Neuroimaging Research in Geriatric Mental Health
Howard J. Aizenstein, MD, PhD, holds a BSc in biology and computer science/math from the University of Illinois at Urbana-Champaign as well as an MS in computer science. In 1995, Dr. Aizenstein graduated from the University of Illinois in Urbana-Champaign with an MD and a PhD in Computer Science (Computational Learning Theory). He completed a residency in general and adult psychiatry (1999), a fellowship in geriatric psychiatry (2000), and a research fellowship in functional neuroimaging (2001), all at the University of Pittsburgh, Western Psychiatric Institute and Clinic. He joined the faculty at the University of Pittsburgh in 2001 and is currently an Associate Professor of Psychiatry with a secondary appointment in the Department of Bioengineering. Dr. Aizenstein is author or coauthor of over 75 articles and book chapters in the area of cognitive neuroscience, neuroimaging in aging populations, and computational methods for analyzing neuroimages. His research program uses different converging methodologies, traditional computer tasks, as well as structural and functional magnetic resonance imaging to identify key brain structures that affect memory, attention, and learning in elderly individuals and how these change over the course of treatment for depression. His research is funded by the National Institute of Mental Health (NIMH) and National Institute on Aging. Charles F. Reynolds III, MD, is the UPMC Endowed Professor of Geriatric Psychiatry, Neurology, and Neuroscience at the University of Pittsburgh. Dr. Reynolds directs the NIMH-sponsored center of excellence in treatment research for older adults with mood disorders and the John A. Hartford Center of Excellence in Geriatric Psychiatry. The principal themes of his research are the prevention of depression and its complications in later life, development of intervention strategies for older adults with difficult-to-treat depression (including the use of biosignatures to improve personalization of treatment), and dissemination of evidence-based treatment strategies to underserved older adults. Dr. Reynolds earned his MD degree from Yale University in 1973 and completed his psychiatry residency at the University of Pittsburgh in 1977; he has been on the faculty there ever since. He has served on the Institute of Medicine’s study of the nation’s workforce issues raised by the growing population of older adults and on the National Mental Health Advisory Council of NIMH. He is current president of the American Association of Geriatric Psychiatry and the American Foundation for Suicide Prevention. Myra Fernandes, MA, PhD, holds a BSc in psychology and biology from the University of Waterloo, Ontario, Canada, as well as an MA and a PhD in Cognitive Neuropsychology from the University of Toronto. She is an Associate Professor in the Behavioural Neuroscience Division of the Psychology Department and is adjunct to the Research Institute of Aging at the University of Waterloo. Dr. Fernandes is author or coauthor of over two dozen articles and book chapters on cognitive neuroscience and neuroimaging in aging populations. Her research program uses various converging methodologies, traditional computer tasks, functional magnetic resonance imaging, as well as paper-and-pencil tests to identify environmental settings and key brain structures that affect memory, attention, and language in young adults and how these change during the normal aging process. In 2007, she was awarded the President’s New Researcher Award of the Canadian Psychological Association; in 2008, she received an Ontario Early Researcher Award and grant. Her research is funded by the Natural Sciences and Engineering Research Council of Canada and the Canadian Institute of Health Research.
Neuroimaging Research in Geriatric Mental Health HOWARD J. AIZENSTEIN, MD, PhD CHARLES F. REYNOLDS III, MD MYRA FERNANDES, MA, PhD Editors
New York
Copyright © 2010 Springer Publishing Company, LLC All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Springer Publishing Company, LLC, or authorization through payment of the appropriate fees to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400, fax 978-646-8600,
[email protected] or on the Web at www.copyright.com. Springer Publishing Company, LLC 11 West 42nd Street New York, NY 10036 www.springerpub.com Acquisitions Editor: Philip Laughlin Project Manager: Laura Stewart Cover design: David Levy Composition: Apex CoVantage, LLC Ebook ISBN: 978-0-8261-1116-6 10 11 12 13 / 5 4 3 2 1 The author and the publisher of this Work have made every effort to use sources believed to be reliable to provide information that is accurate and compatible with the standards generally accepted at the time of publication. The author and publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance on, the information contained in this book. The publisher has no responsibility for the persistence or accuracy of URLs for external or third-party Internet Web sites referred to in this publication and does not guarantee that any content on such Web sites is, or will remain, accurate or appropriate.
Library of Congress Cataloging-in-Publication Data Neuroimaging research in geriatric mental health / editors, Howard J. Aizenstein, Charles F. Reynolds III, and Myra Fernandes. p. ; cm. Includes bibliographical references and index. ISBN 978-0-8261-1099-2 (alk. paper) 1. Geriatric neuropsychiatry. 2. Brain—Imaging. 3. Mental illness— Imaging. I. Aizenstein, Howard J. II. Reynolds, Charles F. III. Fernandes, Myra. [DNLM: 1. Mental Disorders—radiography. 2. Aged. 3. Brain— radiography. 4. Diagnostic Imaging—methods. 5. Geriatric Psychiatry— methods. WT 150 N4935 2009] RC451.4.A5N475 2009 618.97'68—dc22 2009043754 Printed in the United States of America by Hamilton Printing
For my wife, Barbara Baumann, and children, Max, Simon, and Evelyn. You are my inspiration. Thank you. —H. A. For my parents, Metrio and Solange Fernandes. You have always, and continue to be, the ones with the toughest questions about my research, its purpose, relevance, and goals. I hope I have answered at least some of your questions. Your genuine interest and support keep me searching for answers. Thank you—this one’s for you. —M. F. For my mentors, David Kupfer, Barry Lebowitz, and the late Monica David Blumenthal, for their wisdom and powerful example along the way; and for countless students and young faculty, for allowing me to serve as their mentor and for providing inspiration to make our field even better. — C. R.
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Contents
Contributors ix Preface xi Acknowledgments
1
xiii
Introduction: Perspectives From the National Institutes of Health 1 Jovier D. Evans
2
Magnetic Resonance Spectroscopy (MRS)
5
Anand Kumar and Olusola A. Ajilore
3
Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) 17 Julie C. Price, Charles M. Laymon, Rajesh Narendran, and Brian J. Lopresti
4
Magnetic Resonance Imaging (MRI) in Geriatric Psychiatry Research 71 Howard J. Aizenstein and Vijay K. Venkatraman
5
Software Packages for Neuroimage Processing
85
Robert J. Tamburo
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Structural Brain Changes Associated With Normal Aging
101
Faith M. Gunning and Adam M. Brickman
7
Functional Neuroanatomy of Aging and Cognition
125
Myra Fernandes
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Vascular Depression 149 Faith M. Gunning, Vassilios Latoussakis, and George S. Alexopoulos
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Contents
Brain Structural and Functional Correlates of Alzheimer’s Dementia and Mild Cognitive Impairment in the Context of Community-Based Studies 171 James T. Becker, Cyrus A. Raji, Mikel Olabarrieta, Owen T. Carmichael, Weiying Dai, Victoria Maruca, Paul M. Thompson, Lewis Kuller, and Oscar L. Lopez
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Neuroimaging in Late-Life Schizophrenia Heline Mirzakhanian and Lisa T. Eyler
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Psychosis in Dementia Zahinoor Ismail and David Mamo
Index
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Contributors
Howard Aizenstein, MD, PhD University of Pittsburgh Medical Center Department of Psychiatry Pittsburgh, Pennsylvania
Jovier D. Evans, PhD Geriatrics Research Branch Neuroscience Center National Institute of Mental Health Bethesda, Maryland
Olusola A. Ajilore, MD, PhD University of California, Los Angeles Neuropsychiatric Institute Los Angeles, California
Lisa T. Eyler, PhD University of California, San Diego Department of Psychiatry San Diego, California
George S. Alexopoulos, MD Weill Cornell Medical College Department of Psychiatry New York, New York
Myra Fernandes, PhD University of Waterloo Department of Psychology Waterloo, Ontario, Canada
James T. Becker, PhD University of Pittsburgh Medical Center Departments of Psychiatry, Neurology, and Psychology Pittsburgh, Pennsylvania
Faith M. Gunning, PhD Weill Cornell Medical College Department of Psychiatry New York, New York
Adam M. Brickman, PhD Columbia University Medical Center Cognitive Neuroscience Division New York, New York
Zahinoor Ismail, MD Geriatric Mental Health Program Centre for Addiction and Mental Health Toronto, Ontario, Canada
Owen T. Carmichael, PhD University of California, Davis Department of Neurology Davis, California
Lewis Kuller, MD, MPH University of Pittsburgh Medical Center Department of Epidemiology Pittsburgh, Pennsylvania
Weiying Dai, PhD Brigham and Women’s Hospital Department of Radiology Boston, Massachusetts
Anand Kumar, MD University of California, Los Angeles Department of Psychiatry Los Angeles, California
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Contributors
Vassilios Latoussakis, MD Weill Cornell Medical College Department of Psychiatry New York, New York Charles M. Laymon, PhD University of Pittsburgh Medical Center Department of Radiology Pittsburgh, Pennsylvania Oscar L. Lopez, MD University of Pittsburgh Medical Center Department of Neurology Pittsburgh, Pennsylvania Brian J. Lopresti, PhD University of Pittsburgh Medical Center Department of Radiology Pittsburgh, Pennsylvania David Mamo, MD Geriatric Mental Health Program Centre for Addiction and Mental Health Toronto, Ontario, Canada Victoria Maruca Allegheny College Meadville, Pennsylvania Heline Mirzakhanian, MS Department of Psychiatry University of California, San Diego San Diego, California Rajesh Narendran, MD University of Pittsburgh Medical Center Department of Radiology Pittsburgh, Pennsylvania
Mikel Olabarrieta, MD Hospital Vall d’Hebron Department of Neurology Barcelona, Spain Julie C. Price, PhD University of Pittsburgh Medical Center Department of Radiology Pittsburgh, Pennsylvania Cyrus A. Raji, BA, BS University of Pittsburgh Medical Center Department of Pathology Pittsburgh, Pennsylvania Charles F. Reynolds III, MD University of Pittsburgh Medical Center Department of Psychiatry Pittsburgh, Pennsylvania Robert J. Tamburo, PhD University of Pittsburgh Department of Psychiatry Pittsburgh, Pennsylvania Paul M. Thompson, PhD University of California, Los Angeles Department of Neurology Los Angeles, California Vijay K. Venkatraman, MS University of Pittsburgh Department of Bioengineering Pittsburgh, Pennsylvania
Preface
Neuroimaging is playing an increasing role in the research and treatment of mental disorders in the elderly. In part this stems from the fact that aging itself is the greatest risk factor for the neuropathological processes of neurodegeneration and cerebrovascular disease, both of which result in structural and functional brain changes that can be visualized with modern neuroimaging methods. The goal of this edited book is to summarize the current state of the field. Experts in geriatric psychiatry, cognitive neuropsychology, neurology, neuroradiology, and MR physics have contributed. This “primer” can help provide a common reference to help unify the field of neuroimaging in geriatric mental health and promote the development of new ideas. Our goals for the book are to summarize the current state of the field of neuroimaging research in geriatric psychiatry and to promote continued rapid advancement. The target audience is investigators new to the field who are interested in a comprehensive survey. We also aimed for this work to be accessible to interested clinical geriatric psychiatrists (who want a research update) and to serve as a useful reference for established researchers. A main motivation for this book is that we have periodically been approached by new researchers asking how to get started. We hope that this book may serve as a starting point for these new investigators. H.J.A., C.F.R., & M.F.
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Acknowledgments
We would like to acknowledge our greatest support system: our families. Their patience during the times when edits needed to be done, images compiled, and e-mails written made the whole process more enjoyable and smoother. Their interest and enthusiasm in seeing the final product have been an inspiration. We would also like to thank the supportive team at Springer for their dedication to us and to seeing the value in a book of this type. Special thanks to Philip Laughlin for keeping us on our toes, and keeping us organized. In preparing the final manuscript, we most gratefully acknowledge the help of Michelle Manios, Research Assistant extraordinaire at the University of Waterloo. Michelle’s attention to detail and tireless dedication to getting things done on time made all the difference in putting together the final product. We also thank Kerry Vegliando for her efforts in rounding up the final images and materials for publication.
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Neuroimaging Research in Geriatric Mental Health
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1
Introduction: Perspectives From the National Institutes of Health JOVIER D. EVANS
The field of clinical mental health research, compared to other aspects of clinical medicine, has been plagued by a lack of corresponding breakthroughs in basic science and clinical pathophysiology. One of the greatest challenges for psychiatry in the postgenomic era is to “catch up” with the rest of clinical medicine with respect to diagnostic, prognostic, therapeutic, and preventive strategies based on the relevant biology, pathogenesis, and pathophysiology of the disorders of interest. The National Institute of Mental Health (NIMH) is interested in supporting translational research efforts leading to new discoveries that will help cure mental illness. The mission of the institute is to transform the understanding and treatment of mental illnesses through basic and clinical research, paving the way for prevention, recovery, and cure. For the this work to move forward, the institute must foster innovative thinking and ensure that a full array of novel scientific perspectives are used to further discovery in the evolving science of brain, behavior, and experience. In support of this mission, NIMH will generate research and promote research training to fulfill the following four objectives of its Strategic Plan (National Institute of Mental Health, 2008): (a) support basic, translational, and clinical research to gain a more complete understanding of the genetic, neurobiological, behavioral, environmental, and experiential factors that contribute to mental disorders; (b) chart the 1
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course of mental disorders over the life span in order to understand ideal times and methods for intervention to preempt or treat mental disorders and hasten recovery; (c) improve existing approaches and devise new ones for the prevention, treatment, and cure of mental illness, allowing those who may suffer from these disorders to live full and productive lives; and (d) through research, evaluation, and collaboration, further develop the capacity of the institute to help close the gap between the development of new, research-tested interventions and their widespread use by those most in need. The field of geriatric psychiatry is poised at the current time to move forward in these efforts and to develop work in the field of late-life mental disorders along more focused, mechanistic directions. In 2004, the NIMH established an organizational unit focused on aging-related research. In that reorganization, the Geriatrics Research Branch was placed within the Division of Adult Translational Research so as to focus on basic mechanisms and aspects of cognitive and affective neuroscience bearing on clinical issues of aging. In 2007, the Geriatrics Research Branch supported and published the proceedings from a workshop discussing new opportunities in late-life affective disorders that charted a course for future work (Smith, Gunning-Dixon, et al., 2007). One of the workshop’s recommendations was that these new imaging techniques should be harnessed to examine clinical questions around course, pathophysiology, treatment response, and mechanism. This book serves as an important complement to these efforts in providing information about the use of innovative imaging tools and techniques to examine aspects of pathophysiology and mechanism in relation to disorders of aging. It explains basic concepts of neuroimaging and serves as a primer for investigators in this field. It includes chapters from leaders in the field of geriatric neuroimaging using functional, structural, and newer modalities to examine aspects of late-life mental disorders. The following chapters discuss basic elements of what is required to begin scientific investigation in this area. Neurobiological approaches represent unique opportunities for the identification of mechanisms of pathophysiology and vulnerability that could potentially inform the development of intervention and prevention strategies. Such strategies may have relevance to the treatment of younger patients as well as to patients with depression secondary to other neuropsychiatric or medical illnesses (e.g., Alzheimer’s disease, Parkinson’s disease, cerebrovascular disease) and to the development of integrative conceptual models that could be applied to the study of
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other neuropsychiatric disorders. Reaching a better understanding of the complex nature of geriatric mental disorders will require that the approach be both translational and integrative. Ultimately, the use of these integrative translational methods will lead to the development of new mechanism-based treatment studies and proof-of-concept clinical trial designs in which responses to specific medications can be examined in a more focused manner. The results of these clinical trials and the ancillary genetic and neuroimaging studies will lead to important questions that will have to be “reversed” or back-translated to inform the design of future neurobiological studies for these disabling disorders. In addition, for hypothesis-driven, mechanistic research to evolve, continued interaction between investigators across disciplines (including geriatric psychiatry, genetics, neuropsychology, neuroimaging, and neuropathology) will be critical. REFERENCES National Institute of Mental Health. (2008). National Institute of Mental Health Strategic Plan (NIH Publication 08-6368, 2008 revision). Bethesda, MD: U.S. Department of Health and Human Services (HHS), National Institutes of Health. Smith, G. S., Gunning-Dixon, F. M., et al. (2007). Translational research in late-life mood disorders: Implications for future interventions and prevention research. Neuropsychopharamacology, 32(9), 1857–1875.
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Magnetic Resonance Spectroscopy (MRS) ANAND KUMAR AND OLUSOLA A. AJILORE
BASIC PRINCIPLES OF MRS In vivo magnetic resonance spectroscopy (MRS) is a noninvasive technique that allows for the measurement of biochemicals in particular regions of the brain. It takes advantage of the chemical shift effect, which is seen when the same nuclei in different molecules respond differently to a magnetic field. Magnetic field strengths usually range from 1.5 Tesla (T) to 4 T in human studies. The chemical shift is typically measured in parts per million relative to the nuclear frequency and plotted on the horizontal axis of a spectrum, with the vertical axis displaying the amplitude or intensity of a peak (Figure 2.1). Each peak represents a metabolite of interest for a given nucleus. Several signal-detection parameters determine the shape of a spectrum. One is the voxel size, which determines the region of tissue being studied and the concentration of metabolites being measured. Another important parameter is relaxation time, represented by T1 and T2. T1 is the time necessary for hydrogen nuclei to emit 63% of the energy from the stimulating pulse, while T2 is the time for 63% of the transverse energy pulse to be lost because of dephasing. These parameters affect signal strength, since long T1s and short T2s are typically found in intracellular molecules with high molecular weights and thus can lead 5
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Figure 2.1 Sample proton MRS spectrum. Each peak represents a different metabolite, with the area under the peak equivalent to the concentration of the metabolite.
to weak signals. As a result, MRS is particularly useful for measuring low-molecular-weight molecules that are highly concentrated. Typical concentrations of metabolites detected by MRS are in the range of 4 to 10 mM. It is difficult to detect metabolites under 1 mM. These concentrations can be reported in absolute measures or as ratios to creatine concentrations, using creatine as an internal control.
NUCLEAR ISOTOPES USED IN MRS Phosphorus Phosphorus 31 (31P) MRS can be used to measure the following metabolites: adenosine triphosphate (ATP), phosphomonoesters (phosphorylcholine, phosphorylethanolamine, and glycerophosphate), phosphodiesters (glycerylphosphorylethanolamine, glycerylphosphorylserine, and phylycerylphosphorylinositol), phosphocreatine, and inorganic phosphate. This is a useful isotope to measure to understand the bioenergetics of neuronal processes.
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Table 2.1 SUMMARY OF METABOLITES DETECTED USING PROTON MRS METABOLITE
MEASURE
N-acetyl aspartate
Neuronal density and viability
Choline and choline-containing metabolites
Neuronal membrane integrity
Myo-inositol
Glial function
Glutamate/glutamine
Excitatory neurotransmitter
Gamma-aminobutyric acid
Inhibitory neurotransmitter
Proton Hydrogen or proton MRS is used to probe for the following neurochemicals: choline (Cho), creatine (Cr), N-acetyl aspartate (NAA), myoinositol (mI), glutamate/glutamine, and gamma aminobutyric acid (GABA) (Table 2.1). NAA is generally viewed as a marker of neuronal viability and may represent a measure of neuronal volume, number, and /or function. The choline signal represents a composite of choline-containing metabolites such as phosphocholine, glycerophosphocholine, and phosphatidylcholine. These metabolites are involved in cell membrane integrity. Myo-inositol is seen as a marker of gliosis and is a storage form of the inositol phosphate second-messenger system. Glutamate/glutamine and GABA are, respectively, the most ubiquitous excitatory and inhibitory neurotransmitters. Other less commonly used nuclei include carbon, lithium, fluorine, and sodium.
NORMAL AGING Several studies have used MRS to study neurochemical changes associated with normal, healthy aging. Perhaps owing to methodological limitations, early studies using MRS to examine brain alterations associated with normal aging failed to find significant changes over time (Chang, Ernst, Poland, & Jenden, 1996; Saunders, Howe, van den Boogart, Griffiths, & Brown, 1999). However, in a study by Cohen and colleagues comparing brain uptake of choline using proton MRS
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in younger subjects (mean age 32) and older subjects (mean age 73), it was shown that older subjects had decreased choline uptake (Cohen et al., 1995). In a 2000 study, Valenzuela and associates showed that frontal white matter NAA/Cr ratios were significantly correlated with cognitive tasks of executive function. A significant decline in frontal lobe NAA was shown in comparing subjects from ages 20 to 70, with an overall decrease of 12% (Brooks et al., 2001). This decline was interpreted as a decrease in neuronal volume, number, or function, as NAA is thought to be a marker of neuronal viability. These changes are not limited to frontal white matter, as demonstrated in a study that showed age-related decreases in NAA, NAA /Cho, and NAA/Cr ratios across 30 different voxels (Angelie et al., 2001). In a longitudinal study examining similar measures over a 3-year period, Ross and coworkers demonstrated that there were no significant changes in brain metabolites (with the exception of mI) or cognitive function over the period studied (Ross, Sachdev, Wen, & Brodaty, 2006). In a comprehensive review and meta-analysis of MRS studies in normal aging, the majority of studies showed no significant change in metabolites with age; however, the authors did note a trend toward an increase in frontal and parietal choline with increasing age and a trend toward increased parietal and occipital creatine with increasing age (Haga, Khor, Farrall, & Wardlaw, 2009).
LATE-LIFE DEPRESSION Utilization of MRS to study the biochemical changes associated with major depression has yielded a number of findings over the past few years. These include increases in choline and myo-inositol and decreases in NAA and glutamate associated with major depression (Auer et al., 2000; Kumar et al., 2002; Rosenberg et al., 2005; Vythilingam et al., 2003). These metabolic changes associated with major depression seem to be clinically relevant, as demonstrated by studies of treatment effect. For example, an increase in amygdalar region NAA was seen in depressed patients who exhibited a positive response to electroconvulsive therapy (Michael et al., 2003a). In addition, concentrations of Glx (a composite of glutamate and glutamine) in the left dorsal lateral prefrontal cortex were negatively correlated with severity of depression and increased significantly after electroconvulsive treatment (Michael et al., 2003b). In a report examining the effect of antidepressant treatment, it was shown that decreased NAA levels in the anterior cingulate cortex
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in depressed patients were reversed with venlafaxine treatment (Gonul et al., 2006). Other brain regions have been studied as well using MRS in major depression. For example, in one study showing an increase in glutamate levels associated with major depression, the region of interest was in the occipital cortex (Sanacora et al., 2004). In addition, MRS of the mesial temporal lobe of depressed patients demonstrated significant increases in choline/Cr ratios as compared to controls (Mervaala et al., 2000).
DEMENTIA AND COGNITIVE DISORDERS Mild Cognitive Impairment (MCI)/ Cognitively Impaired Nondemented (CIND) MRS studies in MCI have been used to distinguish MCI from normal aging as well as more advanced forms of cognitive dysfunction. An early study by Kantarci and coworkers examined regional metabolic changes associated with MCI. They found that increases in mI/Cr ratios in the posterior cingulate were unique to MCI patients and Alzheimer’s patients, while decreases in NAA/Cr and Cho/Cr seemed present only in patients with more progressive disease (Kantarci et al., 2000). Subsequent studies have shown a similar pattern of changes associated with MCI patients as compared to control subjects (Catani et al., 2001). Other regions have been shown to demonstrate metabolic perturbations restricted to MCI. For example, in their study, Ackl and associates found that hippocampal decreases in NAA occurred in both MCI and Alzheimer’s patients but that NAA changes in the parietal region seemed to occur only in more advanced patients (Ackl et al., 2005). MRS has been studied as a predictor of developing dementia in a number of studies. For example, CIND patients compared to controls have been shown to have reduced NAA in the medial temporal lobe, hippocampus, and neocortical gray matter. There was also a difference between stable CIND patients and CIND patients who developed dementia over 3.6 years of follow-up (Chao et al., 2005). In another study, changes in occipital NAA/Cr ratios predicted conversion to dementia with 100% sensitivity and 75% specificity (Modrego, Fayed, & Pina, 2005). Thus MRS has been a useful tool in determining those early metabolic profiles that distinguish MCI from more advanced dementia and those changes that predict the development of more advanced dementia.
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Alzheimer’s Disease (AD) There have been numerous studies using MRS to measure brain metabolites in AD. The most consistent finding is reduced NAA, a marker of neuronal viability, and increased myo-inositol. This has been demonstrated in a number of cross-sectional studies (Frederick, Satlin, Yurgelun-Todd, & Renshaw, 1997; Lazeyras et al., 1998; Parnetti et al., 1997; Shiino et al., 1993). These patterns also occur longitudinally, as demonstrated in a brief report from Adalsteinsson and colleagues. They showed that gray matter NAA levels (on average) declined 12.26% over the course of a year in AD patients, whereas controls had no significant change over the same period (Adalsteinsson, Sullivan, Kleinhans, Spielman, & Pfefferbaum, 2000). Metabolic alterations associated with AD have been associated with cognitive function as well. For example, NAA reductions in the left medial temporal lobe were associated with impairment in verbal memory, while myo-inositol increases in the right parietotemporal cortex were associated with impairment in visuoconstructional performance (Chantal, Labelle, Bouchard, Braun, & Boulanger, 2002). There have been several studies looking at the effects of treatment of Alzheimer’s disease on brain metabolic profiles. In a study on donepezil, the authors found that patients receiving treatment demonstrated higher NAA concentrations compared to patients receiving placebo over a 24-week period (Krishnan et al., 2003). In a subsequent study, these types of changes were shown to be significantly correlated with cognitive improvement associated with donepezil treatment (Jessen et al., 2006). Rivastigmine has also been shown to positively affect the metabolic profile associated with AD. Patients treated with rivastigmine over a 4-month period exhibited significant increases in frontal cortex NAA/ Cr ratio (Modrego, Pina, Fayed, & Diaz, 2006). Thus, with the evidence from these treatment effect studies, it appears that the metabolic alterations seen in AD are related to the underlying pathophysiology of the disease.
Vascular Dementia (VaD) While there have been a number of studies trying to use MRS to distinguish AD from VaD (Herminghaus et al., 2003; Kattapong, Brooks, Wesley, Kodituwakku, & Rosenberg, 1996), there are more similarities than differences in the patterns of change. The main distinguishing fea-
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ture of VaD is the subcortical predominance of metabolic alterations versus more cortical changes in AD. However, as with AD, patients with VaD have been shown to have decreased NAA as well as increased myoinositol (Herminghaus et al., 2003).
Frontotemporal Dementia (FTD) As expected with FTD, metabolic changes are associated with more frontal structures, as evidenced by a study showing decreased NAA in frontal areas compared to more posterior regional changes in AD patients (Mihara, Hattori, Abe, Sakoda, & Sawada, 2006). In a study comparing FTD patients with AD patients and controls, frontal lobe NAA and Glx concentrations were decreased in conjunction with increases in mI (Ernst, Chang, Melchor, & Mehringer, 1997). The NAA results have been replicated in a later study examining metabolites in the posterior cingulate cortex of FTD patients (Kizu, Yamada, Ito, & Nishimura, 2004).
Lewy Body Dementia (LBD) There are a few studies using MRS to examine brain changes associated with LBD. One report that included 20 patients with LBD showed that choline/Cr ratios were significantly elevated compared to healthy controls (Kantarci et al., 2004). In another study, white matter changes were detected with significantly lower mean NAA/Cr, Glx/Cr, and choline/Cr ratios in LBD patients compared to controls (Molina et al., 2002).
Summary Overall, the studies outlined above demonstrate a general pattern that applies to most of the neurodegenerative diseases. Dementia subtypes appear to differ only in terms of the neuroanatomical regions affected. In a number of areas, including gray and white matter and cortical and subcortical regions, NAA tends to be decreased compared to controls, while increases in myo-inositol and choline have been detected. This is thought to reflect the loss of neurons (with NAA), membrane breakdown (choline), and reactive gliosis (detected by myo-inositol) associated with these diseases.
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STRENGTHS AND LIMITATIONS OF MRS The major strength of MRS is that it allows for the measurement of specific metabolites in a particular region of the brain. Thus the selection of crucial brain regions plays a vital role in elucidating the pathophysiology underlying late-life mental illness. This is also a potential limitation, as many studies choose different brain regions to study the same illness; thus generalizability and broad interpretation across studies are difficult to achieve. Another limitation is poor resolution of specific peaks. Often, GABA, glutamate, and glutamine peaks overlap, making it difficult to determine exact concentrations of these metabolites. One technique designed to surmount this limitation is two-dimensional magnetic resonance spectroscopy (2D-MRS).
2D MAGNETIC RESONANCE SPECTROSCOPY Two-dimensional spectroscopy is a technique that allows for greater resolution. The advantage of 2D spectroscopy is that it allows for the detection of GABA, which is typically masked by the spectral peaks of other metabolites, such as N-acetyl aspartate and glutamate/glutamine. One of the first papers to apply 2D MRS techniques to brain chemistry was done by Thomas and colleagues, showing the feasibility of the 2D Jcoupled point-resolved spectroscopy (JPRESS) technique in human subjects (Ryner, Sorenson, & Thomas, 1995). They were able to detect peaks representing NAA, glutamate/glutamine, and lactate in occipital cortex. Two-dimensional J-resolved spectroscopy has been successfully applied to measure previously hard-to-detect levels of GABA (Jensen, Frederick, Wang, Brown, & Renshaw, 2005). This technique has also been used to measure gray matter/white matter differences. A study by Jensen and colleagues showed that GABA levels are significantly higher in gray matter than in white matter (Jensen, Frederick, & Renshaw, 2005). Localized two-dimensional chemical shift correlated magnetic resonance spectroscopy (2D-COSY) is a method that presents certain advantages over the JPRESS and J-resolved techniques. This method was successfully used in the human brain in a study by Thomas and associates, where peaks for N-acetyl aspartate (NAA), glutamate/glutamine (Glx), myo-inositol (mI), creatine (Cr), choline, aspartate, GABA, taurine, glu-
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tathione, threonine, and macromolecules were identified (Thomas et al., 2001). The authors demonstrated that 2D-COSY generates spectra with better resolution and less overlap of peaks. 2D-COSY has also been shown to be highly reliable and reproducible (Binesh, Yue, Fairbanks, & Thomas, 2002). The advantage of this technique is that it allows for the detection of important metabolites that are difficult to measure by traditional MRS methods. Particularly, it allows for better resolution of aspartate and GABA, important neurotransmitters that have been implicated in mood disorders.
CONCLUSION MRS is an important tool for the localized measurement of brain metabolites; it has been successfully applied to elucidating the nature of geriatric mental illness. Despite its limitations, the method continues to evolve more sophisticated techniques for the improved measurement of clinically important neurochemicals. REFERENCES Ackl, N., Ising, M., Schreiber, Y. A., Atiya, M., Sonntag, A., & Auer, D. P. (2005). Hippocampal metabolic abnormalities in mild cognitive impairment and Alzheimer’s disease. Neuroscience Letters, 384(1–2), 23–28. Adalsteinsson, E., Sullivan, E. V., Kleinhans, N., Spielman, D. M., & Pfefferbaum, A. (2000). Longitudinal decline of the neuronal marker N-acetyl aspartate in Alzheimer’s disease. The Lancet, 355(9216), 1696–1697. Angelie, E., Bonmartin, A., Boudraa, A., Gonnaud, P. M., Mallet, J. J., & Sappey-Marinier, D. (2001). Regional differences and metabolic changes in normal aging of the human brain: Proton MR spectroscopic imaging study. American Journal of Neuroradiology, 22(1), 119–127. Auer, D. P., Putz, B., Kraft, E., Lipinski, B., Schill, J., & Holsboer, F. (2000). Reduced glutamate in the anterior cingulate cortex in depression: An in vivo proton magnetic resonance spectroscopy study. Biological Psychiatry, 47(4), 305–313. Binesh, N., Yue, K., Fairbanks, L., & Thomas, M. A. (2002). Reproducibility of localized 2D correlated MR spectroscopy. Magnetic Resonance Medicine, 48(6), 942–948. Brooks, J. C., Roberts, N., Kemp, G. J., Gosney, M. A., Lye, M., & Whitehouse, G. H. (2001). A proton magnetic resonance spectroscopy study of age-related changes in frontal lobe metabolite concentrations. Cerebral Cortex, 11(7), 598–605. Catani, M., Cherubini, A., Howard, R., Tarducci, R., Pelliccioli, G. P., Piccirilli, M., et al. (2001). (1)H-MR spectroscopy differentiates mild cognitive impairment from normal brain aging. Neuroreport, 12(11), 2315–2317.
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Chang, L., Ernst, T., Poland, R. E., & Jenden, D. J. (1996). In vivo proton magnetic resonance spectroscopy of the normal aging human brain. Life Sciences, 58(22), 2049–2056. Chantal, S., Labelle, M., Bouchard, R. W., Braun, C. M. J., & Boulanger, Y. (2002). Correlation of regional proton magnetic resonance spectroscopic metabolic changes with cognitive deficits in mild Alzheimer’s disease. Archives of Neurology, 59(6), 955–962. Chao, L. L., Schuff, N., Kramer, J. H., Du, A. T., Capizzano, A. A., O’Neill, J., et al. (2005). Reduced medial temporal lobe N-acetylaspartate in cognitively impaired but nondemented patients. Neurology, 64(2), 282–289. Cohen, B. M., Renshaw, P. F., Stoll, A. L., Wurtman, R. J., Yurgelun-Todd, D., & Babb, S. M. (1995). Decreased brain choline uptake in older adults: An in vivo proton magnetic resonance spectroscopy study. JAMA, 274(11), 902–907. Ernst, T., Chang, L., Melchor, R., & Mehringer, C. M. (1997). Frontotemporal dementia and early Alzheimer disease: Differentiation with frontal lobe H-1 MR spectroscopy. Radiology, 203(3), 829–836. Frederick, B. B., Satlin, A., Yurgelun-Todd, D. A., & Renshaw, P. F. (1997). In vivo proton magnetic resonance spectroscopy of Alzheimer’s disease in the parietal and temporal lobes. Biological Psychiatry, 42(2), 147–150. Gonul, A. S., Kitis, O., Ozan, E., Akdeniz, F., Eker, C., Eker, O. D., et al. (2006). The effect of antidepressant treatment on N-acetyl aspartate levels of medial frontal cortex in drug-free depressed patients. Progress in Neuropsychopharmacology and Biological Psychiatry, 30(1), 120–125. Haga, K. K., Khor, Y. P., Farrall, A., & Wardlaw, J. M. (2009). A systematic review of brain metabolite changes, measured with (1)H magnetic resonance spectroscopy, in healthy aging. Neurobiology and Aging, 30(3), 353–363. Herminghaus, S., Frolich, L., Gorriz, C., Pilatus, U., Dierks, T., Wittsack, H. J., et al. (2003). Brain metabolism in Alzheimer’s disease and vascular dementia assessed by in vivo proton magnetic resonance spectroscopy. Psychiatry Research: Neuroimaging, 123(3), 183–190. Jensen, J. E., Frederick, B. B., & Renshaw, P. F. (2005). Grey and white matter GABA level differences in the human brain using two-dimensional, J-resolved spectroscopic imaging. NMR Biomedicine, 18(8), 570–576. Jensen, J. E., Frederick, B. D., Wang, L., Brown, J., & Renshaw, P. F. (2005). Twodimensional, J-resolved spectroscopic imaging of GABA at 4 Tesla in the human brain. Magnetic Resonance Medicine, 54(4), 783–788. Jessen, F., Traeber, F., Freymann, K., Maier, W., Schild, H. H., & Block, W. (2006) Treatment monitoring and response prediction with proton MR spectroscopy in AD. Neurology, 67(3), 528–530. Kantarci, K., Jack, C. R. Jr., Xu, Y. C., Campeau, N. G., O’Brien, P. C., Smith G. E., et al. (2000). Regional metabolic patterns in mild cognitive impairment and Alzheimer’s disease: A 1H MRS study. Neurology, 55(2), 210–217. Kantarci, K., Petersen, R. C., Boeve, B. F., Knopman, D. S., Tang-Wai, D. F., O’Brien, P. C., et al. (2004). 1H MR spectroscopy in common dementias. Neurology, 63(8), 1393–1398. Kattapong, V. J., Brooks, W. M., Wesley, M. H., Kodituwakku, P. W., & Rosenberg, G. A. (1996). Proton magnetic resonance spectroscopy of vascular- and Alzheimer-type dementia. Archives of Neurology, 53(7), 678–680.
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Kizu, O., Yamada, K., Ito, H., & Nishimura, T. (2004). Posterior cingulate metabolic changes in frontotemporal lobar degeneration detected by magnetic resonance spectroscopy. Neuroradiology, 46(4), 277–281. Krishnan, K. R. R., Charles, H. C., Doraiswamy, P. M., Mintzer, J., Weisler, R., Yu, X., et al. (2003). Randomized, placebo-controlled trial of the effects of donepezil on neuronal markers and hippocampal volumes in Alzheimer’s disease. American Journal of Psychiatry, 160(11), 2003–2011. Kumar, A., Thomas, M. A., Lavretsky, H., Yue, K., Huda, A., Curran, J., et al. (2002). Frontal white matter biochemical abnormalities in late-life major depression detected using proton magnetic resonance spectroscopy. American Journal of Psychiatry, 159(4), 630–636. Lazeyras, F., Charles, H. C., Tupler, L. A., Erickson, R., Boyko, O. B., & Krishnan, K. R. (1998). Metabolic brain mapping in Alzheimer’s disease using proton magnetic resonance spectroscopy. Psychiatry Research, 82(2), 95–106. Mervaala, E., Fohr, J., Kononen M., Valkonen-Korhonen, M., Vainio, P., Partanen, K., et al. (2000). Quantitative MRI of the hippocampus and amygdala in severe depression. Psychological Medicine, 30(1), 117–125. Michael, N., Erfurth, A., Ohrmann, P., Arolt, V., Heindel, W., & Pfleiderer, B. (2003a). Metabolic changes within the left dorsolateral prefrontal cortex occurring with electroconvulsive therapy in patients with treatment resistant unipolar depression. Psychological Medicine, 33(7), 1277–1284. Michael, N., Erfurth, A., Ohrmann, P., Arolt, V., Heindel, W., & Pfleiderer B. (2003b). Neurotrophic effects of electroconvulsive therapy: A proton magnetic resonance study of the left amygdalar region in patients with treatment-resistant depression. Neuropsychopharmacology, 28(4), 720–725. Mihara, M., Hattori, N., Abe, K., Sakoda, S., & Sawada, T. (2006). Magnetic resonance spectroscopic study of Alzheimer’s disease and frontotemporal dementia/Pick complex. Neuroreport, 17(4), 413–416. Modrego, P. J., Fayed, N., & Pina M. A. (2005). Conversion from mild cognitive impairment to probable Alzheimer’s disease predicted by brain magnetic resonance spectroscopy. American Journal of Psychiatry, 162(4), 667–675. Modrego, P. J., Pina, M. A., Fayed, N., & Diaz, M. (2006). Changes in metabolite ratios after treatment with rivastigmine in Alzheimer’s disease: A nonrandomised controlled trial with magnetic resonance spectroscopy. CNS Drugs, 20(10), 867–877. Molina, J. A., Garcia-Segura, J. M., Benito-Leon, J., Gomez-Escalonilla, C., del Ser, T., Martinez, V., et al. (2002). Proton magnetic resonance spectroscopy in dementia with Lewy bodies. European Journal of Neurology, 48(3), 158–163. Parnetti, L., Tarducci, R., Presciutti, O., Lowenthal, D.T., Pippi, M., Palumbo, B., et al. (1997). Proton magnetic resonance spectroscopy can differentiate Alzheimer’s disease from normal aging. Mechanisms of Aging and Development, 97(1), 9–14. Rosenberg, D. R., Macmaster, F. P., Mirza, Y., Smith, J. M., Easter P. C., Banerjee, S. P., et al. (2005). Reduced anterior cingulate glutamate in pediatric major depression: A magnetic resonance spectroscopy study. Biological Psychiatry, 58(9), 700– 704. Ross, A. J., Sachdev, P. S., Wen, W., & Brodaty, H. (2006). Longitudinal changes during aging using proton magnetic resonance spectroscopy. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 61(3), 291–298.
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Ryner, L. N., Sorenson, J. A., & Thomas, M. A. (1995). Localized 2D J-resolved 1H MR spectroscopy: Strong coupling effects in vitro and in vivo. Magnetic Resonance Imaging, 13(6), 853–869. Sanacora, G., Gueorguieva, R., Epperson, C. N., Wu, Y. T., Appel, M., Rothman, D. L., et al. (2004). Subtype-specific alterations of gamma-aminobutyric acid and glutamate in patients with major depression. Archives of General Psychiatry, 61(7), 705–713. Saunders, D. E., Howe, F. A., van den Boogart, A., Griffiths, J. R., & Brown, M. M. (1999). Aging of the adult human brain: In vivo quantitation of metabolite content with proton magnetic resonance spectroscopy. Journal of Magnetic Resonance Imaging, 9(5), 711–716. Shiino, A., Matsuda, M., Morikawa, S., Inubushi, T., Akiguchi, I., & Handa, J. (1993). Proton magnetic resonance spectroscopy with dementia. Surgical Neurology, 39(2), 143–147. Thomas, M. A., Yue, K., Binesh, N., Davanzo, P., Kumar, A., Siegel, B., et al. (2001). Localized two-dimensional shift correlated MR spectroscopy of human brain. Magnetic Resonance Medicine, 46(1), 58–67. Vythilingam, M., Charles, H. C., Tupler, L. A., Blitchington, T., Kelly, L., & Krishnan, K. R. (2003). Focal and lateralized subcortical abnormalities in unipolar major depressive disorder: An automated multivoxel proton magnetic resonance spectroscopy study. Biological Psychiatry, 54(7), 744–750.
3
Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) JULIE C. PRICE, CHARLES M. LAYMON, RAJESH NARENDRAN, AND BRIAN J. LOPRESTI
This chapter begins with a basic review of emission imaging technology: single photon emission computed tomography (SPECT) and positron emission tomography (PET), offering a general summary of how this technology is applied to the measurement of physiology. Specific brain imaging applications are then reviewed to provide the reader with a basic understanding of the methods, terminology, and methodology developments that have laid groundwork for emission imaging applications in geriatric mental health. The final section summarizes important challenges associated with emission tomography imaging of the brain in geriatric populations.
EMISSION IMAGING The functional brain imaging studies described here primarily utilize radioactive indicator molecules (i.e., radiotracers) that follow, or trace, steps of a physiological process (Lassen & Perl, 1979). The radiotracer is generally introduced into the subject in very small (trace) amounts that do not perturb the process under study. Emission imaging provides an image of the spatial and temporal radioactivity distribution within a patient through the external detection of emitted gamma rays emanating 17
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from within the body. The utility of such a procedure is found in the fact that minute quantities of a substance that is of relevance to a particular physiological process or target (e.g., enzyme substrate, receptor ligand) can be labeled with a radionuclide useful for SPECT (e.g., technetium 99m, or 99mTc) or PET (e.g., fluorine 18, or 18F) imaging. Emission imaging yields functional information related to the state of a particular physiological process in a noninvasive manner.
Radiotracers for Brain Imaging In general, a radiotracer is synthesized by identifying a close structural analog of a biologically occurring molecule or a pharmaceutical with a known profile and mechanism of action that is conducive to labeling with a radionuclide appropriate for the intended imaging modality. The usefulness of a prospective radiotracer is predicated on its functional similarity to the naturally occurring molecule or pharmaceutical as well as its specificity for the intended target or process. Over the last 25 years, numerous radiotracers have been developed for SPECT and PET imaging, and many of these are useful for the in vivo study of human brain function. Radiotracers that are freely diffusible and highly extracted across membranes, such as [15O]water and [99mTc]hexamethylpropyleneamine oxime (HMPAO), are useful for assessments of cerebral perfusion. Metabolic agents, such as [18F]2-fluoro-2-deoxy-D-glucose (or FDG) and [15O]O2, can be used to assess the energy utilization of brain tissue. Other SPECT and/or PET imaging agents target specific neuroreceptor and neurotransporter binding sites, such as dopamine and serotonin receptor and transporter sites. These and other PET and SPECT neuroreceptor imaging agents are especially useful in the study of neuropsychiatric disorders associated with alterations in neurotransmission. More recently, novel classes of radiotracers have been developed to bind to cerebral amyloid plaque deposits in vivo that are hypothesized to be a primary pathological substrate of Alzheimer’s disease (AD). Specific radiotracers and experimental applications are discussed in a later section of this chapter. An important component of emission imaging is the production of the radionuclides used for radiotracer labeling. Many radionuclides useful for PET imaging (e.g., 18F or 11C) are produced using a cyclotron that accelerates ions (generally negative hydrogen ions) to high energies. The accelerated ions are stripped of their electrons and the remaining protons then bombard a target, thereby inducing a nuclear reaction that
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produces the desired radionuclide. For example, the radionuclide 18F is frequently produced by the reaction: p 18O → 18F n, in which an 18 O target is bombarded by protons producing 18F and neutrons. In some cases, radionuclides for imaging are obtained through decay of other radioactive atoms. For example, 99mTc is obtained from the decay of 99Mo, a nuclear fission product, using a commercial generator. The activity of a radioactive substance is a measure of the number of constitutive radioactive nuclei that decay per unit time. The standard unit of radioactivity is the Becquerel (1 Bq = 1 disintegration/sec). In medical imaging the Curie (Ci) unit is also frequently used for convenience (1 Ci = 3.7 1010 Bq), as typical radiotracer doses are on the order of 107Bq (see Price, 2003). The amount of radiotracer that is administered is based on radiation dosimetry, or the amount of radiation absorbed by critical organs. The dosimetry of a radiotracer is influenced by biological factors, such as metabolism and excretion, as well as physical factors, such as the radionuclide half-life. The half-life (the time required for a quantity of radioactive substance to decrease to half of its initial activity), or t1/2, is an important factor in emission tomography. The radionuclide half-life limits the duration of an imaging study because the statistical quality of incremental data degrades with time as radioactivity, and therefore count rate, decrease. SPECT radionuclides have longer half-lives than those used for PET brain imaging. The shorter-lived PET radiotracers allow for serial injections or repeat PET scans to be performed in the same subject on a given day. This may be advantageous, as it may permit the assessment of multiple aspects of brain function during a single scan session. In other cases, radionuclides with longer half-lives are desirable because they allow for studies of longer duration that may be needed to examine processes with slow kinetics that take longer times to reach equilibrium and /or allow sufficient time to achieve good signal-to-noise ratios for radiotracers with relatively slow clearance of nonspecific binding. As an example, for the SPECT dopamine transporter binding studies that utilize [123I]-CIT (t1/2 = 13 h), the subject is generally imaged 24 hours after injection of the radiopharmaceutical.
Scan Modalities The goal of an emission tomography study is to map the temporal and spatial distribution of radiotracer within a patient and with focus on a particular organ, such as the brain. The data required to produce a SPECT
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or PET image are the count rates from radiation emerging from the patient at a large set of points and directions (Meltzer, Becker, Price, & Moses-Kolko, 2003; Orlov, 1975; Tuy, 1983). Each point of emergence and propagation direction defines what is referred to as a line of response (LOR). SPECT and PET scanners are designed to detect radiation along a sufficient set of LORs to enable production of a 3-dimensional image of tracer distribution. Most SPECT and PET scanners use scintillation detectors for detecting gamma radiation. The main component of these detectors is a scintillating material that converts incident gamma-ray photons (such as those produced by positron annihilation or the decay of radioactive nuclei) into visible light. The scintillator is coupled to a light-detection device that is most frequently an array of photomultiplier tubes (PMTs). The PMTs convert the scintillation light into an electrical signal that is subsequently processed by the system electronics that are position-sensitive (i.e., arranged to allow localization of the point at which the gamma ray hits the detector). A fundamental difference between SPECT and PET is that whereas SPECT is based on the detection of single photons from single radioactive decay, PET is based on the detection of two photons from a single radioactive decay. As the name implies, all PET tracers decay by positron emission. The emitted positron travels a short distance and annihilates with an ambient electron. In this process, two 511-keV gamma rays are emitted in opposite directions. As described below, SPECT and PET utilize different technology to acquire emission data for image production.
