-hemiatrophy syndrome. Neurol- ogy 2007; 69: 1585–1594

. [13]  Sridam N, Phanthumchinda K. Encephalitis lethargica like illness: case report and literature review. J Med Assoc Thai 2006; 89: 1521–1527

. [14]  Vilensky JA, Gilman S, McCall S. A historical analysis of the relationship between encephalitis lethargica and postencephalitic parkinsonism: a com- plex rather than a direct relationship. Mov Disord 2010; 25: 1116–1123

. [15]  Hayase Y, Tobita K. Influenza virus and neurological diseases. Psychiatry Clin Neurosci 1997; 51: 181–184

. [16]  Toovey S. Influenza-associated central nervous system dysfunction: a litera- ture review. Travel Med Infect Dis 2008; 6: 114–124

. [17]  Misra UK, Kalita J. Overview: Japanese encephalitis. Prog Neurobiol 2010; 91: 108–120

. [18]  Centers for Disease Control. Street-drug contaminant causing parkinsonism. Morbidity Mortal Week Rep. 1984; 33: 351–352

. [19]  Langston JW, Ballard P, Tetrud JW, Irwin I. Chronic Parkinsonism in humans due to a product of meperidine-analog synthesis. Science 1983; 219: 979–980

. [20]  Vingerhoets FJ, Snow BJ, Tetrud JW, Langston JW, Schulzer M, Calne DB. Posi-
tron emission tomographic evidence for progression of human MPTP-
induced dopaminergic lesions. Ann Neurol 1994; 36: 765–770

. [21]  Yasuda T, Nakata Y, Choong CJ, Mochizuki H. Neurodegenerative changes ini-
tiated by presynaptic dysfunction. Transl Neurodegener 2013; 2: 16

. [22]  Bondon-Guitton E, Perez-Lloret S, Bagheri H, Brefel C, Rascol O, Montastruc JL. Drug-induced parkinsonism: a review of 17 years’ experience in a regional
pharmacovigilance center in France. Mov Disord 2011; 26: 2226–2231

. [23]  Bower JH, Dickson DW, Taylor L, Maraganore DM, Rocca WA. Clinical corre- lates of the pathology underlying parkinsonism: a population perspective.
Mov Disord 2002; 17: 910–916

. [24]  Kalra S, Grosset DG, Benamer HT. Differentiating vascular parkinsonism from
idiopathic Parkinson’s disease: a systematic review. Mov Disord 2010; 25:
149–156

. [25]  Zijlmans JC, Daniel SE, Hughes AJ, Révész T, Lees AJ. Clinicopathological inves-
tigation of vascular parkinsonism, including clinical criteria for diagnosis.
Mov Disord 2004; 19: 630–640

. [26]  Kimura Y, Sato N, Sugai K et al. MRI, MR spectroscopy, and diffusion tensor
imaging findings in patient with static encephalopathy of childhood with
neurodegeneration in adulthood (SENDA). Brain Dev 2013; 35: 458–461

. [27]  Kruer MC, Boddaert N, Schneider SA et al. Neuroimaging features of neurode- generation with brain iron accumulation. AJNR Am J Neuroradiol 2012; 33:
407–414

. [28]  Kim TJ, Kim IO, Kim WS et al. MR imaging of the brain in Wilson disease of

[32] Snell RG, MacMillan JC, Cheadle JP et al. Relationship between trinucleotide repeat expansion and phenotypic variation in Huntington’s disease. Nat Genet 1993; 4: 393–397

[33] Douglas I, Evans S, Rawlins MD, Smeeth L, Tabrizi SJ, Wexler NS. Juvenile Huntington’s disease: a population-based study using the General Practice Research Database. BMJ Open 2013; 3

[34] Labbadia J, Morimoto RI. Huntington’s disease: underlying molecular mecha- nisms and emerging concepts. Trends Biochem Sci 2013; 38: 378–385

[35] Petersen LR, Brault AC, Nasci RS. West Nile virus: review of the literature. JAMA 2013; 310: 308–315

[36] Sejvar JJ, Haddad MB, Tierney BC et al. Neurologic manifestations and out- come of West Nile virus infection. JAMA 2003; 290: 511–515