Single Photon Emission Computed Tomography (SPECT) SPECT relies on the use of a gamma camera to detect gamma-ray photons emitted from a radioactive decay event. Typically, a gamma camera contains a continuous slab of thallium-doped sodium iodide NaI(Tl) as the scintillating material. Gamma cameras designed for SPECT frequently have a scintillator thickness of 3/8 in. to allow sufficient gammaray stopping power for the energies of most SPECT tracers (35–200 keV). The planar size of the gamma camera varies, with a typical size of 40 by 55 cm (Madsen, 2007). The gamma camera scintillator is covered by a collimator made of highly absorbing material. A parallel hole collimator is most frequently used and consists of closely packed parallel channels that are separated by lead or tungsten septa. The purpose of such a collimator is to allow only those gamma rays with nearly head-on
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incidence to reach the scintillator. This is important in image reconstruction because it is required that the direction of incidence of the gamma rays that strike the camera be known. A SPECT system may consist of one or several gamma camera “heads” mounted on a gantry. To allow data acquisition along LORs at all angles, the gantry (and camera heads) orbit about the patient. The spatial resolution (mm) of SPECT is largely determined by the collimator as well as the source-to-collimator distance (Gunter, 2004). Collimator choice entails trade-offs between resolution and sensitivity. However, for brain imaging with parallel-hole collimators (designed for 99mTc or 123I, see below), the resolution is about 7 to 10 mm (Accorsi, 2008; DeGeeter et al., 1996; Madsen, 2007). The primary radionuclides used in SPECT brain imaging are 99mTc (t1/2 = 6 h) and 123I (t1/2 = 13 h) in studies of blood flow and receptor binding (see Table 3.1). 99mTc is a metastable nuclear isomer of 99Tc (hence the “m” designation), which means that it does not transmute into another chemical element upon decay. 99mTc is produced from the radioactive decay (-) of 99Mo, which is a nuclear fission product with a 66-hour half-life. For medical imaging procedures, 99mTc is provided through the use of commercially available 99Mo/99mTc generators (or “cows”) which separate the 99mTc daughter product from the 99Mo parent (“milking the cow”) by a process of elution. The generator eluant provides 99mTc in a solution that is used in the preparation of 99mTc radiotracers via commercially available preparation kits (Koslowsky, Brake, & Bitner, 2001). The primary gamma emission energy of 99mTc is 140 keV. Iodine 123 is cyclotron-produced by proton irradiation of enriched 124Te and commercially available with a primary emission energy of 159 keV. The 123I radiotracers used for SPECT are generally synthesized on site, although some can be prepared using commercial preparation methods or delivered from radiopharmaceutical vendors (see Kegeles et al., 1999; Meyer et al., 2008).
Positron Emission Tomography (PET) The PET technology takes advantage of the two collinear photons that are produced by positron annihilation to provide functional images. The typical PET scanner consists of multiple rings of scintillation detectors that surround the subject. The detectors, usually bismuth germinate (BGO) or lutetium oxyorthosilicate (LSO), have high detection efficiency for 511-keV photons. The scanner registers an event only when two photons are detected within a narrow time window (~10 ns), which is referred
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Table 3.1 EXAMPLES OF SPECT RADIOTRACERS USED FOR NEUROIMAGING Blood flow and metabolism Blood flow
[99mTc]HMPAO, [99mTc]ECD
Neuroreceptor Acetylcholine Muscarinic
[123I]IQNB
Nicotinic
[123I]5-IA-85380
Benzodiazepine Central
[123I]Iomazenil
Dopamine D2
[123I]IBZM
Transporter/reuptake site
[123I] -CIT
Serotonin 5-HT1A
[123I]p-MPPI
5-HT2A
[123I]5-I-R91150
Transporter/reuptake site
[123I] -CFT, [123I]ADAM
Amyloid
[123I]IMPY
See “The Role of in Vivo Molecular Imaging With PET and SPECT in the Elucidation of Psychiatric Drug Action and New Drug Development,” by P. S. Talbot & M. Laruelle, 2002. European Neuropsychopharmacology, 12, 503–511; and “Positron Emission Tomography: Imaging and Quantification of Neurotransporter Availability,” by M. Laruelle, M. Slifstein, & Y. Huang, 2002b. Methods, 27, 287–299.
to as a coincidence. The two points of detection define the LOR along which the decay occurred and provide necessary directional information for image reconstruction. Spatial resolution (mm) in PET is influenced by several factors that include scanner geometry (e.g., Bailey, 2003), and the resolution is about 5 to 8 mm (Herzog et al., 2004), but it can be higher for special-purpose brain research PET systems. For example, the Siemens HRRT has a resolution of about 3 mm (de Jong et al., 2007). The primary radionuclides used in PET are 15O (t1/2 = 2.07 min), 11C (t1/2 = 20.4 min), 13N (t1/2 = 10 min), and 18F (t1/2 = 110 min); as described above, these decay by positron emission. As a result of short radioactive
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half-lives, these radionuclides must be generated using a cyclotron usually located within or near the PET imaging center. The PET radiotracers (see Table 3.2) are synthesized on site by automated techniques or by stepwise methods performed by radiochemistry personnel working within the time constraints of the radionuclide half-life. Because of their longer half-lives, [18F]-labeled PET radiotracers, most notably [18F] FDG, may be produced by off-site cyclotrons for regional commercial distribution.
DATA ACQUISITION AND ANALYSIS The Emission Study The exact protocol for the emission imaging study depends on the type of scanner, radiotracer, and application. In general it is important to position the patient in a reproducible manner that minimizes head motion during the scan. A head immobilization system is often used (e.g., thermoplastic mask assembly attached to the scanner table). The patient then receives the radiotracer via bolus injection or a combination of a bolus and an infusion (B/I), although some radiotracers are delivered by inhalation (e.g., 133Xe or [15O]O2). For certain studies, there is a waiting or uptake period between the time of tracer injection and the start of emission data acquisition to allow for tracer distribution in tissue. In other studies, particularly in PET, data acquisition begins at the time of tracer injection and multiple time frames of data (dynamic imaging) are acquired over 60 min. Some gamma rays that are emitted within the brain are absorbed by intervening tissue (attenuation). A correction of the emission data for this effect is necessary for the production of quantitatively accurate images. The data required for such a correction are measurements of the attenuation characteristics of the patient. These data can be measured directly by a transmission scan that utilizes an external radiation source or X-ray computed tomography (CT) if an integrated PET/CT scanner is being used. Indirect methods use postemission processing techniques that assume predefined geometry and attenuation characteristics to calculate an approximate correction. It is also important to apply a correction for gamma rays that scatter within the subject and this is particularly important for 3D PET imaging (Watson, 2000). The raw emission data
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Table 3.2 EXAMPLES OF PET RADIOTRACERS USED FOR NEUROIMAGING Blood flow and metabolism Blood flow
[15O]H2O, [13N]NH3
Metabolism Glucose
[18F]2-fluoro-2-deoxy-D-glucose (FDG)
Oxygen
[15O]O2
Transport Glucose
[11C]3-O-methyl glucose
Neuroreceptor Acetylcholine Muscarinic
[11C]NMPB
Acetylcholinesterase inhibitor
[11C]PMP
Benzodiazepine Central
[11C]Flumazenil
Peripheral
[11C]PK11195
Dopamine DA synthesis
[18F]FDOPA
D2
[11C]FLB 457, [18F]Fallypride [11C]Raclopride
Transporter/reuptake site
[11C]CFT
Serotonin 5-HT1A
[11C]WAY 100635, [18F]MPPF
5-HT2A
[11C]MDL 100907, [18F]Altanserin
Transporter/reuptake site
[11C]DASB, [11C]McN5652
Amyloid
[11C]PIB, [11C]SB13 [18F]FDDNP, [18F]BAY94-9172
See “Positron Emission Tomography Radiochemistry,” by N. S. Mason & C. A. Mathis, 2003. Neuroimaging Clinics of North America, 13, 671–687; “PET Study of Neurochemical Systems,” by K. A. Frey, 2002. In P. E. Valk, D. L. Bailey, D. W. Townsend, & M. N. Maisey (Eds.), Positron Emission Tomography (pp. 309–327). New York: Springer; and “Positron Emission Tomography: Imaging and Quantification of Neurotransporter Availability,” by M. Laruelle, M. Slifstein, & Y. Huang, 2002b. Methods, 27, 287–299.
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(i.e., count rates for each measured LOR) are then reconstructed using mathematical algorithms to obtain images of the radioactivity distribution. Conversion of the image data to units of radioactivity concentration is achieved by using a calibration factor obtained by scanning a vessel containing a radioactive source of a known activity concentration. For brain imaging research, study participants usually also undergo structural magnetic resonance imaging (MR imaging or MRI) that is acquired to maximize the gray matter/white matter contrast. The MRI data provide anatomical information used in the analysis of the emission data (e.g., definition of anatomical regions of interest, or ROIs). This requires that the MRI and emission images be spatially aligned or coregistered. For combined PET/CT or SPECT/CT scanners, the CT images may provide the anatomical information.
Experimental Protocols: Fully Dynamic to Single Scan Fully dynamic protocols are the most intensive with respect to subject burden and data acquisition and analysis but uniquely allow for the quantitative assessment of multiple kinetic components of the physiological process (e.g., blood-to-brain transport, free and nonspecific uptake, metabolism or specific binding). Quantitative tracer kinetic studies are important for the characterization of a novel radiotracer, examination of potential group differences in the in vivo kinetics, or validation of simpler approaches (e.g., steady state or single scan). The dynamic emission data are acquired in sequential time frames of increasing duration (e.g., 34 frames over 90 min). In addition to dynamic imaging, tracer kinetic studies also require measurement of the radioactivity concentration in blood (usually plasma), with samples usually collected via an arterial line. The sampling is rapid at early times (e.g., 20 samples over initial 2 min and 14 samples over remaining 88 min). If the radiotracer is metabolized in vivo, it is necessary to measure both the total radioactivity and the radioactivity that arises from radiolabeled metabolites (i.e., radioactive by-products of radiotracer metabolism). The plasma radioactivity is measured by assaying samples of a fixed volume in a gamma well counter (generally cross-calibrated with the emission scanner). The radiolabeled metabolites are measured using radiochemical analysis techniques. The time course of total plasma radioactivity is corrected for the presence of radiometabolites to yield the radioactivity concentration of unmetabolized radiotracer (i.e., plasma input function used for data analysis) (e.g., see Price et al., 2005).
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ROIs are delineated on each subject’s coregistered anatomical MR image (e.g., Figure 3.1A, left). The ROIs are applied to the dynamic emission data and count rate information is extracted using image analysis software that generates the regional kinetic data or time–activity curves. Various in vivo kinetics may be observed that include constant accumulation or irreversible kinetics after bolus injection (see Figure 3.2A and B: top bolus), reversible kinetics and clearance at later times after bolus injection (see Figure 3.2A and B: lower bolus), or constant levels that are consistent with equilibrium kinetics after B/I administration of tracer (see Figure 3.2). The metabolite-corrected plasma data and the regional time–activity data are then analyzed using compartmental modeling analysis. The physiological process is modeled by differential equations that describe the movement of tracer between various compartments that are configured based on a priori knowledge of the underlying physiology. Estimation of the model parameters (rate constants) is achieved using an iterative nonlinear procedure that fits the model solution to the observed time–activity curves (Marquardt, 1963). The model equations and input function can be used in computer simulations to study differences in the in vivo kinetics that can arise from variations in the individual kinetic parameters (e.g., simulations of bolus and B/I data in Figure 3.2). For some radiotracers, a steady-state relationship eventually arises between the radiotracer concentration in blood and brain during the emission study. This may permit imaging over a shortened time or as a single scan (e.g., 20 or 30 min) when radiotracer retention in the brain is primarily reflective of the physiological process of interest. Radiotracer may then be administered to subjects outside of the scanner, with scanning taking place after a predefined uptake period.
RESEARCH APPLICATIONS, OUTCOME MEASURES, AND CHALLENGES Emission imaging in psychiatric research initially focused on alterations in general cerebral function (i.e., blood flow and metabolism). As SPECT and PET neuroreceptor-binding radiotracers became available with sufficient in vivo selectivity and specificity and suitable in vivo kinetics, the dopamine hypothesis of schizophrenia and the monoamine hypothesis of depression motivated investigations of neuroreceptor system function. The recent development of amyloid imaging agents provides new
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Figure 3.1 Progressive data analysis scheme, A–D, from fully dynamic to simplified analyses (PiB PET example). (A) Coregistration of anatomical MR and functional PiB PET emission images with region-of-interest (ROI) definitions (left) and resulting regional time–activity curves (TACs) for the cerebellar reference region (center, nondisplaceable [ND] uptake) and precuneus (right, high amyloid-binding region in AD). (B) Application of a 2-tissue 4-parameter (2T-4k) compartmental model (center). The model analyses utilize metabolite-corrected plasma input function data (left) and regional TAC data (right). The solid lines (right) show the model fits to the TAC data that yielded volume of distribution measures for precuneus (PRC, VT) and cerebellum (CER, VND). (C) Examination of regional tissue/plasma concentration ratios (left) reveals an eventual plateau that supports the use of a simplified graphical analysis, such as the Logan plot (center). It is important to assess the level of agreement between the nonlinear compartmental and linear Logan results; this agreement was found to be good for PiB (right, n=15: 5 AD, 5 MCI, 5 controls). (D) Examination of regional tissue/ cerebellar ratios (left) reveals plateaus at lower ratio values indicating bias (relative to plasma ratios in [C]). It is important to evaluate potential sources of study bias in order to assess whether further analysis simplifications are possible at a regional or voxel level. The voxel level analyses generally require spatial normalization of each subject’s neuroimaging data (i.e., MR and parametric emission images) to an anatomical template (center). The normalized emission images could then be sampled using one set of template ROIs for all subjects or analyzed using voxelwise statistical evaluations (right).
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Figure 3.2 (A) Simulated radiotracer plasma kinetics (or input function) that could result after the bolus injection or bolus constant infusion (B/I) administration of radiotracer. (B) Two-tissue-compartment 4-parameter simulated (2T-4k) PET brain kinetics that can result after bolus injection of a radiotracer that exhibits constant accumulation (e.g., FDG) or irreversible receptor binding during the time frame of the PET study (top bolus curve) relative to the lower bolus curve which is consistent with reversible ligand binding. The B/I PET kinetic curve exhibits a plateau that is consistent with the achievement of equilibrium.
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tools to further research at the interface of neurodegeneration and psychiatric disorder in geriatric populations. The following sections review methodology developments, terminology, and data analysis methods used for imaging applications that may be of future relevance for geriatric research investigations as well as other approaches that continue to be widely applied across the life span in psychiatric research.
Blood Flow Cerebral blood flow and perfusion measures have been performed using SPECT (e.g., [99mTc]HMPAO, [99mTc]ECD, 133Xe, and [123I]Iodoamphetamine) and PET (e.g., [15O]water, [15O]butanol, [13N]ammonia). Although 133Xe has superior characteristics as a quantitative SPECT blood-flow agent, technical issues have limited its use (Murase, Tanada, Fujita, Sakaki, & Hamamoto, 1992; Tatsch et al., 2002). Nonquantitative SPECT perfusion imaging is most often performed using [99mTc] HMPAO or [99mTc]ECD and generally allows for a single assessment of perfusion. The subject is injected outside of the scanner and radiotracer uptake is initially rapid, leading to a distribution that is essentially “trapped” in place. Imaging can begin as early as 30 min postinjection, although [99mTc]HMPAO imaging may occur as many as 24 h after injection (Thomsen et al., 2008). Quantitative dynamic 99mTc SPECT studies have been performed, but limited by radiotracer extraction and temporal resolution issues (e.g., initial frames ~30–40 sec) (Murase et al., 1992). The general kinetics of [99mTc]HMPAO and [99mTc]ECD are similar, but there are differences in the distribution of the radiotracers (Tatsch et al., 2002) that yield greater tissue ratios and clearance for [99mTc]ECD (Koulibaly et al., 2003; Léveillé, Demonceau, & Walovitch, 1992). Comparison of [99mTc]HMPAO and [99mTc]ECD scans acquired for Alzheimer’s disease subjects revealed distribution differences in this group (Koulibaly et al., 2003). This was followed by a report that more consistent correlation was observed between [99mTc]HMPAO and neurophysiological models of verbal memory (than observed for [99mTc]ECD) (Nobili et al., 2007). The authors mention the importance of normative databases. This is consistent with efforts of many emission research groups as ligand- and disease-specific databases are being established and maintained to facilitate research activities at baseline and at longitudinal follow-up (Adams et al., 2004; Minoshima, Frey, Koeppe, Foster, & Kuhl, 1995; Mosconi et al., 2007; Rabiner et al., 2002).
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A clear strength of the 99mTc SPECT perfusion study is found in study feasibility as the radiotracers can be routinely prepared in a nuclear pharmacy laboratory, SPECT scanners are widely available, and subjects can be scanned hours after injection. PET blood-flow studies are commonly performed using [15O]water; although [15O]butanol has superior characteristics (Votaw et al., 1999), it is not commonly used. Clear advantages of PET (relative to SPECT) for blood-flow imaging are found in the strong quantitative properties of PET, which result from superior temporal and spatial resolution and robust attenuation correction as well as the ability to perform repeat scans in a single session (i.e., short [15O]water half-life). [15O]Water PET blood-flow studies have utilized equilibrium, fully dynamic (Frackowiak, Lenzi, Jones, & Heather, 1980; Herscovitch, Markham, & Raichle, 1983; Huang, Carson, & Phelps, 1982; Huang et al., 1983; Lammertsma et al., 1989) and weighted integration (Alpert et al., 1984; Carson, Huang, & Green, 1986; Koeppe, Holden, & Ip, 1985; Ohta et al., 1996) methods. Dynamic [15O]water studies are generally performed over 3 to 4 min with initial frames at about 3 sec. The arterial input function can be measured using a continuous flowthrough radiation monitoring system (e.g., Laymon et al., 2008a) that is practical for short studies, as radiolabeled metabolites are not a concern. Data are analyzed using a 1-tissue-compartmental model and iterative curve fitting suitable for estimating blood-to-brain clearance and efflux (K1: mL · cm3 · min1 and k2: min1) and accounting for input function timing delays and dispersion (Iida et al., 1986, 1988). Blood flow is assessed via K1 (e.g., Figure 3.3A). An analysis simplification is use of an initial summed scan of [15O]water uptake as a proportional blood-flow measure (Kanno, Iida, Miura, & Murakami, 1991), such as that shown in Figure 3.4A, B. [15O]Water PET has been widely used in studies of resting blood flow and in the assessment of blood-flow changes that arise from neuronal activation. About 20 years ago, the field of human brain mapping emerged from early [15O]water PET activation studies that quantified state-dependent focal transients in neuronal activity (Fox & Mintun, 1989); this tool was viewed as a new neuroanatomical technique (Frackowiak & Friston, 1994). With the development of blood oxygen level– dependent (BOLD) functional MRI (fMRI) methods, the field of human brain mapping shifted to fMRI assessments of relative change. It is important to note that developments in brain mapping have provided analytical methods that are becoming standard tools for SPECT and PET.
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Figure 3.3 Examples of parametric images of (A) blood flow ([15O]water K1: general assessment of brain function) relative to (B) amyloid deposition ([11C]PiB or PiB DVR: specific assessment of brain function) for a young elderly patient with mild AD (55 years of age, MMSE = 28) and midlife cognitively normal control (45 years of age, MMSE = 28). The images show generally lower blood flow for the AD subject relative to the control, while amyloid deposition is significant for the AD patient with only nonspecific uptake of PiB in the control.
These tools allow for robust image registration and spatial normalization within and across imaging modalities using established anatomical MR atlases (or templates, see Figure 3.1D) or those newly created that can be either MR-based (e.g., age- or disease-specific) or emission-based (e.g., tracer-specific); all of this can be performed within the statistical parametric mapping framework (SPM5) that is designed for voxelwise
Figure 3.4 Example of structural MR (A) and PET uptake images summed over varying postinjection (p.i.) periods for (B) [15O]water (0–4 min p.i.), (C) [11C]WAY 5-HT1A receptor binding (20–90 min p.i.), (D) [18F]Altanserin 5-HT2A receptor binding (20–90 min p.i.), and (E) [11C]DASB 5-HT transporter binding (20–90 min p.i.). The images were acquired in the same female patient over 4 years (55–59 years of age). The PET emission images are scaled to the injected dose and body mass (% injected dose · kg · g1). The MR images correspond to the same tomographic slice as that for [15O]water and [11C]DASB, while different tomographic slices were selected for [11C] WAY and [18F]Altanserin to better highlight areas of greatest binding for these radioligands. (See section entitled “Ligand-Binding Interactions,” p. 34.)
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statistical analyses of image data within and across modalities (Friston, Ashburner, Kiebel, Nichols, & Penny, 2007). SPECT activation studies have been performed using scans collected on different days (Shimoyama et al., 2004: cognition study) or a splitdose technique involving two radiotracer injections in a single day, which yields a second scan that is vulnerable to contamination by radioactivity from the first scan (Ludwig et al., 2008: working memory study; Zheng, 2002: pre/post drug treatment study in AD). [15O]Water PET activation studies remain of value, as this technique can provide multiple quantitative blood-flow measures that can be particularly useful for assessing the effects of a pharmacological stimulus/treatment and cognitive activation, as evidenced by a recent example in hypertensive subjects (Jennings, Muldoon, Price, Christie, & Meltzer, 2008). The application of brain mapping tools for SPECT and PET imaging is becoming commonplace for many psychiatry-related research investigations. This is reflective of continuing advancements in brain mapping and emission imaging but also underscores the importance of evaluating new steps, old problems, and comparing results across methods (Boles Ponto, Schultz, Leonard Watkins, & Hichwa, 2004: cerebrovascular reserve study in MCI; Borbely et al., 2003: speech activation study).
Glucose Metabolism The measurement of cerebral glucose utilization in vivo is possible only using PET and the glucose analogue [18F]2-fluoro-2-deoxy-D-glucose (or FDG) (Ido, Wan, Fowler, & Wolf, 1977). This assessment is based on the method of Sokoloff (Huang et al., 1980; Sokoloff et al., 1977). Both glucose and FDG are metabolized (phosphorylated) by hexokinase. Glucose is further metabolized along the glycolytic pathway but FDG is irreversibly trapped in tissue as FDG-6-phosphate and is uniquely reflective of the hexokinase step. The transport and phosphorylation rates of glucose and FDG differ and an independently determined parameter, the lumped constant (LC), accounts for these differences. A 2-tissue (3- or 4-parameter) compartment model is applied. The blood–brain barrier (BBB) influx and efflux of FDG is represented by K1 (mL · cm3 · min1) and k2 (min1), and the rate of FDG phosphorylation in tissue is represented by k3 (min1). The model was later extended to allow for dephosphorylation (k4, min1) (Huang et al., 1980; Phelps et al., 1979). Fully dynamic FDG data are generally acquired with scan durations of 70 to 90 min (30–35 frames) and arterial blood sampling throughout
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(no radiolabeled metabolites). The cerebral metabolic rate of glucose (CMRglc) is expressed in terms of K1 – k3, the glucose plasma concentration (CP, mg/100 mL), and the lumped constant (LC), where CMRglc = (K1 k3 /(k2 k3))(CP /LC). The LC value for FDG in normal brain was initially determined to be 0.42 (Reivich et al., 1979), while more recent publications indicate a value that is about twofold greater, 0.78 to 0.89 (Graham et al., 2002; Hasselbalch et al., 1998; Reivich et al., 1979). The Patlak graphical analysis (Patlak, Blasberg, & Fenstermacher, 1983; Patlak & Blasberg, 1985) is appropriate to apply to the steady-state FDG data to obtain the overall uptake rate (or Ki, linear regression slope) that is equivalent to K1 k3 /(k2 k3). Simplified FDG analyses include approaches that utilize population-based rate-constant values and singlescan measures of the FDG tissue concentration (Huang et al., 1980; Hutchins et al., 1984). Other methods simplify the determination of the plasma integral based on a single FDG plasma measure and populationbased input function parameters. The simplified kinetic method (SKM) of Hunter, Hamberg, Alpert, Choi, and Fischman (1996) approximates the FDG plasma integral using venous data and a triexponential equation. This method has proven very useful for assessing brain metabolism in imaging studies of sleep (Nofzinger et al., 2000, 2002). FDG continues to be a valued tool for psychiatric imaging research across the life span (Brodie et al., 1988; Drevets et al., 2002; Kennedy et al., 2001, 2007; Lozano et al., 2008; Mayberg et al., 1997; Smith et al., 1992, 2009; Volkow et al., 1994, 1997, 2006, 2008), including studies of schizophrenia, addiction, depression, and AD.
Ligand-Binding Interactions SPECT and PET studies of neuroreceptor binding in brain provide in vivo measures that are theoretically related to the total concentration of binding sites and inversely related to the affinity of the tracer for the binding site (i.e., Bavail and KD, respectively). Examples of applications for dopamine (DA) and serotonin (5-HT) neuroreceptor system and amyloid plaque imaging are discussed below. The DA and 5-HT receptors are G protein–coupled and can exist in high- and low-affinity states. Antagonist radioligands bind equally to receptors in both states, while agonists bind only to receptors in the high affinity state. (For reviews, see Ginovart et al., 2006; Laruelle, 2000; Narendran et al., 2004.) Most in vivo imaging studies have been performed using antagonist radiotracers, but recently several agonist radiotracers have been developed. Consen-
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sus nomenclature was recently published for the in vivo imaging of reversibly binding radioligands, and this terminology is used (Innis et al., 2007). The initial evaluation of a neuroreceptor radioligand is generally performed using a bolus injection tracer kinetic modeling study performed at high specific activity (i.e., Ci/mmole). At high specific activity, the quantity of nonradioactive ligand in the system is negligible; therefore the occupation of binding sites by nonradioactive ligand is assumed to be negligible. A 2-tissue 4-parameter (2T-4k) model configuration (Figure 3.1B, center) is commonly applied; it describes (a) nondisplaceable uptake of free tracer (F) and nonspecifically (NS) bound ligand in tissue with a summed concentration CND and (b) specifically bound ligand in tissue with concentration CS. The concentration of free radioligand in tissue is CFT and CND = CFT CNS. The compartmental rate constants are K1 (mL · cm3 · min1) and k2 (min1), representing the bidirectional BBB transport of radiotracer, while k3 (min1) and k4 (min1) are respectively reflective of the bimolecular association to (kon, nmol · L1 · min1 or nM · min1) and unimolecular dissociation from (koff, min1) the binding site. When k4 艐 0, the radioligand exhibits irreversible binding. The total radioligand distribution volume in tissue (VT, mL · cm3) is the sum across each compartment, where VT = VND VS. The VT is theoretically defined as the tissue:plasma concentration ratio at equilibrium. A 2T configuration is based on the assumption that the free and nonspecific tracer kinetics are indistinguishable (CT = CFTNS CS), while a 1T configuration does not distinguish either (CFTNSS). More comprehensive presentations of compartmental modeling, theory, data analysis, and related terminology can be found in previous publications (Carson, 2003; Gunn, Gunn, & Cunningham, 2001; Koeppe, 2002; Price, 2003). Measures that are reflective of specific binding include the total volume of distribution (VT) and binding potential (BP, unitless). For the 2T-4k model, VT = K1 / k2(1 k3 / k4), k3 = fND kon Bavail, and BPND = k3 / k4 = fND Bavail / KD. Only Bavail receptors are available for binding in vivo because some may be compartmentalized, in a low-affinity state, or occupied by endogenous ligand (Innis et al., 2007). The fND parameter, or tissue-free fraction, is the fraction of radioligand free from nonspecific binding. The BP can also be derived from the regional VT values. Expression of the VT of receptor-binding regions relative to VND results in a distribution volume ratio, or DVR = VT / VND, where BPND = DVR – 1. The VND is estimated in a reference region for which specific binding of the radioligand is assumed to be negligible and the concentrations of
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free and nonspecifically bound radioligand are representative of other brain regions (e.g., the cerebellum is commonly the reference region for DA, 5-HT, and amyloid binding studies). It is important to note that the in vivo binding process is complex and not fully served by the relationships described above. The reader is referred to Innis and colleagues (2007) for a summary of these issues, further detail regarding the in vivo experiment, and alternate in vivo binding measures. Analyses may also be performed using reference tissue modeling, which essentially utilizes the reference region’s kinetics in place of the plasma input function, resulting in great simplification of the kinetic study and improvement of study feasibility (Cunningham et al., 1991; Gunn, Lammertsma, Hume, & Cunningham, 1997; Lammertsma & Hume, 1996). In addition to the assumption that VND is a valid approximation of nondisplaceable uptake, the simplified reference tissue model (or SRTM) assumes that delivery of tracer from blood to brain is equivalent for the reference and receptor-binding regions. SRTM has been widely used for the generation of parametric BP images (Wu & Carson, 2002). Graphical analyses, using either plasma or reference region data, offer linear alternatives for neuroreceptor binding studies provided that steady-state conditions are established. The Patlak analysis (see “Glucose Metabolism,” above) is applied to obtain measures of the overall uptake for an irreversibly binding radioligand, Ki = K1k3/(k2 k3) (mL · cm3 · min1), but this outcome can be confounded by blood flow. The Logan analysis provides a measure of the total distribution volume (VT) and is applied to reversible radiotracers (Logan et al., 1990, 1996) to obtain DVR or BPND measures. Bias can arise in the outcomes determined using the Logan analysis as a result of noise in the regression variables (Slifstein & Laruelle, 2000). Smoothing of the data prior to analysis can address this bias (Logan et al., 2001), although alternative methods less vulnerable to noise have been implemented (Ichise, Toyama, Innis, & Carson, 2002; Parsey, Ogden, & Mann, 2003). In a B/I study, the radioligand is administered as a bolus followed by a constant infusion and this results in (near) equilibrium conditions and tissue:plasma ratios yield VT. The advantages of the B/I method are that it requires a single radiotracer injection, quantification of BP may be achieved without arterial sampling, and total scan time may be reduced (Carson et al., 2000). However, only one parameter is generally determined and estimates of plasma clearance and specific binding cannot be separately obtained. Simple ratios (i.e., tissue:plasma and region:reference) have also been used as outcome measures when strongly related to kinetic binding measures (see Lopresti et al., 2005).
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The SUV, standardized uptake ratio, is often used in clinical settings and is the summed tissue uptake (over a predefined postinjection period) normalized to injected dose and body mass (unitless, assuming 1 g · cm3 tissue density) (see Price et al., 2005). The SUV ratio (or SUVR) is simply the region:reference tissue ratio.
Dopamine Receptors Of the five dopamine (DA) receptor subtypes identified (Sunahara, Seeman, Van, & Niznik, 1993), the D2 site has been most extensively studied, in part because of its link to antipsychotic medication effects (Creese, Burt, & Snyder, 1975; Grigoriadis & Seeman, 1984; Hall et al., 1994). Dopamine receptors are highly concentrated in the striatum and low in the cerebellum (Hall et al., 1994). Extrastriatal (e.g., frontal cortex) concentrations are higher for D1 than for D2 receptors (Hall et al., 1994). The D2 receptor has been widely studied over the last two decades with radiotracers that predominantly bind to D2 receptors with limited D3 receptor binding. The contribution of the D3 receptor signal is considered to be minimal unless otherwise noted by D2/3. Most in vivo dopamine-D2 receptor studies have utilized antagonists (or inverse agonists) with varying affinities. The highest affinity (e.g., in vitro KD 艐 0.03 nM) tracers exhibit irreversible binding kinetics, while those of lower affinity (e.g., in vitro KD 艐 1 nM) exhibit reversible kinetics. The importance of radioligand affinity in vivo imaging studies was evident in early [11C]NMSP and [11C]RCP studies performed in control and schizophrenic subjects that yielded conflicting results that were partially attributed to differences in radiotracer affinities and thus to differences in the radiotracer sensitivities to endogenous dopamine (Farde et al., 1987a; Seeman, Guan, & Niznik, 1989; Wong et al., 1986). Examples of D2 receptor radiotracers include [11C]FLB 457, KD = 0.02 nM (Halldin et al., 1995), [18F]fallypride, KD = 0.03 (Mukherjee, Yang, Das, & Brown, 1995), [11C]N-methylspiperone (or [11C]NMSP), KD = 0.2 nM (see Wagner et al., 1983); [123I]IBZM, KD = 0.4 nM (Kung et al., 1989), and [11C]raclopride or ([11C]RCP), KD = 1 nM (Farde et al., 1985). Most are used to primarily image D2 receptors in striatum (see Figure 3.5), although ligands of higher affinity (e.g., [11C]fallypride) exhibit some extrastriatal uptake. An age-related decline in D2 receptor binding is established (e.g., Volkow et al., 2000). The test–retest variability is 5%–10% ([11C]FLB-457: Vilkman et al., 2000; [11C]fallypride: Mukherjee et al., 2002; [123I]IBZM: Catafau et al., 2008; [11C]RCP: Volkow et al., 1993), although not all measures were quantitative.
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Figure 3.5 PET imaging of D2 receptor binding in a healthy subject. An example of a typical [11C]raclopride PET image of striatal D2 receptor binding (parametric BPND image created using the Logan analysis) is shown along with the corresponding MR image for a healthy 30-year-old female volunteer. The maximum pixel value corresponds to a BPND value of 4.0.
An important development was the validation of a study paradigm to assess endogenous dopamine activity in vivo. In such studies, dopaminergic activity is inferred by the reduction observed in the D2 radiotracer binding potential measure that results after administration of d-amphetamine (AMPH), an agent that leads to increases in endogenous extracellular dopamine. Reductions in the VT and KI were reported for [11C]RCP and [18F]NMSP during bolus injection PET studies in baboons that were performed pre- and post-AMPH administration (1 mg/kg); these reductions exceeded the measured test–retest variability of the PET D2 binding measures (Dewey et al., 1991, 1993). Computer simulations indicated that the effect of DA on [18F]NMSP binding was greater in vivo than in vitro because the in vivo experiment depends upon the half-time clearance of the radiotracer in tissue and [18F]NMSP binding is not likely to be affected by physiological levels of endogenous DA (Logan et al., 1991). Increases in endogenous dopamine were also detected using a B/I paradigm, SPECT, and [123I]IBZM (Innis et al., 1992; Laruelle et al., 1995). Subsequent validation studies of the [11C]RCP/AMPH bolus and B/I paradigms provided important confirmatory results (Logan et al., 1991, 1994; Laruelle, 2000; Carson et al., 1997; Endres et al., 1997). Similarly, methods were described and evaluated for the in vivo detection of endogenous DA depletion (Ginovart, Farde, Halldin, & Swahn, 1997; Laruelle et al., 1997). Another methodological improvement re-
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sulted from improved PET spatial resolution (6 mm). This allowed for the delineation of striatal subregions and more precise localization of AMPH-induced binding potential changes in functionally important areas, such as of anterior ventral (including nucleus accumbens) and dorsal striatum (Drevets et al., 1999a, 2001; Mawlawi et al., 2001; Martinez et al., 2003). The [11C]RCP/AMPH paradigm has been extensively applied in psychiatric neuroimaging research predominantly in schizophrenia (Breier et al., 1997; Laruelle et al., 1996) but also in mood disorder (Parsey et al., 2001), addiction (Martinez et al., 2005, 2007; Volkow et al., 1997), attention deficit hyperactivity disorder (ADHD) (Volkow et al., 2007), and stress (Montgomery, Mehta, & Grasby, 2006a; Pruessner, Champagne, Meaney, & Dagher, 2004) using bolus and B/I methods (for reviews, see Kennedy & Zubieta, 2004; Laruelle, Talbot, Martinez, & Abi-Dargham, 2002a; Zipursky, Meyer, & Verhoeff, 2007). Reports of extrastriatal D2 receptor binding measures have been limited. Nonquantitative studies by Wang and colleagues (1993) compared extrastriatal measures of [11C]NMSP and [11C]RCP and generally concluded that the low target-to-background ratios limited the visualization of regions such as the frontal and temporal cortices. Later, these investigators reported an age-related decline in D2 receptors ([11C] RCP) in the thalamus and temporal regions (Wang et al., 1996). Extrastriatal measures of D2-receptor binding have been reported using two high-affinity DA D2/3 radiotracers [11C]FLB 457 (Farde et al., 1997; Halldin et al., 1995; Suhara et al., 1999) and [18F]fallypride (Christian, Narayanan, Shi, & Mukherjee, 2000; Kessler et al., 1997). While there is widespread acceptance that both of these imaging agents, [11C]FLB 457 and [18F]fallypride, can be used to reliably measure D2-receptor binding in several extrastriatal regions (Cropley et al., 2008; Mukherjee et al., 2002; Sudo et al., 2001; Vilkman et al., 2000), the question of whether the signal-to-noise ratio of these radiotracers is sufficient to image AMPH-induced dopamine release in regions with medium to low D2-receptor density—such as the midbrain, thalamus, hippocampus, and cortex—is still unresolved in the literature. The successful use of [18F]fallypride to measure AMPH-induced DA release in some extrastriatal regions, such as the midbrain and hippocampus but not the cortex in humans, has now been reported by at least three different groups (Cropley et al., 2008; Riccardi et al., 2005; Slifstein et al., 2007a). In contrast, the data with [11C]FLB 457 to image stimulantinduced DA release in the extrastriatal regions is somewhat mixed and
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inconclusive (Aalto, Bruck, Laine, Nagren, & Rinne, 2005; Aalto et al., 2009; Montgomery, Asselin, Farde, & Grasby, 2006b; Narendran et al., 2009). In a recent human study contrasting these two high-affinity DA D2 radioligands [11C]FLB 457 and [11C]fallypride (Narendran et al., 2009), the [11C]FLB 457 BPND was 30%–70% higher compared to the [11C] fallypride BPND in the cortical regions. In this same study, amphetamineinduced reduction in the radiotracer binding was observed with [11C] FLB 457 but not [11C]fallypride in several prefrontal cortical regions of interest, such as the dorsolateral prefrontal cortex, medial prefrontal cortex, and anterior cingulate cortex. Pending successful validation of this technique, [11C] FLB 457 may allow for the characterization of the role of dopamine in higher cortical functioning in normal aging and disorders such as ADHD, schizophrenia, and Parkinson’s disease. Extrastriatal D1-receptor-binding radiotracers include [11C]SCH 23390 (KD: 0.5 nM) (Farde, Halldin, Stone-Elander, & Sedvall, 1987b), [11C]SCH 39116 (KD: 0.8 nM), and [11C]NNC 756 (KD: 0.4 nM) (Laihinen et al., 1994). Despite the recent selectivity issues (significant binding to 5HT2A receptors) that have plagued these radioligands (Ekelund et al., 2007; Slifstein et al., 2007b), several groups have successfully used these agents and reported abnormalities in DA D1-receptor binding in schizophrenia (Abi-Dargham et al., 2002; Okubo et al., 1997), depression (Cannon et al., 2009), and some (Narendran et al., 2005) but not all addictive disorders (Martinez et al., 2009). Another exciting development is related to the advancement of DA D2/3 agonist radiotracer [11C]PHNO, a radiotracer with modest in vivo preference for D3 relative to D2 receptors (4- to 12-fold) (Narendran et al., 2006). This pharmacological property has been exploited by several groups for its potential to provide information about D3 receptors in studies of nonhuman primates (Narendran et al., 2006; Rabiner et al., 2007). The advancement of similar imaging techniques that combine [11C]PHNO with highly selective D3 or D2 compounds in humans is likely to allow for the successful evaluation of the potential for D3receptor binding in D3 receptor–enriched regions such as the globus pallidus and ventral striatum in health and disease.
Serotonin Receptors The serotonin system is distributed throughout brain, with cell bodies primarily originating in the raphe nuclei of the midline brainstem and neurons that project diffusely over the thalamus, hypothalamus, basal
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ganglia, basal forebrain, and neocortex (Peroutka, 1994). There are over 14 receptor subtypes, with 5-HT1A and 5-HT2A receptors extensively implicated in the antidepressant response (Kreiss & Lucki, 1997; Meltzer et al., 1998a; Peroutka, 1994). Two 5-HT1A receptor antagonist radiotracers that have been used in psychiatric applications are [carbonyl-11C]WAY 100635 ([11C]WAY, KD = 2 nM) (Hall et al., 1997; Pike et al., 1995) and [18F]MPPF (Plenevaux et al., 2000). Several other radioligands have been developed and reviewed in the literature (Passchier et al., 2000). A [18F]-labeled analog of [11C]WAY, [18F]FCWAY, was also introduced for the study of 5-HT1A receptor binding (Carson, 2000), but the application of this tracer has been limited by significant defluorination, which leads to uptake of radioactivity in the skull, thus possibly contaminating the signal of neocortex (Ryu et al., 2007). Recently a new 5-HT1A agonist, [11C]CUMI-101, was introduced and evaluated in humans (Kumar et al., 2007; Milak et al., 2008); it holds promise for imaging of high-affinity 5-HT1A-receptor binding in vivo. The in vivo distribution of 5-HT1A-receptor binding is highest in mesial temporal cortex (including hippocampus) and moderate in anterior cingulate, lateral orbitofrontal, brainstem raphe, and occipital areas and lowest in basal ganglia and thalamus (Figure 3.4C). The cerebellum is the primary reference region. This is consistent with the known rank order of 5-HT1A receptors (Pazos, Probst, & Palacios, 1987a) with the exception of the raphe, which is a small structure whose radioactivity concentration is underestimated owing to the limited resolution of PET imaging. Gunn and colleagues (1998) performed compartmental and reference tissue (SRTM) modeling of [11C]WAY data collected in controls. These authors found a strong linear correlation between the SRTM and compartmental measures and, although the slope was less than 1.0 indicating bias in the SRTM results, the test–retest variation for temporal cortex was about 11%. Low signal in the cerebellar reference region complicated these analyses (Slifstein, Parsey, & Laruelle, 2000). Parsey and colleagues (2000) showed that BP was underestimated by SRTM (particularly in areas of high receptor density) and by the Logan method (particularly in small noisy regions) relative to the kinetic method, but good (i.e., low) test–retest variation (same-day studies) was observed for both the full kinetic and Logan approaches (intraclass correlation coefficient ICC 0.84 0.14 across 15 ROIs). Compartmental analyses have also been performed to evaluate [18F]MPPF using a multi-injection protocol that provided in vivo measures of hippocampal Bavail (2.9 nM) and KD (2.8 nM)
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in healthy males: strong correlation between the regional Logan BPND and Bavail values was observed despite scalp uptake (Costes et al., 2002). Good test–retest variation was found in controls across regions (mean ICC 0.70) using SRTM and Logan BPND (Costes et al., 2007). [11C]WAY has been applied in several studies of depression (for review, see Drevets et al., 2007). Gender effects on binding have been noted for both [11C]WAY (e.g., Meltzer et al., 2001; Parsey et al., 2002) and [18F]MPPF (Costes et al., 2005), with variable findings regarding age (Costes et al., 2005; Meltzer et al., 2001; Parsey et al., 2002; Tauscher et al., 2001). Alterations in 5-HT1A receptor binding have been found in eating disorders (for review, see Kaye, 2008). Sensitivity to endogenous serotonin has not been established (Jagoda et al., 2006; Maeda et al., 2001). Normative databases have been described for [11C]WAY (Rabiner et al., 2002) and [18F]MPPF (Costes et al., 2005). Initial 5-HT2A receptor binding studies utilized PET radiotracers, such as [11C]NMSP (extrastriatal) and N1-([11C]-methyl)-2-Br-LSD (Wong et al., 1987), [11C]ketanserin (Baron et al., 1985), and [18F]setoperone (KD = 0.7, Maziere et al., 1988), but limitations in affinity and selectivity characteristics as well as feasibility of synthesis resulted in the common use of [18F]altanserin (or [18F]ALT, Lemaire et al., 1991) and [11C]MDL 100907 (or [11C]MDL, Lundkvist et al., 1996). The KD values of [18F]ALT and [11C]MDL are about 0.3 nM (Kristiansen et al., 2005). SPECT imaging of 5-HT2A receptor binding has been performed using [123I]R91150 (Catafau et al., 2006a, 2006b). The distribution of 5-HT2A receptor binding (Pazos, Probst, & Palacios, 1987b) is greater in anterior cingulate, frontal, and lateral temporal cortices; moderate in mesial temporal cortex; and lowest in basal ganglia and thalamus (Figure 3.4D), with cerebellum as reference region (Ito, Nyberg, Halldin, Lundkvist, & Farde, 1998). The early [18F]ALT PET studies were evaluated by the full kinetic methods using arterial input (Biver et al., 1994; Sadzot et al., 1995). The regional 5-HT2A receptor binding measures followed the in vitro rank order of 5-HT2A receptor density and reflected displacement of [18F] ALT by ketanserin (5-HT2A antagonist) (Biver et al., 1994; Sadzot et al., 1995). The compartmental analyses indicated the existence of a slow, nonspecific binding component most evident in cerebellum. Studies in baboons showed that this component was partly the result of radiometabolites of [18F]ALT crossing the BBB. Subsequent studies verified that this radioactivity contributed to a background signal of nonspecific radioactivity, which was fairly uniform across regions and adequately ac-
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counted for by the cerebellar reference data (Price et al., 2001a, 2001b). An age-related decline in 5-HT2A receptor binding is established (e.g., Bhagwagar et al., 2006; Meltzer et al., 1998b; Sheline, Mintun, Moerlein, & Snyder, 2002). Bolus [18F]altanserin, [11C]MDL, and [123I]R91150 data are generally analyzed using compartmental modeling and plasma-based graphical (Hinz, Bhagwagar, Cowen, Cunningham, & Grasby, 2007; Ito et al., 1998; Smith et al., 1998) or SRTM (Catafau et al., 2006a, 2006b) analyses. Good test–retest (~10%, 2–16 days apart) was found for [18F]ALT across multiple receptor-rich areas, using the Logan DVR (Smith et al., 1998). For [123I]R91150 SPECT imaging, pseudoequilibrium kinetics were observed and tissue ratios were strongly correlated (and low bias) with kinetic binding measures. [18F]Setoperone was initially evaluated using tissue ratios (Blin, Pappata, Kiyosawa, Crouzel, & Baron, 1988). Subsequent kinetic analyses verified a high correlation between tissue ratios and kinetic BP (Petit-Taboue et al., 1996), and tissue ratio test– retest (6–21 days apart); variation was 5%–7% (Kapur, Jones, DaSilva, Wilson, & Houle, 1997). Equilibrium B/I imaging has been quite useful for [18F]ALT (Pinborg et al., 2003; van Dyck et al., 2000). A normative database was described (Adams et al., 2004). Low test–retest variation (~6%) was measured in cortical areas for the B/I binding potential with several-fold greater variation in receptor-poor areas (Haughbol et al., 2007). The [18F]ALT B/I method has provided stable binding measures after 2 years of follow-up in subjects 33 to 67 years of age, supporting its use for longitudinal studies of aging in neuropsychiatric disorders (Marner et al., 2009). Neuroimaging research applications for the 5-HT2A receptor include aging (Meltzer et al., 1998b); 5-HT2A receptor binding includes depression (Bhagwagar et al., 2006; Meyer et al., 2001), schizophrenia (Trichard et al., 1998), and AD (Blin et al., 1993).
Monoamine Transporters The dopamine (DAT), serotonin (SERT), and norepinephrine (NET) transporters are located on presynaptic nerve terminals and are the mechanism by which monoamine neurotransmitter action is terminated and neurotransmitter cleared from the synapse by reuptake into the dopamine or 5-HT nerve terminal and subsequent degradation. Monoamine transporters have been implicated in many psychiatric disorders and are also potent targets for drugs of abuse as well as widely prescribed
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therapeutic agents. Owing to their localization on monoaminergic nerve terminals, monoamine transporters are important targets for PET and SPECT radiotracer development. In principle, the binding of a radiotracer with specificity for a monoamine transporter reflects the density of nerve terminals and therefore may provide clinically meaningful assessments of monoaminergic denervation in disease. Radiotracers for monoamine transporters have also been effectively used in drug development for in vivo estimates of the occupancy of therapeutic agents that act on the same monoamine transporter (for review, see Talbot & Laruelle, 2002). Despite the importance of NET in psychiatric disorders, this section focuses only on DAT and SERT because of signal-to-noise issues and problems of reference region identification that have limited the application of NET radioligands (for review, see Ding, Lin, & Logan, 2006). In addition to this, numerous DAT and SERT radioligands have been synthesized; only a brief summary follows. Several review publications provide further detail regarding available radioligands, analytic methodology, and psychiatric research applications for these in vivo studies (e.g., Laruelle, Slifstein, & Huang, 2002b; Piccini, 2003; Zipursky et al., 2007). DAT sites are predominantly found in brain areas with significant dopaminergic innervation, including the striatum, nucleus accumbens, substantia nigra, and ventral tegmental area. DAT is responsible for terminating the dopamine signal and is particularly important in a number of dopamine-related disorders, such as ADHD. It is also the site of action of commonly abused psychostimulant drugs, such as cocaine and methamphetamine, that bind to DAT and block (or reverse) the reuptake mechanism. Methylphenidate, a psychostimulant drug that binds with high affinity to both DAT and NET, is frequently prescribed for the treatment of ADHD. Numerous radiotracers have been developed for dopamine transporter (DAT) imaging that include [11C]-CFT, [11C]cocaine, and [11C]methylphenidate for PET and [123I]-CIT, [123I]CIT-FP (DATScan), [99mTc]TRODAT, and [123I]altropane for SPECT (for reviews, see Laruelle et al., 2002b; Piccini, 2003). A limitation of some of these tracers, most notably [123I]-CIT, is limited selectivity for DAT and the potential for outcomes with mixed contributions of DAT and SERT binding. Overall, these radiotracers have been successfully applied, but not without some difficulty, owing to the variability that can arise in the quantification of binding in areas exhibiting nearly irreversible binding coupled with rapid metabolism of the radiotracer in plasma. The quantification of DAT radiotracers has involved compartmental
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modeling, simple tissue ratios, and reference tissue approaches such as the reference Logan graphical analysis and SRTM (Scanley et al., 2000; Yaqub et al., 2007). DAT binding has also been found to decline with age (van Dyck et al., 1995) and is influenced by a range of factors (e.g., age, gender, and smoking habit), as reviewed by Piccini (2003). The SERT binding sites are abundant in limbic cortices (e.g., posterior uncus, anterior cingulate gyrus, entorhinal and insular cortices), striatum, thalamus, and the raphe nucleus (Varnas, Halldin, & Hall, 2004). Examples of SERT radioligands that have been developed include [11C]-CIT, [11C]()-McN5652, and [11C]DASB for PET and [123I]CIT and [123I]ADAM for SPECT (for review, see Laruelle et al., 2002b). These compounds reflect the known distribution of SERT, exhibiting highest uptake in brainstem, thalamus, striatum, temporal cortex, and frontal cortex (see Figure 3.4E), although the -CIT compounds have limited selectivity for SERT (see DAT, above). The PET radioligand [11C]McN5652 has been somewhat limited by high levels of nonspecific binding. The PET radiotracer [11C]DASB is more commonly used because it provides a lower nonspecific binding signal than [11C]McN5652 and is selective for SERT (Frankle et al., 2006; Kim, Ichise, Sangare, & Innis, 2006; Ogden et al., 2007). The SPECT agent [123I]ADAM has been applied in several imaging studies (for summary, see Frokjaer et al., 2008). SERT imaging data have been analyzed using compartmental, graphical, and reference tissue methods as well as summed tissue uptake (e.g., Catafau et al., 2005; Frankle et al., 2004; Frokjaer et al., 2008). Frankle and colleagues (2006) reported test–retest variability for [11C] DASB of 10% (same-day studies, 120-min acquisitions), across regions and analyses (compartmental through graphical). The optimal imaging time for [123I]ADAM SPECT quantification was found to be 4 to 6 h after a single injection (Catafau et al., 2005), with test–retest variation of specific uptake ratios ranging from about 13%–22%. Frokjaer and colleagues (2008) reported good agreement for SRTM and the Logan graphical outcomes relative to the full kinetic measures but overestimation by the late scan tissue ratios; the established sources for this discrepancy were recently summarized by Slifstein (2008). This distribution of SERT is consistent with the widely accepted role of 5-HT in the modulation of mood and emotion. SERT dysfunction has been associated with clinical depression, obsessive compulsive disorder (OCD), and alcohol abuse. SERT is the target of the widely abused psychoactive drug 3,4-methylenedioxy-N-methylamphetamine (MDMA) or
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“ecstasy” as well as a class of frequently prescribed antidepressant medications termed selective serotonin reuptake inhibitors (SSRIs); these include fluoxetine (Prozac), paroxetine (Paxil), and sertraline (Zoloft). Studies have found similar results when [11C]DASB and [11C]McN5652 were used to study MDMA users (McCann et al., 2005).
Amyloid Plaques The definitive diagnosis of Alzheimer’s disease (AD) is based on the presence of numerous abnormal protein deposits, amyloid-beta (A) plaques and neurofibrillary tangles, observed at autopsy. The neuroimaging community has directed much effort toward the in vivo study of early neurodegenerative brain changes related to dementia, mild cognitive impairment (MCI), and normal aging. Initial amyloid-imaging tracers include [18F]FDDNP (Shoghi-Jadid et al., 2002; Small et al., 2006), [11C] PiB (Klunk et al., 2004), and [11C]SB13 (Verhoeff et al., 2004) for PET and [123I]IMPY (Kung et al., 2002) for SPECT. There are several new [18F]-labeled compounds that are also under development and evaluation; these will facilitate amyloid imaging for routine clinical applications (Rowe et al., 2008; for review, see Mathis, Lopresti, & Klunk, 2007). Of these radiotracers, [11C]PiB has been most widely used, partly because of its robust signal-to-noise ratio. The first collaborative [11C] PIB PET studies (Klunk et al., 2004) reported a PiB uptake in AD nearly twice that of controls in specific cortical areas (p 0.002) but similar in “amyloid spared” areas (subcortical white matter, cerebellum) (see Figures 3.1A and 3.3B); specific PIB uptake correlated inversely with FDG metabolism (e.g., parietal) (Klunk et al., 2004). Kinetic modeling of dynamic PET data provided absolute [11C]PIB retention measures that were used to evaluate the results of simplified [11C]PiB methods (including reference-tissue methods and single-scan uptake). Despite bias in the simplified measures (relative to arterial-based analyses), strong correlations, low test–retest variability (~5% in primary cortical areas), and reduction of subject burden supported the use of simplified methods (Lopresti et al., 2005; Price et al., 2005). [11C]PIB retention in MCI subjects ranged from control to AD levels with evidence of early amyloid deposition in frontal and precuneus areas (Lopresti et al., 2005; Price et al., 2005). The original findings with PiB in AD and controls have been reproduced in over 40 PET centers around the world and more than 100 subjects have been scanned at some sites (e.g., Engler et al., 2006; Mintun et al., 2006; Rowe et al., 2007).
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In Figure 3.1, the compartmental model fits assumed a blood volume contribution to the regional PET data of 5% of the vascular radioactivity. A 2-tissue-compartment 4-parameter (2T-4k) model was applied to data for one AD subject and yielded PiB PET cerebellar rate constants (% standard error) of: K1 = 0.273 mL · cm3 · min1 (3.2%), k2 = 0.144 min1 (4.3%), k3 = 0.008 min1 (14.2%), and k4 = 0.005 min1 (49.9%) with precuneus rate constants reflective of amyloid deposition (K1 = 0.260 mL · cm3 · mL [3.9%], k2 = 0.124 min1 [9.2%], k3 = 0.045 min1 [11.5%], and k4 = 0.016 min1 [9.9%]. These parameters were used to determine the total distribution volume (VT) (see “Ligand-Binding Interactions,” above). Instability sometimes results (e.g., standard errors greater than 50%) from noise and/or low signal in the tissue and plasma data and it is common to evaluate simpler methods that are either less sensitive to these issues or for which such issues do not arise. Similar to other previously well-evaluated radioligands, such as [11C] raclopride, reference-tissue analyses and graphical approaches were found to be robust simple alternatives (see Figure 3.1C) and are commonly used for [11C]PiB PET analyses on regional (e.g., Engler et al., 2006; Lopresti et al., 2005; Mintun et al., 2006; Rowe et al., 2007) and voxel (or parametric) bases (e.g., Mikhno et al., 2008; Yaqub et al., 2008; Zhou et al., 2007; Ziolko et al., 2006) (see Figure 3.1D). Lopresti and colleagues (2005) also evaluated the use of the carotid time–activity data in place of the plasma input function and found promising results. Amyloid imaging has been applied to study AD, Parkinson’s disease and Lewy body dementia, mild cognitive impairment, late-life depression, and normal aging (Aizenstein et al., 2008; Butters et al., 2008; Edison et al., 2008; Fodero-Tavoletti et al., 2007; Jack et al., 2008; Villemagne et al., 2008; Wolk et al., 2009).
Emission Imaging Challenges Practical issues of general importance for emission imaging studies can become of great importance for geriatric mental health application. These include cerebral atrophy (e.g., accurate quantification of functional emission measures in gray matter, robust image registration, region delineation, and spatial normalization), age-related changes (i.e., as a potential confound for measurements of interest), motion during the scan, study feasibility vs. quantitative quality of the outcome measures, and medication or therapies (impact on in vivo metabolism of radiotracer and patient compliance during scanning session). These factors lead
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to variability and bias in the regional tissue kinetics and plasma input function. The dilutional effect of expanded cerebrospinal fluid (CSF) spaces resulting from brain atrophy can lead to underestimation of radioactivity concentration in gray matter, particularly in cortical areas (for review, see Metzler, Bowsher, & Jaszczak, 2003). In addition to this, the limited resolution of SPECT and PET leads to measurement bias in the quantification of radioactivity in small brain areas and spillover contamination of low-signal areas adjacent to areas of high radioactivity concentration. The use of a resolution correction scheme that compensates for emission signal blur between areas of gray matter and white matter or CSF has proven useful in emission imaging aging research (Marner et al., 2009; Meltzer et al., 1998b, 2000, 2004). For many years, researchers have developed methods to address these issues, and techniques continue to evolve as scanner characteristics and computational capabilities improve to allow for more robust and sophisticated tissue segmentation and whole-brain applications (Baete et al., 2004; Bowsher et al., 1996; Fazio & Perani, 2000; Meltzer, Leal, Mayberg, Wagner, & Frost, 1990; Meltzer et al., 1999; Muller-Gartner et al., 1992; Muzic, Chen, & Nelson, 1998; Quarantelli et al., 2004; Rousset, Ma, & Evans, 1998; Yang, Huang, Mega, & Lin, 1996). Nevertheless, it is important to consider what level of correction scheme complexity is appropriate to avoid the introduction of spurious noise or bias (Marner et al., 2009; Meltzer et al., 1999). A related issue is a mismatch in the MRI and PET coregistration, which can be complicated by brain atrophy (e.g., enlarged ventricles and sulci), with errors arising at tissue interfaces or along ventricle borders. Registration challenges also arise for late single-scan images when the radioactivity distribution is fairly localized (e.g., [11C]raclopride, high-striatal but low-cortical signal; see Figure 3.5). Simple quality-control steps include simultaneous visual inspection of both the coregistered MRI and PET image sets at key points throughout brain as well as verification of accurate image orientation (Laymon et al., 2008b). Spatial mapping errors also can arise in the spatial normalization (i.e., mapping of image data to a reference space) of emission data in activation analyses (Sychra, Pavel, Chen, & Jani, 1994; SPECT) or automated ROI sampling methods (Figure 3.1D). Spatial normalization may be improved by choosing an MRI template (e.g., appropriate level of atrophy) and normalization parameters that are well suited for the particular study group (Crinion et al., 2007; Rosario, Ziolko, Weissfeld, & Price, 2008). Such template-based methods
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(MRI and PET ligand templates) have been examined for studies of dementia and aging (Fripp et al., 2008; Sun et al., 2007). Artifacts arising from subject motion can be a large problem, particularly when it leads to mismatch between the PET emission and transmission (attenuation correction) data. The impact of motion may be minimized by short scan durations (when possible) or the application of postprocessing approaches that include the use of motion tracking devices and data-driven detection and correction (Kyme et al., 2003), deconvolution or deblurring (Faber, Raghunath, Tudorascu, & Votaw, 2009; Raghunath, Faber, Suryanarayanan, & Votaw, 2009), and simpler frame-by-frame registration techniques (for review, see Tellmann et al., 2006). Several technical issues complicate analyses of studies with radioligands that exhibit certain reference region kinetics or rapid plasma metabolism. As an example, [11C]WAY is a radiotracer with very low free and nonspecific uptake. Reproducible quantification of the reference region VT can be problematic and vulnerable to errors and variability arising from the PET scatter correction, variable location of the reference region (across scans) within the scanner field of view (FOV) due to lower scanner sensitivity at edges of the FOV, and spillover of radioactivity from neighboring areas, such as occipital cortex (Drevets et al., 1999b; Gunn et al., 1998; Hirvonen et al., 2007; Parsey et al., 2000). As an alternative, Parsey and coworkers (2005) utilized white matter to approximate [11C]WAY reference region kinetics. The extent to which this alternative satisfies reference region assumptions is being established. As another example, in [11C]PiB PET studies, there can be uptake in the cerebellar white matter, and the influence of this signal should be minimized when defining the reference region. Finally, the potential for specific binding in the reference region is important to evaluate with each application (Parsey et al., 2005), as is the potential for blood flow as a confound to the specific binding measures. Last, for G-coupled receptor systems, radiotracer internalization can occur (Laruelle, 2000); this can lead to alterations in radiotracer binding that are not consistent with a simple occupancy model for challenge paradigms. The extent to which neuroreceptor-binding compounds may be vulnerable to this process is unclear. Goggi, Sardini, Egerton, Strange, and Grasby (2007) recently used confocal microscopy to quantify agonist-dependent receptor internalization; the authors found this approach to provide a direct method for the investigation of pharmacological and mechanistic aspects of internalization that could inform data interpretation for in vivo neuroreceptor imaging studies.
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CONCLUSIONS Emission imaging uniquely allows for the study of neurophysiological processes in the living brain. Over three decades, methodology developments (e.g., radiotracer, scanner, experimental paradigm, analyses, and standardization) have progressively led to more precise, feasible, and robust tools that can be applied by the psychiatric researcher to better understand neurobiological aspects of a specific disorder or the interface between disorders. Examples of developments include established methods for rigorous and simple assessments of blood flow, glucose metabolism, neuroreceptor and neurotransporter binding (e.g., dopamine and serotonin systems), and amyloid plaque deposition. Normative radiotracer-specific databases are being developed and maintained to faciliate future investigations at baseline and at longitudinal follow-up. These developments and working solutions to challenges in emission imaging will lead to further improvements of this technology and serve to advance translational research in geriatric mental illness.
ACKNOWLEDGMENTS The authors thank Dr. Meltzer for the MR and serotonin PET images (MH067602 and MH064625), Dr. Drevets for the MR and dopamine PET images (NARSAD, MH01713), and the Pittsburgh Amyloid Imaging group for the [15O]water and PiB PET images (K02 AG027998, R01 MH070729, P01 AG025204, P50 AG 0005133R37, and AG025516). This chapter is dedicated to G. Allen and to the families and volunteers who participated in these research studies. REFERENCES Aalto, S., Bruck, A., Laine, M., Nagren, K., & Rinne, J. O. (2005). Frontal and temporal dopamine release during working memory and attention tasks in healthy humans: A positron emission tomography study using the high-affinity dopamine D2 receptor ligand [11C]FLB 457. Journal of Neuroscience, 25, 2471–2477. Aalto, S., Hirvonen J., Kaasinen, V., Hagelberg, N., Kajander, J., Nagren, K., et al. (2009). The effects of d-amphetamine on extrastriatal dopamine D(2)/D(3) receptors: A randomized, double-blind, placebo-controlled PET study with [(11)C]FLB 457 in healthy subjects. European Journal of Nuclear Medicine and Molecular Imaging, 36(3), 475–483.
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Riccardi, P., Li, R., Ansari, M. S., Zald, D., Park, S., Dawant, B., et al. (2005). Amphetamine-induced displacement of [18F] fallypride in striatum and extrastriatal regions in humans. Neuropsychopharmacology, 31, 1016–1026. Rosario, B. L., Ziolko, S. K., Weissfeld, L. A., & Price, J. C. (2008). Assessment of parameter settings for SPM5 spatial normalization of structural MRI data: Application to type 2 diabetes. Neuroimage, 41, 363–370. Rousset, O., Ma, Y., & Evans, A. (1998). Correction for partial volume effects in PET: Principle and validation. Journal of Nuclear Medicine, 39(5), 904–911. Rowe, C. C., Ackerman, U., Browne, W., Mulligan, R., Pike, K. L., O’Keefe G., et al. (2008). Imaging of amyloid beta in Alzheimer’s disease with 18F-BAY94–9172, a novel PET tracer: Proof of mechanism. Lancet Neurology, 7, 129–135. Rowe, C. C., Ng, S., Ackermann, U., Gong, S. J., Pike, K., Savage, G., et al. (2007). Imaging beta-amyloid burden in aging and dementia. Neurology, 68, 1718–1725. Ryu, Y. H., Liow, J. S., Zoghbi, S., Fujita, M., Collins, J., Tipre, D., et al. (2007). Disulfiram inhibits defluorination of 18F-FCWAY, reduces bone radioactivity, and enhances visualization of radioligand binding to serotonin 5-HT1A receptors in human brain. Journal of Nuclear Medicine, 48, 1154–1161. Sadzot, B., Lemaire, C., Maquet, P., Salmon, E., Plenevaux, A., Degueldre, C., et al. (1995). Serotonin 5HT2 receptor imaging in the human brain using positron emission tomography and a new radioligand, [18F]altanserin: Results in young normal controls. Journal of Cerebral Blood Flow and Metabolism, 15, 787–797. Scanley, B. E., Gandelman, M. S., Laruelle, M., Al-Tikriti, M. S., Baldwin, R. M., Zoghbi, S. S., et al. (2000). [123I]IPCIT and [123I]beta-CIT as SPECT tracers for the dopamine transporter: A comparative analysis in nonhuman primates. Nuclear Medicine and Biology, 27, 13–21. Seeman, P., Guan, H.-C., & Niznik, H. (1989). Endogenous dopamine lowers the dopamine D2 receptor density as measured by [3H]raclopride: Implications for positron emission tomography of the human brain. Synapse, 3, 96–97. Sheline, Y. I., Mintun, M. A., Moerlein, S. M., & Snyder, A. Z. (2002). Greater loss of 5-HT(2A) receptors in midlife than in late life. American Journal of Psychiatry, 159, 430–435. Shimoyama, H., Aihara, M., Fukuyama, H., Hashikawa, K., Aoyagi, K., Goldberg, E., et al. (2004). Context-dependent reasoning in a cognitive bias task: Part II. SPECT activation study. Brain Development, 26, 37–42. Shoghi-Jadid, K., Small, G. W., Agdeppa, E. D., Kepe, V., Ercoli, L. M., Siddarth, P., et al. (2002). Localization of neurofibrillary tangles and beta-amyloid plaques in the brains of living patients with Alzheimer disease. American Journal of Geriatric Psychiatry, 10, 24–35. Slifstein, M. (2008). Revisiting an old issue: The discrepancy between tissue ratioderived binding parameters and kinetic modeling-derived parameters after a bolus of the serotonin transporter radioligand 123I-ADAM. Journal of Nuclear Medicine, 49, 176–178. Slifstein, M., Kegeles, L. S., Gonzales, R., Frankle, W. G., Xu, X., Laruelle, M., et al. (2007b). [11C]NNC 112 selectivity for dopamine D1 and serotonin 5-HT2A receptors: A PET study in healthy human subjects. Journal of Cerebral Blood Flow and Metabolism, 27, 1733–1741.
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4
Magnetic Resonance Imaging (MRI) in Geriatric Psychiatry Research HOWARD J. AIZENSTEIN AND VIJAY K. VENKATRAMAN
In 2003, Paul C. Lauterbur and Peter Mansfield were awarded the Nobel Prize in Medicine for their discovery of magnetic resonance imaging (MRI). This technology has revolutionized in vivo imaging of the human brain, allowing for remarkably clear high-resolution images.
PHYSICS MRI is based on the concept of nuclear magnetic resonance (NMR), which involves the measurement of signals coming from nuclei in response to radio waves of the same frequency as the nuclei themselves (Liang & Lauterbur, 2000; Mitchell, 1999). A brief synopsis of MR physics is provided below. For a comprehensive review, the reader is referred to such texts as Liang and Lauterbur’s Principles of Magnetic Resonance Imaging (2000) and Mitchell’s MRI Principles (1999). Protons constitute the most abundant element in the human body, and the behavior of biological protons in the presence of a magnetic field is important for the concept of MRI. The protons have a magnetic axis of random orientation. The presence of a magnetic field aligns the protons’ spin axis along the applied magnetic field, and they continue to spin around this axis (a process called precession). 71
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The spinning protons give rise to secondary magnetic fields, or magnetization. The average magnetization of the protons in the direction of the magnetic field is called the longitudinal net magnetization. This net magnetization is smaller than the main magnetic field. In order to measure the net tissue magnetization to form an MR image, a radio pulse (excitation pulse) is used to disturb the equilibrium. A radio pulse of appropriate frequency rotates this longitudinal magnetization away from equilibrium into the transverse plane, producing transverse magnetization. With the radio pulses, the MR signal is obtained; but locating the source of the MR signals is a complex procedure. A variety of techniques are used in combination to resolve the collection of radio signals in three dimensions and thus to produce useful MR images. Additional magnetic field gradients, called imaging gradients, are used to locate the sources. These gradients create variation in the magnetic field, causing the protons spin slower or faster depending on their location. These gradients are produced by gradient coils within the bore of the main magnet and are applied in three orthogonal axes at different times, allowing threedimensional localization of the MR signal origin. There are three major types of gradients: 1. Slice or section selection: This is applied in three dimensions (Gx,
Gy, Gz) to select the required slice with the help of a shaped radiofrequency (RF) pulse and is called a slice-selection gradient. 2. Frequency encoding: This makes the oscillation frequency of the MR signal linearly dependent on its spatial origin along a particular axis. The process of encoding the spatial location is called frequency encoding, and the gradient is called the frequencyencoding or readout gradient. 3. Phase encoding: The phase-encoding gradient, used in mapping MR signal sources, is based not only on the frequency of signals at readout but also on their phase. This gradient is applied along the phase-encoding axis at different strengths. The combination of spatial encoding and Fourier transform data provides the information needed to construct the MR image. The relaxation time (T1) is a property of a tissue at a given magnetic field strength. With each excitation pulse, the longitudinal magnetization begins to recover; it is reduced to steady state after several pulses. The time between repetitions of the excitation pulse is called the repetition time (TR). The TR and flip angle are the two major parameters that can
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be varied to control the T1 weighting of an image. The transverse magnetization decays at the rate of the T2 relaxation time. The delay between the creation of the transverse magnetization and the measurement of the resulting echo is called the echo time (TE). The images where signal intensity is largely dependent on T2 differences (or TE values) are called T2-weighted images. The various pulse sequences are designed on the basis of TR, TE, and other parameters to obtain the necessary variation of contrast in the MR image. The MR image can have different artifacts, including motion-induced artifacts, susceptibility, wraparound, edge artifacts (partial-volume, chemical shift edge, truncation, relaxation), ghost artifacts, altered signal intensity, stripes, and image distortion. The difference pulse sequences used in imaging sequences—such as T2∗, echo planar imaging (used in BOLD fMRI), fluid-attenuated inversion-recovery imaging (FLAIR), diffusion-weighted imaging, magnetization transfer imaging, and others—are novel variations obtained by optimizing the various parameters described above.
DIFFERENCE BETWEEN 1.5T, 3T, AND 7T IMAGES Typically the MRI scanner is built around a permanent magnet—the instrument’s most expensive and important component (Liang & Lauterbur, 2000; Mitchell, 1999). The strength of the main magnet and its precision (homogeneity of the field strength) are important. Magnetic field strength is a major factor determining image quality. Higher magnetic fields increase the signal-to-noise ratio, permitting higher resolution or faster scanning. The higher field strengths have drawbacks, such as more costly magnets, higher maintenance costs, increased safety concerns as well as increases in certain artifacts as compared with lower field strengths. The 1.0–1.5T (T = tesla) field strengths are a good option for general medical use. Field strengths up to 3.0T may be desirable for research (such as brain imaging). The latest research studies use 7T field strengths. Figure 4.1 illustrates susceptibility-weighted imaging (SWI) using a 7T Siemens imaging scanner.
STRUCTURAL MRI Structural MRI methods can be used to identify and quantify patterns of change in volumetric neuroimaging studies. The various structural
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Figure 4.1 SWI image using 7T Siemens Scanner.
MRI sequences enable the identification of structural alterations such as (a) volume in gray matter, white matter, and cerebrospinal fluid from high-resolution T1-weighted images (Raz, 1996, 2005; Rosano et al., 2005); (b) white matter hyperintensities (WMH) from FLAIR images (Gunning-Dixon & Raz, 2000; Soderlund, Nyberg, Adolfsson, Nilsson, & Launer, 2003); (c) white matter integrity from diffusion-weighted imaging (Pfefferbaum, Adalsteinsson, & Sullivan, 2005; Salat et al., 2005); and (d) myelination from magnetization transfer imaging (Van Es et al., 2006). Figure 4.2 illustrates some of the common MR modalities. Advanced neuroimaging sequences such as diffusion spectrum imaging (DSI) and Q-ball imaging are currently being used to study the white matter tracts (Schmahmann, Pandya, Wang, Dai, & D’Arceuil, 2007). Structural MRI is useful for studying the patterns of neuroanatomical changes in geriatric research. Structural MRI is important for studies of normal aging, late-life depression, dementia, Alzheimer’s disease, and other cognitive disorders to examine the association of age-associated changes in neuroanatomy with specific age-related behavioral changes, such as changes in cognition.
FUNCTIONAL MRI In the 1960s and 1970s, a number of investigators showed that MR could be used not only to visualize neuroanatomy and structural pathology
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Figure 4.2 T1, T2, FLAIR, DTI, and T2* images from 3T scanner.
but also, by tuning the MR contrast appropriately, to visualize dynamic changes in blood oxygenation across the brain; this was the beginning of functional MRI (fMRI). Over the following years, a number of studies showed that this blood oxygenation level–dependent (BOLD) signal could be used to map brain activity during a variety of cognitive and affective tasks. By allowing investigators to test theories of the underlying neural pathways controlling cognitive and emotional processes, fMRI has been a major advance for the fields of cognitive and affective neuroscience. This approach is often referred to as human brain mapping. In addition to studying “normal” human brain function, fMRI can also be used to characterize functional activation patterns in patient groups. This area of clinical fMRI research has recently led to a number of new insights into the nature of psychopathology and treatment—including the recognition of a dorsal-versus-ventral processing imbalance in depression (Phillips, Drevets, Rauch, & Lane, 2003), overlap in response patterns with placebo versus medication (Mayberg et al., 2002), and paradoxical nonlinear activation patterns in mild cognitive impairment (Wierenga & Bondi, 2007), suggesting a compensatory stage prior to the onset of dementia.
THE BASIS OF BOLD fMRI The blood oxygen level–dependent (BOLD) response refers to the cascade of changes that occur with neural activation, resulting in changes in the MR signal. With neural activation there is an increase in blood flow, which leads to an increased ratio of oxygenated versus deoxygenated hemoglobin (Hb); oxygenated Hb has a stronger MR signal and is used as proxy of relative brain activity (illustrated in Figure 4.3).
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Figure 4.3 The BOLD hemodynamic response.
The functional circuit involved in a particular task can be identified by contrasting the MR images acquired while someone is doing the task versus when he or she is doing a control task. There are two main ways in which this can be done—either block design, where the subject performs several minutes on task and then several minutes of control, and the images during these blocks are compared. More recently, though, it has become fairly common to do event-related fMRI (for review, see D’Esposito, Zarahn, & Aguirre, 1999a), in which images are acquired over the course of the individual trials of the task. In this case control trials are interspersed with the experimental trials. There are two primary advantages of event-related fMRI over block-design fMRI. The first is that, for certain types of tasks, an event-related design more closely mimics the structure of the particular cognitive or affective task that was previously used in other (i.e., non-fMRI) research studies. For instance, to model a “go no-go” task, it is important to intermix go and no-go trials; it would change the task considerably to have an entire block of no-go trials. The other primary advantage of event-related designs is that eventrelated fMRI allows the investigator to visualize the BOLD signal over the course of individual trials and thus potentially to capture the dynamics of the regional activity. This is in contrast to a standard block design
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study, where the focus is on the overall level of activity averaged over a block of trials. An important issue in interpreting event-related fMRI is recognizing that the time course of the BOLD hemodynamic response function (HRF) signal (the fMRI signal) is not instantaneous but rather is smoothed out over time. That is, with a short burst of synaptic activity, one would expect a BOLD signal lasting roughly 12–14 sec and peaking at between 4 and 6 sec. Several studies have examined the statistical advantages of block-design versus event-related fMRI, and the general view is that for the same amount of time spent scanning (on the same task), the observed effect size is larger for a block-design study as than for an event-related study (Friston, Holmes, Price, Buchel, & Worsley, 1999).
PERFUSION MRI: AN ALTERNATIVE TO BOLD A significant limitation in BOLD fMRI is concern that the BOLD hemodynamic response is inherently relative. That is, the raw BOLD signal does not provide a reliable estimate of regional blood flow. Rather, it is contrast of the BOLD signal on alternating experimental versus control tasks that provides the meaningful signal. By comparison with this, PET imaging with an O15 radioligand is capable of providing quantitative measures of blood flow. In MRI, a technique analogous to O15 PET is also available; it is referred to as arterial spin label (ASL) or perfusion imaging (Aguirre, Detre, & Wang, 2005; Detre, Leigh, Williams, & Koretsky, 1992). In perfusion MR imaging, the MR excitation signal is inverted to provide a “tagged” signal, which is alternated with an “untagged” image. A comparison of the tagged and untagged images provides a quantitative measure of regional perfusion. Full-brain voxelwise perfusion images can provide a quantitative image of perfusion across the brain. Investigators have recently used perfusion imaging to demonstrate similar findings as with PET blood flow studies—e.g., decreased parietal–temporal resting perfusion with Alzheimer’s disease (Alsop, Detre, & Grossman, 2000). In addition to providing quantitative resting perfusion, ASL has also recently been used for investigating the changes in blood flow associated with tasks. For instance, Fernandez-Seara and associates (2007) have used ASL to show medial temporal lobe activity on an encoding task. The two primary methods for perfusion imaging are referred to as continuous arterial spin labeling (CASL) and pulsed arterial spin labeling
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(PASL). CASL is believed to offer better signal quality, but it generally requires special hardware for providing the continuous tagging pulse.
METHODOLOGICAL CHALLENGES OF fMRI IN GERIATRIC PSYCHIATRY In order to interpret fMRI results appropriately in geriatric psychiatry, it is important to consider whether differences in brain structure may affect the resulting fMRI signal; similarly, it is important to consider whether changes in brain physiology may influence assumptions about the BOLD fMRI signal. These concerns about brain morphometric changes in the elderly and age-related changes in the BOLD signal are the two primary methodological challenges of fMRI in geriatric psychiatry.
THE INFLUENCE OF BRAIN MORPHOMETRIC CHANGES ON fMRI There are well-established patterns of structural brain changes associated with aging. With increasing age, the brain decreases in overall volume, the cortical gyri become smaller, and the sulci and ventricles become larger. These changes vary across individuals and occur even in those who are otherwise apparently healthy. They have been described in a number of large epidemiological studies and seem to vary across the brain, with most prominent decreases in volume reported in the frontal cortex. For further descriptions of the structural brain changes associated with aging, see Chapter 6, “Structural Brain Changes Associated With Normal Aging.” The change in brain volume with age and with the diseases of aging present a particular challenge for fMRI studies in these populations: How should these changes be accounted for in terms of the functional signal? In a standard fMRI analysis plan, the functional scans from all the subjects in a study are lined up together (sometimes referred to as alignment, cross-registration, warping, or normalization). If the brains have significantly different shapes and sizes, then this alignment may bias the results by contributing more CSF (due to the larger sulci and ventricles) of the more atrophic brains as compared with more gray matter from the less atrophic brains. The standard alignment algorithms vary in their ability to account for variability in brain structure (Reuter-Lorenz & Lustig, 2005). Some investigators have addressed this problem by using a larger
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smoothing kernel in studies of aging subjects (e.g., 10 mm instead of the standard smoothness of 6- or 8-mm full-width half-maximum Gaussian). This approach recognizes that the alignment may be worse in the elderly population and corrects for it by making the images blurrier. This allows the statistical voxelwise comparison to find group differences even if there is some discrepancy in the spatial colocalization. An alternative approach involves avoiding the registration problems entirely by focusing on a region-of-interest (ROI)–based analysis (e.g., Aizenstein et al., 2004).
THE BOLD HEMODYNAMIC RESPONSE IN AGING fMRI depends on an intact BOLD hemodynamic response function (HRF), or the cascade of neurophysiological events leading from neural activation to a change in the measured T2∗ MRI signal. Aging and the neuropsychiatric disorders of aging are associated with cerebrovascular changes; one would therefore expect that these conditions would also alter the BOLD signal. This is of critical importance in interpreting whether the signal identified in an fMRI study of aging reflects changes in neural activity (as is often presumed) or whether the changes are due to the age-related changes in the coupling of the neural activity to the fMRI signal (i.e., the BOLD HRF). To examine the BOLD HRF in aging, we studied 8 healthy college-age subjects (3 men, mean age = 24.2, SD = 4.37) and 10 healthy elderly control subjects (6 men, mean age = 67.2, SD = 4.83). While undergoing fMRI, subjects performed a simple visual and motor task, tapping with their index fingers in response to the word TAP in the center of the screen. This task was chosen because it is believed to involve similar neural activity between the groups (Cunningham, Iansek, Bradshaw, & Phillips, 1995; D’Esposito et al., 1999b). Thus any changes in the BOLD signal would likely be due to changes in its coupling to neural activation (Aizenstein et al., 2004; Dennis & Cabeza, 2008). For each subject, we chose the most significant 32 contiguous voxels (based on a t-test comparing the fMRI signal at the expected peak of HRF to baseline) in their motor (premotor and supplementary motor cortex) and visual (occipital cortex, cuneus, and precuneus) cortices. The resulting fMRI time series are shown in Figure 4.4. As can be seen in the figure, there is a similar peak for both the young and the elderly subjects in both the visual and motor regions. This suggests that by focusing the analysis on the peak of the HRF, the difference in signal observed on fMRI will likely reflect differences in neural activation.
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Figure 4.4 fMRI time-series during finger tapping.
FUNCTIONAL MRI IN LATE-LIFE DEPRESSION To date, most of the functional neuroimaging studies reporting on latelife depression (LLD) have focused on the resting state and have identified changes in baseline (i.e., resting) cerebral activity between patients and controls. One of the earliest studies (Sackeim et al., 1990) demonstrated globally decreased cerebral blood flow (CBF) using the xenon inhalation technique. A decrease in global brain metabolism in LLD was also found with PET (Kumar et al., 1993). Others (Baxter et al., 1989; Bench, Friston, Brown, Frackowiak, & Dolan, 1993) using PET, and working with samples with age ranges extending from midlife through late life, have found decreased CBF and metabolism in LLD to be most prominent in the frontal cortex. Other specific areas with reported decreases in LLD versus controls in PET studies include the medial temporal lobe (Gron, Bittner, Schmitz, Wunderlich, & Riepe, 2002) and the caudal anterior cingulate cortex (ACC) (de Asis et al., 2001). The results in the ACC are particularly intriguing. While decreases have been identified in the dorsal (or caudal) ACC (de Asis et al., 2001), other studies have found areas of increased activation in the rostral ACC (Smith et al., 1999). The latter results extend earlier findings of increased activation in these areas in midlife depression (Mayberg, 1997; Wu et al., 1999). Moreover, as in the studies of midlife depression, there is evidence that the increased ACC activity resolves with treatment (Smith et al., 1999).
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Several functional neuroimaging studies of LLD have been conducted during cognitive activation (e.g., de Asis et al., 2001; Gron et al., 2002). This approach offers some advantages over studies performed at rest. Group differences in resting blood flow or cerebral metabolism could be confounded by different baseline thought processes between groups (e.g., a tendency for the depressed group to be worrying rather than relaxing while in the scanner). Functional imaging performed during controlled cognitive tasks is inherently less prone to this confound because the concurrent behavioral measures demonstrate that the groups are performing similarly. Moreover, the controlled cognitive task standardizes behavior and therefore decreases variability in brain response. The cognitive activation functional imaging studies conducted in LLD have replicated the general patterns of regional activity found during resting studies. In a study using a word-generation task, de Asis and colleagues (2001) found reduced CBF bilaterally in the dorsal anterior cingulate and hippocampus (as measured compared with controls); on a verbal declarative memory task, Gron and coworkers (2002) found decreased left ventrolateral prefrontal cortex (VLPFC) and hippocampal activation compared with elderly controls. On a cognitive control task comparing LLD subjects with elderly controls (Aizenstein et al., 2005), we found decreased BOLD activation in the dorsolateral prefrontal cortex (DLPFC) and ACC; moreover, in a sequence learning task comparing LLD subjects with elderly controls, we found the depressed elderly to have decreased prefrontal activation and increased striatal activation. This increased striatal activation on trials that violated the predictive sequential pattern is consistent with reports of increased negative reward activity in midlife depression. Overall, the functional neuroimaging findings in LLD show a pattern similar to that in midlife depression, with increased activity in ventral “affective” regions and decreased activity in dorsal “cognitive” regions. This supports the model of an imbalance whereby cognitive regions are not adequately regulating affective regions. To test this altered regulation model, we recently explored the functional connectivity in geriatric depression (Aizenstein et al., 2009) and found that although the regional changes in dorsal activation in LLD normalize with treatment, the altered connectivity remains lower than in a nondepressed comparison group. We believe that these structural changes to the white matter, known to be present in LLD, account for the persistently altered
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functional connectivity, and can explain the vulnerability of these individuals to recurrent depressive episodes.
SUMMARY There has been a tremendous increase in the use of MRI (functional and structural) in studying the brain. These neuroimaging studies have provided insight into normal aging and the neuropsychiatric diseases of aging. In this chapter we reviewed the basis of both structural and functional MRI and also the functional imaging results in geriatric depression. REFERENCES Aguirre, G. K., Detre, J. A., & Wang, J. (2005). Perfusion fMRI for functional neuroimaging. International Review of Neurobiology, 66, 213–236. Aizenstein, H. J., Butters, M. A., Figurski, J. L., Zmuda, M. D., Stenger, V. A., Becker, J. T., et al. (2005). Prefrontal and anterior cingulated cortex function during cognitive control in late-life depression. Presented at the American Association of Geriatric Psychiatry Meeting, San Diego, CA. Aizenstein, H. J., Butters, M. A., Wu, M., Mazurkewicz, L. M., Stenger, V. A., Gianaros, P. J., et al. (2009). Altered functioning of the executive control circuit in late-life depression: Episodic and persistent phenomena. American Journal of Geriatric Psychiatry, 17(1), 30–42. Aizenstein, H. J., Clark, K. A., Butters, M. A., Cochran, J., Stenger, V. A., Meltzer, C. C., et al. (2004). The BOLD hemodynamic response in healthy aging. Journal of Cognitive Neuroscience, 16(5), 786–793. Alsop, D. C., Detre, J. A., & Grossman, M. (2000). Assessment of cerebral blood flow in Alzheimer’s disease by spin-labeled magnetic resonance imaging. Annals of Neurology, 47(1), 93–100. Baxter, L. R., Schwartz, J. M., Jr., Phelps, M. E., Mazziotta, J. C., Guze, B. H., Selin, C. E., et al. (1989). Reduction of prefrontal cortex glucose metabolism common to three types of depression. Archives of General Psychiatry, 46, 243–250. Bench, C. J., Friston, K. J., Brown, R. G., Frackowiak, R. S., & Dolan, R. J. (1993). Regional cerebral blood flow in depression measured by positron emission tomography: the relationship with clinical dimensions. Psychological Medicine, 23, 579–590. Cunningham, R., Iansek, R., Bradshaw, J. L., & Phillips, J. G. (1995). Movement-related potentials in Parkinson’s disease: Presence and predictability of temporal and spatial cues. Brain, 118, 935–950. de Asis, J. M., Stern, E., Alexopoulos, G. S., Pan, H., Van Gorp, W., Blumberg, H., et al. (2001). Hippocampal and anterior cingulate activation deficits in patients with geriatric depression. American Journal of Psychiatry, 158, 1321–1323. D’Esposito, M., Zarahn, E., & Aguirre, G. (1999a). Event-related fMRI: Implications for cognitive psychology. Psychological Bulletin, 125, 155–164.
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D’Esposito, M., Zarahn, E., Aguirre, G. K., & Rypma, B. (1999b). The effect of normal aging on the coupling of neural activity to the bold hemodynamic response. Neuroimage, 10, 6–14. Dennis, N. A., & Cabeza, R. (2008). Neuroimaging of healthy cognitive aging. In F. I. M. Craik & T. A. Salthouse (Eds.), Handbook of aging and cognition (3rd ed.). Mahwah, NJ: Erlbaum. Detre, J. A., Leigh, J. S., Williams, D. S., & Koretsky, A. P. (1992). Perfusion imaging. Magnetic Resonance in Medicine, 23(1), 37–45. Fernandez-Seara, M. A., Wang, J., Wang, Z., Korczykowski, M., Guenther, M., Feinberg, D. A., et al. (2007). Imaging mesial temporal lobe activation during scene encoding: Comparison of fMRI using BOLD and arterial spin labeling. Human Brain Mapping, 28(12), 1391–1400. Friston, K. J., Holmes, A. P., Price, C. J., Buchel, C., & Worsley, K. J. (1999). Multisubject fMRI studies and conjunction analyses. Neuroimage, 10, 385–396. Gron, G., Bittner, D., Schmitz, B., Wunderlich, A. P., & Riepe, M. W. (2002). Subjective memory complaints: Objective neural markers in patients with Alzheimer’s disease and major depressive disorder. Annals of Neurology, 51, 491–498. Gunning-Dixon, F. M., & Raz, N. (2000). The cognitive correlates of white matter abnormalities in normal aging: A quantitative review. Neuropsychology, 14(2), 224–232. Kumar, A., Newberg, A., Alavi, A., Berlin, J., Smith, R., & Reivich, M. (1993). Regional cerebral glucose metabolism in late-life depression and Alzheimer disease: A preliminary positron emission tomography study. Proceedings of the National Academy of Sciences of the United States of America, 90, 7019–7023. Liang, Z. P., & Lauterbur, P. C. (2000). Principles of magnetic resonance imaging: A signal processing perspective. Bellingham, WA: SPIE Optical Engineering Press. Mayberg, H. S. (1997). Limbic-cortical dysregulation: A proposed model of depression. Journal of Neuropsychiatry & Clinical Neurosciences, 9, 471–481. Mayberg, H. S., Silva, J. A., Brannan, S. K., Tekell, J. L., Mahurin, R. K., McGinnis, S., et al. (2002). The functional neuroanatomy of the placebo effect. American Journal of Psychiatry, 159(5), 728–737. Mitchell, D. G. (1999). MRI principles. Philadelphia: Saunders. Pfefferbaum, A., Adalsteinsson, E., & Sullivan, E. V. (2005). Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging. Neuroimage, 26(3), 891–899. Phillips, M. L., Drevets, W. C., Rauch, S. L., & Lane, R. (2003). Neurobiology of emotion perception II: Implications for major psychiatric disorders. Biological Psychiatry, 54(5), 515–528. Raz, N. (1996). Neuroanatomy of aging brain: Evidence from structural MRI. In E. D. Bigler (Ed.), Neuroimaging II: Clinical applications (pp. 153–182). New York: Academic Press. Raz, N. (2005). The aging brain observed in vivo: Differential changes and their modifiers. In R. Cabeza, L. Nyberg, & D. Park (Eds.), Cognitive neuroscience of aging (pp. 19–57). New York: Oxford University Press. Reuter-Lorenz, P. A., & Lustig, C. (2005). Brain aging: Reorganizing discoveries about the aging mind. Current Opinion in Neurobiology, 15(4), 245–251. Rosano, C., Becker, J., Lopez, O., Lopez-Garcia, P., Carter, C. S., Newman, A., et al. (2005). Morphometric analysis of gray matter volume in demented older adults:
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Exploratory analysis of the cardiovascular health study brain MRI database. Neuroepidemiology, 24(4), 221–229. Sackeim, H. A., Prohovnik, I., Moeller, J. R., Brown, R. P., Apter, S., Prudic, J., et al. (1990). Regional cerebral blood flow in mood disorders: I. Comparison of major depressives and normal controls at rest. Archives of General Psychiatry, 47, 60–70. Salat, D. H., Tuch, D. S., Greve, D. N., van der Kouwe, A. J., Hevelone, N. D., Zaleta, et al. (2005). Age related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiology of Aging, 26(8), 1215–1227. Schmahmann, J. D., Pandya, D. N., Wang, R., Dai, G., & D’Arceuil, H. E. (2007). Association fibre pathways of the brain: Parallel observations from diffusion spectrum imaging and autoradiography. Brain, 130, 630–653. Smith, G. S., Reynolds III, C. F., Pollock, B., Derbyshire, S., Nofzinger, E., Dew, M. A., et al. (1999). Cerebral glucose metabolic response to combined total sleep deprivation and antidepressant treatment in geriatric depression. American Journal of Psychiatry, 156, 683–689. Soderlund, H., Nyberg, L., Adolfsson, R., Nilsson, L. G., & Launer, L. J. (2003). High prevalence of white matter hyperintensities in normal aging: Relation to blood pressure and cognition. Cortex, 39(4–5), 1093–1105. Van Es, A. C., Vander Flier, A. W., Admiraal-Behloul, F., Olofsen, H., Bollen, E., Middelkoop, H., et al. (2006). Magnetization transfer imaging of gray and white matter in mild cognitive impairment and Alzheimer’s disease. Neurobiology of Aging, 27(12), 1757–1762. Wierenga, C. E. & Bondi, M. W. (2007). Use of functional magnetic resonance imaging in the early identification of Alzheimer’s disease. Neuropsychology Review, 17(2), 127–143. Wu, J., Buchsbaum, M. S., Gillin, J. C., Tang, C., Cadwell, S., Wiegand, M., et al. (1999). Prediction of antidepressant effects of sleep deprivation by metabolic rates in the ventral anterior cingulate and medial prefrontal cortex. American Journal of Psychiatry, 156, 1149–1158.
5
Software Packages for Neuroimage Processing ROBERT J. TAMBURO
This chapter provides an overview of software packages commonly used within the neuroimaging community. The primary focus is on software for the analysis of structural and functional magnetic resonance imaging (MRI) brain data, although the software summarized may be extensible to other neuroimaging modalities such as computed tomography (CT) and positron emission tomography (PET). The breadth of available software and its features are substantial and cannot possibly be covered in a single chapter. Instead, this chapter is meant to serve as a software guide for clinicians who are inclined to utilize neuroimaging methodologies in their own research. Neuroimaging software may address a number of components of the image analysis pipeline. These include preprocessing, processing, and postprocessing. In this chapter, image preprocessing refers to methods typically employed to prepare images for analysis (e.g., noise filtering, motion correction, brain extraction, etc.). Image processing refers to methods that extract, interrogate, manipulate, and formulate the image to arrive at a result for interpretation. Image postprocessing refers to methods that operate on the processed image to better represent the results of the processing (e.g., surface rendering, image overlay, image tiling, histograms, etc.). A discussion of these methodologies is outside of the scope of this chapter. The reader is instead referred to the end of 85
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this chapter for a list of suggested image analysis textbooks dedicated to these topics. Prior to highlighting the features and capabilities of available neuroimaging software, several issues to be considered prior to the processing of medical images are discussed.