[37] Vilensky JA, Gilman S, McCall S. Does the historical literature on encephalitis lethargica support a simple (direct) relationship with postencephalitic Par- kinsonism? Mov Disord 2010; 25: 1124–1130

[38] Anderson LL, Vilensky JA, Duvoisin RC. Review: neuropathology of acute phase encephalitis lethargica: a review of cases from the epidemic period. Neuropathol Appl Neurobiol 2009; 35: 462–472

[39] Finkbeiner S. Huntington’s Disease. Cold Spring Harb Perspect Biol 2011; 3: a007476

[40] Wardle M, Morris HR, Robertson NP. Clinical and genetic characteristics of non-Asian dentatorubral-pallidoluysian atrophy: a systematic review. Mov Disord 2009; 24: 1636–1640

[41] Yoshii F, Tomiyasu H, Shinohara Y. Fluid attenuation inversion recovery (FLAIR) images of dentatorubropallidoluysian atrophy: case report. J Neurol Neurosurg Psychiatry 1998; 65: 396–399

[42] Takeda S, Takahashi H. Neuropathology of dentatorubropallidoluysian atro- phy. Neuropathology 2007; 16: 48–55

[43] Takahashi H, Yamada M, Takeda S. [Neuropathology of dentatorubral-pallid- oluysian atrophy and Machado-Joseph disease] [in Japanese] No To Shinkei 1995; 47: 947–953

[44] Wong JC, Armstrong MJ, Lang AE, Hazrati LN. Clinicopathological review of pallidonigroluysian atrophy. Mov Disord 2013; 28: 274–281

[45] Singh P, Ahluwalia A, Saggar K, Grewal CS. Wilson’s disease: MRI features. J Pediatr Neurosci 2011; 6: 27–28

[46] Jacobs DA, Markowitz CE, Liebeskind DS, Galetta SL. The “double panda sign” in Wilson’s disease. Neurology 2003; 61: 969

[47] Demirkiran M, Bozdemir H, Sarica Y. Vascular parkinsonism: a distinct, heter- ogeneous clinical entity. Acta Neurol Scand 2001; 104: 63–67

[48] Zijlmans JC, Thijssen HO, Vogels OJ et al. MRI in patients with suspected vascular parkinsonism. Neurology 1995; 45: 2183–2188

[49] Choi SM, Kim BC, Nam TS et al. Midbrain atrophy in vascular Parkinsonism. Eur Neurol 2011; 65: 296–301

[50] Zijlmans JC, Katzenschlager R, Daniel SE, Lees AJ. The L-dopa response in vascular parkinsonism. J Neurol Neurosurg Psychiatry 2004; 75: 545–547

[51] Gerschlager W, Bencsits G, Pirker W et al. [123I]beta-CIT SPECT distinguishes vascular parkinsonism from Parkinson’s disease. Mov Disord 2002; 17: 518–

childhood: findings before and after treatment with clinical correlation. AJNR
Am J Neuroradiol 2006; 27: 1373–1378 523

. [29]  Racette BA, Aschner M, Guilarte TR, Dydak U, Criswell SR, Zheng W. Patho- physiology of manganese-associated neurotoxicity. Neurotoxicology 2012; 33: 881–886

. [30]  Ala A, Walker AP, Ashkan K, Dooley JS, Schilsky ML. Wilson’s disease. Lancet 2007; 369: 397–408

. [31]  Martino D, Stamelou M, Bhatia KP. The differential diagnosis of Huntington’s disease-like syndromes: ‘red flags’ for the clinician. J Neurol Neurosurg Psy- chiatry 2013; 84: 650–656

[52] Ondo WG, Chan LL, Levy JK. Vascular parkinsonism: clinical correlates pre- dicting motor improvement after lumbar puncture. Mov Disord 2002; 17: 91–97

[53] Akiguchi I, Ishii M, Watanabe Y et al. Shunt-responsive parkinsonism and reversible white matter lesions in patients with idiopathic NPH. J Neurol 2008; 255: 1392–1399