RECONSTRUCTION Data collected at a medical imaging site are not initially acquired as spatial images. The data are acquired in a frequency domain referred to as k-space. A variety of image reconstruction methods are available for transforming the raw data to a spatial image. Each of these reconstruction methods depends on a variety of parameters, which present as a tedious and complex process. Fortunately image reconstruction is typically performed automatically by the scanner system itself. There are, however, several advantages to performing image reconstruction manually. For example, reducing image noise, correcting artifacts due to fluid flow or physical motion, fixing radio frequency and static field inhomogeneities, and so on may be more efficiently addressed prior to image reconstruction (Bracewell, 1995; Mitchell, 1999; Yoo, 2004). Clinical researchers interested in obtaining and processing unprocessed raw data should consult with their scanner technicians, who may also be able to tailor the parameters of the scanner reconstruction software to meet desired specifications. This chapter does not address the details of image reconstruction (refer to Chapter 4, “Magnetic Resonance Imaging [MRI] in Geriatric Psychiatry Research,” for these details). This chapter also does not provide an overview of image reconstruction software. The remainder of this chapter assumes that the data have been reconstructed and stored in one of the many available imaging formats.
FORMAT Medical images are eventually stored on a computer hard disk in a variety of image formats. Generally, these can be categorized as (a) an unprocessed (i.e., raw) image format, (b) a proprietary image format (e.g., SIGNA by General Electric), and (c) a standardized format (e.g., DICOM). The raw image format is one of the most basic and fundamental means for storing data. Raw images are stored as either ASCII or binary
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files. ASCII files store each byte of data as a text character, whereas binary files encode bytes of data as an array of text characters and binary data. Binary data files are more compact than ASCII files and are therefore frequently used to store the large amounts of data collected in neuroimaging research. Raw image files do not have a standardized format for data organization, but they usually have, preceding the image data, a small header structure embedded in the file that describes basic file information necessary for reading the image. Such information may but does not necessarily include the following: (a) spatial resolution—the size of the image in data space (e.g., 100 voxels cubed), (b) voxel size and spacing, (c) voxel format—the data type used to store the output from the imaging sensor (e.g., 8-bit), (d) dimensionality of the image (e.g., 2D), (e) endianness— the manner in which bytes of data are stored (e.g., little endian or big endian), and (f ) organization of the image matrix—raw major or column major. Since raw images do not include the extensive metadata that describe image properties, scan parameters, and subject information, the user must obtain these from an external source in attempting to analyze and visualize the image (Cacioppo, Tassinary, & Berntson, 2007). In an effort to store and share data more effectively, the National Electrical Manufacturers Association (NEMA), in the early 1980s, created the Digital Imaging and Communications in Medicine (DICOM) standard for storing and transmitting medical imaging information. The DICOM standard includes a file-format definition as well as a network communication protocol for transmitting data between systems. The DICOM standard has been widely adopted by manufacturers of medical imaging scanners and by medical facilities. DICOM is not, by itself, an image format. A single DICOM file consists of a number of attributes such as subject name, date of scan, pixel data, and so on. In order to view the pixel data stored within the DICOM file, software is needed to extract the data and assemble it into an image. DICOM files acquired during a single scan are typically stored as a series of files usually representing 2D slices of a 3D volume and may include repeated acquisitions of the volume. This means of storage results in a large number (hundreds to thousands) of files, which can be disadvantageous when one is working with DICOM-formatted images for neuroimaging researchers. Thus, researchers have devised 3D image formats to more easily process imaging data (NEMA, 2008). Researchers have found it useful to develop more compact imaging formats to process images more effortlessly. Examples of commonly
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used 3D formats include Analyze 7.5 (BIR, 1986–1995), developed by the Biomedical Imaging Resource at Mayo Clinic (Robb, 2009); MINC, developed by the McConnell Brain Imaging Centre of the Montreal Neurological Institute (Neelin, 1993); and NIf TI, from the Data Format Working Group in the Neuroimaging Informatics Technology Initiative (NIf TI, 2005). There are many software utilities and packages capable of converting images between various image formats. A portion of the software summarized later in this chapter is capable of performing such conversions. Before proceeding to the next section, it is worth emphasizing that it is critically important to ensure that images retain proper orientation information when converting between image formats and processing imaging data. Neglecting to keep track of such spatial information may cause confusion in viewing an image or lead to the misinterpretation of data analysis results.
ORIENTATION The primary objective in viewing or analyzing an image is to relate the voxels in the coordinate system of the image to the anatomy in the coordinate system of the subject. In order to do so, one must be able to precisely associate the coordinate system of the image and the subject to one another. Coordinate directions are typically given in relation to the subject’s coordinate system (e.g., right denotes the right side of the subject). For clarity, a distinction between coordinate systems may be made in viewing an image on a computer screen (e.g., one might say that the right side of the subject appears on the right side of the screen). In practice, neuroimaging MRI researchers favor two conventions for viewing MRI images: the neurological (anatomical) and radiological orientations. An image acquired in the anatomical orientation, when properly displayed on a computer screen, will have the subject’s right/ left side on the right/left side of the screen. Conversely, an image acquired in the radiological orientation will have the right/left subject’s side on the left/right side of the screen. The image orientation is usually stored in the image file header and the image voxels are stored in the appropriate order to reflect this orientation. Converting a raw image to a different format may alter this ordering, which should be harmless if the image format is capable of storing the voxels in the requested orientation (Wideman, 2005).
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For a 3D, rectilinear image, there are a maximum of 48 different orientation schemes for an image. There are, however, image formats that have orientation limits incapable of properly storing data in the proper order. For example, the Analyze 7.5 image format is capable of storing only six orientations (Cacioppo et al., 2007). Therefore, if one converts a raw image in an orientation unrecognized by the Analyze 7.5 image format, one is at the mercy of the conversion software to handle the discrepancy. By and large, visualization software reads the orientation of an image header to display the image and often assigns the appropriate orientation labels to the viewer. One must be wary in determining how the viewer reads the image data, displays the image, and assigns labels to the coordinate axes. Owing to the right-left swap in the coordinate direction of these two orientations, the importance of this understanding and bookkeeping should be apparent in processing and visualizing image data and interpreting analysis results.
SOFTWARE OVERVIEW In recent years, there has been a large influx of image analysis software made freely available to the public. Free (i.e., nonproprietary, with no fee, unpatented, uncopyrighted) software is advantageous to the community by providing a platform on which to perform analysis without having to reimplement and reprogram the details of the algorithms themselves. Free software also promotes open science, the dissemination of source code, data, and publication, with the goals of research reproducibility, method validation, and advancement (Yoo & Metaxas, 2005). Thus, only free software is included in this chapter’s software compilation. Although neuroimaging software makes the analytical technicalities of computational neuroimaging methodologies less complicated, the user must ensure that the implementation of the method is well understood in order to interpret the results accurately. It is the user’s responsibility to demand that the software be well documented, validated, and kept up to date in order to ensure that it is meets the high quality standards of the neuroimaging community. The listings of freely available neuroimaging software included in this chapter are by no means exhaustive. The given software was included because the author was familiar with it. Not all of the software is compatible with multiple operating systems. That delineation is not made here, since software constantly evolves and may be made newly
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available for specific platforms after publication. The software included is listed below in alphabetical order. A final caveat of free software is that it is left to the reader to learn any specific license restrictions for software use (e.g., some free software is restricted to academic research and not available for commercial use). GUI stands for graphical user interface and CLP stands for command line program. Specific usage of the software is not included, as the dynamic nature of software development will not permit this. ■
3D Slicer was initially developed for surgical planning by the Surgical Planning Lab at Brigham and Women’s Hospital and at the MIT AI Lab. Slicer has since evolved into a versatile, opensource software suite for visualization and image analysis. Slicer is designed to be compatible across platforms. ■
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Features: A GUI environment. Flexible image input/output capable of reading and writing DICOM, raw, Analyze, NIf TI, and other data formats. Preprocessing methods such as denoising, cropping, blurring, thresholding, resampling, and so on. Automated segmentation routines and an interface for manual delineation of region of interest. Region-of-interest surface rendering and visualization. Diffusion-weighted processing techniques for diffusion tensor imaging, which includes white matter tractography. Affine and deformable registration routines. Contains an embedded brain atlas with over 200 defined structures. Web site: http://www.slicer.org
AFNI (Analysis of Functional NeuroImages) was originally developed and is currently maintained by Robert Cox at the NIH. AFNI is a rich software package for processing and displaying f MRI data. ■
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Features: A GUI environment with CLPs for specific functions and methods. An interface for AC-PC alignment and transformation to Talairach coordinates. Computation of f MRI activation maps and 2- and 3D overlays of activation maps onto structural images. Temporal data processing and visualization utilities. A CLP for removing extraneous brain voxels from structural images. Plug-ins for Fourier and wavelet filtering, real-time f MRI analysis, histogram generation, and so on. Web site: http://afni.nimh.nih.gov/afni
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Features: BioImage Suite provides a GUI environment. Image preprocessing tools for smoothing, cropping, reorienting, reslicing, thresholding, edge detection, bias field correction, and so on. A GUI for FSL’s brain extraction algorithm and a process for refining the results. Linear and nonlinear image registration for 2D-to-3D, 4D-to-2D, and 3D-to-3D images. Also provides landmark-, surface-, and point-based image registration. Includes a GUI for assessing the results of image registration and a user-friendly GUI for functional MRI analysis using AFNI. Visualization tools for viewing activation maps. Histogram segmentation and mathematical morphology. Diffusion-weighted imaging analysis and neurosurgery analysis and visualization tools. Web site: http://www.bioimagesuite.org
BrainSuite2 was developed in the in the UCLA Laboratory of Neuroimaging. ■
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Features: CLP utilities for linear and nonlinear image registration and image preprocessing utilities for reslicing, reorienting, resizing, reuniting, averaging, and so on. Web site: http://bishopw.loni.ucla.edu/AIR5
BioImage Suite was developed at Yale University and has extensive capabilities for neuroimage analysis and visualization. It is integrated with other image-analysis processing software such as FSL, AFNI, and SPM. ■
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AIR (Automated Image Registration) was developed by Roger P. Woods at the UCLA School of Medicine and includes routines for registering images of the same and differing imaging modalities. AIR is designed to be compatible across platforms. ■
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Features: BrainSuite2 was designed to guide users through the process of cortical surface extraction with minimal user interaction. The individual components of BrainSuite2 may also be used for soft tissue, skull, and scalp segmentation and for surface analysis and visualization. Web site: http://www.loni.ucla.edu/Software/Software_Detail. jsp?software_id=19
BrainVisa/Anatomist was developed as a collaborative effort between L’Institut Fédératif de Recherche 49 (IFR49) and the Frederic Joliot Hospital Service.
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DTIQuery/CINCH was developed at Stanford for giving neuroimaging researchers an interactive means to explore and interpret DTI tractography data. ■
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Features: A Matlab toolbox that provides an integrated GUI environment to manage, process, and analyze f MRI data in a single framework. Specifically it provides the necessary processes to apply data-driven independent component analysis to f MRI data. Web site: http://www.sccn.ucsd.edu/fmrlab
Debabeler was developed in the UCLA Laboratory of Neuroimaging. ■
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Features: DTIQuery and CINCH provide a GUI for interacting with tractography data calculated using other software (e.g., AFNI). Box- or ellipse-shaped regions can be selected to display pathways that pass through specific anatomical areas. These regions can be used in coordination with a simple and flexible query language, which allows for arbitrary combinations of these queries using Boolean logic operators.
■ Web site: http://graphics.stanford.edu/projects/dti/software FMRLab was developed at the University of California, San Diego for f MRI data analysis with independent component analysis (ICA). ■
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Features: BrainVisa has tools for f MRI, MEG/EEG, and MR diffusion imaging analysis as well as measuring cortical thickness, gyrification index, sulcal length, and sulcal depth. BrainVisa utilizes its own Anatomist software to visualize data and analysis results. Web site: http://www.brainvisa.info
Features: Debabeler manages the conversion of imaging data from one file format and convention to another. It consists of a graphical user interface to visually program the translations and a data translation engine to read, sort and translate the input files and write the output files to disk. Web site: http://www.loni.ucla.edu/Software/Software_Detail. jsp?software_id=11
DPTools was developed at the Laboratoire d’Imagerie Médicale Expérimentale et Clinique and provides a suite of functional and structural MRI processing utilities.
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Features: A GUI-based environment. Analysis for f MRI, diffusion, and perfusion imaging. Includes a visualization tools for all forms of analysis. Tools for registration and segmentation, measuring MR flow, performing brain extraction, and calculating statistics from f MRI maps and diffusion tensor maps. Web site: http://www.fmritools.org
Features: GUI environment. Methods for brain stripping, tissue segmentation, and measuring cortical thickness. Methods for volume-based and surface-based region labeling, and surfacebased statistical analyses. Web site: http://surfer.nmr.mgh.harvard.edu
FSL is a comprehensive library of GUI- and CLP-based image analysis and statistical tools for f MRI, MRI, and DTI brain imaging data. FSL is written mainly by members of the Analysis Group, FMRIB, Oxford, UK. ■
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FreeSurfer was developed as a collaboration between CorTechs and the Athinoula A. Martinos Center for Biomedical Imaging. It is a set of automated tools for reconstructing the brain’s cortical surface from structural MRI data and overlaying f MRI data onto the reconstructed surface. ■
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Features: Features f MRI preprocessing (e.g., motion correction, denoising, and analysis, first-level time-series analysis, and Bayesian mixed effects analysis methods). A suite of structural MRI utilities like brain extraction, tissue segmentation, linear and nonlinear inter- and intramodality registration, modelbased segmentation, distortion correction, structural brain change analysis, voxel-based morphometry of gray matter, and nonlinear noise reduction. Tools for diffusion MRI to diffusion parameter reconstruction, tractography, and voxelwise analysis of multisubject data. Web site: www.fmrib.ox.ac.uk/fsl
ImageJ was developed by Wayne Rasband at the Research Services Branch, National Institutes of Mental Health. ImageJ is a public domain Java image processing program with an open architecture that provides extensibility via Java plug-ins. ■
Features: ImageJ can display, edit, analyze, and process data of a large number of image formats. Preprocessing methods include cropping, reorienting, reslicing, smoothing, sharpening,
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Insight Toolkit (ITK) is an NLM project that was developed by a handful of organizations. ITK is an open-source, cross-platform library of segmentation, registration, and preprocessing algorithms. ■
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Features: ITK-SNAP provides an intuitive interactive GUI for semiautomated segmentation methods using active contours as well as for manual delineation and image navigation. Web site: http://www.itksnap.org
LONI Pipeline Processing Environment was developed at the UCLA Laboratory of Neuroimaging. ■
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Features: ITK is an extensive image analysis toolkit written in C⫹⫹ with wrappers available for translating it to different programming languages like Java, Python, Matlab, etc. ITK provides many classical and modern image-processing functions and algorithms. ITK also provides a basic image architecture that allows any researcher to develop their own methodologies without “reinventing the wheel,” which has consequently led to the Insight Journal (http://www.insight-journal.org), an online publication open to accessing articles, data, and peer reviews. No GUI is provided with ITK, but visualization toolkits such as FLTK, VTK, QT, Java, and others can be interfaced with ITK. Web site: http://www.itk.org
ITK-SNAP is a software application developed by Cognitica Corporation under a contract from the National Library of Medicine for segmenting structures in 3D medical images. ■
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mathematical morphology, and so on. ImageJ by itself has limited functionality for neuroimaging researchers. However, a large number of plug-ins have been contributed to make it possible to solve almost any image processing or analysis problem. Web site: http://rsb.info.nih.gov/ij
Features: The LONI pipeline is a free workflow application primarily aimed at neuroimaging researchers. With the LONI Pipeline users can quickly piece together workflows that utilize neuroimaging tools available elsewhere (e.g., AIR, ITK, FSL, etc.). Web site: http://pipeline.loni.ucla.edu/?software_id=2
MedINRIA was developed by the ASCLEPIOS Research Team and is a free collection of state-of-the-art algorithms dedicated to medical image processing and visualization.
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Features: MedINRIA provides a GUI environment for diffusion imaging analysis and visualization, and fiber tracking and extraction. Additionally, MedINRIA provides a fusion of f MRI results and diffusion weighted imaging analysis. Rigid, affine, and diffeomorphic registration tools are available, as well as manual, semiautomated, and fully automated segmentation tools. Web site: http://www-sop.inria.fr/asclepios
Features: MIPAV enables quantitative analysis and visualization of medical images of numerous modalities. Using MIPAV’s standard user-interface and analysis tools, researchers at remote sites (via the Internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders. MIPAV is a Java application that should run on any Java-enabled platform. Web site: http://mipav.cit.nih.gov
MRIcro was developed by Chris Rorden at the University of South Carolina. ■
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MIPAV (Medical Image Processing, Analysis, and Visualization) is a Java-based medical image-processing, analysis, and visualization application developed by the center of information technology at the National Institutes of Health. ■
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MRIcro allows users to view medical images in a stand-alone program. It includes tools to complement the analysis tools available with SPM. Web site: http://www.sph.sc.edu/comd/rorden/mricro.html
Multitracer was developed in the in the UCLA Laboratory of Neuroimaging as a Java application for displaying 3D images and defining anatomic structures within these images. ■
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Features: MultiTracer provides a GUI interface for the manual delineation brain regions. Tracings of regions can be saved in a format that facilitates review and revision, and the volumes, areas, and lengths of traced structures can be computed. These include tools for simple measurements (e.g., areas, volumes, lengths, width, surface areas, etc.) as well as tools that resample the tracings into surface meshes that can be analyzed and displayed. Web site: http://bishopw.loni.ucla.edu/MultiTracer/MultiTracer. html
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NeuroLens was developed at the Unité de neuroimagerie fonctionnelle in Montreal as a Mac application for the analysis and visualization of functional neuroimages. ■
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OsiriX is an open-source DICOM viewer for Mac OSX computer systems. ■
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Features: OsiriX is an image processing software dedicated to DICOM images and confocal microscopy. It is fully compliant with the DICOM standard for image communication and image file formats. OsiriX is able to receive images transferred by DICOM communication protocol from any PACS or imaging modality. OsiriX provides navigation and visualization of multimodality and multidimensional images. The 3D viewer offers modern rendering modes: multiplanar reconstruction (MPR), surface rendering, volume rendering, and maximum intensity projection (MIP). Web site: http://www.osirix-viewer.com
ParaView is an open-source visualization data analysis and visualization platform that was developed by Kitware. ■
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Features: GUI-based environment. Provides tools for region painting. Flexible viewing of numerous dataset types, including volumetric time-series and cortical surfaces, in multiple windows. Input/output for many commonly used image formats in neuroimaging research. Web site: http://www.neurolens.org
Features: ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView’s batch processing capabilities. ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of terascale as well as on laptops for smaller data. Web site: http://www.paraview.org
PLS (Partial Least Squares) was developed by Randy McIntosh at the Rotman Research Institute. ■
Features: PLS is a Matlab toolkit for performing multivariate analysis of neuroimaging data. PLS is a method for extracting distributed signal changes related to changing task demands.
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It has also been applied to measuring distributed patterns that affect task performance and task-dependent changes in the relation between brain regions. PLS analysis has been used to characterize distributed signals measured by neuroimaging methods such as PET, event-related f MRI, blocked f MRI, structural MRI, event-related potentials, and magnetoencephalography. Web site: http://www.rotman-baycrest.on.ca/index.php?sec tion=84
Features: SPM focuses on the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data. The SPM software package has been designed for the analysis of brain imaging data sequences. The sequences can be a series of images from different cohorts or time-series from the same subject. The current release is designed for the analysis of fMRI, PET, SPECT, EEG, and MEG. Web site: http://www.fil.ion.ucl.ac.uk/spm
SnPM (Statistical non-Parametric Mapping) was developed by Andrew Holmes of Astra Zeneca and Tom Nichols of the University of Michigan. ■
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SPM (Statistical Parametric Mapping) was developed by members and collaborators of the Wellcome Trust Centre for Neuroimaging for analyzing functional imaging data. ■
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Features: SnPM provides an extensible framework for voxel-level nonparametric permutation/randomizsation tests of functional neuroimaging experiments with independent observations. The SnPM toolbox provides an alternative to the statistics section of SPM. SnPM uses the general linear model to construct pseudo t-statistic images, which are then assessed for significance using a standard nonparametric multiple comparisons procedure based on randomization/permutation testing. Web site: http://www.sph.umich.edu/ni-stat/SnPM
Visualization Toolkit (VTK), developed by Kitware, is an opensource, freely available software system for 3D computer graphics, image processing, and visualization. ■
Features: VTK is a software toolkit that supports a wide variety of visualization algorithms including scalar, vector, tensor, texture, and volumetric methods as well as advanced modeling
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techniques such as implicit modeling, polygon reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation. In addition, dozens of imaging algorithms have been directly integrated to allow the user to mix 2D imaging with 3D graphics algorithms and data. Web site: http://www.vtk.org
VoxBo was developed by the Center for Functional Neuroimaging at the University of Pennsylvania. VoxBo is a software package for the processing, analysis, and display of data from functional neuroimaging experiments. ■
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Features: VoxBo is a free software package for the processing, analysis, and display of data from functional neuroimaging (chiefly f MRI) experiments. VoxBo includes preprocessing tools for structural and functional images. Web site: http://www.voxbo.org
For a large collection of neuroimaging tools and resources, the reader is directed to the Web site of the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) (NITRC, 2007). The NITRC finds and compares neuroimaging resources for f MRI and structural MRI analyses, which include common and uncommonly used software tools. NITRC collects and points to standardized information about tools, thus simplifying the task of finding and comparing software. NITRC strives to find the right neuroimaging tool or resource for the neuroimaging researcher. SUGGESTED READING Bankman, I. (2000). Handbook of medical imaging: Processing and analysis. San Diego, CA: Academic Press. Davies, E. R. (2004). Machine vision: Theory, algorithms, practicalities. San Francisco, CA: Morgan Kaufmann. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification. New York: WileyInterscience. Friston, K. J. (Ed.). (2006). Statistical parametric mapping: The analysis of functional brain images. New York: Academic Press. Gonzalez, R. C., & Woods, R. E. (2007). Digital image processing. Englewood Cliffs, NJ: Prentice Hall. Hader, D. P. (Ed.). (2000). Image analysis: Methods and applications. Boca Raton, FL: CRC Press. Hajnal, J. V., Hill, D. L. G., & Hawkes, D. J. (Eds.). (2001). Medical image registration. Boca Raton, FL: CRC Press.
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Jezzard, P. (2003). Software Packages for Neuroimage Processing. In P. M. Matthews & S. M. Smith. Functional MRI: An introduction to methods. Oxford, UK: Oxford University Press. Parker, J. R. (1996). Algorithms for image processing and computer vision. New York: Wiley. Russ, J. C. (2006). The image processing handbook. Boca Raton, FL: CRC Press. Shapiro, L. G., & Stockman, G. C. (2001). Computer vision. Englewood Cliffs, NJ: Prentice Hall. Snyder, W. E., & Qi, H. (2004). Machine vision. Cambridge, UK: Cambridge University Press. Toga, A. W., & Mazziotta, J. C. (2000). Brain mapping: The systems. New York: Academic Press. Toga, A. W., & Mazziotta, J. C. (2002). Brain mapping: The methods. New York: Academic Press. Webb, A. G. (2002). Introduction to biomedical imaging. New York: Wiley–IEEE Press. Yoo, T. (Ed.). (2004). Insight into images. Wellesley, MA: A. K. Peters.
REFERENCES BIR. (1986–1995). Analyze 7.5 File Format. Mayo Foundation. Retrieved November 2008 from http://eeg.sourceforge.net/ANALYZE75.pdf Bracewell, R. N. (1995). Two-dimensional imaging. Englewood Cliffs, NJ: Prentice Hall. Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (2007). Psychophysiological science: Interdisciplinary approaches to classic questions about the mind. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd ed., pp. 1–16). New York: Cambridge University Press. Mitchell, D. G. (1999). MRI principles. Philadelphia: Saunders. Neelin, P. (1993). Medical imaging NetCDF. McConnell Brain Imaging Centre of Montreal Neurological Institute. Retrieved November 2008 from http://www.bic.mni. mcgill.ca/software/minc/ NEMA. (2008). DICOM. Medical Imaging & Technology Alliance. Retrieved November 2008 from http://medical.nema.org/ NIf TI. (August 12, 2005). Neuroimaging Informatics Technology Initiative. Nif TI. Retrieved November 2008 from http://nifti.nimh.nih.gov/ NITRC. (2007). NITRC. Neuroimaging Informatics Tools and Resources Clearinghouse. Retrieved November 2008 from http://www.nitrc.org/ Robb, R. A. (2009). Mayo Biomedical Imaging Resource Lab Homepage. Mayo Foundation for Medical Education and Research. Retrieved November 2008 from http:// mayoresearch.mayo.edu/mayo/research/robb_lab/index.cfm Wideman, G. (August 29, 2005). Orientation and voxel-order terminology: RAS, LAS, LPI, RPI, XYZ and all that. Graham Wideman. Retrieved November 2008 from www. grahamwideman.com/gw/brain/orientation/orientterms.htm Yoo, T. (Ed.). (2004). Insight into images. Wellesley, MA: A. K. Peters. Yoo, T. S., & Metaxas, D. N. (2005). Open science—combining open data and open source software: Medical image analysis with the Insight Toolkit. Medical Image Analysis, 9(6), 503–506.
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6
Structural Brain Changes Associated With Normal Aging FAITH M. GUNNING AND ADAM M. BRICKMAN
Cerebral aging is a complex and heterogeneous process. Thus it is no surprise that cerebral aging is associated with a high degree of interindividual variability. Significant effort has been devoted to characterizing alterations that occur in the brain with advancing age. This is particularly important because understanding patterns of healthy aging will better enable us to understand the neurobiology of brain disorders that either present or are exacerbated during late life. Furthermore, thorough characterization of non-disease-related cerebral aging will enable us to elucidate anatomical alterations associated with the cognitive and affective symptoms of late-life psychiatric illnesses. In this chapter, we discuss work that has used various structural magnetic resonance imaging (MRI) modalities to understand the pattern of neuroanatomical changes occurring with normal aging, and we examine the question of how ageassociated changes in neuroanatomy mediate specific age-related declines in cognitive abilities.
POSTMORTEM FINDINGS Prior to presenting the in vivo MRI findings, we provide a brief description of the diverse array of microscopic changes associated with normal 101
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aging. What is visualized on MRI in the aging brain most likely reflects a diminution of morphological complexity across a number of cellular processes (Dickstein et al., 2007; Morrison & Hof, 2002) in addition to a subtle amount of neuronal loss (Morrison & Hof, 1997). For example, microscopic studies have documented neuronal shrinkage (Haug, 1985), reduction in synaptic density (Morrison & Hof, 1997), deafferentation (Bertoni-Freddari et al., 2002), loss of dendritic spines (de Brabander et al., 1998; Jacobs et al., 1997; Uylings & de Brabander, 2002), and loss of neuronal bodies in the neocortex (Pakkenberg & Gundersen, 1997), the hippocampus (West, 1993), and the cerebellum (Nairn et al., 1989). Brain aging is also characterized by degradation of the white matter, including myelin pallor (Kemper, 1994), loss of myelinated fibers (Bartzokis, 2004; Marner et al., 2003), and malformation of myelin sheaths (Peters, 2002). Importantly, although the total number of neocortical neurons appears to decline by approximately 10% (Uylings & de Brabander, 2002) and dendritic complexity decreases dramatically across the adult life span (Duan et al., 2003), this decline is characterized by significant interindividual as well as within-subject variability among cortical regions. That is, postmortem age-related changes appear to be specific to certain cortical regions, with the association cortices being most affected by aging, whereas the primary visual and somatosensory cortices are relatively spared (Uylings & de Brabander, 2002).
STRUCTURAL MRI STUDIES OF NORMAL AGING Structural MRI methods can be used to identify and quantify typical patterns of age-associated cerebral change. Further, structural MRI enables the identification of structural alterations associated with cognitive aging as well as the exploration of factors that contribute to the heterogeneity of brain aging.
VOLUMETRIC STUDIES Region-of-interest (ROI) morphometry studies have focused on gross regions, such as total parenchyma; or ventricular volume as well as specific structures, and have included both cross-sectional and longitudinal aging analyses. In adults, there is little doubt that the brain shrinks dramatically with age. This decline accelerates with age and is most
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precipitous after about age 50 (Raz & Rodrigue, 2006). For example, one cross-sectional study showed that healthy individuals between the ages of 71 and 80 had total brain volumes that were similar to those of healthy 2- and 3-year-old subjects (Courchesne et al., 2000). Gauging the amount of annual tissue loss in adults by examining the amount of change in parenchyma volume suggests a modest volume loss per year, ranging from less than 1% to about 2.5% (Cardenas et al., 2003; Raz & Rodrigue, 2006; Thompson et al., 2003). Jernigan and colleagues showed about 14% and 26% reductions in gray and white matter tissue volume, respectively, across ages 30 to 90 years (Greenwood, 2007; Jernigan et al., 2001). Studies of total tissue loss through measurement of the ventricular system have shown a similar pattern of change, though at a higher rate (Hu et al., 2001; Mueller et al., 1998; Raz & Rodrigue, 2006; Raz et al., 2004a; Resnick et al., 2003; Sullivan et al., 2002). Ventricular expansion likely increases nonlinearly, with rates increasing with advancing age (Raz & Rodrigue, 2006). The question of the relative prominence of global gray versus white matter in aging has received particular attention in recent years (Peters & Rosene, 2003). Bulk volume loss of white matter may reflect myelin degeneration (Peters, 2002) and is in line with the idea that the aging brain is characterized by declining neuronal connectivity (Albert, 1993). Several studies have shown a relatively greater age-associated decline in white matter volume or the presence of white matter volume loss in the absence of gray matter loss (Allen et al., 2005; Bartzokis et al., 2003; Guttmann et al., 1998; Jernigan et al., 2001; Resnick et al., 2003). However, several other studies have shown the opposite effect, with more prominent age-associated gray matter loss (Blatter et al., 1995; Sullivan et al., 2004; Thompson et al., 2003). A number of factors could account for these discrepancies. First, white matter volume loss may be a feature of more advanced age (Salat et al., 1999); that is, gray matter loss may begin earlier and progress gradually, whereas white matter loss may start later and progress more precipitously (Raz et al., 2005). Second, gray/ white matter segmentation algorithms and intensity thresholds may differ across laboratories, producing variable results. Finally, white matter changes may be heterogeneous and best captured through examination of its microstructure. Despite the well-replicated finding of total brain volume loss with advancing age, there is a lack of uniformity of age-associated volume loss, and a number of studies have begun to elucidate the pattern of regional effects across the adult life span. The prefrontal cortex appears
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to be most vulnerable to the effects of age (Raz, 2000; Raz & Rodrigue, 2006; Raz et al., 2004a). Most structural MRI studies that have examined the regional distribution have shown the greatest effects in frontal lobe (Allen et al., 2005; DeCarli et al., 1994; Raz et al., 1997, 2004a; Resnick et al., 2003; Salat et al., 1999; Tisserand et al., 2002), followed by the temporal lobes (Cowell et al., 1994; Sullivan et al., 1995), with relative sparing of primary sensory areas and the occipital lobes (Bartzokis et al., 2001; Good et al., 2001; Raz et al., 1997, 2005). In a study comparing the volumes of the frontal, temporal, parietal, and occipital lobes, Allen and colleagues (2005) found that temporal lobe volume was relatively stable across ages 20–60 but then decreased; frontal lobe volume showed a linear trend across the adult life span. On the other hand, the parietal and occipital lobes showed very little aging effect. A recent longitudinal morphometry study reported about a 0.91% loss of volume in the lateral prefrontal cortex and a 0.85% loss in the orbital frontal cortex per year (Raz et al., 2005). Our recent cross-sectional analysis of gray and white matter prefrontal volume in 70 individuals across the adult life span paralleled these findings (Brickman et al., 2005). Gray matter volume in the lateral prefrontal cortex showed the greatest age-associated effect and appeared to decline linearly across ages. Importantly, white matter volume appeared relatively stable except in the oldest participants, who had reduced white matter volume in the dorsolateral and orbital regions, consistent with the idea that change in white matter volume may be most prominent among the very old (Raz et al., 2005; Salat et al., 1999). Medial temporal lobe structures (see Figure 6.1) are affected greatly in Alzheimer’s disease and mild cognitive impairment (MCI). However, the influence of normal aging on the structure of the medial temporal lobe is not clear. From cross-sectional studies there appears to be a significant albeit modest median effect size between age and the volume of the hippocampus (r = –.31) (Raz, 2000). Nonetheless, the variability between studies is striking, with some showing that in well-screened samples of nondemented adults, there is a relative preservation of hippocampal volume across the adult life span (Sullivan et al., 1995, 2005; Van Petten, 2004), whereas others show a large effect size of the impact of age (r = –.65) (Jernigan et al., 2001). In a longitudinal study of medial temporal lobe structures, Raz and colleagues demonstrated almost a 1% per annum loss in hippocampal volume and a 0.53% loss in entorhinal cortex volume per year in individuals above age 50 (Raz et al., 2004b). There are a number of issues related to the samples studied that likely contribute to the discrepant cross-sectional findings. First, aging of the
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hippocampus appears to be nonlinear and may be significant only beginning in the sixth or seventh decade of life (Jack et al., 1998; Moffat et al., 2000; Raz et al., 2004). Second, dysfunction or volume loss in medial temporal lobe structures, particularly the entorhinal cortex, may be specific to Alzheimer’s disease (AD); positive findings could reflect the impact of a proportion of individuals with incipient AD pathology. Third, the inclusion of individuals with vascular risk factors is likely to contribute to more significant aging-related changes in the hippocampus (Du et al., 2006; Raz et al., 2005). Regarding subcortical nuclei, most researchers who have examined age effects in the neostriatum in cross-sectional studies have detected a moderate age-related decrease in the caudate and putamen coupled with minimal aging of the globus pallidus (Gunning-Dixon et al., 1998; Jernigan et al., 1991; Krishnan et al., 1990; McDonald et al., 1991; Murphy et al., 1992; Raz, 2000; Raz et al., 1995). We showed an age-associated
Figure 6.1 Three-dimensional volumetric reconstruction of the left and right hippocampus derived through region-of-interest analysis. View is from the anterior/dorsal orientation, with the head of the hippocampus at the bottom of the figure and the tail oriented upward toward the top of the figure. Image provided courtesy of Scott A. Schobel, MD.
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decrease in absolute volume of the caudate but not the putamen, though we found no apparent age-associated difference in relative striatal size (Brickman et al., 2003). In a 5-year follow-up study of 53 healthy adults ranging from 20–77 years of age at baseline, we also observed linear age-related trends for all striatal nuclei, with the rates of decline varying across nuclei (Raz et al., 2003). The caudate and putamen evidenced the largest annual shrinkage rate (0.83% and 0.73%, respectively), whereas the globus pallidus evidenced a weaker but still statistically significant rate of shrinkage (0.51%). Findings of age-related volume reductions in the thalamus vary greatly (Raz & Rodrigue, 2006). Cross-sectional and longitudinal studies of regional neuromorphology across the adult life span have relied on either ROI or voxel-based approaches. These studies have been essential in identifying regions that are vulnerable to the effects of age, but typically they have not considered the interrelationship among brain regions. That is, they treat each unit of measurement (e.g., ROI or voxel) as an independent unit without explicit examination of how they might correlate with one another. We recently conducted the first study that applied a multivariate “covariance” approach to identify spatially distributed patterns of ageassociated morphological effects (Brickman et al., 2007). Using a version of principal components analysis termed subprofile scaling model applied to voxel-based morphometry, which captures sources of betweenand within-group variability, we identified patterns of gray and white matter that reliably distinguished between young and older neurologically healthy adults. These patterns included prominent frontal regions but also showed some posterior and subcortical involvement. A recent application of the identified patterns to an independent sample (Brickman et al., 2008) showed good stability of the gray matter network but not the white matter network. The findings are consistent with a report by Alexander and colleagues (2006), and suggest that multivariate approaches may be particularly useful for discriminating between age groups or identifying patterns of neuromorphometric difference across disease states (see Chapter 7, “Functional Neuroanatomy of Aging and Cognition,” for a similar use of multivariate approaches to functional neuroimaging). A number of methodological limitations must be considered in interpreting morphometry studies. Bearing in mind that loss of neuronal bodies, decrease in neuronal size, and decreases in dendritic arborization are all potential sources of volume reductions on T1-weighted images, the specific neurohistological processes that account for the observed
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volume reductions cannot be distinguished. Regarding manual ROI morphometry, a significant limitation is the lack of standard guidelines used for the delineation of ROIs across laboratories. Thus differences between studies may be influenced by differences between research groups in the neuroanatomical boundaries used to identify ROIs. The advantage of voxel-based morphometry (VBM) is the relative ease with which it can be carried out as well as its ability to interrogate the entire brain at once, meaning that novel findings can be more easily detected. However, VBM studies of aging have yielded results that vary slightly from those that have used ROI approaches (Raz & Rodrigue, 2006); the one study that directly compared the two methods applied to the same dataset showed that they are not in full agreement (Tisserand et al., 2002). Probably a large source of error in studies using VBM stems from the requirement that MR images are transformed from native space to standardized stereotaxic space, which is most often defined by a template reflecting a young, healthy brain. Through nonlinear “warping” transformation algorithms corrected by the Jacobian determinant, true volumetric information can be estimated, but dramatic atrophy among individuals can significantly affect the reliability of coregistration. Newer approaches create study-specific templates or apply templated ROI maps to MRI data in native space, which may increase both reliability and validity.
THE RELATIONSHIP OF AGE-RELATED MORPHOMETRIC CHANGES TO COGNITIVE AGING Cognitive aging is a selective process marked by significant declines on tasks that demand substantial mental effort for successful performance, rely heavily on processing speed, and are characterized by complexity and novelty of the stimuli. On the other hand, performance on tasks that depend on semantic knowledge and/or overlearned skills are relatively preserved. It is critical to understand how this pattern of cognitive aging is related to the observed age-related changes in neuromorphology. Correlations between regional volumes and cognitive abilities tend to be modest; however, the strength of these relationships increases with advancing age (Greenwood, 2007; Zimmerman et al., 2006), suggesting that variability in neuromorphometry among older adults at least partially accounts for age-associated variability in cognition. For example, Raz and colleagues (1998) examined the association between several
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cortical volumes and performance on tests of executive function and memory in healthy adults across the life span; they found that a decrease in dorsolateral prefrontal gray matter volume was associated with poorer performance on tests of executive abilities. We have shown a stronger positive relationship between lateral frontal lobe gray matter volume and executive functioning among older adults compared with younger adults (Zimmerman et al., 2006) and that relative frontal lobe white matter volume mediates the association between age and performance on tasks of memory and executive functions (Brickman et al., 2006). Furthermore, using a multivariate approach, the degree to which older adults manifested a pattern of age-associated density loss was associated with poorer performance on tasks of memory and executive abilities (Brickman et al., 2007, 2008). Regarding the neuroanatomical underpinnings of learning, the observed relationships between medial temporal volumes and age-related episodic memory declines have generally been relatively weak (Van Petten, 2004). However, there is evidence that the prefrontal cortex, cerebellum, and neostriatal nuclei may mediate some of the age-related declines in acquisition of new skills. Evidence suggests that larger volumes of the putamen and cerebellum (but not neocortical regions or hippocampus) are associated with proficiency in a perceptual motor task and in part mediate age-related differences in performance (Raz et al., 2000). In addition, larger caudate and prefrontal cortex volumes partially mediate age-related performance differences on a mirror drawing task, a more cognitively demanding perceptual motor task (Kennedy & Raz, 2005), whereas acquisition of a cognitive skill (e.g., the mathematical puzzle/game Tower of Hanoi) is reliant upon the prefrontal cortex (Head et al., 2002). Longitudinal studies may clarify the role of volumetric reductions in age-related cognitive declines. Evidence from a longitudinal voxelbased morphometry study showed that the degree of loss of gray matter density in the frontal and temporal lobes during a 3-year interval was related to the rate of cognitive decline among older adults (Tisserand et al., 2004). In another longitudinal study of normal aging, the volume of the hippocampus predicted age-related declines in episodic memory, whereas total cortical gray matter predicted age-related changes in executive skills (Kramer et al., 2007). In contrast, analysis of the relationship of age-related shrinkage of cortical volumes during a 5-year interval revealed that reductions in the volume of enthorhinal cortex mediated agerelated changes in episodic memory, whereas volumes of the prefrontal cortex and the hippocampus did not (Rodrigue & Raz, 2004). Such lon-
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gitudinal investigations can provide critical information about the neural underpinnings of age-related cognitive change; however, at this point there are too few studies to describe a typical pattern of findings.
AGING OF THE CEREBRAL WHITE MATTER White Matter Hyperintensities Traditionally, the most common method to study white matter abnormalities on MRI has been the examination of white matter hyperintensities (WMHs). WMHs are areas of increased lucency appearing on FLAIR or T2-weighted images and are taken to indicate white matter damage. They can be discrete, or punctate, or may appear more confluent with the lateral ventricles (see Figure 6.2). Until recently, WMHs were considered clinically irrelevant, but a culmination of sample- and population-based research has demonstrated their functional significance (Gunning-Dixon & Raz, 2000; Malloy et al., 2007). With the exception of those that appear as smooth “rims” or “caps” along the surface of the lateral ventricles, WMHs are thought to be ischemic in nature and reflect rarefaction of myelin, breakdown of vessel endothelium, and microvascular disease (Fazekas et al., 1993). The presence of WMHs is common among normal elderly adults, and chronological age appears to be the most salient predictor of severity (de Leeuw et al., 2001; Jernigan et al., 1991). Vascular risk factors, such as hypertension, also account for much variability in severity of WMHs (de Leeuw et al., 2001). The severity of WMHs is associated with poorer performance in agesensitive domains, including executive functions, episodic memory, and slowed processing speed among older adults (Gunning-Dixon & Raz, 2000). The contribution of WMHs to age-related declines in executive skills may be independent from that of the prefrontal volume (GunningDixon & Raz, 2003). Furthermore, one study of older individuals suggested that WMH burden accounts for all of the age-related variance in processing speed and executive skills (Rabbitt et al., 2007). Longitudinal analyses have also supported the importance of WMHs in age-related declines in executive skills (Cook et al., 2004; Kramer et al., 2007), working memory (Raz et al., 2007), and fluid intelligence (Raz et al., 2007). The methods of quantifying WMHs are beset with some of the same limitations as morphometry studies. First, there are numerous WMH rating scales, and these vary widely in scope, sensitivity, and reliability.
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Figure 6.2 Signal hyperintensities on a FLAIR image.
Second, postmortem studies of the histology of WMHs suggest that they reflect a number of pathological processes; however, the methodology prevents a reliable discrimination among such mechanisms. Third, analysis of WMHs does not permit the identification of specific affected white matter tracts. One hopes that the introduction of new MRI approaches (e.g., diffusion tensor, magnetization transfer) will help to achieve not only better differentiation between true white matter lesions and spurious findings but also help to clarify the underlying pathological causes of WMHs.
Diffusion Tensor Imaging Diffusion tensor imaging (DTI; see Figure 6.3) offers a promising new technique for the identification of cerebral networks most vulnerable to the aging process. This method measures the magnitude and direction
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Figure 6.3 Fractional anisotropy image from a diffusion tensor scan.
of self-diffusion of water. When no barriers to such diffusion are present, diffusion occurs equally in all directions (i.e., it is isotropic). However, when barriers to this diffusion are present, the diffusion tends to follow the long axis of those barriers (i.e., diffusion is anisotropic). Barriers to diffusion in the brain include cell membranes, myelin sheaths, and white matter fiber tracts. Diffusion anisotropy can be quantified by a number of different metrics, including fractional anisotropy (FA) and mean diffusivity (MD). MD is a measure of the averaged rate of diffusion,
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whereas FA is a measure of the strength of the directional dependence of diffusion. Developmental studies show that FA generally increases with maturation of the brain, while MD decreases as the brain matures and becomes more structured and organized (Wozniak & Lim, 2006). Tissue disruption, by removal or degradation of structural barriers to water molecular motion, typically increases MD values and decreases FA values. Commensurate with the morphometry findings of selective agerelated vulnerability of the prefrontal cortex, the most consistent finding from DTI studies of advancing age is a predilection of the prefrontal white matter to exhibit significant age-associated reductions in FA and increases in MD. Following the initial reports of greater age-related FA reductions in frontal ROIs relative to more posterior ROIs (O’Sullivan et al., 2001; Sullivan et al., 2001), several studies have provided converging support for an anterior-posterior gradient of age-associated decreases in FA and increases in MD (Ardekani et al., 2007; Grieve et al., 2007; Head et al., 2004; Pfefferbaum & Sullivan, 2003; Pfefferbaum et al., 2005; Salat et al., 2004). Furthermore, there appear to be differential aging effects on FA within the frontal regions, with specific white matter areas including the ventromedial prefrontal and deep frontal white matter perhaps exhibiting the most robust relationship with age (Salat et al., 2005). The application of voxelwise techniques to explore the relationship of FA with age throughout the entire brain generally supports the pattern of findings from ROI studies, with frontal regions exhibiting greater white matter reductions than more posterior regions (Ardekani et al., 2007; Salat et al., 2004). In addition, these studies detect a pattern of decreased FA and increased MD in select striatal regions (Abe et al., 2008; Ardekani et al., 2007; Salat et al., 2004). Voxelwise analysis of the relationship of atrophy to age-related reductions in FA suggests that although in some regions atrophy may contribute to decrease of FA, FA appears to be a sensitive marker of aging of the brain that may precede atrophy in many brain regions (Hugenschmidt et al., 2008). Furthermore, results from voxelwise analyses indicate that diffusion metrics and brain volume may be complementary indices of brain aging (Abe et al., 2008). The corpus callosum is the major commissure connecting the cerebral hemispheres. Postmortem studies reveal subtle aging of the corpus callosum, with greater susceptibility of the thin, less myelinated fibers present in the genu of the corpus callosum to the deleterious effects of aging (Aboitiz et al., 1992; Kemper, 1994). Abnormalities of the corpus
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callosum are correlated with abnormalities in cognition and behavior, and degradation in interhemispheric transfer in older adults likely contributes to specific patterns of cognitive aging (Janowsky et al., 1996; Jeeves & Moes, 1996). Studies of regional FA and MD of the corpus callosum also follow an anterior-to-posterior gradient, with the genu and rostral body exhibiting the strongest relationship with advancing age, whereas FA and MD in the splenium is relatively stable (Ota et al., 2006; Pfefferbaum et al., 2005; Salat et al., 2005). Application of a quantitative fiber tracking approach revealed that within the corpus callosum, older subjects had lower FA and higher diffusivity coupled with fewer fibers than younger subjects. Furthermore, there was disproportionately lower FA and higher diffusivity in the older versus the younger group in anterior fiber bundles relative to posterior bundles (Sullivan et al., 2006). Diffusion tensor imaging indices also distinguish normal from pathological aging. For example, Head and colleagues (Head et al., 2004) used diffusion tensor imaging to characterize the influence of both normal and pathological (dementia of the Alzheimer’s type) aging on the microstructural integrity of the white matter. ROI analysis of each of the four lobes as well as the corpus callosum revealed age differences in all ROIs examined, with lower FA and higher ADC in older adults relative to their younger counterparts. Age effects were greater in anterior than the posterior corpus callosum as well as in frontal relative to parietal, temporal, and occipital regions. Furthermore, demented subjects exhibited only minimal differences in anterior regions relative to their age-matched counterparts, but they did show reduced FA and increased ADC in posterior regions. Taken together, these results suggest that microstructural white matter integrity differentiates normal from pathological aging, because normal aging is associated with microstructural deterioration that generally occurs in an anterior-to-posterior gradient, whereas dementia is associated with deterioration of more posterior lobar regions (Head et al., 2004).