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Part VII Vascular Dementia

. 21  Vascular Dementia 194

. 22  Neuroimaging of Vascular Dementias 199

. 23  Imaging of Specific Hereditary Microangiopathies 210

. 24  Vasculitis and Dementia 216

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Vascular Dementia

21 Vascular Dementia

A.M. Barrett and Vahid Behravan

A spectrum of ischemia-related cognitive problems has been identified, including vascular dementia, vascular cognitive impairment,1 multi-infarct dementia,2 and subcortical vascular dementia.3,4 The syndrome of clinical stroke or subclinical vas- cular brain injury and functionally relevant memory and cogni- tive impairment5 may have first been introduced to the field by Binswanger and Alzheimer in the 19th century.6 In contrast to dementia caused by neurodegenerative conditions affecting the brain, such as Alzheimer’s disease, frontotemporal dementia and variants (primarily cortical), Parkinson-plus syndromes, Huntington’s disease, and other neurodegenerative disorders primarily affecting the subcortical systems, vascular and ische- mic-related dementia can be considered a “secondary” form of dementia.7 These forms can be distinguished from primary neurodegenerative disorders by the mechanism of cognitive impairment: not only is there direct damage to cortical and subcortical cells and white matter circuitry, but there is indirect damage via abnormal cellular homeostasis (for example, the brain in vascular dementia may be affected by the common co- occurrence of diabetes and hyperglycemia, and the mechanics of brain perfusion may be altered related to co-occurring car- diac problems).

Brain imaging is a critical part of the diagnosis of vascular dementia because its constellation of cognitive symptoms can overlap with that associated with cortical dementia like Alzheimer’s disease. Although vascular dementia can com- monly be distinguished from Alzheimer’s disease by its early

deficits in motivation, initiation, and organization of thinking (subcortical deficits, sparing crystallized knowledge), one or more strokes in the posterior cortical parietal regions, either directly affecting the cortex or damaging its white matter input from the thalamus or other cortical regions, may mimic cortical neurodegenerative disease (▶ Fig. 21.1).8

21.1 Diagnostic Criteria

Although different definitions of vascular dementia have been provided by Alzheimer’s Disease Diagnostic and Treat- ment Centers (ADDTC),9 International Statistical Classifica- tion of Diseases, 10th Revision (ICD-1010), National Institute of Neurological Disorders and Stroke-Association Internatio- nale pour la Recherche et l’Enseignement en Neurosciences (NINDS-AIREN11), and the Hachinski ischemic score,12,13 an excellent general set of diagnostic criteria are provided by the American Psychiatric Association Diagnostic and Statisti- cal Manual (DSM, 4th edition).14 The DSM specifies vascular dementia as (1) memory impairment (impaired ability to learn new information or to recall previously learned infor- mation), required to make the diagnosis of a dementia and a prominent early symptom; and (2) one (or more) cognitive disturbances, including aphasia (language disturbance), apraxia (dysfunctional skilled learned purposive movement despite intact strength), agnosia (failure to recognize or identify faces, objects, pantomimes or other domain-specific

Fig. 21.1 Schematic coronal slice representing different pathologic syndromes in vascular dementia. Gray areas mark brain regions affected by ischemia and infarction. Multi-infarct dementia is characterized by small- and large-vessel lesions all over the gray matter (left side); strategic infarct dementia is characterized by fewer lesions in critical regions for memory function: hippocampal formation or paramedian thalamus. In subcortical vascular encephalopathy, multiple periventricular confluent white matter lesions may co-occur with small-vessel lesions. Amy, amygdala; Bgl, basal ganglia; CAI, hippocampus CA1 region; Cing, cingulate gyrus; ER, entorhinal cortex; F, frontal neocortex; Hypoth, hypothalamus; NBM, basal nucleus of Meynert; T, temporal neocortex; Thal, thalamus. (Used with permission from Thal DR, Grinberg LT, Attems J. Vascular dementia: different forms of vessel disorders contribute to the development of dementia in the elderly brain. Exp Gerontol 2012;47(11):816 –824.)

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information despite intact sensory function), and distur- bance in executive functioning (e.g., planning, organizing, sequencing, abstracting). Anomia (pathologic difficulty pro- ducing the names of words or objects) is sufficient to meet criteria for aphasia. The cognitive deficits must cause signifi- cant impairment in social or occupational functioning and represent a significant decline from a previous level of func- tioning.

The upcoming fifth edition of the DSM is stated to propose that diagnosis of a major neurocognitive disorder of vascular origin (dementia) can involve memory; however, a minor neu- rocognitive disorder, which allows the person to continue func- tional activities but only while using compensatory measures, can be defined by a single cognitive deficit, such as executive dysfunction.

In contrast to other types of dementia, vascular dementia is defined by focal neurologic signs and symptoms (e.g., exaggera- tion of deep tendon reflexes, extensor plantar response, pseu- dobulbar palsy, gait abnormalities, weakness of an extremity) or laboratory evidence indicative of cerebrovascular disease (e.g., multiple infarctions involving cortex and underlying white matter) that are judged to be causally related to the distur- bance.8,15 Lastly, in vascular dementia, cognitive deficits and

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neurologic signs may not occur exclusively during the course of the delirium.

21.2 Pathophysiology

Five different but interrelated pathologic sets of changes (▶Table 21.1) contribute to the evolution and progression of cognitive and functional deficit in this disorder.5,15,16 These changes include large-vessel embolic strokes, either of cardiac or artery-to-artery origin, and large watershed infarcts caused by brain hypoperfusion; small-vessel lacunar strokes (▶Fig. 21.2), chronic periventricular white matter ischemia (▶ Fig. 21.2), cerebral microbleeds (▶ Fig. 21.2, ▶ Fig. 21.3), and medial temporal atrophy.

Some current reviews that report the co-occurrence of medial temporal and hippocampal atrophy with pathologic evi- dence of vascular dementia have been criticized on the basis that subjects with medial temporal atrophy may have had undetected Alzheimer’s disease in addition to cerebrovascular pathology. Even if medial temporal atrophy has independent causes compared with other radiologic signs of vascular dementia, it is independently predictive of progression of cog- nitive symptoms over time.17

Vascular Dementia

Table 21.1 Pathology related to progressive cognitive impairment in vascular dementia

Pathologic disorder Cause Useful references

Large artery and watershed infarction

Embolic, hypoperfusion

Wright, 201315; Leys et al, 200540

Small artery infarctions or lacunes, counted as part of NINDS-AIREN criteria

Thrombosis affecting caudate, putamen, globus pallidus, thalamus, internal capsule, cerebellum, brainstem

Arvanitakis et al, 201141; Roman et al, 199311

Chronic subcortical ischemia affecting > 25% periventricular white matter

Cerebral vessel disease, atherosclerosis/ lipohyalinosis

Chui et al, 20009; Thal et al, 20125

Cerebral microbleeds

Cerebral vascular abnormality

Kirsch et al, 200942; Van der Flier and Cordonnier, 201236

Hippocampal atrophy and sclerosis

Neurodegeneration, presumably from metabolic insufficiency or chronic ischemia

Gorelick et al, 201143; Zarow et al, 200844

Abbreviations: NINDS-AIREN, National Institute of Neurological Disorders and Stroke-Association Internationale pour la Recherche et l’Enseignement en Neurosciences.

Fig. 21.2 (a) Brain magnetic resonance imaging illustrating periventricular white matter abnor- mality (areas of increased signal intensity, black arrows) on fluid-attenuated inversion recovery imaging. (b) Microbleeds (areas of decreased signal intensity, white arrows) are seen on T2*- weighted imaging. (Used with permission from Van der Flier WM, Cordonnier C. Microbleeds in vascular dementia: clinical aspects. Exp Gerontol 2012;47(11): 853–857.)

195

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Vascular Dementia

Fig. 21.3 (a) Brain magnetic resonance imaging illustrating lacunar strokes (areas of decreased signal intensity, white arrows) on fluid-attenuated inversion recovery imaging. (b) Microbleeds (areas of decreased signal intensity, white arrows) are seen on T2*-weighted imaging. (Used with permission from Van der Flier WM, Cordonnier C. Microbleeds in vascular dementia: clinical as- pects. Exp Gerontol 2012;47(11):853–857.)

21.3 Genetics

Vascular dementia genetics have codeveloped along with the genetics of Alzheimer’s disease and other primary neuro- degenerative diseases, as the distinction between these condi- tions has been clarified (▶Table 21.2). Genetic risk factors in Alzheimer’s disease have been widely studied, and these same genes, as well as different genes, have been studied in relation to the risk of development of vascular dementia.