THE RELATIONSHIP OF MICROSTRUCTURAL AGING TO AGE-RELATED COGNITIVE DECLINE DTI is a promising tool to investigate the neurobiological underpinnings of age-related cognitive decline. O’Sullivan and colleagues (2001) observed early evidence that DTI indices are related to cognitive performance in healthy older adults. In particular, lower attentional set shifting
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scores correlated with greater diffusivity in a frontal ROI, whereas lower verbal fluency scores correlated with lower FA in a middle white matter ROI. The authors interpreted these relationships between DTI and attention and executive performance as evidence of cortical “disconnection,” contributing to age-related cognitive decline. Subsequent studies have provided additional evidence for the contribution of microstructural white matter reductions to select deficits in working memory and executive skills. Voxelwise analysis of FA and two measures of attention/executive skills in a sample of adults ranging in age from 20 to 73 years, detected a relationship between FA and performance on a task reliant on planning and response speed in extensive frontal, parietal, and thalamic regions, whereas no relationship was detected between performance on the attention switching task and FA (Grieve et al., 2007). In an examination of the relationship between FA and MD in specific ROIs (anterior, middle, and posterior white matter of centrum semiovale) and executive skills, working memory, and processing speed, FA and MD were correlated with the working memory domain only, irrespective of ROI (Charlton et al., 2006). Furthermore, in a subsample of individuals for whom MRS data were available, N-acetyl aspartate correlated with FA, suggesting that age-related FA reductions may be mediated by axonal loss (Charlton et al., 2006). Taken together, these findings provide further support for the idea that loss of microstructural white matter integrity may contribute to poorer performance in age-sensitive domains including executive skills, working memory, processing speed, and episodic memory. Regarding information processing speed, Madden and colleagues (2004) observed that higher FA in the anterior limb of the internal capsule was associated with faster response times in older adults, whereas higher FA in the splenium of the corpus callosum was associated with faster reaction times in younger adults only. Furthermore results of another study suggest that age-related reductions in FA in the pericallosal frontal region and in the genu of the corpus callosum, but not in other regions, mediate the relationship between processing speed and episodic retrieval (Bucur et al., 2008). Thus, these findings provide preliminary evidence that the relationship between FA and visual attention/ information processing speed may differ between young and older adults and that white matter integrity in prefrontal regions may be one mechanism underlying the relationship between age-associated individual differences in perceptual speed and episodic memory retrieval.
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Overall, results from studies examining the relationship between DTI measures and cognitive performance provide preliminary support for the idea that loss of microstructural white matter integrity may contribute to poorer performance in age-sensitive domains including executive skills, working memory, processing speed, and episodic memory.
MAGNETIZATION TRANSFER IMAGING A number of interpretation issues about DTI remain. Many authors have characterized DTI as a measure of white matter, whereas in reality it is simply a measure of anisotropic diffusion. Some have argued that DTI primarily measures axonal membrane integrity, with myelin playing a modulatory role. Magnetization transfer ratio (MTR) is another method that can be used to study white matter abnormalities. MTR imaging provides information about the macromolecular structure of cerebral white matter based on the interaction of the normally observed tissue water signal with protons contained in large macromolecules (including myelin). Macromolecular semisolid structures in the brain, such as myelinated axons, are ordinarily invisible to MRI because of their extremely short transverse relaxation times. However, protons bound to them can be selectively excited using off-resonance radiofrequency pulses. To achieve MTR contrast, two MR sequences are used. The first is a proton density (PD) weighted sequence, which reflects the total water signal. The second employs an additional pulse prior to the basic proton density sequence and serves to null the signal from water molecules associated with macromolecules. Thus, the second sequence reflects the signal from free water. The percent contrast difference between the two image sets is usually expressed as the magnetization transfer ratio: MTR = (M0 – MSAT)/M0. Studies conducted in early development and multiple sclerosis suggest that DTI and MTR may provide complementary information about white matter integrity, with DTI primarily reflecting organization of fiber tracts and MTR being particularly sensitive to myelin and axonal integrity (Wozniak & Lim, 2006). MTR in early development shows an increase during the initial years of life that coincides with brain myelination and follows the posterior-toanterior temporal pattern occurring in neurodevelopment (Wozniak & Lim, 2006). However, the relationship of MTR to age is more controversial at the other end of the spectrum. For example, in 52 healthy older
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adults between the ages of 20 and 86 years, MTR histograms were significantly lower in the group above age 50 than in the younger group. The MTRs started to decline after the age of approximately 40 years in both tissues (Ge et al., 2002). In contrast, Armstrong and colleagues (2004) examined MTRs in the whole brain and multiple ROIs in 27 healthy adults ranging from 18–69 years of age. They concluded that if WMH are excluded, MTR remains relatively stable throughout adulthood, with a tendency for MTRs in some regions to increase with age, suggesting that MTR may increase in late adulthood owing to redundant myelin (Armstrong et al., 2004). The idea that age-related reductions in MTR are attributable to the presence of WMH is only partially supported. One of the first studies of MTR in elderly individuals evaluated overall mean MTR values from gray matter, normal-appearing white matter, and areas of WMH in 23 healthy elderly subjects (mean age 75). MTR values were approximately 8% lower in WMH than in normal-appearing white matter. However, MTR showed a moderately negative correlation with age in all three tissue types (Tanabe et al., 1997). In a subsequent study, analysis of MTR maps from 198 neurologically asymptomatic elderly participants in regard to WMH and ROIs of normal-appearing brain tissue revealed that MTR of WMH was significantly lower than that of normal-appearing white matter, with an overall relative reduction of approximately 10%, and that it decreased significantly with greater WMH burden. However, MTR of normal-appearing white matter did not differ between elderly individuals with minimal WMH versus subjects with extensive WMH (Fazekas et al., 2005). In yet another study, mean MTR of normalappearing white matter was lower in elderly subjects compared with the young, but it again did not differ between subjects with minimal and those with extensive WMH (Spilt et al., 2005). Thus these studies all support the existence of reduced MTR in aging of the brain, which is exacerbated in areas of WMH but occurs even in normal-appearing white matter, suggesting that MTR and WMH measures can provide complementary information about cerebral aging.
INTERVENTIONS THAT MAY MODIFY CEREBRAL AGING Preliminary findings suggest that some interventions (e.g., hormone replacement therapy, aerobic fitness) have the potential to modify the influence of aging on the brain. For example, in a small longitudinal study
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of 12 women on hormone replacement therapy and 12 age-matched control subjects, controls exhibited the expected pattern of significant shrinkage in association cortices and hippocampal regions, whereas the women on hormone replacement therapy showed significant aging only in the hippocampus, coupled with relative preservation of association cortices (Raz et al., 2004c). Furthermore, a voxel-based morphometric analysis found that hormone replacement therapy is associated with relative preservation of tissue density in prefrontal, temporal, and parietal regions in a cognitively intact group of older women (Erickson et al., 2005). Regarding aerobic fitness, data from cross-sectional studies suggest that it is associated with reduced loss of brain tissue density in frontal, parietal, and temporal association cortices (Colcombe et al., 2003), and aerobic fitness is related to higher FA in adults, independent of age, in the uncinate fasciculus and the cingulate (Marks et al., 2007). Furthermore, tissue density has been compared in individuals who were assigned to an aerobic exercise condition versus others assigned to a nonaerobic exercise condition. At 6-month follow-up, the first group showed increases in white and gray matter in select prefrontal and anterior cingulate regions whereas the second group did not exhibit such increases (Colcombe et al., 2006).
CONCLUSIONS Over the past 20 years, investigators have exploited the remarkable capacity of structural magnetic imaging (MRI) to visualize and quantify ageassociated changes in brain morphology. These efforts have uncovered effects in the brain’s macrostructure—gross variability in local or global tissue volume—and its microstructure, such as subtle markers of white matter or myelin-associated pathology. As with most measurable attributes of normal aging, observations from structural MRI aging studies are notable for increased variability and individual differences with advancing age. However, in considering multiple structural MRI modalities across the vast number of studies that have been completed, a general pattern of age-related preservation and decline has emerged. Furthermore, efforts that combine imaging approaches with behavioral measures have begun to elucidate associations between neuromorphology and cognition in normal aging. Finally, we are in the early stages of understanding how factors including aerobic fitness and hormone replacement therapy may be able to modify the pattern and course of cerebral aging.
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Morrison, J. H., & Hof, P. R. (2002). Selective vulnerability of corticocortical and hippocampal circuits in aging and Alzheimer’s disease. Progress in Brain Research, 136, 467–486. Mueller, E. A., Moore, M. M., Kerr, D. C., Sexton, G., Camicioli, R. M., Howieson, D. B., et al. (1998). Brain volume preserved in healthy elderly through the eleventh decade. Neurology, 51(6), 1555–1562. Murphy, D. G., DeCarli, C., Schapiro, M. B., Rapoport, S. I., & Horwitz, B. (1992). Agerelated differences in volumes of subcortical nuclei, brain matter, and cerebrospinal fluid in healthy men as measured with magnetic resonance imaging [erratum appears in Archives of Neurology (1994) 51(1), 60]. Archives of Neurology, 49(8), 839–845. Nairn, J. G., Bedi, K. S., Mayhew, T. M., & Campbell, L. F. (1989). On the number of Purkinje cells in the human cerebellum: Unbiased estimates obtained by using the “fractionator.” Journal of Comparative Neurology, 290(4), 527–532. O’Sullivan, M., Jones, D. K., Summers, P. E., Morris, R. G., Williams, S. C., & Markus, H. S. (2001). Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology, 57(4), 632–638. Ota, M., Obata, T., Akine, Y., Ito, H., Ikehira, H., Asada, T., et al. (2006). Age-related degeneration of corpus callosum measured with diffusion tensor imaging. Neuroimage, 31(4), 1445–1452. Pakkenberg, B., & Gundersen, H. J. (1997). Neocortical neuron number in humans: Effect of sex and age. Journal of Comparative Neurology, 384(2), 312–320. Peters, A. (2002). The effects of normal aging on myelin and nerve fibers: A review. Journal of Neurocytology, 31(8–9), 581–593. Peters, A., & Rosene, D. L. (2003). In aging, is it gray or white? Journal of Comparative Neurology, 462(2), 139–143. Pfefferbaum, A., Adalsteinsson, E., & Sullivan, E. V. (2005). Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging. Neuroimage, 26(3), 891–899. Pfefferbaum, A., & Sullivan, E. V. (2003). Increased brain white matter diffusivity in normal adult aging: Relationship to anisotropy and partial voluming. Magnetic Resonance in Medicine, 49(5), 953–961. Rabbitt, P., Scott, M., Lunn, M., Thacker, N., Lowe, C., Pendleton, N., et al. (2007). White matter lesions account for all age-related declines in speed but not in intelligence. Neuropsychology, 21(3), 363–370. Raz, N., Gunning-Dixon, F., Head, D., Rodrigue, K. M., Williamson, A., & Acker, J. D. (2004a). Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: Replicability of regional differences in volume. Neurobiology of Aging, 25, 377–396. Raz, N. (2000). Aging of the brain and its impact on cognitive performance: Integration of structural and functional findings. In F. I. M. Craik & T. A. Salthouse (Eds.), Handbook of aging and cognition (2nd ed., pp. 1–90). Mahwah, NJ: Erlbaum. Raz, N., Gunning, F. M., Head, D., Dupuis, J. H., McQuain, J., Briggs, S. D., et al. (1997). Selective aging of the human cerebral cortex observed in vivo: Differential vulnerability of the prefrontal gray matter. Cerebral Cortex, 7(3), 268–282. Raz, N., Gunning-Dixon, F. M., Head, D., Dupuis, J. H., & Acker, J. D. (1998). Neuroanatomical correlates of cognitive aging: Evidence from structural magnetic resonance imaging. Neuropsychology, 12(1), 95–114.
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Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., et al. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15(11), 1676–1689. Raz, N., & Rodrigue, K. M. (2006). Differential aging of the brain: Patterns, cognitive correlates and modifiers. Neuroscience Biobehavioral Review, 30(6), 730–748. Raz, N., Rodrigue, K. M., Head, D., Kennedy, K. M., & Acker, J. D. (2004b). Differential aging of the medial temporal lobe: A study of a five-year change. Neurology, 62(3), 433–438. Raz, N., Rodrigue, K. M., Head, D., Kennedy, K. M., Head, D., Gunning-Dixon, F., & Acker, J. D. (2003). Differential aging of the human striatum: Longitudinal evidence. AJNR: American Journal of Neuroradiology, 24, 1849–1856. Raz, N., Rodrigue, K. M., Kennedy, K. M., & Acker, J. D. (2004c). Hormone replacement therapy and age-related shrinkage: Regional effects. Neuroreport, 15, 2531–2534. Raz, N., Rodrigue, K. M., Kennedy, K. M., & Acker, J. D. (2007). Vascular health and longitudinal changes in brain and cognition in middle-aged and older adults. Neuropsychology, 21(2), 149–157. Raz, N., Torres, I. J., & Acker, J. D. (1995). Age, gender, and hemispheric differences in human striatum: A quantitative review and new data from in vivo MRI morphometry. Neurobiology of Learning and Memory, 63(2), 133–142. Raz, N., Williamson, A., Gunning-Dixon, F., Head, D., & Acker, J. D. (2000). Neuroanatomical and cognitive correlates of adult age differences in acquisition of a perceptual-motor skill. Microscopy Research & Technique, 51(1), 85–93. Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003). Longitudinal magnetic resonance imaging studies of older adults: A shrinking brain. Journal of Neuroscience, 23(8), 3295–3301. Rodrigue, K. M., & Raz, N. (2004). Shrinkage of the entorhinal cortex over five years predicts memory performance in healthy adults. Journal of Neuroscience, 24(4), 956–963. Salat, D. H., Buckner, R. L., Snyder, A. Z., Greve, D. N., Desikan, R. S., Busa, E., et al. (2004). Thinning of the cerebral cortex in aging. Cerebral Cortex, 14(7), 721–730. Salat, D. H., Kaye, J. A., & Janowsky, J. S. (1999). Prefrontal gray and white matter volumes in healthy aging and Alzheimer disease. Archives of Neurology, 56(3), 338–344. Salat, D. H., Tuch, D. S., Hevelone, N. D., Fischl, B., Corkin, S., Rosas, H. D., et al. (2005). Age-related changes in prefrontal white matter measured by diffusion tensor imaging. Annals of the New York Academy of Sciences, 1064, 37–49. Spilt, A., Geeraedts, T., de Craen, A. J. M., Westendorp, R. G. J., Blauw, G. J., & van Buchem, M. A. (2005). Age-related changes in normal-appearing brain tissue and white matter hyperintensities: More of the same or something else? American Journal of Neuroradiology, 26(4), 725–729. Sullivan, E. V., Adalsteinsson, E., Hedehus, M., Ju, C., Moseley, M., Lim, K. O., et al. (2001). Equivalent disruption of regional white matter microstructure in ageing healthy men and women. Neuroreport, 12(1), 99–104. Sullivan, E. V., Adalsteinsson, E., & Pfefferbaum, A. (2006). Selective age-related degradation of anterior callosal fiber bundles quantified in vivo with fiber tracking. Cerebral Cortex, 16, 1030–1039. Sullivan, E. V., Marsh, L., Mathalon, D. H., Lim, K. O., & Pfefferbaum, A. (1995). Agerelated decline in MRI volumes of temporal lobe gray matter but not hippocampus. Neurobiology of Aging, 16(4), 591–606.
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Sullivan, E. V., Marsh, L., & Pfefferbaum, A. (2005). Preservation of hippocampal volume throughout adulthood in healthy men and women. Neurobiology of Aging, 26(7), 1093–1098. Sullivan, E. V., Pfefferbaum, A., Adalsteinsson, E., Swan, G. E., & Carmelli, D. (2002). Differential rates of regional brain change in callosal and ventricular size: A 4-year longitudinal MRI study of elderly men. Cerebral Cortex, 12(4), 438–445. Sullivan, E. V., Rosenbloom, M., Serventi, K. L., & Pfefferbaum, A. (2004). Effects of age and sex on volumes of the thalamus, pons, and cortex. Neurobiology of Aging, 25(2), 185–192. Tanabe, J. L., Ezekiel, F., Jagust, W. J., Schuff, N., & Fein, G. (1997). Volumetric method for evaluating magnetization transfer ratio of tissue categories: Application to areas of white matter signal hyperintensity in the elderly. Radiology, 204(2), 570–575. Thompson, P. M., Hayashi, K. M., de Zubicaray, G., Janke, A. L., Rose, S. E., Semple, J., et al. (2003). Dynamics of gray matter loss in Alzheimer’s disease. Journal of Neuroscience, 23(3), 994–1005. Tisserand, D. J., Pruessner, J. C., Sanz Arigita, E. J., van Boxtel, M. P., Evans, A. C., Jolles, J., et al. (2002). Regional frontal cortical volumes decrease differentially in aging: An MRI study to compare volumetric approaches and voxel-based morphometry. Neuroimage, 17(2), 657–669. Tisserand, D. J., van Boxtel, M. P. J., Pruessner, J. C., Hofman, P., Evans, A. C., & Jolles, J. (2004). A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time. Cerebral Cortex, 14(9), 966–973. Uylings, H. B. M., & de Brabander, J. M. (2002). Neuronal changes in normal human aging and Alzheimer’s disease. Brain & Cognition, 49(3), 268–276. Van Petten, C. (2004). Relationship between hippocampal volume and memory ability in healthy individuals across the lifespan: Review and meta-analysis. Neuropsychologia, 42(10), 1394–1413. West, M. J. (1993). Regionally specific loss of neurons in the aging human hippocampus. Neurobiology of Aging, 14(4), 287–293. Wozniak, J. R., & Lim, K. O. (2006). Advances in white matter imaging: A review of in vivo magnetic resonance methodologies and their applicability to the study of development and aging. Neuroscience & Biobehavioral Reviews, 30(6), 762–774. Zimmerman, M. E., Brickman, A.M., Paul, R. H., Grieve, S. M., Tate, D. F., Gunstad, J., et al. (2006). The relationship between frontal gray matter volume and cognition varies across the healthy adult lifespan. American Journal of Geriatric Psychiatry, 14(10), 823–833.
7
Functional Neuroanatomy of Aging and Cognition MYRA FERNANDES
Our understanding of the relationship between the aging brain and behavioral measures of cognitive performance has led to the development of a new domain of research: the cognitive neuroscience of aging. Studies investigating the effects of aging on brain anatomy and physiology suggest targeted areas that are most affected by aging and are characterized by cerebral atrophy, synaptic loss, and changes in receptor numbers and function, among other anatomical changes (see Chapter 6, “Structural Brain Changes Associated With Normal Aging,” for more details). These changes are undoubtedly responsible for most of the observed age-related changes in cognitive function reported in many studies of aging. Identifying precisely how these brain changes affect cognition is a formidable challenge, though new testing methodologies and advances in neuroimaging analysis techniques have led to testable hypotheses and models of the link between brain and behavior. In cognitive psychology, the effects of aging on task performance are measured in accuracy and/or response time, whereas the effects on the brain are measured as changes in blood flow and metabolism (with positron emission tomography [PET] or functional magnetic resonance imaging [fMRI]) or changes in electrical activity (with electroencephalography [EEG]). fMRI methods rely of the fact that neural activity must be supported by a local blood supply, as the brain does not store 125
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any oxygen and stores very little glucose. As energy requirements in a region of the brain increase, there is an increase in blood flow to that region exceeding the demands. In fMRI, the relative amounts of oxygenated (oxyhemoglobin) and deoxygenated (deoxyhemoglobin) blood can be compared while a participant is carrying out a cognitive task; this measure is called the blood oxygen level–dependent (BOLD) response. An area showing increases in BOLD during task performance is indicative of that region’s need for increased metabolism (energy) as well as its involvement in the performance of that task. Changes in BOLD signal within the brains of older and younger adults are often compared in studies of the cognitive changes of aging. This chapter reviews studies that use neuroimaging to investigate how cognitive processing is affected by normal aging. Recent advances in neuroimaging have allowed an exploration of the brain basis of age-related declines in memory function and changes in basic sensory processing; these studies suggest a combination of differential recruitment of brain regions and strategies applied to solve tasks that account for the observed differences in cognitive performance across younger and older adults. A common complaint among older adults is a perceived decline in memory functions with increasing age. The new phrase “a senior moment” has been coined to capture those situations in which the older adult fails to recall a name, source of information, or intention to do something. These examples reflect what cognitive researchers call episodic, source, and prospective memory. Numerous behavioral studies concur with this view and reveal significant age differences in memory for lists of words (Smith, 1977), text (Dixon, Simon, Nowak, & Hultsch, 1982), and details about the context in which items were initially experienced (source memory) (Park & Puglisi, 1985; Park, Puglisi, & Lutz, 1982). These deficits are not limited to verbal material, as declines are also seen in memory for faces (Bartlett, Leslie, Tubbs, & Fulton, 1989), and abstract visual material (Smith Park, Cherry, & Berkovsky, 1990). Yet, despite declines in these sorts of memory functions, others remain intact or even increase with advancing age. Semantic memory for names and labels of objects (Park, Polk, Mikels, Taylor, & Marshuetz, 2001; Salthouse, 1982, 1991), vocabulary levels (Park, 2000; Salthouse, 1991), and general word knowledge (Salthouse, 1982) are unaffected and even continue to increase over the life span. As well, older adults have relatively preserved performance on tests of nondeclarative memory, in which memory is assessed indirectly as an increase in processing efficiency upon encountering an item for a second time (Light & Singh, 1987), as in the case of
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preserved repetition priming in old age (Fleischman, Wilson, Gabrieli, Bienias, & Bennett, 2004). Thus it is performance on short-term and episodic memory tasks that shows a marked decline with age (Park, 2000). These age-related impairments appear to depend on the amount of executive control or the attentional demands of the task. For example, older adults show better performance on short-term memory tasks that require the simple maintenance of information, as in repeating a list of numbers in the order they were presented; this is preserved in old age, though repeating those same numbers in the reverse order of presentation is impaired (Bopp & Verhaeghen, 2005). In addition, episodic memory tests that provide a retrieval cue, such as a recognition test in which the previously presented item is re-presented, call on the senior only to discriminate old from new. Such tests rarely reveal age differences in performance (Craik, 1986). Similarly, when the test constrains the search for the previously presented item, as in cued-recall tests where the first letter of the word may be given, small or no age-related deficits are observed. It is when the senior must reconstruct the episode without environmental support or hints that larger memory deficits are seen, as on tests of free recall or when source judgments about where or when information was initially acquired is needed (Craik, 1986; Craik & McIntyre, 1987). Multiple theories have been proposed to explain the memory deficits observed with age. These deficits have been framed in terms of a general slowing in processing speed (Salthouse, 1996), less effective inhibition processes (Hasher & Zacks, 1988), or reduced attentional resources (Craik, 1986). Although no single framework has been able to adequately describe all of the data on memory and aging, most theories converge on the idea that changes in brain function, particularly in the frontal lobe, underlie these deficits (Baddeley & Wilson, 1988; Craik & Byrd, 1982; Fuster, 1997; Knight, Grabowecky, & Scabini 1995; Luria, 1966; Rabinowitz, Craik, & Ackerman, 1982; Shallice & Burgess, 1991). During aging, the brain undergoes many deleterious changes, including reductions in volume (Raz et al., 1997) stemming from gray and white matter atrophy, synaptic degeneration, and reductions in regional cerebral blood flow (rCBF) to the brain. Evidence has been accumulating that changes in rCBF may underlie the pattern of preserved and impaired cognitive and motor function associated with normal aging (Pugh & Lipsitz, 2002). Postmortem studies show that the weight and volume of the brain decline at a persistent rate of 2% per decade (Kemper, 1994), and there is a negative correlation between brain volume and age (r = –.41
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in Raz et al., 1997). The largest age–volume correlation is found in the prefrontal cortex, with smaller correlations in other brain regions such as the temporal lobes, parietal cortices, hippocampus, and cerebellum; no change is found in primary sensory areas and pons (Raz, 2000). Studies of living seniors have examined rCBF over the cortex and throughout the entire brain and found that aging is associated with a decrease over the entire cortex (Madden & Hoffman, 1997). Interestingly there is a shift in distribution of rCBF with advancing age: middle-aged adults show more rCBF in anterior than posterior regions of the cortex (termed hyperfrontality), whereas older adults show greater rCBF in posterior than anterior regions (termed hypofrontality) (West, 1996). These findings supplement the behavioral results in that those memory tasks involving executive (organization, reordering) or attentional control (selection) processes, which are believed to rely on frontal lobe structures, are most impaired by age. According to the reduced-resource view of cognitive aging (Craik, 1986; Craik & Byrd, 1982), seniors have less efficient cognitive resources available to perform a cognitive task (Kahneman, 1973) owing to neural changes in the frontal lobe of the aging brain. According to this view, older adults as opposed to younger ones must engage additional neural regions to perform a given cognitive task. This pattern has been found using fMRI, with older adults compared with younger adults showing additional neural activity in the frontal lobes during episodic encoding (Stebbins et al., 2002), episodic memory retrieval (Cabeza, McIntosh, Tulving, Nyberg, & Grady, 1997b), working memory (Reuter-Lorenz et al., 2000), perception (Grady et al., 1994), and inhibitory control (Nielson, Langernecker, & Garavan, 2002), (for review, see Cabeza, 2002). Similarly, in Moscovitch’s (1989) component–process model of memory, if the memory task is one that requires significant frontal lobe resources—such as organization, response monitoring, or attentional control (selection) functions—this loss in available resources with advancing age begins to affect behavioral performance, unless additional resources can be recruited. One challenge for neuroimaging research, however, is that any given cognitive task is associated with a myriad of increases and decreases in brain activation, and age differences can emerge such that older participants show increased, decreased, or similar levels of brain activation relative to younger adults (Park, 2000). Furthermore, decreases in the volume of certain brain structures may limit the amount of activation that can be observed in older adults relative to younger ones, particularly in the frontal lobes (Raz, 2000; Raz, Gunning, Head, Dupuis, &
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Acker, 1998). Another difficulty is that the type of response an individual can make while undergoing functional neuroimaging is limited to button presses to indicate perception or memory for a stimulus, because the head movement required to make a verbal response introduces excessive motion artifact in functional images, making the interpretation of activations problematic. Therefore most studies of memory involve recognition rather than recall tests, for which age differences in performance are minimal. Nevertheless neuroimaging is a unique tool that allows one to directly map levels of behavioral performance to levels of brain activation. What is more, unlike behavioral studies, in which it is difficult to determine whether an observed age difference is due to differences in effort or elaboration of stimuli during encoding or to difficulties discriminating between response options during retrieval, neuroimaging research can measure brain changes unique to each stage of memory processing. More recently, neuroimaging studies have begun to explore how an aging brain manages to perform well on a given task. Some studies report no differences in overt cognitive task performance across age groups but striking differences in how the brains of young and old achieve performance. Adaptive versus maladaptive modes of processing among groups of seniors are now being explored. It is this aspect of neuroimaging that is the focus here, as it highlights how seniors, despite many neural structural differences relative to younger adults, make use of these modes to achieve their cognitive goals. The ability to examine how the brains of older adults find alternate routes to achieving performance is the real strength of the neuroimaging research of aging and also highlights the superb adaptability of the human brain when faced with less than optimal hardware. In her seminal neuroimaging study of aging, Grady and colleagues (1995) performed PET scans on younger and older adults while they were encoding faces for a later memory test. These investigators showed that during encoding, younger adults engaged the left prefrontal cortex to a greater degree than did older adults; greater activation of medial temporal lobe areas was also shown. Interestingly, the correlation between left prefrontal and hippocampal structures was .94 in young and .02 in older adults. These results suggest that performance in older adults is characterized by a change in both activity and in connectivity of brain structures during encoding. Similar results were reported by Cabeza and colleagues (1997a), in addition to an observed bilaterality in activation of prefrontal cortex in older adults, but unilateral left
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activation in young participants. From these data came the first suggestion that older adults may adapt to the decline in neural structures by increasing activation in other, homologous parts of the brain. That is, the compensatory recruitment theory was born. In the next section, evidence for this theory from studies of sensory function, working memory, and episodic encoding and retrieval is reviewed.
STUDIES OF BASIC SENSORY FUNCTIONS Neuroimaging studies of cortical regions required for early stages of perceptual analysis have consistently shown an age-related decline in activation of these regions For example, Ross and colleagues (1997) showed a decreased BOLD response in Brodmann area (BA) 17 and 18 in older as compared with younger adults in response to a visually presented red flash. Although there is disagreement about whether the spatial extent of visual cortical activity is also reduced in older adults (Huettel, Güzeldere, & McCarthy, 2001; Ross et al., 1997), it is clear that early visual processing regions are affected by aging. In line with a decline in the neural system that mediates visual feature perception, studies involving more complex materials led to the same finding. Regional cerebral blood flow measures in the occipital lobe and ventral processing pathway are reduced during a face-matching and location judgment task in older as compared with younger adults (Grady et al., 1992, 1994; Grady, McIntosh, Horwitz, & Rapoport, 2000). Reduced activation was also observed in left extrastriate cortex (BA 18) during the processing of verbal material in a lexical decision task (Madden et al., 1996), a word-pair encoding task (Cabeza et al., 1997a), and retrieval (Anderson et al., 2000). Similarly, reductions in visual processing regions were observed on a letter-working memory task (Rypma, Prabhakaran, Desmond, & Gabrieli, 2001). Although these studies report a common decline in occipital lobe activation in response to visual stimuli, they each represent changes due to different task demands, and it is unclear whether the same region was affected across all studies. Thus any claim for a pervasive deficit with aging due to a common factor must be limited. Cabeza and colleagues (2004), in a novel study, addressed this issue by considering age-related differences in brain activity on three different tasks within the same individual. Each participant completed a working memory, visual attention, and episodic retrieval task to words while in an fMRI scanner. These subjects showed that exactly the same occipital region (BA 18 bilaterally, BA 19 in the left, and BA 31 in the
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right hemisphere) showed age-related decreases in activity across these diverse tasks. The consistent finding of reduced occipital activation in aging has been interpreted as reflecting an age-related decline in the quality of bottom-up sensory input (Madden, 2007). Another consistent finding in the neuroimaging literature is that activation in frontal regions tends to increase on cognitive tasks, in older as compared with younger adults. These studies are reviewed further on and have been interpreted as reflecting a heavier reliance on top-down attentional control processes in older adults (Cabeza, Anderson, Locantore, & McIntosh, 2002; McIntosh et al., 1999) to compensate for deficits in bottom-up processing. In line with this suggestion, Madden and colleagues (2007) showed that during a letter-search task, older adults showed activation in frontal and parietal regions correlated with search performance; in younger adults, however, performance correlated with activation in the occipital lobes. Notably, these patterns were observed only in the more difficult search task and not in the neutral control condition. This suggests that older adults may cope with increased task demands in a visual search task by placing greater reliance on top-down than on bottom-up processing strategies. Thus older adults engage the frontal lobe at task difficulty levels for which young adults do not, as the latter can still rely on their intact sensory function to guide performance. As shown below, this is a theme that resonates throughout the literature, whether the task in question taps sensory functions, working memory, encoding, or episodic retrieval. Although there have been relatively few neuroimaging studies of age-related changes in processing of the auditory sensory modality, studies of event-related potentials in the brain show a decrease in amplitude of the mismatch negativity component in older adults (Alain, McDonald, & Ostroff, 2004; Bertoli, Smurzynski, & Probst, 2002; but see also Cooper, Todd, McGill, & Mitchie, 2006), which reflects the first phase of auditory sensory memory (Näätänen, 2000). Further neuroimaging studies are needed to determine whether the changes seen in brain regions involved in early visual processing are similarly observed for early auditory processing and whether the brain compensates similarly by increasing top-down processing.
STUDIES OF WORKING MEMORY The concept of working memory, first proposed by Baddeley and Hitch in 1974, has been the basis of a great deal of research over the past
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few decades. It suggests a type of mental work space that can be used to maintain current thoughts through rote rehearsal or manipulation of images. We can reorganize, sort, and check over our responses to a task prior to output using working memory; once task-related goals are met, this information can be discarded. There is a vast behavioral literature on the topic of working memory; reviewing this is beyond the scope of the present chapter, although the key components of processing within working memory include (a) executive functions, which serve to help one focus attention on task-relevant information (Posner & Paterson, 1990); (b) inhibitory processes, which serve to keep irrelevant information from entering the working memory work space and to resolve interference and conflict; and (c) cognitive flexibility, which allows a person to quickly change decision criteria or incorporate changing task demands. There is a large literature examining how aging affects working memory capacities; this generally show age-related declines in both the maintenance of information in working memory and in the ability to manipulate its contents (Belleville, Rouleau, & Caza, 1998; Hartman, Dumas, & Nielson, 2001; Van der Linden, Bredart, & Beerten, 1994). Although deficits in maintenance due to age are more reliably found for complex over simple materials (Salthouse & Babcock, 1991), age effects in executive, inhibitory, and cognitive flexibility are pervasive (Hasher & Zacks, 1988); this forms the basis of several theories of aging and decline in function (Engle, 2002; Hasher & Zacks, 1988; McDowd & Shaw, 2000). The general consensus from neuroimaging studies is that maintenance of information in working memory is subserved by ventrolateral frontal and parietal regions of the brain, lateralized to the left and right for verbal and visuospatial materials, respectively, whereas selective attention, inhibition, and more executive processing relies on dorsolateral prefrontal cortex (DLPFC), with lateralization of activation possibly dependent on the type of material (Smith, Jonides, & Koeppe, 1996). In their influential theory of normal cognitive aging, Hasher and Zacks (1988) suggest that older adults have trouble ignoring distracting information—a deficit in selective attention. Irrelevant background information enters working memory, thereby slowing information processing, which leads to difficulties in processing or retrieving task-relevant information. In line with this, studies have indicated that older adults show greater priming for to-be-forgotten or no-longer-relevant information compared with younger adults (Hartman & Hasher, 1991). These studies suggest that extraneous distracting information in the environment impairs the performance of older adults. Neuroimaging studies of
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working memory function have shown that aging is associated with an overactivation of brain regions supporting executive functions—that is, of DLPFC. For example, in a PET study comparing working memory capacity in younger and older adults, Grady and coworkers (1998) asked participants to view an unfamiliar face; after a delay of 1–21 sec, they had to select the face from among two alternatives. Both groups showed increased bilateral activity in prefrontal cortex (PFC), but the locus of activation differed across age groups: younger adults activated ventrolateral PFC (BA 45) related to stimulus representation aspects of processing, whereas older adults showed greater activation in left DLPFC (BA 9), which reflects greater reliance on attentional control (top-down) processing on tests of working memory. Interestingly, even when the material to be held in working memory comprised elementary visual forms (sine-wave gratings) in a same/different judgment task, older adults activated left DLPFC but younger ones did not, and increases in activation in older adults were correlated with better performance on the task (Bennett, Sekuler, McIntosh, & Della-Maggiore, 2001). A similar pattern was observed in a PET study by Reuter-Lorenz and colleagues (1999, 2000, 2001) in which a verbal or spatial item was held in working memory over a period of 3 sec; thereafter, participants had to say whether or not the verbal or spatial items matched a probe item. The investigators found that younger adults recruited left-lateralized PFC for verbal and right-lateralized PFC for spatial materials, but older adults recruited bilateral PFC regions during processing of both materials; notably, older adults with faster response times (RTs) showed greater bilateral DLPFC activation than did slow responders. Following the suggestion by Rypma and associates (1999) that DLPFC activation reflects recruitment of executive processes to augment simple rehearsal processes, Reuter-Lorenz and colleagues interpret this increase in older adults as reflecting their need to invoke executive processes even on memory tasks with lesser memory loads in order to maintain performance. Based on correlations with RT data, this activity is compensatory; it helps performance. As shown below, this bilateral recruitment of brain regions in older adults is among the most pervasive findings in the cognitive neuroscience of aging and has led to the development of the influential “HAROLD” model (hemispheric asymmetry reduction in older adults) (Cabeza, 2002). In support of this claim, a meta-analysis comprising comparisons across age groups was conducted; it included perception of faces, working memory, and episodic memory (Grady, 2002) and showed increased
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bilateral PFC activity on all tasks in older adults. Grady suggests that this increase in PFC involvement reflects an increased reliance on executivetype resources on lower-level, easier tasks, which is not necessary for young participants. The discussion below covers first advances in neuroimaging research in the area of long-term memory and then goes on to consider this model more fully.
STUDIES OF ENCODING AND RETRIEVAL In behavioral work, “deep” encoding involves semantic processing of an item, usually in terms of its meaning or relation to oneself, in contrast to “shallow” encoding, in which surface characteristics—such as font, number of syllables, or color—are processed. Deep encoding leads to better memory (Craik & Lockhart, 1972). Recent neuroimaging work has shown that relative to younger adults, older adults showed decreased activation of the left prefrontal cortex during intentional (Logan, Sanders, Snyder, Morris, & Buckner, 2002), and deep encoding conditions (Daselaar, Veltman, Rombout, Raaijmakers, & Jonker, 2003; Grady, Bernstein, Beig, & Siegenthaler, 2002; Grady, McIntosh, Rajah, Beig, & Craik, 1999; Stebbins et al., 2002) and a corresponding decrease in subsequent memory performance in those cases where it was assessed. These results suggest that one reason why older adults may have poorer memories than younger adults is an inability to recruit brain regions involved in semantic processing (Demb et al., 1995; Poldrack, Prabhakaran, Seger, & Gabrieli, 1999) in left PFC, which the young use to more richly encode items, leading to improved memory. In line with this, when younger adults perform encoding under conditions of distraction (divided attention), their memory performance decreases to the level seen in seniors, and there is a corresponding decrease in left PFC activation (Fletcher et al., 1995; Iidaka, Anderson, Kapur, Cabeza, & Craik, 2000; Kensinger, Clarke, & Corkin, 2003; Shallice et al., 1994). Thus one reason for poorer memory in seniors may be their deficient recruitment of left PFC regions that could aid in semantic/deep processing of information. Although these studies suggest a general pattern of decreased left PFC activity during deep encoding, not all studies find this effect. Logan and colleagues (2002) report similar levels of activation in young and old during a deep encoding task; they suggest that when seniors are oriented to process items more semantically (deeply), they can recruit the same areas as do the young; seniors differ in that they do not do so spontane-
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ously on an intentional test of memory in which level of processing is not determined by experimenter instruction. Another exception was noted by Lustig and colleagues (2003). Under deep encoding conditions, they found that healthy older and early Alzheimer’s disease patients showed similar memory performance coupled with an increase in left PFC by comparison with younger subjects. Clearly more work is needed to determine those conditions under which older adults show deficiencies in activation and, more importantly, whether these are meaningfully related to cognitive task performance. In addition to diminished activation of left PFC, older adults also show less activation in hippocampal structures during encoding (Daselaar et al., 2003; Grady et al., 1999); moreover, a significant positive correlation between prefrontal and hippocampal regions is observed in younger but not older adults (Stebbins et al., 2002). Of note, these last authors also report a significant correlation between right PFC and parietal cortex in older subjects only, suggesting that an entirely different network subserves encoding in that group. When brain activations during retrieval are examined, there is again evidence for bilateral brain activation in older relative to younger adults. Across numerous studies of retrieval, young adults show greater involvement of the right than left side of the brain, whereas older adults show bilateral activations. For example, in their study of face recognition, Grady and associates (1995) found that younger adults had right PFC and parietal activations and showed a significant correlation between right frontal and right hippocampal activity. In subsequent work, Cabeza and colleagues (1997a) found that older adults had less right and some left PFC activation during retrieval, leading to a bilateral pattern of brain activity in that group. Other investigators have found that although older adults show reduced hippocampal activity at retrieval (Cabeza et al., 2004; Grady, McIntosh, & Craik, 2005), they often show greater parahippocampal activity than younger adults (Bäckman et al., 1997; Cabeza et al., 2004; Grady et al., 2005). It has been suggested that differential activity of these regions is related to the quality of the memories that younger and older adults retrieve. That is, Daselaar and colleagues (2006) have shown that although hippocampal activity was associated with the retrieval of memories that were rich in contextual details (recollection), activity in rhinal cortex was associated with the retrieval of memories based on feelings of familiarity (context-free). Older adult were more likely to report having the latter than the former types of memories and showed greater
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activation of rhinal than hippocampal areas during a recognition memory test. What is missing from these studies, however, is a consideration of how these reported changes in brain activation directly relate to memory performance in older adults. If the decrease in left PFC (during encoding) and right PFC (during retrieval) and more bilateral involvement in older adults is compensatory (Cabeza et al., 1997a, 1997b), it should be possible to take advantage of the variability in memory performance inherent in group studies and to split up data depending on level of performance so as to compare the brain activation patterns in high versus low performers. If bilateral activation reflects compensatory recruitment for declining neural efficiency, it should be evident in high-performing relative to low-performing seniors. That is, according to the HAROLD model, to counteract cognitive decline, older adults recruit both hemispheres during task conditions under which younger adults require only one hemisphere (Cabeza et al., 2002). To test this claim, Cabeza and colleagues (2002), according to performance on a battery of neuropsychological tests that assess memory functions, grouped seniors into high and low performers before scanning (Glisky, Polster, & Routhieaux, 1995). During scanning, a group of younger adults and the two groups of older adults performed a source memory retrieval task known to produce right PFC lateralized activity in younger subjects (Cabeza, Locantore, & Anderson, 2003). The results offered clear support for the compensation view. That is, as shown in Figure 7.1, high-performing older adults showed bilateral PFC activation, whereas younger adults and low-performing seniors showed unilateral right activation. Although the Cabeza study concerned activation patterns during retrieval, similar results are reported in an episodic encoding task. Rosen and colleagues (2002) scanned both younger adults and older adults (who had been split into high and low performers based on neuropsychological testing) during deep and shallow encoding of verbal material. High-performing seniors recruited similar left PFC regions as younger participants, as well as a homologous region in right PFC, whereas lowperforming seniors showed lower left and right PFC activation compared to younger participants. Another way of examining how the aging brain affects cognition is to consider conditions in which behavior in the two groups is equivalent and study the different ways in which the tasks are accomplished in the two groups. This approach is informative, as it highlights brain adaptations among older participants that lead to successful performance (or at
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Figure 7.1 Brain activity in frontal cortex during a source memory task was lateralized to the right in young and old low-performing senior participants but bilateral in old high-performing senior participants.
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least performance similar to that of younger participants). Neuroimaging can be used to examine conditions in which behavioral measures of memory may indicate no age effects. The way the brain achieves performance can differ drastically across age groups, and knowing these differences may help pinpoint successful patterns of performance. This aspect of neuroimaging is the focus of the following discussion, which highlights how seniors, despite many neurostructural differences relative to younger adults, make use of these differences to achieve their cognitive goals. In our research, we examine the effects of distraction on the encoding and retrieval processes. Our work and that of others has shown that carrying out two tasks simultaneously (divided attention) has a greater effect when the distracting task is introduced during encoding (Baddeley, Lewis, Eldridge, & Thomson, 1984; Craik, Govoni, Naveh-Benjamin, & Anderson, 1996; Park, Smith, Dudley, & Lafronza, 1989; Puglisi, Park, Smith, & Dudley, 1988) than during retrieval, though performance on the distracting task suffers significantly more in older than younger adults under divided attention (DA) conditions at retrieval (Anderson, 1999; Fernandes & Moscovitch, 2003; Macht & Buschke, 1983; Nyberg, Nilsson, Olofsson, & Bäckman, 1997; Park et al., 1989; Whiting & Smith, 1997). In line with these behavioral findings, functional neuroimaging studies have shown that activation in the PFC is reduced by DA during encoding but not during retrieval (Anderson et al., 2000; Fletcher et al., 1995; Iidaka et al., 2000; Kensinger et al., 2003; Shallice et al., 1994). These results suggest that memory deficits in old age stem from defective or poorer encoding rather than changes in retrieval-related processing. Alternatively, older versus younger adults may simply increase activity in bilateral PFC (BA 9/44 and 46/10) under DA conditions, as they have been shown to do during memory retrieval (Cabeza et al., 2004; Cabeza et al., 1997a, 1997b) in order to maintain a comparable level of memory performance. Increases in PFC activation have been associated with better memory performance in high-performing older adults (Cabeza et al., 2002), leading to the suggestion that such brain activity compensates for age-related changes elsewhere in the brain. We hypothesized, therefore, that under DA conditions at retrieval, an increase in DLPFC in the older group would be needed to maintain a level of performance comparable to that of younger adults (Fernandes, Pacurar, Moscovitch, & Grady, 2006). We used fMRI to measure brain activity in younger and older adults while they were engaged in verbal recognition memory tasks under either
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full or DA condition during retrieval. The distracting tasks were either an animacy judgment for words or an odd-digit judgment for numbers. As others have reported, we found that older adults in the full attention condition had more activity in bilateral medial and right middle frontal gyri as well as in cingulate and temporal regions (see Figure 7.2). Replicating our earlier work (Fernandes & Moscovitch, 2003; Fernandes, Davidson, Glisky, & Moscovitch, 2004), we found no age differences in the magnitude of memory interference from these distracting tasks, although older adults performed less well overall on the memory tests. Because the memory interference effects were equivalent in younger and older adults, any differences in brain activity during DA
Figure 7.2 Brain areas with age differences in activity during the FA condition are shown on representative slices of the average structural MRI from the older adults. (A) An axial slice at Z = 20 mm above the AC–PC line; (B) an axial slice at Z = 40 mm; (C) a sagittal slice at ⫻ = ⫺11 mm from the AC (left hemisphere). The numbered circles in (A–C) indicate the regions whose mean activity is plotted in (D) (percent change from the auditory control task). The left PFC (#1) had greater activity in younger adults, and the right PFC (#2) and medial PFC (#3) showed greater activity during FA in older adults. Percent signal change is relative to an auditory baseline condition.
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between groups, after taking into account activity during full attention, reflects age differences in the way the brain copes with the distracting tasks and not differences in performance level per se. Our results suggest that older adults may be able to perform as well as younger adults under DA conditions (equivalent memory interference) because they rely more heavily on activity in inferior parietal and temporal cortex during recognition under DA conditions while also increasing activation in bilateral PFC regions. Roland and Gulyas (1995) and Krauss and colleagues (1999) suggest that activity in posterior parietal cortex during episodic memory retrieval represents reactivation of stored engrams. In line with this suggestion, Dobbins, Rice, Wagner, and Schacter (2003) and Wheeler and Buckner (2004) found that increased activity in the parietal cortex was associated with successful memory performance. Increased activity in these areas in our study of older adults may indicate greater demand on these regions to recover stored representations by directing parietal-based attention toward them; the increased PFC activity may reflect the increased resource demands of such an approach to memory retrieval (see Figure 7.3). Thus, while aging does not affect successful retrieval, it does change the way in which retrieval is accomplished.
CONCLUSIONS Although the work discussed in the previous sections inform us about the neural substrates that are active during certain conditions, it does not tell us how the brain as a whole changes dynamically to achieve performance under various conditions. Cognitive processes are believed rarely to be the result of activation of individual brain areas but rather are thought to result from the integrated activity of brain regions known as neural networks (Finger, 1994; Friston, 1994; Lashley, 1933). Although some studies note sets of brain regions that are active during a task, multivariate imaging techniques such as the partial least squares method (McIntosh, Bookstein, Haxby, & Grady, 1996) enable researchers to identify the patterns of brain activity associated with different tasks and behaviors and to determine how these covary with task performance. This analytical technique differs from a univariate one in that it highlights relationships between brain regions rather than activations in a single region. As suggested by earlier work, multivariate analyses support the idea that age-related differences in brain activation during cognitive tasks are due in part to a shift in the neural networks used to mediate performance
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Figure 7.3 Brain areas with age differences in activity during the DA digit condition are shown on representative slices of the average structural MRI from the older adults. The image on the left is a coronal slice at Y = ⫺34 mm posterior to the AC; the one on the right is a coronal slice at Y = ⫺50 mm. Mean activity in the circled regions is plotted in the graphs below each image, which show significantly increased activity in the older adults in left temporal and right parietal regions during the DA digit condition (black bars) compared with the younger adults. Mean activity in DA animacy (gray bars) is also plotted for comparison. Percent signal change is relative to the FA condition.