Traditionally, to find a genetic link for a certain condition, researchers need large families or ethnicities affected with the disease. When we consider the major pathologic conditions associated with vascular dementia, genetic predictors are rele- vant to all three types: multi-infarct dementia, small-vessel and strategic infarct-type dementias, and subcortical arterioscle- rotic leukoencephalopathy (Binswanger encephalopathy). Some of the genetic associations with vascular dementia, however, may primarily reflect the genetics of Alzheimer’s disease; Alzheimer’s disease and vascular dementia co-occur in up to half of the people diagnosed with Alzheimer’s disease.15

An extensively studied gene in Alzheimer’s disease is apo- lipoprotein E (ApoE), a cholesterol carrier that supports injury repair in the brain. Polymorphic alleles of ApoE are the main genetic determinants of Alzheimer’s disease risk. The Apo-E 4 allele, which is most relevant to increased risk of Alzheimer’s disease, is also considered to increase the risk of vascular dementia.18,19,20

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is considered the most common heritable cause of stroke and vascular dementia in adults. It is a familial form of vascular dementia21,22 caused by mutations in the NOTCH3 gene located on chromosome 19 and is clinically characterized by migraines with aura, recurrent ischemic strokes, cognitive and behavior impairments, and dementia. Cerebral autosomal recessive arteriopathy with sub- cortical infarcts and leukoencephalopathy (CARASIL, also known as Maeda syndrome), is also a heritable small-vessel dis- ease clinically characterized by nonhypertensive leukoence- phalopathy associated with alopecia and spondylosis.23 Diagno- sis relies on brain MRI findings and molecular genetic testing of HTRA1 , a gene located on chromosome 10.

Other causes of hereditary cerebral vasculopathy that can lead to strokes, and potentially cognitive impairment and dementia, are less common. These causes include but are not limited to hereditary vascular retinopathy (HVR); cerebroreti- nal vasculopathy (CRV); and hereditary endotheliopathy with retinopathy, nephropathy, and stroke (HERNS).24 HERNS is characterized by retinal capillary obliteration and CNS vascul- opathy. These patients commonly have dementia and, like patients with CADASIL, they can suffer migraine headaches. HERNS is linked to chromosome 3p21, and transmission is autosomal dominant.

Hereditary cerebral amyloid angiopathy is a condition that can cause a progressive dementia, stroke, and other neurologic problems. Many different types are associated with kindred of different nationalities. The Dutch type of hereditary cerebral amyloid angiopathy is the most common form. Flemish, Italian, Icelandic, and Arctic types of hereditary cerebral amyloid angi- opathy and two other types, known as familial British dementia (FBD) and familial Danish dementia, are known to be associated with dementia.25,26,27,28 FBD with amyloid angiopathy is an

Table 21.2 Conditions associated with subcortical, stroke-related and vascular dementia, and genetic associations. See text for details.

Condition Gene Location

Alzheimer’s disease and vascular dementia

Apolipoprotein E

Chromosome 19

CADASIL

NOTCH 3

Chromosome 19

CARASIL

HTRA1

Chromosome 10

CRV, HERNS, HVR

TREX1

Chromosome 3

HDLS

CFR1R

Chromosome 5

FBD

BRI

Chromosome 13

Hereditary cerebral amyloid angiopathy

APP, CST3,ITM2B

Chromosome 21, 20, 13

Abbreviations: CADASIL, Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; CARASIL, cerebral autosomal recessive arteriopathy with subcortical infarcts and leu- koencephalopathy; CRV, cerebroretinal vasculopathy; FBD, familial British dementia; HDLS, hereditary diffuse leukoencephalopathy with neuroaxonal spheroids; HERNS, hereditary endotheliopathy with retin- opathy, nephopathy, and stroke; HVR, hereditary vascular retinopathy.