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Raz, N., Gunning, F. M., Head, D., Dupuis, J. H., McQuain, J., Briggs, S. D., et al. (1997). Selective aging of the human cerebral cortex observed in vivo: Differential vulnerability of the prefrontal gray matter. Cerebral Cortex, 7, 268–282. Reuter-Lorenz, P. A., Jonides, J., Smith, E. S., Hartley, A., Miller, A., Marshuetz, C., et al. (2000). Age differences in the frontal lateralization of verbal and spatial working memory revealed by PET. Journal of Cognitive Neuroscience, 12, 174–187. Reuter-Lorenz, P. A., Marshuetz, C., Jonides, J., Smith, E. E., Hartley, A., & Koeppe, R. (2001). Neurocognitive ageing of storage and executive processes. European Journal of Cognitive Psychology, 13(1–2), 257–278. Reuter-Lorenz, P. A., Stanczak, L., & Miller, A. C. (1999). Neural recruitment and cognitive aging: Two hemispheres are better than one, especially as you age. Psychological Science, 10(6), 494–500. Roland, P. E., & Gulyas, B. (1995). Visual memory, visual imagery, and visual recognition of large field patterns by the human brain: Functional anatomy by positron emission tomography. Cerebral Cortex, 5, 79–93. Rosen, A. C., Prull, M. W., O’Hara, R., Race, E. A., Desmond, J. E., Glover, et al. (2002). Variable effects of aging on frontal lobe contributions to memory. Neuroreport, 13(18), 2425–2428. Ross, M. H., Yurgelun-Todd, D. A., Renshaw, P. F., Maas, L. C., Mendelson, J. H., & Mello, N. K. (1997). Age-related reduction in functional MRI response to photic stimulation. Neurology, 48, 173–176. Rympa, B., Prabhakaran, V., Desmond, J. D., & Gabrieli, J. D. E. (2001). Age differences in prefrontal cortical activity in working memory. Psychology and Aging, 16, 371– 384. Rympa, B., Prabhakaran, V., Desmond, J. D., Glover, G. H., & Gabrieli, J. D. E. (1999). Load-dependent roles of frontal brain regions in the maintenance of working memory. Neuroimage, 9(2), 216–226. Salthouse, T. A. (1982). Adult cognition. New York: Springer-Verlag. Salthouse, T. A. (1991). Theoretical perspectives in cognitive aging. Hillside, NJ: Erlbaum. Salthouse, T. A. (1996). The processing speed theory of adult age differences in cognition. Psychology Review, 103, 403–428. Salthouse, T. A., & Babcok, R. L. (1991). Decomposing adult age differences in working memory. Developmental Psychology, 27(5), 546–554. Shallice, T., & Burgess, P. (1991). Higher-order cognitive impairments and frontal lobe lesions in man. In H. S. Levin, H. M. Eisenberg, & A. L. Benton (Eds.), Frontal lobe function and dysfunction (pp. 125–138). New York: Oxford University Press. Shallice, T., Fletcher, P., Frith, C. D., Grasby, P., Frackowiak, R. S. J., & Dolan, R. J. (1994). Brain regions associated with acquisition and retrieval of verbal episodic memory. Nature, 368, 633–635. Smith, A. D. (1977). Adult age differences in cued recall. Developmental Psychology, 13(4), 326–331. Smith, A. D., Park, D. C., Cherry, K., & Berkovsky, K. (1990). Age differences in memory for concrete and abstract pictures. Journal of Gerontology, 45(5), P205–P209. Smith, E. E., Jonides, J., & Koeppe, R. A. (1996). Dissociating verbal- and spatialworking memory using PET. Cerebral Cortex, 6, 11–20.
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8
Vascular Depression FAITH M. GUNNING, VASSILIOS LATOUSSAKIS, AND GEORGE S. ALEXOPOULOS
HISTORICAL OVERVIEW Both late-life depression (Burton, 1638) as well as the contribution of atherosclerotic changes in depression development (Gaupp, 1905) have long been described. The last few decades have seen a renewed interest in the study of the relationship between aging- or disease-associated cerebral lesions, impaired cognitive functioning, and geriatric depressive syndromes. The distinction between early- versus late-onset depression was one of the factors that focused attention on a subgroup of elderly depressives with neurological brain abnormalities conferring vulnerability to geriatric depressive syndromes (Alexopoulos et al., 1988; Blazer, 1982; Meyers & Alexopoulos, 1988; Winokur, 1979). Although confounded by methodological as well as conceptual issues, studies using age of onset as the distinguishing clinical characteristic revealed differences between early- and late-onset elderly depressives in family history of depression (Baron et al., 1981), dementia prevalence (Alexopoulos et al., 1993a, 1993b), and neuropsychological and neuroradiological abnormalities (Alexopoulos et al., 1992; Jacoby & Levy, 1980). The syndrome of late-onset depression stimulated investigations of the biological substrates of geriatric depression. Those investigations underscored the significance of cerebrovascular disease as a contributor to 149
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both the development of a late-onset depressive syndrome as well as to pharmacotherapy resistance (Coffey et al., 1988; Krishnan, 1993; Krishnan & McDonald, 1995). In 1997, the vascular depression hypothesis was proposed, which states, “Cerebrovascular disease may predispose, precipitate, or perpetuate a depressive syndrome in many elderly patients” (Alexopoulos et al., 1997; Krishnan et al., 1997). The hypothesis that vascular disease is associated with a variant of latelife depression spurred the descriptions of two entities: “clinically defined vascular depression” (Alexopoulos et al., 1997) and “MRI-defined vascular depression” (Krishnan et al., 1997). Clinically defined vascular depression was characterized by first onset of depression at 60 years of age or older and a vascular score of 1–4 on the Cumulative Illness Rating Scale-Geriatrics. Using these clinical criteria, the vascular depression group compared to the nonvascular group showed more cognitive dysfunction, disability, psychomotor retardation, lack of insight as well as and less depressive ideation. In MRI-defined vascular depression, a modified Fazekas classification system was adopted, which assesses the extent of subcortical gray matter, deep white matter, and periventricular white matter changes on magnetic resonance imaging (MRI). Patients were assigned to the vascular depression group if they had a score of at least 2 on either deep white matter hyperintensities or subcortical gray matter ratings. When contrasted with the nonvascular group, patients with MRI-defined vascular depression were significantly older, had a later age of depression onset, and were less frequently psychotic. They also showed a trend toward decreased family history of mental illness and increased anhedonia and disability. Since its initial inception, the vascular depression hypothesis has stimulated studies on the clinical presentation, course, pathogenesis, and treatment of geriatric depression. In this chapter we review lines of research most relevant to the concept of vascular depression.
MEDICAL COMORBIDITY AND RISK OF DEPRESSION Medical comorbidity, including vascular disease, predicts development of depression in diverse clinical populations. Some postulate that stress, major depression, and medical illness are reciprocally linked—stress promotes adaptation (allostasis); but when mediators of the stress response are not inhibited (allostatic state), immunity is impaired, arteriosclerosis promoted, and obesity, bone demineralisation, and atrophy of brain cells arise (McEwen, 2003).
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Cerebrovascular risk factors predict severity of depressive symptoms. In one study of 130 inpatients with major depression and 64 normal control subjects over the age of 50, cumulative cerebrovascular risk factor burden did not differ between depressed patients and control subjects (Lyness et al., 1998). However, diabetes mellitus and atrial fibrillation were both associated with depression, and atrial fibrillation retained an independent association after medical disability was statistically controlled (Lyness et al., 1998). Furthermore, data from 3,660 participants of the Cardiovascular Health Study (CHS) were used to examine the relationship of peripheral vascular factors to depressive symptoms. Participants with coronary heart disease, diabetes, or a history positive for smoking were significantly more likely to report more severe depressive symptoms, even when adjusting for age, race, gender, and physical and cognitive impairment (Byers et al., 2007). These findings imply that heart disease, diabetes, and smoking are potential risk factors for vascular depression. Furthermore, Lyness and colleagues (2000) found that before controlling for overall medical comorbidity, the severity of initial cumulative cerebrovascular risk factors predicted 1-year depressive symptoms and diagnoses in primary care patients over the age of 60.
POSTSTROKE DEPRESSION Major depression occurs frequently after a cerebrovascular accident. In fact, major depression is more likely to occur after a stroke than following other medical illnesses, even when functional disability is comparable (Folstein et al., 1977). The prevalence of depression varies with the time since the stroke. At 6 months, the prevalence rate of major depression after stroke ranges from approximately 9% to 34% (Whyte & Mulsant, 2002). The peak prevalence appears to be around 3– 6 months after the stroke; however, the prevalence of depression remains high up to 3 years after a stroke (Whyte & Mulsant, 2002). The high incidence rate of depression following stroke may be related to ischemic cerebral lesions that disrupt the neural circuitry critical for mood regulation (Robinson et al., 1984; Starkstein et al., 1988). In addition, the clinical presentation of poststroke depression is consistent with frontosubcortical dysfunction (Naarding et al., 2007). Thus it stands to reason that lesion location may be important to the development of late-life depression (Robinson et al., 1984; Starkstein et al., 1988) and some evidence supports this assumption. Lesions localized in the basal
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ganglia and their frontal projections (Starkstein et al., 1988) are associated with a high incidence of depression. Although the influence of hemispheric location on the presence of poststroke depression is controversial, meta-analytic review of the literature suggests that relative to right hemisphere lesions, left hemisphere lesions, particularly in anterior frontal regions, are strongly associated with the severity of depressive symptoms in the postacute recovery period following a stroke (Narushima et al., 2003).
EXECUTIVE DYSFUNCTION IN LATE-LIFE DEPRESSION Depressed older adults often develop cognitive deficits. Neuropsychological findings can provide indirect evidence about the cerebral networks involved in the pathophysiology of late-life depression. Executive dysfunction, a disturbance typically associated with compromised integrity of frontostriatal networks, is present in a considerable number of older individuals with major depression (Alexopoulos, 2002; Elderkin-Thompson et al., 2003; Lockwood et al., 2002; Nebes et al., 2001). Observations from acute treatment trials (Butters et al., 2000; Nebes et al., 2001) and from longer-term follow-up of depressed elders receiving uncontrolled treatment (Murphy & Alexopoulos, 2004) suggest that impairment of executive functions remains present, albeit to a milder extent, even after depressive symptoms subside. Thus, in some depressed older patients, executive dysfunction is a relatively stable trait only mildly exacerbated during depressed states. The syndrome of depression with executive dysfunction (DED) is conceptualized as major depression with prominent frontostriatal dysfunction. DED is characterized by psychomotor retardation, reduced interest in activities, impaired instrumental activities of daily living, and limited insight and vegetative signs (Alexopoulos et al., 2002b). The clinical presentation of DED resembles that of medial frontal lobe syndromes. Although cerebrovascular disease and vascular illness may predispose individuals to the development of DED, nonvascular factors may contribute as well. Executive dysfunction may increase the risk of poor outcomes of geriatric depression. Abnormal scores in executive function tested with Trails B, card-sorting perseveration (Simpson et al., 1998), and perseverative errors in the Controlled Oral Word Association Test and Animal Naming (Potter et al., 2004) predicted poor outcomes of geriatric de-
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pression. Another geriatric study documented that elderly patients who failed to respond to therapeutic dosages of antidepressants had greater impairment in initiation /perseveration tasks than those who responded to treatment (Kalayam & Alexopoulos, 1999). Moreover, abnormal initiation /perseveration scores may be associated with increased risk for relapse and recurrence of geriatric major depression (Alexopoulos et al., 2000), although some disagreement exists (Butters et al., 2004). In a sample of elderly depressed patients, both abnormal initiation /perseveration and abnormal response inhibition were associated with an unfavorable response to citalopram (Alexopoulos et al., 2005). Furthermore, in one recent study of elderly depressed patients, executive dysfunction, as measured by impaired response inhibition, predicted nonresponse to antidepressant treatment, whereas measures of psychomotor speed, reaction time, spatial judgment, and memory had no significant impact on antidepressant response (Sneed et al., 2007a).
EXECUTIVE DYSFUNCTION, CEREBROVASCULAR RISK FACTORS, AND GERIATRIC DEPRESSION In one study of elderly depressed patients, cerebrovascular risk factors predicted worse performance in processing speed, executive function, and episodic memory (Sheline et al., 2006). Similarly, a study of geriatric rehabilitation patients detected a significant interaction between cerebrovascular risk factors and executive functioning on depressive symptoms. That is, patients with poorer baseline executive functioning scores and two or more cerebrovascular risk factors demonstrated significantly greater depression scores than did the other patients during both the inpatient baseline evaluation and at 18-month follow-up (Mast et al., 2004). Furthermore, in a study of elderly depressed patients (mean age 83) that matched late-onset and early-onset depressed subjects for age, the late-onset group had both significantly more cerebrovascular risk factors as well as more impairment in executive function relative to the early-onset group. In contrast, those with a recurrent illness that started at an earlier age had more impairment in episodic memory (Rapp et al., 2005), suggesting that the pathophysiology may differ between the early- and late-onset patients. A controlled antidepressant treatment trial of elderly subjects suffering from major depression revealed that initiation /perseveration and response inhibition are associated with low remission rates of depression
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(Alexopoulos et al., 2004). Furthermore, heart disease burden but not general medical burden also predicted low remission rates of depression independent of the influence of executive dysfunction. The relationship of heart disease burden to remission was not mediated by executive dysfunction. Thus executive dysfunction and heart disease burden may constitute independent vulnerability factors that increase the risk for chronicity of geriatric depression.
NEUROIMAGING CORRELATES OF GERIATRIC DEPRESSION Structural neuroimaging reveals the presence of cerebrovascular disease in many elderly depressed patients. Signal hyperintensities (SHs), which are common on T2-weighted and Flair MRI scans of older adults, are believed to reflect cerebrovascular disease. SHs are more prevalent and severe in older depressed individuals than image-matched controls and mainly occur in subcortical gray matter regions and frontal white matter projections (Coffey et al., 1990; Greenwald et al., 1996; Krishnan, 1993; Krishnan et al., 1997; Lenze et al., 1999; O’Brien et al., 1996, 2006; Taylor et al., 2003a; Tupler et al., 2002); they are often associated with cognitive deficits, particularly executive dysfunction (Boone et al., 1992; Gunning-Dixon & Raz, 2000, 2003; Lesser et al., 1996; Simpson et al., 1998). SH may influence the development, course, and outcome of latelife depression. A cross-sectional examination of the CHS demonstrated that depressive symptoms were related to small lesions in the basal ganglia. Longitudinal CHS data found, in addition, that large cortical white matter lesions and severe subcortical white matter grade were significant risk factors for developing depressive symptoms (Steffens et al., 2002a). Furthermore, SHs have been associated with responses to both electroconvulsive therapy (ECT) (Hickie et al., 1995; Steffens et al., 2001) and antidepressant pharmacotherapy (Hickie et al., 1995; Simpson et al., 1998), although disagreement exists (Janssen et al., 2007; Salloway et al., 2002a, 2002b; Sneed et al., 2007b). A recent study noted that basal ganglia lesions predicted failure to respond to antidepressant monotherapy with a sensitivity of 80% and a specificity of 62% (Patankar et al., 2007). The early studies of SH are limited by the use of subjective and often unreliable rating scales. However, in recent years volumetric measurement techniques have been developed (e.g., Firbank et al., 2004). In
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addition, methods of examining regional white matter lesions have also been developed (Macfall et al., 2006), which allow for more specific lesion location. The application of these more sophisticated and precise measurement approaches may help to elucidate the significance of SHs to the course and the outcomes of the illness. For example, a study that used a quantitative rating scale to examine the relationship of overall SH burden to response to controlled treatment with escitalopram revealed that patients who failed to remit following escitalopram treatment had significantly greater SH burdens than both patients who remitted and elderly comparison subjects (Gunning-Dixon et al., under review). Furthermore, SH burden did not differ between depressed patients who remitted and elderly comparison subjects. Along with short-term treatment outcomes, percent increase in SH over a 2-year period may be related to failure to sustain remission (Taylor et al., 2003b). In a more recent longitudinal study of elderly depressives and elderly controls followed for up to 4 years, treatment outcome was associated with changes in total and deep white matter lesions over time (Chen et al., 2006). The wealth of research on cerebrovascular disease and late-life depression provided the impetus for Krishnan and colleagues (2004) to propose the classification of subcortical ischemic vascular depression. This is a condition defined by the combination of depression and MRI evidence of subcortical ischemic changes. According to this definition, deep white matter hyperintensities that scored either as 2 (beginning confluence of foci) or 3 (large confluent areas) on the Coffey classification system are considered reliable indications of ischemia as well as diffuse (score 3) subcortical gray matter hyperintense lesions. A casecontrol study of depressed elderly subjects of whom 75 (54%) of the subjects met neuroimaging criteria for subcortical ischemic vascular depression revealed that age, lassitude, and a history of hypertension were positively associated with the diagnosis of subcortical ischemic vascular depression, whereas a family history of mental illness and loss of libido were negatively associated with the diagnosis (Krishnan et al., 2004). In addition to the frequent presence of SHs, volume reductions are also present on MRI in the brains of older depressives in multiple frontal, limbic, and subcortical regions including the anterior cingulate cortex, prefrontal cortices, neostriatum, hippocampus, and amygdala (Krishnan et al., 1992; Kumar et al., 2000; Lai et al., 2000; Lee et al., 2003; Steffens et al., 2002b; Taylor et al., 2003c). The anterior cingulate cortex (ACC) and prefrontal regions demonstrate the most consistent alterations. MRI-based parcellation of prefrontal regions in elderly depressed
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patients confirms significant attenuation of gray and white matter in the ACC and the gyrus rectus along with reductions of gray matter in the orbitofrontal cortex (Ballmaier et al., 2004). In nondepressed elderly, vascular risk factors, particularly hypertension, appear to account for some of the observed age-related declines in both the prefrontal cortex and hippocampus (Raz et al., 2007). Thus the body of work on normal aging and MRI morphometry provides indirect support that at least some of the volume reduction observed in late-life depression is likely to be related to vascular disease. Diffusion tensor imaging (DTI) provides information about the microstructural integrity of white matter areas relevant to the pathophysiology of depression. Recent findings suggest that microstructural white matter abnormalities in frontal–subcortical areas are associated with executive dysfunction of late-life depression (Murphy et al., 2007) and predict a poor or slow response of geriatric depression to an antidepressant (Alexopoulos, Kiosses, Choi, Murphy, & Lim, 2002a). The relationship of vascular disease to microstructural indices of white matter integrity is not well characterized. However, one study that used voxelwise analysis to examine the relationship of blood pressure and white fractional anisotropy in elderly depressed patients reported significant associations between fractional anisotropy and diastolic blood pressure throughout the anterior cingulate and in multiple frontotemporal and frontostriatal regions (Hoptman et al., 2009). These preliminary findings suggest that reduced microstructural integrity of frontal–subcortical white matter may be one of the mechanisms by which high blood pressure, a known vascular risk factor, confers vulnerability to geriatric depression.
NEUROPATHOLOGICAL EVIDENCE FOR CEREBROVASCULAR DISEASE IN LATE-LIFE DEPRESSION The most frequently observed pathological correlates of white matter hyperintensities (WMHs) in elderly nondemented subjects include gliosis (Chimowitz et al., 1992; Fazekas et al., 1993, 1998), myelin pallor (Awad et al., 1986; Fazekas et al., 1993, 1998; Takao et al., 1999), atrophy of the neuropil (Fazekas et al., 1998), and breakdown of the ependymal ventricular lining (Leifer et al., 1990; Scarpelli et al., 1994). Despite the diverse underlying pathology, WMHs on the MRIs of depressed individuals provide compelling support for a vascular contribution to the
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pathophysiology of late-life depression. Thomas and colleagues completed an examination of neuropathological correlates of WMH in 20 depressed patients and 20 elderly comparison subjects (Thomas et al., 2002a, 2002b, 2003). Periventricular hyperintensities (PVHs) were found to be due to ependymal loss, differing degrees of demyelination, and cerebral ischemia with no differences in pathological correlates detected between patients and controls (Thomas et al., 2003). However, all of the deep WMHs (DWMHs) examined in the depressed subjects showed evidence of ischemia compared with less than one-third of DWMHs in control subjects. This difference was driven primarily by punctuate lesions being characterized by ischemia in depressed patients but typically not in controls. Larger DWMHs were usually ischemic in both groups (Thomas et al., 2002a). Postmortem data support the central role of frontostriatal systems in the pathophysiology of vascular depression. In the depressed subjects, the ischemic DWMHs showed a marked predilection for the dorsolateral prefrontal white matter (Thomas et al., 2002a). Furthermore, cell-packing density of neurons and glia, density of pyramidal and nonpyramidal neurons, and cortical and laminar width were measured from the orbitofrontal cortex of elderly depressed subjects (Rajkowska et al., 2005). Relative to age-matched controls, the overall packing density of orbitofrontal neurons with pyramidal morphology was decreased by 30% in MDD. Further laminar analysis of pyramidal neuron density revealed the most marked reductions in layers IIIc and V, the source of prefrontal projections to the striatum, amygdale, and cortical regions. Relative to controls, elderly depressed subjects also exhibit increased expression of cell adhesion molecules in the dorsolateral prefrontal cortex but not in either the anterior cingulate or occipital cortex. Such an increase suggests an inflammatory reaction consistent with ischemia in the dorsolateral prefrontal regions (Thomas et al., 2001).
NEUROBIOLOGICAL MODEL OF THE PATHOPHYSIOLOGY OF LATE-LIFE DEPRESSION A model of the neurobiology of emotion based on neuroimaging findings has identified two neuroanatomical systems, a ventral and a dorsal, that appear critical to the processing of emotional stimuli (Phillips et al., 2003a). The ventral system—which includes the amygdala, insula, ventral striatum, ventral anterior cingulate, and orbitofrontal cortex—is
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important for the evaluation of the emotional significance of incoming stimuli and generation of an affective response (Phillips et al., 2003a). The dorsal system—comprising the dorsal anterior cingulate, dorsolateral prefrontal cortex, and hippocampus—is critical for the cognitive regulation of affective responses. The ventral and dorsal systems have reciprocal connections; dysfunction in either network may result in poor emotional regulation. Depression is believed to be characterized by increased negative bias in the assessment of incoming stimuli within the ventral system, accompanied by decreased regulation of the affective response by the dorsal system (Phillips et al., 2003b). Functional neuroimaging studies generally support a pattern of hypermetabolism in select ventral limbic and paralimbic regions along with hypometabolism of dorsal cortical regions during depressive states, some of which normalize with remission of depression (Aizenstein et al., 2009; Buchsbaum et al., 1997; de Asis et al., 2001; Drevets et al., 2002; Kennedy et al., 2001, 2007; Saxena et al., 2002). Thus, a plausible neurobiological model of geriatric depression is that reduced white matter integrity in older depressed patients (i.e., reduced FA and greater WMH) interferes with the reciprocal regulation of dorsal cortical–ventral limbic networks critical to the experience of emotions and leads to a “disconnection state” associated not only with the predisposition of experiencing major depression in late life, but also with poor antidepressant response. The etiology of this reduced white matter integrity includes but is not restricted to vascular disease.
GENETIC CONTRIBUTIONS Genetic influences on the expression and outcomes of geriatric depressive syndromes are beginning to emerge. Although the overall importance of family history to vulnerability for mood disorders may decline in late life, it may be replaced by associations with structural abnormalities of the brain, some of them related to cerebrovascular lesions. Genes under study include those of the serotonin transporter (5-HTT) and the angiotensin receptors (AGTRs). Among elderly depressives, 5HTTLPR heterozygotes (l /s) showed an increased volume of total and white matter magnetic resonance imaging (MRI) lesions compared to homozygotes (l /l or s/s), a finding partly or wholly explained by the development of hypertension (Steffens et al., 2008). Moreover, elderly carriers of the S allele with major depression had lower caudate nucleus volume than
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L homozygotes (Hickie et al., 2007). In contrast, other groups implicated the LL genotype with low hippocampal volume in patients with first onset of depression in late life (Taylor et al., 2005). Heredity may predispose to geriatric depressive syndromes by increasing the risk of vascular lesions. Patients with late-onset major depression carried the C677T mutation of the enzyme methylene tetrahydrofolate reductase more frequently than controls (Hickie et al., 2001). Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, a condition characterized by recurrent subcortical strokes and frequently presenting with depression, is caused by mutations in the notch3 gene (Joutel et al., 1996). The ε4 allele of apolipoprotein-E increases the risk for two conditions associated with geriatric depression, Alzheimer’s disease (Saunders et al., 1996), and cerebrovascular disease (Kuller et al., 1998), but it was not associated with late-life depression in a community sample (Blazer et al., 2002). A recent study found an association between the A1166C polymorphism in the angiotensin II receptor, vascular type 1 (AGTR1), and treatment outcome in geriatric depression. This finding implicated a polymorphism previously shown to be associated with a number of vascular events such as ischemic stroke, myocardial infarction, and so on. Of note, though, the severity of the subcortical lesion was not found to moderate treatment outcome. Moreover, an earlier report (Bondy et al., 2005) failed to find an association between the AGTR1 A1166C polymorphism and treatment response in a mixed-age sample of inpatients with unipolar and bipolar depression. Preliminary data of a double-blind trial comparing a stimulant (methylphenidate) plus citalopram to citalopram plus placebo suggest that elderly depressives with the DAT VNTR 10/10 genotype preferentially respond to the combination of active agents. Furthermore, the subjects with the above genotype were more impaired in their executive functions at baseline (Lavretsky et al., 2008).
FUTURE DIRECTIONS FOR TREATMENT OF VASCULAR DEPRESSION Vascular lesions, including white matter changes, predate and are associated with the development of depressive symptoms. The assumption that vascular lesions predispose to depression has implications for the
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treatment and prevention of depression in late life. Addressing vascular risk factors such as hypertension and hypercholesterolemia is the rational first step. Agents promoting vascular integrity such as antiplatelet agents, calcium channel blockers, and antioxidants are other sensible options. In subjects with vascular depression, nimodipine added to conventional antidepressant monotherapy improved response rates and reduced recurrences compared with antidepressant alone (Taragano et al., 2001, 2005). This added support to the argument that augmenting standard antidepressant therapy with an agent shown to affect cerebrovascular status may be a pathogenetically targeted intervention for vascular depression. The effect of antidepressants on the rate of progression of WMHs is still a matter of debate. Different classes of antidepressants may be associated with different rates of progression. One study found that use of tricyclic antidepressants predicts worse rate of progression compared to the use of selective serotonin reuptake inhibitors (SSRIs) or no antidepressant use (Steffens et al., 2008). Brain stimulation methods may have a unique role in the treatment of vascular depression. There is evidence to suggest that patients with vascular stigmata shown to be resistant to pharmacotherapy may be treated successfully with electroconvulsive therapy (ECT) (Coffey et al., 1988; Ramos-Rios et al., 2007). Preliminary data indicate that repetitive transcranial magnetic stimulation may be a safe and effective treatment option for vascular depression (Fabre et al., 2004). Consistent with those data, a double-blind sham-controlled study in poststroke depression (PSD) resistant to antidepressants found that active repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex led to a significant reduction of depressive symptoms (Jorge et al., 2004). Animal studies raise the possibility that the efficacy of rTMS may be related to its potential to activate the mesolimbic dopaminergic pathway and thus influence processes such as attention, motivation, reward reinforcement, and so on, which are disturbed in patients with vascular depression (Ohnishi et al., 2004). Other cognitive processes may be beneficially targeted by rTMS, as in the example of working memory, which improved after stimulation at a midline parietal site, was not influenced by stimulation at a frontal location, and deteriorated after stimulation at an occipital site (Luber et al., 2007). A recent meta-analysis concluded that prophylactic use of antidepressant medications reduced the rate of newly developed poststroke
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Steffens, D. C., Chung, H., Krishnan, K. R., Longstreth, W. T., Jr., Carlson, M., & Burke, G. L. (2008). Antidepressant treatment and worsening white matter on serial cranial magnetic resonance imaging in the elderly. The Cardiovascular Health Study. Stroke, 39(3), 857– 862. Steffens, D. C., Conway, C. R., Dombeck, C. B., Wagner, H. R., Tupler, L. A., & Weiner, R. D. (2001). Severity of subcortical gray matter hyperintensity predicts ECT response in geriatric depression. JECT Journal of Electroconvulsive Therapy, 17, 45– 49. Steffens, D. C., Payne, M. E., Greenberg, D. L., Byrum, C. E., Welsh-Bohmer, K. A., Wagner, H. R., et al. (2002b). Hippocampal volume and incident dementia in geriatric depression. American Journal of Geriatric Psychiatry, 10, 62–71. Steffens, D. C., Taylor, W. D., McQuoid, D. R., & Krishnan, K. R. (2008). Short/long heterozygotes at 5HTTLPR and white matter lesions in geriatric depression. International Journal of Geriatric Psychiatry, 23(3), 244 –248. Takao, M., Koto, A., Tanahashi, N., Fukuuchi, Y., Takagi, M., & Morinaga, S. (1999). Pathologic findings of silent hyperintense white matter lesions on MRI. Journal of Neurological Sciences, 167, 127–131. Taragano, F. E., Allegri, R., Vicario, A., Bagnatti, P., & Lyketsos, C. G. (2001). A double blind, randomized clinical trial assessing the efficacy and safety of augmenting standard antidepressant therapy with nimodipine in the treatment of “vascular depression.” International Journal of Geriatric Psychiatry, 16, 254 –260. Taragano, F. E., Bagnatti, P., & Allegri, R. F. (2005). A double-blind, randomized clinical trial to assess the augmentation with nimodipine of antidepressant therapy in the treatment of “vascular depression.” International Psychogeriatrics, 17, 487– 498. Taylor, W. D., MacFall, J. R., Steffens, D. C., Payne, M. E., Provenzale, J. M., & Krishnan, K. R. (2003a). Localization of age-associated white matter hyperintensities in late-life depression. Progress in Nneuropsychopharmacology & Biological Psychiatry, 27, 539 –544. Taylor, W. D., Steffens, D. C., MacFall, J. R., McQuoid, D. R., Payne, M. E., Provenzale, J. M., et al. (2003b). White matter hyperintensity progression and late-life depression outcomes. Archives of General Psychiatry, 60, 1090 –1096. Taylor, W. D., Steffens, D. C., McQuoid, D. R., Payne, M. E., Lee, S. H., Lai, T. J., et al. (2003c). Smaller orbital frontal cortex volumes associated with functional disability in depressed elders. Biological Psychiatry, 53, 144 –149. Taylor, W. D., Steffens, D. C., Payne, M. E., MacFall, J. R., Marchuk, D. A., Svenson, I. K., et al. (2005). Influence of serotonin transporter promoter region polymorphisms on hippocampal volumes in late-life depression. Archives of General Psychiatry, 62, 537– 544. Thomas, A. J., Ferrier, I. N., Kalaria, R. N., Perry, R. H., Brown, A., & O’Brien, J. T. (2001). A neuropathological study of vascular factors in late-life depression. Journal of Neurology Neurosurgery & Psychiatry, 70, 83– 87. Thomas, A. J., O’Brien J. T., Barber, R., McMeekin, W., & Perry, R. (2003). A neuropathological study of periventricular white matter hyperintensities in major depression. Journal of Affective Disorders, 76, 49– 54. Thomas, A. J., O’Brien, J. T., Davis, S., Ballard, C., Barber, R., Kalaria, R. N., et al. (2002a). Ischemic basis for deep white matter hyperintensities in major depression: A neuropathological study. Archives of General Psychiatry, 59, 785 –792.
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Thomas, A. J., Perry, R., Barber, R., Kalaria, R. N., & O’Brien J. T. (2002b). Pathologies and pathological mechanisms for white matter hyperintensities in depression. Annals of the New York Academy of Sciences, 977, 333– 339. Tupler, L. A., Krishnan, K. R., McDonald, W. M., Dombeck, C. B., D’Souza, S., & Steffens, D. C. (2002). Anatomic location and laterality of MRI signal hyperintensities in late-life depression. Journal of Psychosomatic Research, 53, 665– 676. Whyte, E. M., & Mulsant, B. H. (2002). Post stroke depression: Epidemiology, pathophysiology, and biological treatment. Biological Psychiatry, 52, 253–264. Winokur, G. G. (1979). Unipolar depression: Is it divisible into autonomous subtypes? Archives of General Psychiatry, 36, 47– 52.
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Brain Structural and Functional Correlates of Alzheimer’s Dementia and Mild Cognitive Impairment in the Context of Community-Based Studies JAMES T. BECKER, CYRUS A. RAJI, MIKEL OLABARRIETA, OWEN T. CARMICHAEL, WEIYING DAI, VICTORIA MARUCA, PAUL M. THOMPSON, LEWIS KULLER, AND OSCAR L. LOPEZ
Brain imaging is now one of the most important components in evaluating behavioral abnormalities that result from brain injury. With the advent of magnetic resonance imaging (MRI), the clinician /scientist has access to high-resolution images of the brain that can be acquired quickly and with minimal risk to the patient /subject. These techniques have had a profound impact on the study of the degenerative diseases of the elderly, relating patterns of cognitive impairment and regional brain atrophy that might be responsible for such functional abnormalities. Structural MRI data have been used to examine the risk of impending dementia or the likelihood of developing mild cognitive impairment (MCI), the earliest clinical manifestation of Alzheimer’s disease (AD). Our goal here is to present data relevant to the question of how brain structure and function change as individuals age and as cognitive functions decline. This question is important because age is a key risk factor for neurodegenerative conditions such as AD (Kuller et al., 2003). We are particularly interested in risk modifiers that may mediate the effects of one condition (e.g., hypertension) on brain structure or function; consequently this is not an exhaustive review of the literature on aging and dementia. Furthermore, we focus on studies using participants drawn from the community, which is particularly important in studying the transition from normal to impaired cognition. Studies conducted in referral clinics 171
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have the advantage of providing well-characterized subjects—usually in good health and with higher socioeconomic status. However, such studies have the disadvantage that, in order for a subject to attend the clinic, someone (patient, caregiver, physician) had to observe a change in behavior or cognition sufficient to warrant a referral. By contrast, although epidemiological studies may lack the depth of evaluation of a clinic study, they have the advantage that they do not require a referral for evaluation. Consequently individuals identified through a community study as having MCI or dementia will be, on average, earlier in the course of the clinical syndrome and thus earlier in the pathological process. For example, in the Cardiovascular Health Study–Cognition Study (CHS-CS), 938 individuals were evaluated at the Pittsburgh site in 1998 –1999. Of 115 participants who were classified with dementia, only 12 (10.4%) reported being told by their family physician that they had a dementia syndrome. Of the 103 participants who said they had not been told of a cognitive disorder, 13 (12.6%) were taking donezepil (Aricept) or another cholinesterase inhibitor. Thus as many as 21.7% of the individuals with dementia (and only 1% of those with MCI) may have been identified by the primary care physician. Obviously, this is substantially lower than the nearly 100% rate seen in referral. We first discuss the structural MRI (sMRI) data before moving to functional MRI (fMRI). Within each section we generally progress from a discussion of normal aging through conversion to MCI and AD. With regard to “normal” aging, we assume that chronological age itself is not responsible for changes in brain anatomy, although we do not preclude this possibility. Rather, we assume that, by and large, age is a proxy or indicator variable for other medical conditions that affect brain anatomy and that become more prevalent with age and we present some data describing this phenomenon. With regard to MCI and dementia, we explicitly acknowledge two levels of analysis: the pathological and the clinical. In terms of AD, we try to consistently refer to the pathological process as Alzheimer’s disease and to the clinical expressions of the pathological process as Alzheimer’s dementia. Because a clinical dementia may be caused by a variety of factors, we restrict our data presentation only to those individuals who meet criteria for probable AD (McKhann et al., 1984). That is, they had no condition that could, in and of itself, account for the dementia syndrome; these individuals have a very high likelihood of having AD at autopsy (Lopez et al., 2000). Similarly, we restrict our discussion of MCI to those individuals with “probable” MCI (Lopez et al., 2003b), who
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account for approximately 25% of all those with MCI identified in the CHS-CS.
STRUCTURAL NEUROIMAGING The analysis of brain structure using MRI generally falls into one of three categories: visual ratings of atrophy and white matter hyperintensities, as in the CHS-HS (Bernick et al., 2001; Heckbert et al., 1997); manual tracing or semiautomated segmentation of either specific individual brain regions (e.g., Jack et al., 1997, 1999) or of the entire brain (e.g., Jernigan et al., 1991a; Jernigan, Salmon, Butters, & Hesselink, 1991); or automated (or semiautomated) analysis on a brainwise basis, as in voxel-based morphometry (Ashburner & Friston, 2000). Each of these techniques makes different assumptions about the principles underlying the analysis of brain structure, and thus they yield complementary information. Aging is independently associated with decline in brain volume (Raz, Rodrigue, Head, Kennedy, & Acker, 2004); there is a loss of neuronal cell bodies in neocortical, hippocampal, and cerebellar areas (Simic, ´ Kostovic, ´ Winblad, & Bogdanovic, ´ 1997), shrinkage of neurons (Haugh, 1985), and less efficient DNA repair ability (Rutten et al., 2007), leading to compromised neuronal integrity and a reduction in synaptic density (Morrison & Hof, 2007). Consequently the brains of older, cognitively normal individuals have lower brain weight, reduced volume, and expansion of both the cerebral ventricles and the sulci (Skullerand, 1985) as well as reduction in cortical gray matter thickness (Sowell et al., 2003, 2007). In an effort to understand the nature of such age-related changes, Raji and colleagues completed an analysis of age-associated alterations in brain volume among participants in the CHS-CS (Raji, Lopez, Kuller, Carmichael, & Becker, in press). They used voxel-based morphometry (VBM) (Ashburner & Friston, 2000), a brainwide voxel-level analysis of brain gray matter (GM) integrity, which has the advantage of not being constrained a priori to predefined regions of interest. The cognitively normal CHS-CS participants were carefully selected to include only those who had remained nondemented for at least 5 years after their MRI scan, thus reducing the likelihood of preclinical neurodegenerative changes at the time of their imaging study. From a total of 532 individuals with valid scans in 1998 –1999, a total of 295 were scanned with 3D
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volumetric spoiled gradient echo MRI; from this group, the investigators selected 107 elderly persons who were cognitively nondemented in 1997–1999 and 2002–2003. The remaining subjects (n = 169) either had possible AD (n = 11), MCI (n = 61), had converted to MCI or AD after 5 years (n = 78), or were lost to follow-up (n = 19). The scans were reoriented into the axial plane and then normalized to a custom “older brain” template based on 419 nondemented elderly subjects (69 ± 7.5 years of age). The normalized images were then segmented using a mixed model cluster analysis that assigns each voxel a value reflective of a tissue type based on prior probabilities from the custom template. The segmented images were modulated by multiplying these files by the Jacobian determinant of their spatial transformation matrix; the resulting images were then smoothed using a 12-mm isotropic Gaussian filter, which rendered the data more normally distributed for use in the parametric statistical analysis of SPM2. The initial processing of the scans was run as a semiautomated script in SPM2 (http://dbm. neuro.uni-jena.de/vbm/vbm2-for-spm2/). Focal differences in GM volume were assessed within the framework of the general linear model as implemented in SPM2. First, multiple linear regression was used to identify those GM voxels with statistically significant negative associations with the age, while also controlling for gender, race (White versus African American), and total intracranial volume (TIV). All t values were statistically thresholded under a false discovery rate (Genovese, Lazar, & Nichols, 2002) PFDR 0.01 (100-voxel extent threshold). As age increased, the total GM volume decreased (rp[121] = –.44, p .001, controlling for TIV, gender, and race); the VBM analysis found that the volume of the voxels within the hippocampus was significantly negatively associated with age, as were various subdivisions of the frontal lobes (see Figure 9.1). Indeed, loss of hippocampal and mesial temporal lobe volume with aging has been a consistent finding in multiple MRI studies (Raz, 2005), and the strength of the association between age and hippocampal GM volume is in the range of –.93 r –.10 (median = –.40). Our partial correlation rp values agree with these data (r p = –.45 for left hippocampus and r p = –.42 for right hippocampus). Other cross-sectional and longitudinal manual volumetric MRI studies have shown volume decline with aging in the frontal lobe regions, most prominently in the prefrontal cortex, followed by the temporal, parietal, and occipital lobes; small agerelated changes are reported in the thalamus, globus pallidus, and pons (Jernigan et al., 1991a; Resnick et al., 2000). The anatomical sequence
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Figure 9.1 Results of the voxel-based morphometry analysis of gray matter volume and age. The volume of the voxels within the hippocampus was significantly negatively associated with age, as were various subdivisions of the frontal lobes, after controlling for total intracranial volume.
of cortical GM atrophy with aging follows a pattern that is roughly the reverse of the order in which the cortex matures during development, a phenomenon known as retrogenesis (Thompson et al., 2005). Age-related volume loss was seen in a VBM analysis in the prefrontal cortex, insula, anterior cingulate gyrus, superior temporal gyrus, inferior parietal lobule, and precuneus. Other studies have found age-associated
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volume decline in bilateral hippocampus, insula, superior temporal gyrus, caudate nuclei, frontal lobes, and occipital lobes (Ohnishi, Matsuda, Tabira, Asada, & Uno, 2001; Van Laere & Dierckx, 2001). The largest VBM study of normally aged adults involved 465 persons (age range 18 –79) and showed lower GM volume as a function of increasing age in bilateral superior parietal regions, pre- and postcentral gyri, insula, cerebellum, and anterior cingulate (Good et al., 2001) but not the mesial temporal lobes.
TRANSITION TO ALZHEIMER’S DEMENTIA Many studies have sought to define the early imaging manifestations of AD, particularly those that precede evident clinical dementia (Elias et al., 2000; Galvin et al., 2005; Killiany et al., 2000; Schott et al., 2003; Tierney, Yao, Kiss, & McDowell, 2005). The normal progression of the disorder includes a prodromal period in which the disease is present without clinical signs, and this may extend as long as 10 years (Desai & Grossberg, 2005; Elias et al., 2000). The prodromal period typically progresses insidiously to a phase of mild cognitive impairment (MCI), the earliest symptomatic indicator of an evolving AD (Morris & Cummings, 2005; Morris et al., 2001). Recent positron emission tomography (PET) (Jagust et al., 2006) and structural MRI (Rosano, et al., 2005b) studies suggest that it is possible to identify persons at high risk for AD even while they are cognitively normal. Hall and colleagues (Hall, Moore, Lopez, Kuller, & Becker, 2008) provide evidence for a presymptomatic marker of early brain changes of AD from structural MRI scans by analyzing cross-sectional data from 162 participants in the CHS-CS who were cognitively normal in 1998 –1999 and were studied again in 2002–2003. When Hall and colleagues compared the MRI scans of subjects who developed AD during the study with those who remained normal (Figure 9.2) (p .001, 30 voxels), they found changes in the MRIs (a) bilaterally in the basal forebrain and (b) in the left amygdala and hippocampal formation in those individuals who went on to develop dementia. The atrophy of the basal forebrain extended from the level of the isocortical– allocortical transition posteriorly through the medial ventral striatum and into the medial nucleus basalis and diagonal band; posteriorly it extended into the lateral chiasmal hypothalamus. The atrophy in the left mesial temporal lobe was centered in the posterior and medial portions of the amygdala with extension into the anterior hippocampus.
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Figure 9.2 Brain regions that were atrophic among individuals who developed AD (from normal cognition) from 1998–1999 and 2002–2003 relative to the participants who remained cognitively normal. Atrophy in the basal forebrain extended from the level of the isocortical–allocortical transition posteriorly through the medial ventral striatum and into the medial nucleus basalis and diagonal band. The atrophy in the left mesial temporal lobe was centered in the posterior and medial portions of the amygdala, with extension into the anterior hippocampus.
Hall and associates then measured the volumes in the affected regions at the cluster level within the regions identified in the AD patients and established a lower cutoff of the normal limit at the 10th percentile of the distribution of the subjects who remained normal throughout follow-up. An individual subject’s likelihood of developing AD during follow-up increased by a factor of 8.70 (95% CI = 3.07–25.0) when the volume of his or her basal forebrain fell below the 10th percentile of normal. Although left hippocampal atrophy was associated with an increased risk of conversion, it was not statistically significant (RR = 3.06, 95% CI = 0.85 –11.0). The investigators then used a machine-learning algorithm in the Waikato Environment for Knowledge Analysis (J48) (Witten & Frank, 2005) to classify the subjects based on their regional volumes; once again, the volume of the basal forebrain was the most important factor
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in determining cognitive status 4 years later. Of the 21 normal subjects who went on to develop AD, 17 were correctly classified, and none of the subjects who remained normal were classified as AD. This yields a positive predictive value of 1.00 and a sensitivity of the classification relative to the final diagnosis of 82.8%, with a specificity of 96.7%. Thus basal forebrain atrophy is a precursor to the future evolution of the clinical manifestations of AD. Atrophy in both basal forebrain and the temporal lobe appears necessary for the full expression of the syndrome, and the extent of temporal lobe atrophy when combined with basal forebrain atrophy predicts the rate at which normal subjects become demented. It is unclear currently whether basal forebrain atrophy is a necessary event in the evolution of AD. Other MRI studies have emphasized atrophy of mesial temporal lobe structures as a necessary component of the dementing process, particularly as it appears to occur concomitantly with early cortical changes (Teipel, Flatz, Heinsen, Bokde, & Schoenberg, 2005a). However, two other recent studies have demonstrated the basal forebrain changes in early AD; one has shown this with both imaging and pathology (Teipel et al., 2005a) and another shows the changes in MRI reconstructions (Karas et al., 2004). Most recently, Raji and colleagues showed bilateral basal forebrain atrophy in a group of 19 persons from CHS-CS with a diagnosis of probable AD (Raji et al., in press).