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

autosomal dominant condition characterized by vascular dementia, progressive spastic paraparesis, and cerebellar ataxia; onset is in the sixth decade of life. A point mutation in the BRI gene on chromosome 13 is the genetic abnormality.29

Other rare reported causes of vascular dementia with genetic associations include Sneddon’s syndrome (livedo reticularis with cerebrovascular disease), which causes a progressive arte- riopathy. Sneddon’s syndrome occurs sporadically, but familial cases have been reported, inherited in an autosomal dominant (with incomplete penetrance) or autosomal recessive fashion.30

Dementia has also been reported in other hereditary leu- koencephalothies, such as hereditary diffuse leukoencephalop- athy with neuroaxonal spheroids (HDLS)31 or vanishing white matter disease.32 This is relevant because neuroradiologic abnormalities in leukodystrophies of heritable or metabolic ori- gin may be mistaken for ischemic changes (▶ Fig. 21.4).33

21.4 Classification

Before computerized protocols were widely available to calcu- late the volume of brain lesions and the extent of white matter abnormality, visual rating methods were commonly used to stage ischemic injury in vascular dementia. The Fazekas scale34 rates white matter hyperintensities both generally and in periventricular regions as a marker of small-vessel disease. Scheltens et al35 suggested further modifications to quantify basal ganglia hyperintensities; however, these methods did not correlate well with computerized volumetric assessments with confirmed objective reliability and validity. Even computerized volume measurements for ischemic lesions did not correlate well with progression of cognitive and functional deficits, and therefore the value of these techniques has been questioned; because of evidence of threshold values definitely associated with cognitive deficits, however, and a stronger relation to gait and motor functional problems in small- and large-vessel ische- mia, a clinical role for these techniques still seems possible.15

Clear predictive associations with clinical diagnosis of vas- cular dementia or progression of symptoms are not yet avail- able for microbleeds. However, counting microbleed sites might be useful in the future to predict cognitive functional progression.36

Kanekar, Imaging of Neurodegenerative Disorders (ISBN 978-1-60406-854-2), copyright © 2016 Thieme Medical Publishers All rights reserved. Usage subject to terms and conditions of license.

Visual rating is still a widely used method for classification of medial temporal atrophy, despite the availability of computer- ized techniques. One method is a 1 to 5 scale recommended by Scheltens and colleagues.37 In Alzheimer’s disease, visual inspection for hippocampal atrophy had greater than 80% sensi- tivity and greater than 90% specificity for diagnosis.16

21.5 Additional Neuroanatomic-

Behavioral Considerations

Barrett and colleagues38 and Cramer et al39 pointed out that modality-specific outcomes accurately assess the natural evolu- tion of specific poststroke conditions, such as aphasia, spatial neglect, and limb apraxia, which are themselves tremendously disabling. A strategic infarct can affect critical regions for mem- ory and cognitive function, such as the left temporal-parietal junction. When people with preexisting aphasia, spatial neglect, or other focal cognitive syndromes develop an addi- tional, stepwise loss of functional ability, such as loss of ability to dress independently, clinicians commonly obtain a brain image to determine whether a new large-vessel stroke contrib- utes to that process. Given the potential relevance of assessing the volume of white matter lesion burden, counting lacunar lesions, assessing the volume of microbleeds, and assessing hip- pocampal atrophy in people with vascular dementia, we can expect that they may also assist with predicting progress of functional disability and the need for more aggressive manage- ment and treatment of risk factors after large-artery stroke. This means that, in the future, clinicians may routinely examine the risk of future vascular dementia in patients with a new or chronic large-artery stroke by evaluating neuroradiologic-neu- ropathologic data, making new therapies for vascular dementia more widely available as they are identified.

21.6 Acknowledgments

This work was supported by the Kessler Foundation, the National Institutes of Health, and the Department of Education/ National Institute of Disability and Rehabilitation Research (Grants K24 HD062647, H133 G120203, PI: Barrett). Study

Vascular Dementia

Fig. 21.4 Axial (a) DWI and (b) ADC images in a patient with hereditary leukoencephalopathy (HDLS) reveals increased signal intensity in both diffusion-weighted imaging (arrows in a) and on apparent diffusion coefficient mapping (arrows in b). (Used with permission from Boissé L, Islam O, Woulfe J, Ludwin SK, Brunet DG. Neurological picture: hereditary diffuse leukoencephalopathy with neuroaxonal spheroids: novel imaging find- ings. J Neurol Neurosurg Psychiatry 2010;81 (3):313–314.)

197

198

Vascular Dementia

contents do not necessarily represent the policy of the Depart- ment of Education, and one should not assume endorsement by the federal government.

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