STRUCTURAL IMAGING ANALYSIS OF MILD COGNITIVE IMPAIRMENT MCI is considered a transitional state between normal aging and dementia (Bowen et al., 1997; Lopez et al., 2003b; Morris et al., 2001; Petersen et al., 1999) and thus warrants identification as a potential target for therapeutic intervention. MCI includes those individuals who have subjective and objective evidence of abnormal memory functioning for age in the context of otherwise normal general cognitive function and with no alteration in activities of daily living (Petersen et al., 1999). The requirement of a subjective complaint is, as discussed earlier, reasonable in the context of a referral clinic such as an Alzheimer’s Disease Research Center. However, this is a rare phenomenon in the general community. In the CHS-CS, we found that approximately one-third of MCI patients did not complain of deficits, and a similar proportion of families noted no difficulties. Thus we would again conclude that individuals with MCI,
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like those with AD, who are identified through community studies are in fact earlier in the course of their clinical decline than those drawn from referral clinics. A number of brain structural abnormalities have been identified among MCI patients with abnormal memory; there is significant reduction in the volume of the hippocampus (De Santi et al., 2001; Soininen et al., 1994; Wolf et al., 2001), medial occipitotemporal lobe (Convit et al., 2000), parahippocampal gyrus, entorhinal cortex, superior temporal gyrus, and anterior cingulate gyrus (Killiany et al., 2000; Visser et al., 1999). These morphological abnormalities are particularly severe among those MCI patients who progress to AD compared with those who do not (Killiany et al., 2000; Visser et al., 1999; Visser, Verhey, Hofman, Scheltens, & Jolles, 2002). Although the most common definition of MCI relies heavily on the presence of memory dysfunction, a growing number of studies have concluded that performance in other cognitive domains is not entirely normal (Hanninen et al., 1995, 1997; Lopez et al., 2003a; Petersen et al., 1999). Patients with MCI can have deficits in verbal fluency, attention, and executive function. On neuropsychological tests, some MCI patients can have altered performance in multiple cognitive areas, which may or may not include memory, and have mild or no significant deterioration in activities of daily living. In the CHS-CS and the Pittsburgh ADRC, we identified a group of subjects with an idiopathic amnestic disorder, which represents 25%– 35% of all MCI cases (Lopez et al., 2000, 2006; Lopez et al., 2003c). Those subjects with impairments in multiple domains constitute the majority of MCI cases. Using a VBM analysis, Bell-McGinty and colleagues (2002) compared and contrasted the cortical volumes of these two types of MCI patients: the idiopathic amnestic disorder (MCI-A; n = 9) and the MCI-multiple cognitive domain type (MCI-MCD; n = 28). All of the MCI patients were from the Pittsburgh ADRC and all had subjective complaints of cognitive impairment with little alteration of activities of daily living. The MCI-A patients had an isolated memory deficit with at least two standard deviation units below the level expected on measures of memory and learning, with otherwise normal cognitive functions. MCI-MCD patients performed abnormally (–1 z –2) on at least one measure in two or more cognitive domains, which may or may not have included memory. Using VBM, Bell-McGinty and colleagues (2002) found that as a group, the MCI subjects had significantly atrophic hippocampi and middle temporal gyri (see Figure 9.3). The left inferior parietal, left middle
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frontal, and right superior frontal volumes were also reduced in MCI patients relative to healthy control subjects. The MCI-A subjects had significantly reduced volume of the mesial temporal lobe on the right— including the hippocampus, entorhinal cortex, and amygdala—compared with control subjects. Subjects classified with MCI-MCD had significant bilateral volume loss of the hippocampus, middle and superior temporal, and inferior frontal gyri compared with controls. Among MCI patients who progressed to AD during follow-up (both MCI-A and MCI-MCD), there was greater atrophy in the left entorhinal cortex, bilateral superior temporal gyri, and right inferior frontal gyrus relative to those who did not progress. Using the peak coordinates from these four regions, Bell-McGinty and colleagues extracted the eigenvariates (with age as a covariate) from within SPM99. Partial correlation analyses, controlling for age, showed significant positive correlations between the subjects’ score on the Mini Mental State Examination (MMSE) and the volumes of the left entorhinal cortex (rp = .35; df = 34; p = .04) and right inferior frontal gyrus (r p = .36; df = 34; p = .03).
Figure 9.3 Results of VBM analysis comparing gray matter volumes in MCI-A (left column) and MCI–MCD (right column) with normal control subjects. The top row shows the regional atrophy projected onto a coronal section through an average MCI patient’s brain. The bottom row shows the regions of cortical atrophy projected onto an average brain image.
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Thus there are distinct brain structural abnormalities in the two subgroups of MCI patients. The MCI-A patients have abnormalities in the hippocampus and entorhinal cortex as well as the medial temporal lobes (amygdala) and neocortex (inferior parietal, inferior and middle frontal, and superior temporal gyri on the left). The MCI-MCD subjects, who have a wider range of cognitive deficits, showed more diffuse and extensive volume loss in the neocortical heteromodal association areas (Mesulam, 1985), including the middle and superior temporal and inferior frontal gyri, right superior frontal gyrus, and left inferior parietal gyrus; they had less involvement of the medial temporal lobe structures compared with those diagnosed with MCI-A. Another method of analysis of brain structure uses three-dimensional (3D) reconstructions of hippocampus to determine not only total regional volume but also shape and size abnormalities (Thompson et al., 2004). In normal aging, hippocampal volume loss is on average 1.6%–1.7% per year (Jack et al., 1998, 2000), while that of the entorhinal cortex is about 1.4% per year (Du et al., 2004). Much higher rates of hippocampal volume loss are seen in MCI and AD patients, with faster atrophic rates in MCI subjects who progress to AD relative to those who remain stable (annual hippocampal atrophy rate for MCI patients who remain stable = 2.8%; for MCI converters = 3.7%; and for AD = 3.5%–4%) (Jack et al., 2004). We compared hippocampal volumes and 3D shapes among patients with AD (n = 20), MCI-MCDT (n = 20), MCI-A (n = 6), and age- and education-equivalent control subjects (n = 20) (Davis et al., 2004). Figure 9.4 shows the results of the analysis. The AD patients had significantly atrophic hippocampi compared with the controls, and the pattern of atrophy among the MCI patients depended on whether they were MCI-A or MCI-MCD. Those with MCI-A showed significant atrophy along the inferior regions of the hippocampus bilaterally compared with the controls. However, there was no significant difference between the MCI-A and AD groups. By contrast, the MCI-MCDT patients showed significant differences from the AD patients but no significant differences from the controls. The finding that the MCI-A group is more like the AD group than the controls is consistent with the idea that these patients have more AD pathology (at least in the temporal lobe) than do the MCI-MCDT patients. Thus, although the rate of conversion from MCI to AD may be the same regardless of the initial pattern of presentation (i.e., 12%–15% per year), the focus of the neuropathological abnormalities differs between the groups. In related work, Apostolova and colleagues (2006) used this
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Figure 9.4 Three-dimensional mesh images of the hippocampus showing areas of atrophy in AD patients relative to cognitively normal individuals. The head of the hippocampus is pointed toward the viewer; the image on the right side of the page is of the left hippocampus.
atrophy mapping approach to study 20 MCI subjects followed clinically and neuropsychologically for 3 years. Over this period, 6 patients developed AD, 7 remained stable, and 7 improved. Smaller hippocampi— specifically CA1 and subiculum—were associated with increased risk for conversion from MCI to AD. MCI patients who improved and no longer met MCI criteria at follow-up tended to have larger hippocampal volumes, and their subiculum and CA1 regions were relatively preserved (Apostolova et al., 2006). The annual rate of hippocampal atrophy also increases with worsening diagnosis. The rates of hippocampal atrophy among 490 subjects scanned twice 1 year apart (Morra et al., 2009) found that the mean rate of hippocampal volume loss was greatest among patients with the more “severe” diagnosis (AD = 5.59% per year; MCI = 3.12% per year; normal = 0.66% per year).
VENTRICULAR EXPANSION Understanding how the rate of brain atrophy differs between “stable” subjects—who remain cognitively normal or mildly impaired over a pro-
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longed period—and “declining” subjects—who transition from normal cognitive function to various forms of MCI or dementia—has the potential to provide valuable indications of dementia risk and progression. Understanding the interactions between rates of cognitive decline and rates of brain structure atrophy could be useful for assessing the time course of cognitive decline. Clinical studies have shown that the time course of conversion to dementia can be highly variable, since some subjects can remain in an MCI state for many years while others convert to dementia rapidly or improve over time (Ganguli, Dodge, Shen, & DeKosky, 2004; Ritchie, Artero, & Touchon, 2001). Furthermore, relationships between prevalent comorbidities and rates of brain structure atrophy among subjects transitioning from normal cognitive function to dementia are unclear. For example, there is an association between cerebral ventricular volume and clinical conditions that are common in the elderly, such as hypertension, diabetes, and depression (Ferguson et al., 2005; Goldstein, Bartzokis, Guthrie, & Shapiro, 2002; Pantel et al., 1998), but previous studies on rates of brain structure atrophy in dementia progression vary with respect to accounting for these comorbidities. Cortical neuronal loss associated with dementia-related pathology is reflected in the expansion of the cerebrospinal fluid, especially the in lateral ventricles. Carmichael and colleagues (2005, 2007) analyzed the rate of expansion of the lateral ventricles between MRI scans at two time points. They focused on ventricular expansion because earlier studies suggested that accelerated ventricular expansion is associated with dementia, general cognitive decline, and higher loads of dementia-related pathology at autopsy (Adak et al., 2004; Bradley et al., 2002; DeCarli et al., 1992; Ezekiel et al., 2004; Luxenberg, Haxby, Creasey, Sundaram, & Rapoport, 1987; Silbert et al., 2003; Thompson et al., 2004). We analyzed a set of 145 image pairs from the CHS-CS (1997–1999 to 2002–2003) using a fully automated segmentation technique (Carmichael et al., 2005). The images were anisotropically smoothed (Smith & Brady, 1997) and the skull and nonbrain tissue was removed (Smith, 2002); the resulting images were then cropped to remove all-zero planes and geometrically aligned to images on which the lateral ventricles had been traced. For each image pair, the first image was aligned to a single randomly selected subject image on which the lateral ventricles had been manually traced by a trained rater (Narr et al., 2001); the second image was then aligned to the first. The volume of the ventricles was quantified using the ventricle-tobrain ratio (VBR), the ratio of lateral ventricle volume to whole-brain volume. Ventricular expansion between first and second scans was quantified
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by the VBR change rate—that is, the difference between VBR in the first and second images divided by the interscan interval (see Table 9.1 for data). We found that the ventricles expanded at a significantly faster rate in the demented subjects and the MCI subjects who progressed to dementia relative to cognitively normal subjects remained normal and normal subjects progressed to MCI (see Morra et al., 2009). Significantly faster ventricular expansion was also seen in MCI subjects who progressed to dementia compared with normal subjects who progressed to MCI. MCI subjects who remained MCI had faster ventricular expansion than normal subjects who progressed to MCI, but the difference was not statistically significant (see Table 9.2 for values). Thus the lateral ventricles expand faster among subjects with dementia and subjects progressing from MCI to dementia; they expand more slowly in initially normal subjects regardless of dementia progression, and the expansion rates for MCI subjects who remained MCI are highly variable. This supports our hypothesis that ventricular expansion may be a consequence of the pathological processes that occur relatively late in the progression from normal cognitive function to dementia. MCI appears to represent a transitional state between the relatively slow ventricular expansion of aging (but cognitively normal) individuals, and the faster expansion of dementia. The finding that ventricles expanded significantly faster among individuals making the MCI-to-dementia transition compared with those transitioning from normal to dementia may seem paradoxical. The transition from normal cognitive function to dementia represents a more sudden cognitive decline, which might be expected to be accompanied by sudden changes in brain structure. However, normal-to-dementia decliners had larger ventricles at baseline (Carmichael et al., 2007), and larger ventricles are associated with susceptibility to cognitive decline (Kuller et al., 2003). Thus the difference in expansion rates may be explained in terms of differing associations between cognitive and structural changes between the more sudden and gradual decliners. Thus both groups of individuals have similar time courses of dementia-related pathology reflected in initially slow and then accelerating ventricular dilation. In our view, the normalto-dementia decliners, with their initially large ventricles, were especially vulnerable to expressing clinical dementia early in the pathological process; they therefore experienced precipitous cognitive decline early in the pathological time course even though ventricular dilation was relatively slow. In the more gradual decliners, however, the same time course of structural changes was associated with more gradual cognitive changes,
Table 9.1 VENTRICULAR VOLUMES AT TWO TIME POINTS BY PROGRESSION GROUP FIRST IMAGE
SECOND IMAGE
CHANGE RATE
DEMENTIA PROGRESSION GROUP (N)
VENTRICULAR VOLUME (cm (cm3 )
VBR a
VENTRICULAR VOLUME (cm (cm3 )
VBR
VBR (UNITS/ YEAR)
Normal to normal (52)
30.7 12.6
0.0221
33.6 13.9
0.0238
2.64e - 4
Normal to MCI (29)
33.1 14.3
0.0249
35.9 15.4
0.0265
2.70e - 4
Normal to dementia (23)
36.1 16.6
0.0271
39.5 16.0
0.0293
3.65e - 4
MCI to MCI (12)
34.6 16.2
0.0259
35.9 17.1
0.0266
12.9e - 4
MCI to dementia (13)
33.8 13.4
0.0261
38.7 16.6
0.0297
9.93e - 4
Dementia to dementia (12)
35.3 8.6
0.0254
37.8 11.3
0.0269
26.3e - 4
a
Ventricle:brain ratio.
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Table 9.2 VENTRICULAR EXPANSION AS A FUNCTION OF CHANGING COGNITIVE STATUS F RATIO
p
Normal to normal (52) vs. normal to MCI (29)
0.17
0.67
0.09
Normal to normal (52) vs. normal to dementia (23)
0.11
0.73
0.07
Normal to MCI (29) vs. normal to dementia (23)
0.46
0.50
0.14
Normal to MCI (29) vs. MCI to dementia (13)
9.69
0.00
0.56
Normal to MCI (29) vs. dementia to dementia (12)
6.05
0.02
0.47
Normal to dementia (23) vs. MCI to dementia (13)
5.60
0.02
0.45
Normal to dementia (23) vs. dementia to dementia (12)
3.47
0.07
0.37
MCI to dementia (13) vs. dementia to dementia (12)
0.03
0.85
0.04
DEMENTIA GROUPS (N)
rcontrast
so that the subjects transitioned to MCI during the earlier, slower ventricular expansion and from MCI to dementia during the later, faster expansion. In related work, healthy control subjects who were carriers of the dementia risk gene, ApoE∗4, showed local ventricular abnormalities comparable to those with AD (Chou et al., 2008). Conversely, in a study of 676 subjects, 1 of 6 of the cognitively normal control subjects carrying the protective gene, ApoE∗2, showed reduced ventricular expansion, suggesting differences in brain integrity with genotype (Hua et al., 2008). Even so, the factors that mediate or moderate the association between ventricular and cognitive changes are still in need of careful study.
STRUCTURAL IMAGING ANALYSIS OF DEMENTIA Structural neuroimaging studies are a standard component of any clinical evaluation of an individual with a suspected neurodegenerative de-
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mentia (Knopman et al., 2001). They are useful for identifying causal factors of a neurobehavioral abnormality and the pattern of brain atrophy can be informative for the differential diagnosis. However, because of the cost (both in terms of data acquisition and data analysis) and the time involved (especially for the study volunteers), these are less common among epidemiological studies; longitudinal brain imaging studies are even less common. As part of the CHS-CS, subjects who failed the neuropsychological exam had a neurological study. The neurological exam included a brief mental status examination, cranial nerve testing, motor tone, abnormal movements, strength, deep tendon reflexes, release signs, plantar response and clonus, cerebellar testing, primary sensory testing, gait, postural stability, the Unified Parkinson’s Disease Rating Scale, and the Hachinski Ischemic Scale (Lopez et al., 2003a). AD diagnosis was made using the NINCDS–ADRDA criteria for probable AD by a consensus panel of neurologists, psychiatrists, and clinicians (e.g., nurse practitioners) (McKhann et al., 1984). The adjudication process has been described in detail elsewhere (Lopez et al., 2003d). Figure 9.5 shows the typical pattern of neurodegeneration in 19 newly diagnosed probable AD patients who were identified in the CHS-CS cohort in 1997–1998. AD results in a characteristic pattern of cerebral degeneration that can easily be distinguished on MRI scans even early in the course of the dementia (Becker, Aizenstein, & Butters, 2005; Jack et al., 1997; Jernigan et al., 1991b; Killiany et al., 2000, 2002). There is significant volume loss in a contiguous bilateral band of cortical and subcortical structures including the mesial temporal lobes (entorhinal cortex, hippocampal formation, amygdaloid complex), basal forebrain area (nucleus basalis), ventral striatum, and hypothalamus. In a related study, Raji and colleagues examined the independent effects of age and AD on brain structural integrity (Raji et al., in press). In Figure 9.6, we show three different t-contrasts. The voxels labeled in green had statistically significant lower GM volumes as age increased. These effects are seen in the frontal lobes, primary somatosensory cortices, superior temporal lobe, occipital lobe, and cerebellum. The voxels labeled in red had lower GM in the probable AD subjects compared to controls; this effect is localized to the mesial temporal lobe. The voxels labeled in blue had both an age and AD main effect, also located in the mesial temporal lobe. There is also a main effect of AD in the substantia innominata of the basal forebrain (see Figure 9.7), identified as a GM extension of the
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Figure 9.5 The results of the VBM analysis comparing “probable AD” patients with healthy, elderly controls. The voxels that had significantly decreased volume in the patients compared to the controls is rendered onto the surface of an individual, control subject’s brain. Note that the atrophy is focused in the inferior and mesial temporal lobes, with sparing of other cortical regions.
ventral striatum including the nucleus basalis of Meynert and located inferior to the anterior commissure (Mesulam & Geula, 1988; Teipel, Flatz, Heinsen, Bokde, & Schoenberg, 2005b). These effects are seen in both the medial aspects, which project to the hippocampus and the lateral divisions and to other regions of the mesial temporal lobe, including the amygdala (De Olmos & Heimer, 1999).
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Figure 9.6 Brain regional atrophy attributable to age and to AD. The voxels labeled in green had lower GM volumes with increasing age; the voxels labeled in red had lower GM in “probable AD” subjects compared with controls. The voxels labeled in blue had both an age and an AD effect; this is also located in the mesial temporal lobe.
Normal aging and AD affect distinct areas of the brain, with age targeting cortical areas and AD affecting primarily mesial temporal lobe structures with overlap primarily in the posterior hippocampus and parahippocampal gyrus. Therefore the main effects on hippocampal GM volume of age and AD exist along an anatomical continuum such that the head of the hippocampus is affected primarily by AD but is relatively
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Figure 9.7 Coronal section of the standard MNI template showing voxel-level atrophy among “probable AD” patients relative to healthy controls. In addition to the decreased volume in the temporal lobes, there is significant atrophy in the ventral striatum, including the basal forebrain.
unaffected by age, whereas more posterior aspects of the hippocampus are affected by both age and AD. AD-related volume loss in hippocampus and thalamus have been found in prior studies (Jernigan et al., 1991b; Xuereb et al., 1991). Additionally, we found AD-specific GM volume loss in the basal forebrain.
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Age and AD have both independent and synergistic effects in different areas of the hippocampal formation. Lower hippocampal GM volume related to older age was localized in posterior hippocampal areas, whereas AD’s main effects were seen most prominently in the anterior hippocampus, including the entorhinal cortex. Differential effects of age and AD on the hippocampus, with AD affecting more anterior portions and age affecting more posterior aspects, have been previously shown (Raz et al., 2004). Therefore AD may exert some of its deleterious reductions in GM hippocampal volume by strengthening the association between aging and GM decline. Alternatively, it may be the case that for a given brain area, persons who exhibit more age-associated volume loss are more likely to express the clinical syndrome for a given degree of AD-related atrophy. Our study suggests that the hippocampus is one such area. The finding of lower volume in the basal forebrain of probable AD subjects warrants particular attention because it includes the nucleus basalis of Meynert, a major source of acetylcholine in the brain (Mesulam & Geula, 1988), which has also been shown to possess tau pathology in AD (Mesulam, Shaw, Mash, & Weintraub, 2004). There is an association between basal forebrain atrophy and ventricular volumes, prefrontal and posterior cingulate GM volumes (Teipel et al., 2005b), and atrophy of the basal forebrain; these are more common in AD than normal aging (Whitwell et al., 2007). Persons who convert from normal cognition to AD have lower GM volumes in this area (Hall et al., 2008). Understanding structural changes in the basal forebrain can therefore lend basic insight into AD pathophysiology.
FUNCTIONAL NEUROIMAGING WITH MRI Recent advances in functional magnetic resonance imaging (fMRI) have permitted the measurement of “absolute” cerebral blood flow (CBF) using arterial spin labeling. This technique uses nonionizing radiofrequency radiation to tag protons in arterial water before they enter the brain, and CBF maps are derived from the difference images acquired with (label) and without (control) tagging. Multislice continuous arterial spin labeling (CASL) techniques allow whole-brain coverage using clinical imaging systems (Alsop & Detre, 1998). Perfusion measurements using arterial spin labeling in humans have a high correlation with alternative techniques such as 15O PET and dynamic susceptibility contrast MRI (Weber & Gunther, 2003; Ye & Berman, 2000).
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Hypertension (HTN) is frequent in the elderly; 30%– 40% of individuals age 65 or greater have the syndrome (Wolf-Maier et al., 2003). It has a significant impact on cardiovascular function as well as brain structure and function (Kilander, Nyman, Boberg, Hansson, & Lithell, 1998; Kivipelto et al., 2001a; Launer, Masaki, Petrovitch, Foley, & Havlik, 1995). The most common explanation for the deleterious effect of HTN on cognition is that HTN increases the risk of cerebrovascular disease (CVD) (Struijs et al., 2005) by causing vascular hypertrophy and microvascular remodeling. This occurs as the disease promotes arteriosclerosis in large vessels and lypohyalinosis in penetrating arterioles, with subsequent regional CBF dysfunction (Moossy, 1993), a process that can lead to lacunar infarcts and white matter disease (van Dijk et al., 2004) and eventually to neuronal loss. HTN is a risk factor for cognitive decline and for dementia, especially AD (Kivipelto et al., 2001b; Qiu, Winblad, Fastbom, & Fratiglioni, 2003; Skoog, Lernfeldt, & Landahl, 1996), likely secondary to a vulnerability state caused by the associated CVD. However, the factors that predispose individuals with HTN to developed AD are unknown. As a first step in understanding this process, it is crucial to study the effects of HTN on brain structure and function in cognitively normal subjects. PET studies conducted in middle-aged cognitively normal subjects with HTN show a pattern of reduced rCBF and compensation (Jennings et al., 1998, 2005). Other PET studies show decreased metabolism in the striatum and thalamus (Fujishima, Imaizumi, Abe, Takeshita, & Fujishima, 1995; Mentis et al., 1994). fMRI studies found an association between activation in the anterior cingulate gyrus, insula, thalamus, and periaqueductal gray matter and blood pressure when cognitively normal individuals with HTN performed the Stroop test (Gianaros et al., 2005). We investigated the determinants of rCBF in cognitively normal individuals using CASL MRI (Dai et al., 2008). Of a group of 51 cognitively normal individuals who had an MRI of the brain in 2002–2003, we selected 41 (80%) for study who had no evidence of structural central nervous system (CNS) lesions or a history of clinical strokes. The anatomical MRI of each subject was aligned to the standard single-subject MNI template brain using a fully deformable registration method (Chen, 1999) that allows for a high degree of spatial deformation for a more accurate regional alignment. There were significant clusters of hypoperfusion in hypertensive as opposed to normotensive subjects (see Figure 9.8). These included the right and left anterior cingulate gyrus with extension to the subcallosal
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region, left posterior cingulate gyrus, medial precuneus, left lateral inferior and superior frontal, inferior parietal, left orbitofrontal, left superior and middle temporal cortices, left hippocampus, bilateral putamen, and globus pallidus. There were no areas of hyperperfusion in the participants with HTN. These data are consistent with previous observations of diminished rCBF in subcortical and cortical areas in subjects with HTN (BeasonHeld, Moghekar, Zonderman, Kraut, & Resnick, 2007; Fujishima et al., 1995; Mentis et al., 1994). HTN affects the volume of the hippocampus and amygdala (den Heijer et al., 2005; Korf, White, Scheltens, & Launer, 2004) and can affect the same cerebral structures that are targeted by AD (Beason-Held et al., 2007; Gianaros et al., 2005; Jennings et al., 1998, 2005). One important feature of these data is that they show that HTN can alter rCBF beyond the cerebral regions localized at the end of the tree of the perforant arteries in subcortical regions such as the striatum. Cognitively normal subjects with HTN have a diminished cerebrovascular dilative response to physiological stimuli (Gianaros et al., 2005; Jennings
Figure 9.8 Maps of rCBF in cognitively normal volunteers calculated using continuous arterial spin labeling (CASL) techniques. There were significant clusters of hypoperfusion in the hypertensive compared with normotensive subjects.
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et al., 1998, 2005), and there is a pattern of reduced rCBF and compensation in middle-aged subjects with HTN. Elevated systolic blood pressure in untreated hypertensive, cognitively normal subjects correlated with gray matter volumes in superior frontal, anterior cingulate, and middle temporal gyri (Gianaros, Greer, Ryan, & Jennings, 2006). HTN can contribute to cognitive deficits in the absence of radiological evidence of brain infracts (Kilander et al., 1998; Kivipelto et al., 2001b), and CVD can modulate the clinical manifestation of AD (Snowdon et al., 1997) by expressing the clinical symptoms of dementia with fewer AD pathological changes (Petrovitch et al., 2000). HTN plays a critical role in this process, and our data suggest one mechanism that could account for these observations. One important aspect of the transition from normal cognition to dementia is that in spite of the presence of progressive neuronal loss and atrophy, some individuals are able to maintain a certain degree of functional plasticity (Becker et al., 1996; Stern et al., 2000). AD patients have greater activation than normals in brain regions involved in memory functions as well as in other brain regions (e.g., frontal lobes) that activate only with increasing task difficulty. However, this compensatory response can occur without necessarily improving cognitive function to normal levels. MCI subjects have greater functional activation of the hippocampus compared with normal controls and AD subjects in memory tasks and in the dorsolateral prefrontal cortex and anterior cingulate gyrus during the performance of attentional/executive tasks compared with cognitively normal subjects (Rosano et al., 2005a). Dai and colleagues (2009) acquired whole-brain maps of rCBF using CASL perfusion MRI; they detected voxel- and region-level differences in rCBF between normal subjects and those with MCI or AD. This study revealed that changes in metabolic activity (as reflected by changes in rCBF) are highly variable during the transition from normal cognition to dementia and that, during the transition, there is evidence of a compensatory hyperactivation of brain regions critical for normal memory. CASL data were acquired from 104 CHS-CS participants who had had an MRI of the brain in 2002–2003: 38 were normal controls, 37 had probable AD (McKhann et al., 1984), and 29 had MCI. Per subject global mean CBF differed significantly among the three groups (F[2, 101] = 5.09), p = .008), with significant differences between normal and AD (t = 2.23, p = .03), and between MCI and AD (t = –3.16, p = .002) groups. However, there were no significant differences between the normal and MCI groups (t = 1.06, p = .29). Statistically significant group
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differences from the voxel-level analysis are shown on orthogonal slices of the colin27 brain (Figure 9.9). MCI subjects had decreased rCBF in the posterior cingulate gyrus with extension to the medial precuneus and increased rCBF in the left hippocampus, right caudate nucleus and putamen/globus pallidus (striatum), and right amygdala compared with normals. The AD group had further decreases in rCBF in the left superior parietal, lateral frontal, superior temporal (anterior), and orbitofrontal cortices as well as the left hippocampus, left thalamus, and right amygdala compared with the MCI group. AD subjects had decreased rCBF relative to normal control subjects in the posterior cingulate gyrus with extension to the medial precuneus, along with the inferior parietal cortex, left lateral frontal cortex, and left superior temporal and left orbitofrontal cortices. AD subjects had increased rCBF in the right anterior cingulate gyrus. There is both increased and decreased rCBF in subjects with MCI and early AD relative to cognitively normal subjects in the limbic subcortical (rostral ventral striatum), and cortical (hippocampus, amygdala,
Figure 9.9 Maps of rCBF using CASL comparing individuals with mild cognitive impairment to those who were cognitively normal. There were significant clusters of hyperperfusion in the MCI subjects compared to cognitively normal participants.
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orbitofrontal cortex, and cingulate gyrus) regions. The cortical association areas (frontal, superior temporal, and parietal) had decreased rCBF only in those with AD. Thus in addition to the CNS functional changes that can be detected in vivo prior to the development of the clinical dementia syndrome, increased rCBF in the hippocampus, anterior cingulate gyrus, and ventral rostral striatum also suggest the presence of an active compensatory mechanism in MCI and the early phases of Alzheimer’s dementia.
SUMMARY AND CONCLUSIONS The purpose of this chapter was to describe the alterations in brain structure and function that occur during normal aging and in the process of neurodegeneration associated with AD. We described the results of a series of studies emanating from the CHS-CS, which has a rich database of more than 20 years of clinical information, MRI scans of the brain, and neurobehavioral evaluations (with diagnostic adjudications). In addition, we presented the results of two studies that revealed alterations in rCBF associated with aging and dementia. Several conclusions may be drawn from these data. First, the CNS alterations usually associated with normal aging are a consequence of medical disorders, such as hypertension and diabetes (and their physiological consequences), that are more prevalent with age. Both brain structure and brain function are affected by these factors—often as long as 25 – 30 years before any clinical effects are noted. Therefore aggressive prevention and treatment of such afflictions may lessen the effects of “age-related brain atrophy.” Second, the transition from normal cognition to clinical dementia is not linear, and the trajectory is moderated by a host of cofactors. There is evidence of the presence of Alzheimer’s disease well before there is evidence of Alzheimer’s dementia. Even with clear evidence of changes in brain structure or function, this does not necessarily mean that a clinical dementia is imminent—the brain has compensatory mechanisms that may help to delay the clinical expression of the pathological state. Third, while much of our attention has focused on changes in the mesial temporal lobes and especially the hippocampus, evidence is accumulating rapidly that this is but one area that is affected—and affected early—during the pathological progression to AD. The basal forebrain, with its rich cholinergic pathways, is atrophic before the development of
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AD (in clinically normal individuals) and is affected by a host of risk modifiers. Thus our attention must broaden to study areas not traditionally linked to AD. Fourth, from a clinical perspective, the changes in brain structure and function that occur with aging are significant, and may render the identification of a “dementia” profile of brain atrophy difficult as individuals age (i.e., beyond 75 years). Among elders with very mild AD, the associated brain atrophy is primarily localized to the mesial temporal lobe and related structures. However, significant cortical atrophy occurs as a consequence of normal aging. Thus it is likely that the use of cortical abnormalities as an index of dementia risk provides less specific results than we had previously believed. ACKNOWLEDGMENTS Preparation of this chapter was supported in parts by funds from the National Institute on Aging (AG05133).
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Neuroimaging in Late-Life Schizophrenia HELINE MIRZAKHANIAN AND LISA T. EYLER
Neuroimaging has revolutionized the neuroscientific study of the brain and become an indispensable tool in mental health research, allowing noninvasive examination of the human brain. Furthermore, different neuroimaging methodologies offer various approaches to studying brain structure, size, metabolism, and function. In humans, neuroimaging measures are of primary interest in the endeavor to understand disordered brain systems and therefore provide important information about underlying pathological processes that may play a role in mental illness. In addition, neuroimaging techniques can potentially be used not only as a diagnostic aid but also to help inform the treatment of populations with disabling brain disorders (Gur, Keshavan, & Lawrie, 2007). Neuroimaging, by providing a glimpse into the disordered brain, is an exciting tool for the field of psychiatry. Considerable neuroimaging research has investigated a variety of patient populations, including those with mood disorders, neurodegenerative disorders, and psychotic disorders (Aizenstein et al., 2005; Eyler, Jeste, & Brown, 2008; Wierenga & Bondi, 2007). Much is learned about brain anatomy and physiology by delineating differences in brain structure and function in healthy individuals compared with those who are impaired. These differences are presumed to be associated with observable deficits in psychiatric populations. 207
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Neuroimaging investigational techniques have also been implemented across the life span, in pediatric as well as geriatric populations (Rapoport & Gogtay, 2008; Wierenga & Bondi, 2007). Neuroimaging not only allows one to observe the brain’s maturing and developmental process in pediatric populations but also permits direct observation of the brain’s aging process in geriatric populations. Findings in healthy individuals provide a basis for examining the effect of aging on mental illness and for understanding new-onset psychiatric disorders in late life. Thus neuroimaging helps elucidate multidimensional changes in the disordered brain associated with aging. Significant knowledge has been gained about the pathophysiology of schizophrenia over the past several decades. These advances have been prompted partly by newer, more advanced research techniques such as imaging and genetics. Yet the topic of aging has been largely neglected in the study of schizophrenia. Only recently has research shifted some focus to geriatric patients suffering from the disorder. This lack in geriatric schizophrenia research has led to a gap in understanding and explaining the changes (or lack thereof) associated with disease progression and chronicity into old age. Several questions remain unanswered: What, if anything, happens when cognitive, brain, and functional changes accumulate over a lifetime of disease? What characterizes the progression of schizophrenia into old age? And, specifically, does deterioration have direct structural and functional brain correlates over time? Theoretically, there are many ways that neural processes related to aging and schizophrenia could combine. Figure 10.1 presents some of the most likely scenarios. One possibility is that aging processes and schizophrenia produce additive effects even when they involve similar brain systems. In this case, an older patient would have different brain findings than a younger patient, but the differences would parallel those seen in normal aging. This could be true either of age-related decrements or increments, such as compensatory functional brain response. For instance, brain volume in a certain region that is abnormally small in young patients with the disorder might shrink further with age, but at the same rate as seen in healthy individuals. Or brain activation during a cognitive challenge that increases in a compensatory manner with age in healthy individuals may show parallel increases with age in patients with schizophrenia. Under this scenario of parallel aging changes, older patients with schizophrenia might still have greater functional deficits than younger patients because their declines or compensatory responses have started from an already abnormal level. A second possible scenario is
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that the effects of age and schizophrenia on the brain may involve similar neural systems, and these influences may interact with each other. The synergistic nature of this interaction could take several forms. One possibility is that aging processes could trigger new late-onset or very lateonset forms of schizophrenia, perhaps among individuals with a genetic or environmental liability. A second possibility is that aging might alter the brain profile observed in younger individuals beyond that predicted by the effects of aging alone. For example, if white matter integrity is influenced by age and schizophrenia in an interactive fashion, we might observe a steeper decline in white matter integrity with age among patients than among healthy individuals. The interaction could also take the form of less or absent compensatory functional brain response with age in schizophrenia. A final possibility is the occurrence of multiple interactive effects. For example, aging might alter the brain profile observed in individuals with schizophrenia beyond that predicted by the effects of aging alone only when the onset of schizophrenia occurs late in life. In fact, some researchers argue that such individuals are at increased risk for developing dementia (Korner et al., 2009). In this chapter, we review studies of brain changes in late-life schizophrenia that are relevant to disentangling the above possibilities. Most of these studies have involved (a) elderly patients with a typical age of onset earlier in life (which we refer to as early-onset schizophrenia (EOS) or (b) elderly patients with new onset of the disorder late in life (late-onset schizophrenia, or LOS). Most studies of EOS compare these individuals to age-matched healthy individuals, or, more rarely, include a comparison group of younger patients with a similar age of onset. Studies of LOS often compare findings with an age-comparable healthy participant group and may include a group of EOS of comparable age. Although a design that includes early- and late-onset, young and old patients, and young and old healthy individuals would be needed to completely disentangle the effects of age from age of onset and examine the nature of how age and illness interact, this type of study has rarely been used in brain research on schizophrenia. The study of geriatric schizophrenia is not only important for understanding the pathophysiology of the disorder but it is also extremely timely from a societal standpoint. Comparable to that among younger adults, the prevalence of schizophrenia among older adults remains around 1% (Cohen, 2000). With the increasing growth of the overall elderly population and the associated increase in the number of older patients with schizophrenia, numerous critical problems may surface. For
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Figure 10.1 Hypothetical patterns of age-related change in healthy individuals (dashed gray line) and individuals with schizophrenia (solid black line).
instance, older psychiatric patients may be faced with the double stigma of mental illness and aging. In addition, a host of possible developmental changes, such as the severity of symptoms or changes in treatment response patterns associated with aging, need to be addressed (Graham et al., 2003; Sproule, Hardy & Shulman, 2000). A growing body of literature suggests both structural as well as functional brain changes associated with aging (Buchsbaum & Hazlett, 1997; DeLisi et al., 2004). These age-related changes are likely to interact in a synergistic fashion with preexisting neurocognitive deficits. The use of neuroimaging techniques offers an avenue of research for understanding the unique features of geriatric psychiatric patients and their impact on quality of life. Although structural neuroimaging approaches in elderly psychiatric patients can highlight anatomical differences, functional imaging techniques can identify deficient brain response to challenges such as cognitive activity and treatment.
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Modern neuroimaging methodologies provide various noninvasive approaches to the visualization of brain structure and activity. Structural neuroimaging techniques measure the size and shape of tissues and structures within the brain, whereas functional neuroimaging measures the activity of the brain. All approaches have advantages as well as disadvantages and caveats in general, but some are probably more specific to schizophrenia; these should be taken into consideration in interpreting the literature on schizophrenia neuroimaging. They are discussed later in this chapter. Magnetic resonance imaging (MRI) is the preferred structural neuroimaging technique for research in schizophrenia; it is useful clinically in documenting brain structure at baseline or the first sign of increasing psychosis. In research settings, the use of diffusion tensor imaging (DTI) to measure the brain’s organization, integrity, and connectivity in schizophrenia patients is gaining interest. Functional magnetic resonance imaging (fMRI) using blood oxygen level–dependent (BOLD) and positron emission tomography (PET) have been implemented in schizophrenia research, although to a lesser extent in geriatric schizophrenia populations (Buchsbaum & Hazlett, 1997; Eyler et al., 2008). fMRI studies in schizophrenia have predominantly focused on cognitive challenges in the areas of verbal and spatial working memory, attention, and verbal learning as well as basic emotional tasks such as the identification of emotions. Going a step further and addressing more ecologically valid concepts, such as social cognitive deficits in schizophrenia, recent studies have started to examine brain activation patterns in patients during cognitive tasks of “social intelligence” (Marjoram et al., 2006). Still in its infancy, this line of research has not begun to investigate age-related changes in schizophrenia.
COURSE AND FEATURES OF LATE-LIFE SCHIZOPHRENIA Schizophrenia is a heterogeneous disorder and its heterogeneous presentation appears to continue into old age. The essential features of schizophrenia are characterized by positive symptoms of psychosis, such as hallucinations and delusions, as well as negative symptoms, such as affective blunting, avolition, and alogia. These symptoms are accompanied by marked impairment in social and occupational functioning (Green et al., 2000). The typical age of onset is in late adolescence. Although
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most individuals experience their first psychotic episode in their early to mid-20s, a subgroup has an age of onset in middle age; a final subgroup develop symptoms of schizophrenia-like psychosis after age 60 (Howard et al., 2000). Although the natural course of schizophrenia can vary, with some individuals experiencing exacerbations of symptoms or remissions, the majority of individuals will remain chronically ill into old age. Recent studies suggest that the age of onset may have both pathophysiological as well as prognostic significance (Girard & Simard, 2008; Schuerhoff et al., 2004). For example, LOS patients with schizophrenia onset in middle age have similar levels of positive symptoms, functional impairment, and family history of psychosis but fewer negative symptoms, lower average doses of antipsychotic medication than to elderly EOS patients, and somewhat better adjustment in young adulthood. Similarly, individuals with a very late onset are more often female, have the lowest levels of thought disorder and negative symptoms, and experience a better overall functional outcome. Widespread cognitive deficits are a hallmark of schizophrenia. Deficits in attention, executive functioning, memory, decision making, and planning are present and prominent into old age (Howard et al., 2000; Rajji & Mulsant, 2008). Executive dysfunction in late-life schizophrenia has repeatedly been reported irrespective of age of onset and symptom severity. For example, both EOS and LOS patients show deficits in verbal and nonverbal abstraction, cognitive flexibility, and novel problem solving. Verbal learning and memory deficits are probably among the most consistent findings in geriatric schizophrenia (Heaton et al., 1994). Specifically, patients appear to have impairments in learning and a delayed free recall (Depp et al., 2007). Similarly, deficits during attention tasks have been documented and linked to working memory and executive deficits (Nuechterlein & Dawson, 1984; Strauss, Alphs, & Boekamp, 1992). Impairments in visuospatial abilities are less conclusive. Although one study suggests more severe visuospatial impairment in older schizophrenia patients compared with aged-matched demented patients (Davidson et al., 1996), other studies have failed to find such impairments (Evans et al., 1999); Heaton and colleagues (1994) reported visuospatial deficits only in the LOS group. Longitudinal studies of cognition in schizophrenia are rare and confounded by either small sample size or lack of a healthy comparison group. Overall, a more rapid age-related decline in cognitive performance has been reported in some EOS individuals but not in others.
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Specifically, studies of EOS patients who have been institutionalized suggest that a subgroup with very poor outcome shows a steep decline in cognitive function late in life that mimics dementia (Harvey et al., 1999). Conversely, EOS outpatients living in the community were found to have declines in neuropsychological performance that did not differ from those of age-comparable healthy individuals (Eyler Zorrilla et al., 2000; Palmer et al., 2003). Thus the notion of more rapid age-related cognitive decline in schizophrenia other than that expected on the basis of age alone is debatable, and the scarcity of longitudinal designs prohibits the examination of incremental rates of cognitive decline with age.
STRUCTURAL NEUROIMAGING Schizophrenia is associated with abnormalities in brain structure, such as generalized brain atrophy (Wright et al., 2000) as well as with more selective focal loss. Although studies report a diffuse loss of cortical tissue, there appears to be selectivity for greater gray matter loss (Whitworth et al., 2005; Zhou et al., 2003). MRI studies of individuals with schizophrenia consistently find ventricular enlargement as well as smaller frontal and temporal lobe volumes (DeLisi et al., 2004; Shenton et al., 2001). Specifically, individuals with schizophrenia appear to show reduced volumes of the amygdala, hippocampus, and parahippocampus (Kasai et al., 2002; Steen et al., 2006). Abnormalities in the limbic system and basal ganglia have also been reported. The idea that abnormal brain changes are present early in life, possibly even prenatally, is a question of debate. There is more compelling evidence, however, suggesting gray matter loss just prior to and during the first psychotic episode. Although conclusions about chronicity and brain abnormality are difficult to draw owing to the dearth of true longitudinal designs, some studies have confirmed an association between chronicity and brain structure. Significant progressive decreases in brain volume over time in patients as compared with healthy individuals have been found in wholebrain tissue volume, frontal lobe, and left temporal gray matter as well as in the caudate nucleus and thalamus. Despite these reported findings, the concept of progressive cortical and subcortical atrophy in schizophrenia is still unsettled. There is more conclusive evidence of increases in ventricular volume with age in patients suffering from schizophrenia. In one study, patients followed for 10 years subsequent to their first episode had continual ventricular enlargement at a significantly higher
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rate than that observed in healthy individuals (Saijo et al., 2001). Saijo and colleagues report a higher mean percent change of 22.9% in lateral ventricular volume in the schizophrenia group compared with a 5.1% increase in volume in controls. Other studies have found similar agerelated volumetric changes or enlargements in schizophrenia compared with healthy individuals, but to a lesser degree (DeLisi et al., 2004; Mathalon et al., 2001; Nair et al., 1997; Van Haren et al., 2007). Progression may be particularly common within the subgroup of EOS patients with very poor outcome and steep cognitive decline in old age. These patients have larger ventricles than either healthy individuals or patients with better outcome, show a progressive increase in ventricular size, and have a more posterior distribution of deficits in gray and white matter volume. Evidence of more severe and widespread deficits in white matter organization as measured by fractional anisotropy based on DTI have also been reported, suggesting an age-associated reduction of frontotemporal connectivity in schizophrenia (Friedman et al., 2008; Jones et al., 2006; Rosenberger et al., 2008). Using fiber tractography, a method that delineates the whole course of the fiber bundles in the brain, Rosenberger and colleagues (2008) found significant age-related decline of fiber tract integrity within the cingulum and uncinate fasciculi in patients with schizophrenia, whereas such decline was not observed in healthy individuals. Similarly, comparing first-episode schizophrenia patients, chronic schizophrenia patients and healthy individuals, Friedman and associates (2008) found less widespread changes in white matter integrity in the first-episode group compared with the chronic schizophrenia group, thus further confirming the possibility of progressive changes in white matter fiber integrity in schizophrenia over time. LOS also shows structural deficits relative to age-matched healthy individuals, although at least one study has suggested that volume of the thalamus is greater in LOS than EOS (Corey-Bloom, Jernigan, Archibald, Harris, & Jeste, 1995). Some studies have reported more signs of white matter abnormality and focal cerebrovascular findings among patients with very late onset schizophrenia-like psychosis, but other investigations that carefully ruled out organic pathologies have not. A recent study found greater cerebellar atrophy among patients with very late onset schizophrenia-like psychosis compared with EOS patients (Barak et al., 2002). Less is known about the course of change in brain structure; that is, it is still unclear, for example, whether progressive structural changes
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occur in a gradual linear fashion or stepwise. In fact, some studies suggest a nonlinear sporadic progression. For example, one study reported progressive curvilinear change in brain abnormality in patients in their twenties to late forties, with a decreasing rate of change beyond the late forties (Van Haren et al., 2008).
FUNCTIONAL NEUROIMAGING Several neuroimaging studies in geriatric schizophrenia have demonstrated abnormal cortical and subcortical brain activation patterns in schizophrenia patients when compared with age-matched comparison groups during resting states as well as during cognitive challenge conditions. Specifically, the prefrontal cortex, hippocampus, striatum, thalamus, and cerebellum seem to be implicated. PET and fMRI studies that have compared the brain function of older patients with schizophrenia with that of age-matched comparison groups have found abnormalities similar to those seen in younger patients, including hypofunction of the frontal cortex and abnormal hippocampal activation (Dupont et al., 1994; Eyler Zorrilla et al., 2000, 2002; Eyler Zorrilla, Jeste, & Brown, 2002). In particular, underactivation in the prefrontal cortices and the medial temporal lobe has been observed during tasks of attention, learning, memory and language even when, behaviorally, the patients and healthy participants performed equally. For example, previous studies from our group have found underactivation in the medial temporal lobe, specifically in the parahippocampal gyrus and hippocampal region, during learning of novel pictures in middleaged and elderly schizophrenia patients when compared with healthy comparison participants (Eyler et al., 2002). Patients with schizophrenia showed this abnormal brain response despite adequate behavioral performance comparable to that of the comparison group. Hypofunction of distributed brain networks was also found in a different group of schizophrenia patients during a verbal learning paradigm. During a verbal association task, patients with chronic schizophrenia compared with healthy individuals showed a diminished brain response in the left hippocampus and bilaterally in the inferior frontal gyri (Eyler, Jeste, & Brown, 2008). In terms of neural activity over time, cross-sectional studies have generally found evidence of negative correlations of hemispheric and regional cerebral blood flow (CBF) and metabolism with age; those
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that included a control group showed steeper negative slopes among patients in some regions, including the frontal and temporal cortex and caudate (Ingvar & Franzen, 1974; Mathew et al., 1988) (see Table 10.1). For example, using measures of regional blood flow, Mathew and colleagues (1988) reported a negative association between cerebral blood flow (CBF) and age. In a subsequent study, Mathew and Wilson (1990) reported an association between chronicity and CBF, suggesting that patients with a longer duration of illness show lower anterior–posterior gradients compared with less chronic patients. Additional PET studies have directly examined age-related change in brain functioning within schizophrenia. However, most of the studies were cross-sectional; moreover, the samples were generally small and included mostly middle-aged subjects, in whom brain function was measured predominantly at rest (Buchsbaum & Hazlett, 1997). Importantly, the only longitudinal study observed no change in the degree of hypofrontality over an 18-year follow-up period, suggesting stability of this feature with age (CantorGraae et al., 1991). In a recent investigation—the first to examine age associations during a cognitive challenge task with fMRI (Eyler et al., 2009)—correlations of age with functional brain response during a verbal learning task were examined in both patients with schizophrenia and healthy individuals. The authors did not find significant negative correlations with age among the schizophrenia patients, who specifically failed to show normal positive age correlations in regions associated with the “default mode” (a network of brain regions that is more active during passive baseline conditions than during active experimental tasks; Figure 10.2). Relative to healthy individuals, LOS patients have been found to have deficits in brain function similar to those seen in separate studies of younger patients with schizophrenia and EOS patients (Howard et al., 2000). Specifically, deficient blood flow in frontal and temporal regions has been consistently observed in LOS (Sachdev et al., 1997). Using single photon emission computed tomography (SPECT), Sachdev and associates investigated differences between LOS and EOS patients and concluded that findings of temporal and frontal regional CBF abnormalities in LOS were similar to those reported in schizophrenia in general. More studies with a direct comparison of brain function in LOS and EOS are necessary to draw firm conclusions about the presence or magnitude of differences or the lack thereof in these groups. Effects of medication and treatment on blood flow have also been explored. In a comprehensive review of longitudinal fMRI studies of
Table 10.1 SUMMARY TABLE OF NEUROIMAGING STUDIES EXAMINING THE ASSOCIATION OF BRAIN ACTIVATION AND AGE OR DURATION OF ILLNESS IN SCHIZOPHRENIA
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CITATION
N
AGE
SEX
Ingvar & Franzen, 1974
9 older SZ; 11 younger SZ
Old SZ: 61.3 (7.0); range = 49–71; young SZ: 24.5 (5.2); range = 17–33
Old SZ: 0M,9F; Young SZ: 7M, 4F
Mathew et al., 1988
108 SZ; 108 HC
SZ: M = SZ: 60M, 31.5 (8.9) 48F; HC: F = 38.3 60M, 48F (12.4); HC: M = 32.0 (13.1) F = 38.9 (13.1)
CHRONICITY/ MEDICATION IMAGING STATUS TECHNIQUE TASK
ANALYSIS MEASURE(S)
RESULTS
SUMMARY OF AGERELATED EFFECT IN SZ GROUP
Old SZ: Xenon chronically injection institutionalized; young SZ: duration of 8 months to 15 years; all medicated (almost all on typical antipsychotics)
Rest vs ROI (detest tectors) (Old SZ: Object naming; Young SZ: Raven’s progressive matrices)
Ratio of left frontal rCBF to left postcentral rCBF
Old SZ more Negative hypofrontal slope than young SZ at rest; old SZ show fewer task-related increases than young SZ
Chronic Xenon (mean inhalation duration 11 years); 46 were medication withdrawn for 2 weeks, rest
Rest
Right and left mean CBF and anterior-toposterior gradient
Negative Negative correlation slope in left and right hemisphere mean CBF; negative association between
ROI (detectors)
(Continued )
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Table 10.1 SUMMARY TABLE OF NEUROIMAGING STUDIES EXAMINING THE ASSOCIATION OF BRAIN ACTIVATION AND AGE OR DURATION OF ILLNESS IN SCHIZOPHRENIA (Continued )
CITATION
N
AGE
SEX
CHRONICITY/ MEDICATION IMAGING STATUS TECHNIQUE TASK
ANALYSIS MEASURE(S)
on mostly typicals
CantorGraae et al., 1991
SZ: 7 (of the 11 young SZ reported in Ingvar & Franzen, 1974)
Time 1: 5M, 2F 23.0 (5.3), range = 17–32; time 2: 41.4 (5.0), range = 34-49
Chronic: Xenon 22.9 (2.9) inhalation years duration of illness; typical antipsychotics; 5/7 had ECT
RESULTS
SUMMARY OF AGERELATED EFFECT IN SZ GROUP
duration of illness and left and right anterior-toposterior gradient Rest, word ROI (defluency, tectors) WCST and associated control task
Normalized Left Hemisphere regional CBF (prefrontal, frontotemporal, temporal, central, parietotemporal, occipital) and anteriorto-posterior gradient
Mean left No sighemisphere nificant CBF and longitudinal anterior-tochange posterior gradient not significantly different after 18 years; no effect of cumulative medication dose
Mathew & LongWilson, duration 1990 SZ: 27; shortduration SZ: 27; HC: 2 matched groups of 27 each
Longduration SZ: 41.8 (10.4); shortduration SZ: 31.4 (12.8); HC matched to long duration: 41.5 (11.4); HC matched to short duration: 32.4 (13.9)
Longduration SZ: 11M, 16F; shortduration SZ: 17M, 10F; HC matched to long duration: 11M, 16F; HC matched to short duration: 17M, 10F
Medicated Xenon inpatients, inhalation long duration: 21.6 (8.5) years; short duration: 2.4 (1.7) years
Rest
ROI (detectors)
Anterior-toposterior gradient
Lower anterior/posterior gradients in long duration, even when age covaried or controlled for with matched comparisons
Negative slope
Goldstein SZ: 15; et al., major af1990 fective: 15; HC: 15
SZ: 34.6 (11.9); major affective: 36.9 (9.7); HC: 36.2 (12.2)
SZ: 8M:7F; major affective: 4M, 11F; HC: 9M, 6F
SZ: duration Xenon 2 to 28 years, inhalation 12/15 medicated; major affective: duration 5 to 26 years, 14/15 medicated
Rest
ROI (detectors)
Frontal deviation score (frontal minus posterior to sylvian sulcus/total)
Negative correlation with age in frontal, posterior, whole brain, and frontal deviation in CBF
Negative slope
SZ: 18M, 0F; HC: 22M, 0F
Never medicated;
Degraded stimulus CPT
ROI
Relative and Negative absolute correlation GMR, and with age in
Negative slope
Siegel et al., 1994
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18 SZ; 22 SZ: 29.6 (7.2); HC range = 21–45; HC: 28.2
FDG PET
(Continued )
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Table 10.1 SUMMARY TABLE OF NEUROIMAGING STUDIES EXAMINING THE ASSOCIATION OF BRAIN ACTIVATION AND AGE OR DURATION OF ILLNESS IN SCHIZOPHRENIA (Continued )
CITATION
N
AGE (7.2); range = 19 –42
SEX
CHRONICITY/ MEDICATION IMAGING STATUS TECHNIQUE TASK most first episode
ANALYSIS MEASURE(S)
RESULTS
SUMMARY OF AGERELATED EFFECT IN SZ GROUP
frontal/occip- relative GMR and ital ratios frontal/ occipital ratio in medial frontal cortex; Relative GMR in left dorsal hippocampus, precuneus, posterior cingulate, superior colliculus Positive as- Positive sociation slope with age in relative GMR
in lateral temporal cortex and left lateral inferior frontal/occipital ratio Shihabud- SZ: 18; din, 1998 HC: 24 (age results reported in Buchsbaum & Hazlett, 1997)
SZ: 38.5 (14.8), range = 18– 65); HC: 37.0 (13.1), range = 21–52
SZ: 12M; 6F; HC: 15M, 9F
Never medicated (7/18) or medication withdrawn for 12 days to 2 years (11/18)
Eyler et al., 2009
SZ = 45.2 (11.3); HC = 44 (15.2)
SZ: 25M, 5F; HC: 10M, 4F
Chronic fMRI (mean duration 21 years [11]); all medicated
SZ: 30; HC: 14
FDG PET
Verbal list learning and memory task
MRI Relative based GMR anatomical ROIs; clusterbased approach within the caudate ROI
Verbal ROI; post BOLD paired hoc whole response associates brain task
More Negative negative slope correlation with age in SZ than HC in lateral and medial frontal regions, anterior temporal region, and a small region within right caudate No significant correlation with age in SZ (and
No significant age-related change
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(Continued )
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Table 10.1 SUMMARY TABLE OF NEUROIMAGING STUDIES EXAMINING THE ASSOCIATION OF BRAIN ACTIVATION AND AGE OR DURATION OF ILLNESS IN SCHIZOPHRENIA (Continued )
CITATION
N
AGE
SEX
CHRONICITY/ MEDICATION IMAGING STATUS TECHNIQUE TASK
ANALYSIS MEASURE(S)
RESULTS
SUMMARY OF AGERELATED EFFECT IN SZ GROUP
HC ⬎ SZ) in right lateral prefrontal cortex Schultz et al., 2002
SZ: 49
SZ: 32.6 (8.1), range = 20 –51
49M, 0F
Chronic [15O] Rest (mean water PET duration 10.25 [7.5] years); medication withdrawn for 3 weeks
Voxelbased regression; global CBF
Quantified blood flow
Negative Negative correlation slope with age in anterior cingulate, bilateral premotor frontal cortex (BA 8), bilateral parietal lobe (BA 40), and for whole brain CBF
No significant correlation with age in SZ (and HC ⬎ SZ) in right superior temporal gyrus, middle precuneus (post hoc)
No significant age-related change
BOLD, blood oxygen level–dependent; CPT, continuous performance test; ECT, electroconvulsive therapy; F, female; FDG, fluorodeoxyglucose; fMRI, functional magnetic resonance imaging; GMR, glucose metabolic rate; HC, healthy comparison; M, male; PET, positron emission tomography; rCBF, regional cerebral blood flow; ROI, region of interest; SZ, schizophrenia; WCST, Wisconsin Card Sorting Test.
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Figure 10.2 Area of significant difference in the magnitude of the correlation between age and blood oxygen level–dependent (BOLD) response during a verbal paired associates learning task as measured by functional magnetic resonance imaging. Hottest colors indicate those areas in which the comparison group correlation was more positive than that of the schizophrenia group (see inset scatter plot).
medication effects in schizophrenia, Davis and associates (2005) concluded that despite numerous methodological limitations, some consistent findings could be drawn. Overall, several studies have indicated increased basal ganglia activity and decreased hippocampal and cortical activity in patients with antipsychotic treatment compared with patients naïve to medication. Often, these changes were in the direction of normalization of brain function. Since these studies generally focused on younger individuals, it is not yet known whether the effects of treatment on brain physiology are comparable among older individuals with either EOS or LOS. The electrophysiology of late-life schizophrenia has also been examined. Electroencephalographic (EEG) abnormalities, such as decreased amplitude of the P300 orienting response, have been repeatedly replicated in young individuals with schizophrenia and in elderly EOS patients compared with healthy individuals (Ford, Roth, & Pfefferbaum, 1992). The latency of the P300 response but not its amplitude has been found to be associated with age in several studies of patients with schizophrenia. Since P300 latency and amplitude also become more abnormal in healthy aging, it is not clear whether the age-related changes in schizophrenia are greater than expected, although some studies report findings consistent with a neurodegenerative process. For example, some studies suggest that there appears to be a stronger relationship between age and
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P300 latency in schizophrenia patients compared with healthy controls (Frangou et al., 1997; Yu et al., 2005). Illness duration in schizophrenia has also been related to P300 amplitude and response. Mathalon and associates (2000), for example, reported reduced P300 amplitude and latency as well as prolongation in patients with longer duration of illness compared with those having less chronic schizophrenia. The mismatch negativity (MMN) component, in contrast, does not seem to be significantly impaired in first-episode, younger patients, whereas deficits are commonly found in more chronic and older patients with schizophrenia (van der Stelt & Belger, 2007). A meta-analysis of MMN in schizophrenia concluded that deficits in MMN are a robust feature of chronic schizophrenia, but it is unclear whether these develop with illness progression (Umbricht & Krljes, 2005). Longitudinal studies will be needed to confirm that these differences reflect steeper age-related decreases among the schizophrenia group rather than cohort effects. Interestingly, general EEG and P300 deficits have not been found among LOS patients with schizophrenia (Olichney et al., 1998; Reeves & Struve, 2003). However, one small study (Olichney et al., 1997) observed an increased latency of the N400 semantic incongruity response in LOS patients but not EOS patients compared with controls.
LIMITATIONS OF EXISTING LITERATURE The existing literature has several methodological shortcomings that should be noted. First, as mentioned at the beginning of this chapter, existing findings are drawn predominantly from cross-sectional studies comparing patients with schizophrenia with age-matched healthy controls. In order to begin to disentangle the effects of aging and illness, one must follow early- and late-onset patients over time. Since longitudinal studies are difficult to conduct, a cross-sectional design can compare three groups of patients (early-onset young, early-onset old, and lateonset old) to two groups of healthy individuals (young and old). None of the reviewed studies contained all of these groups; for many of the neuroimaging findings, therefore, it is difficult to know whether the effects are different depending on age of onset. The few longitudinal and cross-sectional studies that compared young and old healthy individuals to young and old schizophrenia patients suggest structural age-related changes and more complicated functional age-related activation patterns that warrant further investigation. Second, many of the neuroimaging
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abnormalities (e.g., reduced frontal volume, white matter hyperintensities, hypofunction of the prefrontal cortex) are common to multiple psychiatric disorders such as major depression. Current study designs fail to address the question of specificity in terms of direct comparisons between groups of elderly patients with different disorders, although at least one study did find differences between the structural abnormalities of late-life mood disorders compared with late-life psychotic disorders (Rabins et al., 2000). Further work is needed to determine the specificity of late-life neuroimaging findings in schizophrenia. Third, abnormal neuroimaging findings can be confounded by several other factors, such as age-related illness-specific changes in the hemodynamic response, longterm medication effects, and comorbid conditions. Few of the studies reviewed have investigated potential associations between other factors and brain structure and function in geriatric schizophrenia. Furthermore, despite our growing knowledge of compensatory mechanisms in healthy aging, little thought has been given to this possibility in geriatric schizophrenia. Finally, despite its usefulness in providing a wealth of data about brain structure and function, neuroimaging tools have several caveats specific to the geriatric schizophrenia population. Constraints based on MRI safety and eligibility requirements are likely contributing to limited generalizability of findings in this population. For example, scanning is difficult for those who are very obese, which may preferentially exclude some patients with schizophrenia for whom weight gain is a side effect of medication. Similarly, imaging is not recommended for those who are claustrophobic (an anxiety that may be more common among schizophrenia patients, especially those with paranoid ideation). Additionally, image quality and reliability is severely reduced in MRI by participant movement during the scan. This is a particular problem in schizophrenia patients whose medication side effects are marked by tremors or other motor symptoms. These constraints may inevitably compromise external validity of research findings owing to a biased sample and may also limit the usefulness of MRI as a clinical tool for some patient groups.
FUTURE DIRECTIONS In addition to addressing the limitations above, future neuroimaging studies in geriatric schizophrenia will benefit from combining multiple imaging technologies. For example, to better elucidate the functional systems involved in late-life schizophrenia, results from metabolic and
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blood-flow imaging techniques can be combined with mapping of white matter tracts using DTI. Similarly, combining the spatial resolution of fMRI with the temporal resolution of EEG or MEG is likely to yield sensitive measures of pathology. The future is also likely to bring an increased emphasis on the use of fMRI to examine predictors and mechanisms of treatment response. It is likely that metabolic, blood flow, blood oxygenation, and neurochemical imaging all will be brought to bear in these types of studies, which ultimately promise to help address difficulties in treating geriatric patients. One day, it may be possible to use neuroimaging measures to aid in diagnosis and guide treatment decisions, although much more work is needed to establish the validity and reliability of such techniques before they can be put to clinical use. Studies that combine imaging information with genetic testing are becoming more common and could also be useful in helping to bring individualized medicine to geriatric psychiatry. Neuroimaging techniques are powerful tools that have already provided important clues in the search for knowledge about the etiology, course, and treatment of late-life mental disorders. In the coming years, advances in these techniques and their use in combination with each other and with genetic technologies will lead to even greater advances, to the benefit of the growing numbers of elderly mentally ill.
SUMMARY Neuroimaging studies in late-life schizophrenia are limited, and there are even fewer longitudinal studies investigating brain changes over time in individuals with schizophrenia. Neuroimaging studies suggest abnormalities in similar brain structures across all age groups in schizophrenia. Brain structures implicated in schizophrenia but not in aged-matched healthy individuals include the hippocampal and ventricular system as well as several regions of the frontal cortex. Young and older patients with schizophrenia consistently show ventricular enlargement, reduced hippocampal volumes, and mild overall brain atrophy. Several studies that have investigated age-related changes have documented an accelerated decline in brain volume with age among patients compared with controls in amygdala and overall gray matter volume. Cortical thinning of the posterior third of the superior temporal gyrus on gray matter parcellated images, another consistent imaging finding in younger patients with schizophrenia, has not been systematically investigated in older
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patients. Several researchers have recently investigated the degree of white matter integrity as a function of age in schizophrenia. These studies suggest age-associated declines in white matter integrity in schizophrenia beyond those seen in normal aging. Results of studies examining agerelated changes in resting cerebral metabolism in schizophrenia have been mixed, with cross-sectional studies showing a negative correlation with age (and duration of illness) and a longitudinal investigation showing stability. A more recent fMRI study utilizing a cognitive challenge paradigm suggests that patients with schizophrenia might lack normal agerelated increases in brain response in default-mode regions. Findings from P300 and N400 studies of late-onset schizophrenia patients suggest that differences in functional brain activation of early- and late-onset patients might exist. In general, although the scarcity of neuroimaging late-life schizophrenia research does not allow clear conclusions about age-related progressive brain changes in schizophrenia, there appears to be at least a subgroup of patients who show accelerated structural abnormalities with age, and these may or may not lead to abnormalities of brain function and behavior. REFERENCES Aizenstein, H. J., Butters, M. A., Figurski, J. L, Stenger, V. A., Reynolds III, C. F., & Carter, C. S. (2005). Prefrontal and striatal activation during sequence learning in geriatric depression. Biological Psychiatry, 58, 290 –296. Barak, Y., Aizenberg, D., Kirecki, I., Mazeh, D., & Achiron, A. (2002). Very late-onset schizophrenia-like psychosis: Clinical and imaging characteristics in comparison with elderly patients with schizophrenia. Journal of Mental Disease, 190, 733 –736. Buchsbaum, M. S., & Hazlett, E. A.(1997). Functional brain imaging and aging in schizophrenia. Schizophrenia Research, 27, 129 –141. Cantor-Graae, E., Warkentin, S., Franzen, G., Risberg, J., & Ingvar, D. H. (1991). Aspects of stability of regional cerebral blood flow in chronic schizophrenia: An 18-year follow-up study. Psychiatry Research, 40, 253 –266. Cohen, C. I. (2000). Directions for research and policy on schizophrenia and older adults: Summary of the GAP Committee report. Psychiatric Services, 51, 299 – 302. Corey-Bloom, J., Jernigan, T., Archibald, S., Harris, M., J., & Jeste, D. V. (1995). Quantitative magnetic resonance imaging of the brain in late-life schizophrenia. American Journal of Psychiatry, 152, 447– 449. Davidson, M., Harvey, P., Welsh, K. A., Powchik, P., Putnam, K. M., & Mohs, R. C. (1996). Cognitive functioning in late-life schizophrenia: A comparison of elderly schizophrenic patients and patients with Alzheimer’s disease. American Journal of Psychiatry, 153, 1274 –1279. Davis, C. E., Jeste, D. V., & Eyler, L. T. (2005). Review of longitudinal functional neuroimaging studies of drug treatments in patients with schizophrenia. Schizophrenia Research, 78, 45 – 60.
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disorders: A technical consensus statement. International Journal of Geriatric Psychiatry, 18, 670 – 678. Green, M. F., Kern, R. S., Braff, D. L., & Mintz, J. (2000). Neurocognitive deficits and functional outcome in schizophrenia: Are we measuring the “right stuff”? Schizophrenia Bulletin, 26, 119 –136. Gur, R. E., Keshavan, M. S., & Lawrie, S. M. (2007). Deconstructing psychosis in human brain imaging. Schizophrenia Bulletin, 33, 921– 931. Harvey, P. D., Silverman, J. M., Mohs, R. C., Parrella, M., White, L., Powchik, P., et al. (1999). Cognitive decline in late-life schizophrenia: A longitudinal study of geriatric chronically hospitalized patients. Biological Psychiatry, 45, 32– 40. Heaton, R., Paulsen, J. S., Mcadams, L. A., Kuck, J., Zisook, S., Braff, D., et al. (1994). Neuropsychological deficits in schizophrenics: Relationship to age, chronicity, and dementia. Archives of General Psychiatry, 51, 469 – 476. Howard, R., Rabins, P. V., Seeman, M. V., & Jeste, D. V. (2000). Late-onset schizophrenia and very-late-onset schizophrenia-like psychosis: An international consensus. The International Late-Onset Schizophrenia Group. American Journal of Psychiatry, 157, 172–178. Ingvar, D. H., & Franzen, G. (1974). Abnormalities of cerebral blood flow distribution in patients with chronic schizophrenia. Acta Psychiatrica Scandinavica, 50, 425 – 462. Jones, D. K., Catani, M., Pierpaoli, C., Reeves, S. J., Shergill, S. S., & O’Sullivan, M. (2006). Age effects on diffusion tensor magnetic resonance imaging tractography measures of frontal cortex connections in schizophrenia. Human Brain Mapping, 27, 230 –238. Kasai, K., Iwanami, A., Yamasue, H., Kuroki, N., Nakagome, K., & Fukuda, M. (2002). Neuroanatomy and neurophysiology in schizophrenia. Neuroscience Research, 43, 93 –110. Korner, A., Garcia Lopez, A., Lauritzen, L., Andersen, P. K., & Kessing, L.V. (2009). Late and very-late first-contact schizophrenia and the risk of dementia: A nationwide register based study. International Journal of Geriatric Psychiatry, 24(1), 61– 67. Marjoram, D., Job, D. E., Whalley, H. C., Goutouna, V. E., McIntosh, A. M., Simonotto, E., et al. (2006). A visual joke fMRI investigation into theory of mind and enhanced risk of schizophrenia. Neuroimage, 31, 1850 –1858. Mathalon, D. H., Ford, J. M., Rosenbloom, M., & Phefferbaum, A. (2000). P300 Reduction and prolongation with illness duration in schizophrenia. Biological Psychiatry, 47, 413 – 427. Mathalon, D. H., Sullivan, E. V., Lim, K. O., & Pfefferbaum A. (2001). Progressive brain volume changes and the clinical course of schizophrenia in men: A longitudinal magnetic resonance imaging study. Archives of General Psychiatry, 58, 148 –157. Mathew, R. J., & Wilson, W. H. (1990). Chronicity and a low anteroposterior gradient of cerebral blood flow in schizophrenia. American Journal of Psychiatry, 147, 211–213. Mathew, R. J., Wilson, W. H., Tant, S. R., Robinson, L., & Prakash, R. (1988). Abnormal resting regional cerebral blood flow patterns and their correlates in schizophrenia. Archives of General Psychiatry, 45, 542–549. Nair, T. R., Christensen, J. D., Kingsbury, S. J., Kumar, N. G., Terry, W. M., & Garver, D. L. (1997). Progression of cerebroventricular enlargement and the subtyping of schizophrenia. Psychiatry Research, 74, 141–150.
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Nuechterlein, K. H., & Dawson, M. E. (1984). Information processing and attentional functioning in the developmental course of schizophrenic disorders. Schizophrenia Bulletin, 10, 160 –203. Olichney, J. M., Iraguim V. J., Kutas, M., Nowacki, R., & Jeste, D. V. (1997). N400 abnormalities in late life schizophrenia and related psychoses. Biological Psychiatry, 42, 13–23. Olichney, J. M., Iraguim V. J., Kutas, M., Nowacki, R., Morris, S., & Jeste, D. V. (1998). Relationship between auditory P300 amplitude and age of onset in older schizophrenia patients. Psychiatry Research, 79, 273 –278. Palmer, B. W., Bondi, M. W., Twamley, E. W., Thal, L., Golshan, S., & Jeste, D. V. (2003). Are late-onset schizophrenia spectrum disorders neurodegenerative conditions? Annual rates of change on two dementia measures. Journal of Neuropsychiatry and Clinical Neuroscience, 15, 45 –52. Rabins, P. V., Aylward, E., Holroyd, S., & Pearlson, G. (2000). MRI findings differentiate between late-onset schizophrenia and late-life mood disorder. International Journal of Geriatric Psychiatry, 15, 954 – 960. Rajji, T. K., & Mulsant, B. H. (2008). Nature and course of cognitive function in late life schizophrenia: A systematic review. Schizophrenia Research, 102, 122–140. Rapoport, J. L., & Gogtay, N. (2008). Brain neuroplasticity in healthy, hyperactive and psychotic children: Insights from Neuroimaging. Neuropsychopharmacology, 33, 181–197. Reeves, R. R., & Struve, F. A. (2003). Quantitative electroencephalography in late-onset schizophrenia. International Psychogeriatrics, 15, 273 –278. Rosenberger, G., Kubicki, M., Nestor, P. G., Connor, E., Bushell, G. B., Markant, D., et al. (2008). Age-related deficits in fronto-temporal connections in schizophrenia: A diffusion tensor imaging study. Schizophrenia Research, 102, 181–188. Sachdev, P., Brodaty, H., Rose, N., & Haindl, W. (1997). Regional cerebral blood flow in late-onset schizophrenia: A SPECT study using 99mTc-HMPAO. Schizophrenia Research, 27, 105 –117. Saijo, T., Abe, T., Someya, Y., Sassa, T., Sudo, Y., Suhara, T., et al. (2001). Ten year progressive ventricular enlargement in schizophrenia: An MRI morphometrical study. Psychiatry and Clinical Neurosciences, 55, 41– 47. Schuerhoff, F., Golmard, J., Szoeke, A., Bellivier, F., Berthier, A., Meary, A., et al. (2004). Admixture analysis of age of onset in schizophrenia. Schizophrenia Research, 71, 35–41. Schultz, S. K., O’Leary, D. S., Boles Ponto, L. L., Arndt, S., Magnotta, V., Watkins, G. L., et al. (2002). Age and regional cerebral blood flow in schizophrenia: Age effects in anterior cingulate, frontal, and parietal cortex. Journal of Neuropsychiatry and Clinical Neurosciences, 14, 19 –24. Shenton, M. E., Dickey, C. C., Frumin, M., & McCarley, R. W. (2001). A review of MRI findings in schizophrenia. Schizophrenia Research, 49, 1–52. Shihabuddin.L., Buchsbaum, M.S., Hazlett, E.A., Haznedar, M.M., Harvey, P.D., et al. (1998). Dorsal striatal size, shape, and metabolic rate in never-medicated and previously medicated schizophrenics performing a verbal learning task. Archives of General Psychiatry, 55, 235 –243. Siegel, B. V., Reynolds, C., Lohr, J. B., Nuechterlein, K. H., Bracha, H. S., Potkin, S. G., et al. (1994). Changes in regional cerebral metabolic rate with age in schizophrenics and normal adults. Developmental Brain Dysfunction, 7, 132–146. Sproule, B. A., Hardy, B. G., & Shulman, K. I. (2000). Differential pharmacokinetics of lithium in elderly patients. Drugs & Aging, 16, 165 –177.
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Steen, R. G., Mull, C., McClure, R., Hamer, R. M., & Lieberman, J. A. (2006). Brain volume in first-episode schizophrenia: Systematic review and meta-analysis of magnetic resonance imaging studies. British Journal of Psychiatry, 188, 510 –518. Strauss, M. E., Alphs, L., & Boekamp, J. (1992). Disengagement of attention in chronic schizophrenia. Psychiatry Research, 43, 87– 92. Umbricht, D., & Krljes, S. (2005). Mismatch negativity in schizophrenia: A meta-analysis. Schizophrenia Research, 76, 1–23. Van der Stelt, O., & Belger, A. (2007). Application of electroencephalography to the study of cognitive and brain functions in schizophrenia. Schizophrenia Bulletin, 33, 955 – 970. Van Haren, N.E.M., Hulshoff Pol, H. E., Schnack, H. G., Cahn, W., Brans, R. Carati, I., et al. (2008). Progressive brain volume loss in schizophrenia over the course of the illness: Evidence of maturational abnormalities in early adulthood. Biological Psychiatry, 63, 106 –113. Van Haren, N. E., Hulshoff Pol, H. E., Schnack, H. G., Cahn, W., Mandl, R. C., Collins, D. L., et al. (2007). Focal gray matter changes in schizophrenia across the course of the illness: A 5-year follow-up study. Neuropsychopharmacology, 32, 2057–2066. Whitworth, A. B., Kemmler, G., Honeder, M., Kremser, C., Felber, S., Hausrnann, A., et al. (2005). Longitudinal volumetric MRI study in first- and multiple-episode male schizophrenia patients. Psychiatry Research, 140, 225 –237. Wierenga, C. E., & Bondi, M. W. (2007). Use of functional magnetic resonance imaging in early identification of Alzheimer’s disease. Neuropsychological Review, 17, 127–143. Wright, I. C., Rabe-Hesketh, S., Woodruff, P.W.R., David, A. S., Murray, R. M., & Bullmore, E.T. (2000). Meta-analysis of regional brain volume in schizophrenia. American Journal of Psychiatry, 157, 16 –25. Yu, Y.W.Y., Chen, T., Chen, M., Tsai, S., & Lee, T. (2005). Effect of age and global function score on schizophrenic P300 characteristics. Neuropsychobiology, 51, 45 –52. Zhou, S. Y., Suzuki, M., Hagino, H., Takahashi, T., Kawasaki, Y., Nohara, S., et al. (2003). Decreased volume and increased asymmetry of the anterior limb of the internal capsule in patients with schizophrenia. Biological Psychiatry, 54, 427– 36.
11
Psychosis in Dementia ZAHINOOR ISMAIL AND DAVID MAMO
Psychosis in dementia is broadly included within the numerous neuropsychiatric or behavioral and psychiatric symptoms of dementia (BPSD). The significance of these noncognitive manifestations of dementia is that they are the primary reason for admission to a psychiatric hospital for patients suffering from dementia (Wiener, Kiosses, Klimstra, Murphy, & Alexopoulos, 2001) with an enormous impact on resources, suffering, caregiver distress, and economic cost. This notwithstanding, the pathophysiology and pharmacotherapy of BPSD remain largely unknown. The root of this state of affairs is likely the broad inclusion of behavioral and psychiatric manifestations in diagnostic criteria as well as the heterogeneous nature of both psychosis and dementia. Neuropsychiatric symptoms present in up to 80% of people diagnosed with dementia (Assal & Cummings, 2002), and agitation is often included as a symptom of psychosis, further clouding the definition of this diagnostic entity. Delusional thinking in Alzheimer’s disease (AD) may take various forms, including delusions of persecution, infidelity, abandonment, or that the patient’s deceased loved ones are still living. It is further complicated by the difficulty in differentiating between a true delusion and a belief that is secondary to other cognitive difficulties (e.g., misplacing personal belongings and concluding that the items were stolen). On the other hand, misidentification delusions frequently seen in AD often represent true 233
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neuropsychiatric manifestations of neurodegeneration. These include the Capgras delusion (people replaced by impostors), phantom boarder syndrome, the mirror sign (patient’s mirror image misidentified), and the TV sign (television images misidentified as real). Auditory and visual hallucinations are the most common forms of perceptual disturbances, although they can occur in any sensory modality, and the etiology is often complicated by misidentification or environmental factors (e.g., “sundowning”) (Karim & Burns, 2003; Tariot et al., 1995). Classically, visual hallucinations have been associated with Lewy body dementia (LBD), although LBD and AD pathology may coexist in the same patient. Notwithstanding these limitations, psychosis in dementia represents a critical cluster of symptoms that warrants systematic investigation of its pathophysiology and psychopharmacology, especially in view of its impact on clinical outcome of the dementing illness, increased caregiver burden, and the effects of exposure to antipsychotic medications in this frail elderly population. Not surprisingly, therefore, a PubMed search using the terms psychosis and dementia revealed 629 articles on the topic. The goal of this chapter is not to provide an exhaustive review of this published literature but rather to highlight the areas that we believe may have direct relevance to the application of neuroimaging in the study of this group of conditions. We first define the term psychosis in dementia in light of recently proposed diagnostic classifications and review the epidemiology of the syndrome, focusing on Alzheimer’s dementia. The theoretical constructs and biology of psychosis in dementia are then explored, focusing on neuropathological data and genetic studies. Finally, we discuss the available literature on the neuroimaging of psychosis in dementia within the context of these mechanistic theoretical constructs, highlighting the strengths and limitations of these studies and possible directions for future study.
DIAGNOSTIC CRITERIA Strict diagnostic criteria for psychosis of Alzheimer’s disease (AD⫹P), written in the DSM-IV format, have been proposed by Jeste and Finkel (2000) and include hallucinations (auditory or visual) and/or delusions in the context of clinically diagnosed AD. Psychotic symptoms must not predate the onset of the dementia in this diagnostic construct, so that a history of primary psychotic disorders such as schizophrenia, schizoaffective disorder, delusional disorder, or mood disorder with psychotic
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features must be ruled out. Similarly, psychotic symptoms presenting in the context of delirium or resulting from a general medical illness, drugs, or other ingested substances are excluded from this proposed diagnostic category. This construct has been validated by Schneider and colleagues in a reanalysis of a clinical trial of behavioral and psychological symptoms of dementia (BPSD) in which the Behavioral Pathology in Alzheimer’s Disease BEHAVE-AD rating scale was used (Schneider et al., 2003). Of all the patients enrolled in the original trial for behavioral issues, 75% met the criteria for psychosis in dementia; thus, while overlapping with agitation or aggression, AD⫹P was a discrete clinical entity with differing patient demographics than those of nonpsychotic patients with dementia (AD⫺P). An alternative set of criteria for psychosis in dementia, under the broader rubric of neuropsychiatric disturbance in AD, has been suggested by Lyketsos and colleagues (Lyketsos, Breitner, & Rabins, 2001). In this construct, discrete affective and psychotic syndromes of AD have been defined and these criteria have recently been validated. In a consecutive series of 771 patients with probable AD, delusions were subclassified into paranoid and expansive domains and were associated with anosognosia and depression, highlighting the fact that psychotic symptoms rarely occur in isolation and that multiple psychiatric comorbidities are the rule rather than the exception (Mizrahi, Starkstein, Jorge, & Robinson, 2006). A contrasting approach to that of Lyketsos and colleagues is used by Cook and associates (Cook et al., 2003), by identifying and classifying psychosis in AD subtypes based on analysis of the individual psychotic symptoms present in isolation from other behavioral symptoms. In a cross-sectional study of 188 possible and probable AD patients, factor and cluster analysis of Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) behavioral rating scale items identified two separate factors: a misidentification/hallucination factor, which includes symptoms of simple and delusional misidentifications as well as hallucinations, and a persecutory delusion factor (Cook et al., 2003). Subsequent analysis has demonstrated that excess cognitive impairment is restricted to the misidentification/hallucination subtype and that the paranoid subtype does not differ from nonpsychotic patients on cognitive measures (Perez-Madrinan et al., 2004). The variability in definitions of AD⫹P underscores the most fundamental difficulty in reconciling the literature in this area; that is, different studies quantify psychosis differently. The literature describes psychosis as a whole entity but also subclassifies it into hallucinations and delusions and sometimes further subclassifies delusional thinking.
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Studies do not consistently control for the numerous other neuropsychiatric manifestations of AD, especially agitation. The variability in study design reflects the variability in methods of diagnosis, and this also confounds the neuroimaging literature on the topic. This introduction is meant to highlight the limitations in the existent literature on psychosis in dementia (AD⫹P). Unless the “psychosis” is described explicitly, including whether the primary symptoms are hallucinations, delusions, or both, it will not be possible to arrive at an understanding of neurophysiological mechanisms, which may differ between the specific symptoms and behavioral manifestations of psychosis.
EPIDEMIOLOGY Historically, the results of epidemiological studies of psychosis in dementia have been variable, given the heterogeneity of the study group. A more systematic review of the subject by Ropacki and Jeste (2005) reports that 41% of patients with AD experience psychosis, including 36% with delusions and 18% with hallucinations. Psychosis is associated with a more rapid cognitive decline, its incidence increasing progressively over the first 3 years of the illness, with a subsequent plateau. This early presentation is contrary to widespread belief that psychosis in AD represents a condition restricted to the later stage of the illness, and the impact on outcome highlights its public health significance. Inadvertent inclusion of LBD patients as well as poor recall of family history are considered confounds in these epidemiological studies (Ropacki & Jeste, 2005).
NEUROPATHOLOGY A number of studies have looked at psychosis in dementia from neuropathological, neurochemical, and postmortem neuroimaging perspectives. Using 27 autopsy-confirmed AD cases, Zubenko and colleagues (1991) found that psychosis was associated with significantly increased densities of senile plaques and neurofibrillary tangles in the presubiculum and middle frontal cortex; they also noted trends toward increases in the superior temporal and entorhinal cortices. This is consistent with the epidemiological data on the impact of psychosis on cognitive outcome. Further, psychosis was associated with the relative preservation
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of norepinephrine in the substantia nigra and a significant reduction of serotonin in the prosubiculum, with trends toward serotonin reduction in the middle frontal gyrus, superior temporal cortex, entorhinal cortex, substantia nigra, thalamus, amygdale, and caudate nucleus (Zubenko et al., 1991). Förstl, Burns, Levy, and Cairns (1994b) investigated 56 patients with definite AD and observed that misidentification was associated with lower neuron counts in the area CA1 of the hippocampus while delusions and hallucinations were more likely in patients with less severe cell loss in the parahippocampal gyrus but with lower cell counts in the dorsal raphe nucleus (which is primarily serotonergic) (Förstl et al., 1994b). Farber and colleagues (2000) followed 109 subjects with AD and upon death pathologically examined them, comparing those with and without psychosis. Although there were no differences in total senile plaques or cored senile plaques, a greater density of neocortical neurofibrillary tangles was found in AD⫹P versus AD alone, and this increase was independent of dementia severity (Farber et al., 2000). Lai and colleagues (2001) used postmortem radioligand binding assays to quantify muscarinic receptors in AD subjects. M2 receptor density was increased in BA 11 (orbitofrontal cortex) of patients with delusions and in BA 21 (midtemporal gyrus) of patients with hallucinations (Lai et al., 2001). Although not all studies have found associations between plaque and tangle severity and AD⫹P compared with AD⫺P (Sweet et al., 2000), a postmortem MRI spectroscopy study has identified markers suggesting greater neuropil disruption in the dorsolateral prefrontal cortex (DLPFC), superior temporal gyrus (STG), and inferior parietal cortex (IPC) in AD⫹P versus AD⫺P. It is speculated that this phenotype represents an acceleration of the mechanisms of AD-related neurodegeneration (Sweet et al., 2002b). In sum, postmortem studies emphasize the important roles of plaque and tangle density in AD⫹P, suggesting that frontal and temporal plaques and tangles are associated with psychosis. The effect of these plaques and tangles on neurotransmitter systems may underscore their role in manifestation of the illness. Serotonergic systems are consistently disrupted in psychosis, both cortically and subcortically, and are associated with hallucinations and delusions rather than misidentification. The location of significant muscarinic receptor loss is linked with psychosis—the orbitofrontal cortex (OFC) with delusions and the midtemporal gyrus with hallucinations. Further, cortical cell loss is associated with hallucinations and delusions and hippocampal loss with misidentifications. The triaxial inclusion of (a) plaque and tangle density, (b) plaque and tangle location,
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and (c) effect on neurotransmitter systems results in a complicated neuropathological model of AD⫹P. Difficulty in controlling for the various permutations and combinations of these three variables may in part contribute to the heterogeneity of data in this area.
GENETICS Genetic studies have investigated psychosis in dementia from a number of perspectives, including linkage and heritability, the role of apolipoprotein E ε4, genes associated with schizophrenia and psychosis modification, and genes associated with various neurotransmitters. These studies highlight important areas for exploration in neuroimaging studies. Utilizing family data from the National Institute of Mental Health (NIMH) Alzheimer’s Disease Genetic Initiative, Sweet, Nimgaonkar, Devlin, Lopez, and DeKosky (2002a) found that the risk ratio for a sibling of a proband with AD⫹P is 2.4 times higher than that for a sibling of an AD proband without psychosis (Sweet et al., 2002a). Using the same database, heritability of psychosis in late-onset AD (LOAD) in this cohort was estimated at 60%–70% when the proband had multiple psychotic symptoms (Bacanu et al., 2005), supporting the genetic basis for psychosis in AD. Genome screen studies have indicated that genes implicated in the etiology of AD⫹P may be located on chromosomes 7 and 15 (Hollingworth et al., 2007) as opposed to chromosome 14 for AD⫺P (Avramopoulos, Fallin, & Bassett, 2005), suggesting discrete diagnostic entities. The role of apolipoprotein E ε4 (APOE ε4) in AD⫹P is unclear, as evidenced by conflicting studies with varying methodologies (Chang et al., 2004; Gabryelewicz et al., 2002; Harwood, Barker, Ownby, St. GeorgeHyslop, & Duara, 1999; Hirono et al., 1998b; Lehtovirta et al., 1996; Levy, Cummings, Fairbanks, Sultzer, & Small, 1999; Lopez, Kamboh, Becker, Kaufer, & DeKosky, 1997; Lyketsos et al., 1997; Ramachandran et al., 1996; Scarmeas et al., 2002). Three recent studies have implicated a role for APOE ε4 in AD⫹P. In a sample of 171 patients consecutively admitted to a memory clinic and diagnosed with LOAD, presence of the APOE ε4 allele increased the risk of developing delusions in comparison with patients who were not carriers of this allele. This association did not hold for hallucinations or psychosis in general (Spalletta et al., 2006). In a study looking at 110 AD patients, the number of APOE ε4 alleles has been associated with the prevalence and severity of delusions in AD (van
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der Flier et al., 2007). In a large independent cohort of 388 patients with longitudinal measures of BPSD assessed by the Neuropsychiatric Inventory, the exon 4 ε2/ε3/ε4 haplotype of the APOE ε4 gene is associated with hallucinations (Pritchard et al., 2007a). In future studies of AD⫹P, APOE ε4status and age of onset of illness may be important factors in the characterization of the sample group. Neuregulin-1 (NRG1), a gene in the 8p11–12 region, has been linked to schizophrenia (Stefansson et al., 2002, 2003). NRG1 has also been associated with late-onset AD. In a study examining families from the NIMH Alzheimer’s Disease Genetic Initiative with late-onset pedigrees multiplex for psychosis, a linkage was found with NRG1, suggesting that this gene is involved in the neuropathology of psychosis in AD (Go et al., 2005). This intriguing notion that “psychosis-modifier genes” may be involved both during neurodevelopment to produce schizophrenia and throughout neurodegeneration to produce AD⫹P has been supported by other studies. Functional interleukin IL-1 promoter polymorphisms have been associated with AD⫹P, and it has been speculated that diminished IL-1 output may promote the psychotic phenotype through altered neurotransmitter interaction (via dopamine or serotonin) or reduced neuronal repair after amyloid-mediated neurotoxicity (Craig, Hart, McCool, McIlroy, & Passmore, 2004). Other studies also point to a significant role of inflammatory processes in dementia endophenotypes (Tarkowski et al., 2003). Genes involved in neurotransmitter systems have been identified as important in the pathogenesis of AD⫹P. The strongest evidence lies with the serotonin 2A (5HT-2A) receptor, with several studies implicating alleles of this gene with the manifestation of psychosis in dementia. The C allele of the 5HT-2A polymorphism has been associated with auditory hallucinations (Holmes, Arranz, Powell, Collier, & Lovestone, 1998; Pritchard et al., 2006), delusions (Lam, Tang, Ma, Zhang, & Chiu, 2004; Pritchard et al., 2006), and psychosis in general (Nacmias et al., 2001; Pritchard et al., 2006; Rocchi et al., 2003). The T allele has also been associated with delusions (Assal et al., 2004). The variable-number tandem repeat 10-repeat allele of the serotonin receptor (SERT) gene has also been associated with psychosis (Pritchard, Pritchard, Bentham, & Lendon, 2007b), as has the 5HT-2C receptor (Holmes et al., 1998), the dopamine receptors DRD1 and DRD3 (Holmes et al., 2001; Sweet et al., 1998) and catechol-O-methyl transferase (Sweet et al., 2005).
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In summary, genetic studies to date have established strong heritability for psychosis in AD, suggesting a role for APOE ε4 load in delusional thinking, providing potential links with genes associated with primary psychotic disorders, and starting to identify potential neurotransmitter targets for pharmacological intervention. A recent clinical trial demonstrated the efficacy of citalopram in treating the psychosis of dementia, further implicating serotonergic mechanisms (Pollock et al., 2007). Relating these findings to neuropathology through the use of structural and functional imaging studies holds promise for the identification of endophenotypes that can then be used in mechanistic and pharmacological studies.
NEUROIMAGING Numerous studies have addressed AD⫹P from both structural and functional perspectives (Tables 11.1 and 11.2). Taken together, the most consistent findings are that psychosis is associated with right frontal degeneration, frontal lobe hypoperfusion, hypometabolism or white matter changes (unilateral and bilateral), and temporal hypoperfusion or hypometabolism (unilateral or bilateral). Structural neuroimaging studies have addressed delusions—both misidentification (Förstl, Burns, Jacoby, & Levy, 1991, 1994a; Lee et al., 2006) and paranoid (Geroldi, Bresciani, Zanetti, & Frisoni, 2002)—as well as combined delusions (Binetti et al., 1995) in patients with senile dementia (Förstl et al., 1991), multi-infarct and probable AD (Binetti et al., 1995), mild probable AD (Geroldi et al., 2002), and probable AD of all severities (Lee et al., 2006). Despite the diagnostic variability in these studies, the frontal lobes are consistently implicated in delusional thinking associated with a dementing illness. Functional neuroimaging has also, to a significant extent, implicated the frontal lobes in delusional thinking. Although the original study in this area (Gustafson & Risberg, 1974) found paranoid delusions associated with frontal hyperperfusion in patients with presenile dementia of multiple etiologies, subsequent studies more consistent with the intended patient population have found hypoperfusion (Kotrla, Chacko, Harper, Jhingran, & Doody, 1995; Lopez, Smith, Becker, Meltzer, & DeKosky, 2001; Nakano, Yamashita, Matsuda, Kodama, & Yamada, 2006; Staff et al., 1999) and hypometabolism (Mentis et al., 1995; Sultzer et al., 2003) in frontal regions. The sole magnetic resonance spectroscopy
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Table 11.1 STRUCTURAL NEUROIMAGING STUDIES OF PSYCHOSIS IN DEMENTIA STUDY
MODALITY
PSYCHOSIS
FINDINGS
Förstl et al., 1991 (senile dementia, Alzheimer’s type)
CT
Delusions (misidentification); Geriatric Mental State Schedule
Right frontal degeneration
Förstl et al., 1994a
CT
Delusions (misidentification)
Right frontal degeneration
Binetti et al., 1995 (multi-infarct and probable AD)
CT
Delusions
Frontal lobe white matter lesions
Geroldi et al., 2002 (mild probable AD)
CT
Delusions (theft and paranoid, no misidentification); neuropsychiatric interview (NPI)
Normal asymmetry of frontal and temporal horns (symmetry in nondelusional subjects)
Lee et al., 2006 (mild, moderate, and severe probable AD)
MRI- T2
Delusions (misidentification); CERAD BRSD
Bilateral frontal or parietooccipital and left basal ganglia white matter changes
Table 11.2 FUNCTIONAL NEUROIMAGING STUDIES OF PSYCHOSIS IN DEMENTIA STUDY
MODALITY
PSYCHOSIS
FINDINGS
Gustafson & Risberg, 1974 (presenile dementia not specifically AD)
Intra-arterial injections of xenon 133
Delusions
Frontal hyperperfusion with posterotemporal hypoperfusion
Starkstein et al., 1994 (AD)
SPECT
Delusions
Bilat temporal hypoperfusion
Ponton et al., 1995 (probable AD)
SPECT
Delusions
R ant temporal hypoperfusion (Continued )
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FUNCTIONAL NEUROIMAGING STUDIES OF PSYCHOSIS IN DEMENTIA (Continued ) STUDY
MODALITY
PSYCHOSIS
FINDINGS
Mentis et al., 1995 (mild, moderate, and severe, probable and possible AD)
PET
Delusions Bilat OFC, L ACC, (misidentification)— bilat PCC, and L clinical interview medial temporal hypometabolism Trend toward dorsolateral frontal hypometabolism Bilat hypermetabolism in sensory association cortices (STG, IPG)
Kotrla et al., 1995 (mild or moderate probable AD)
SPECT
Delusions—clinical interview
L frontal hypoperfusion
Hirono et al., 1998a (mild to moderate probable AD)
PET
Delusions—NPI or behavioral pathology in Alzheimer’s Disease Rating Scale (BEHAVE-AD)
L inf temporal gyrus hypermetabolism L medial occipital lobe hypometabolism
Staff et al., 1999 (mild, moderate, and severe probable AD)
SPECT
Delusions— semistructured neuropsychiatric assessment
R frontal and R limbic hypoperfusion
Lopez et al., 2001 (mild and moderate probable AD, n = 2)
PET
Delusions— semistructured interview
L dorsolateral PFC and L medial temp cortical hypoperfusion
Fukuhara et al., 2001 (mild probable AD, female)
SPECT
Delusions (theft)— NPI
R medial posterior parietal hypoperfusion
Sultzer et al., 2003 (probable AD, 24 male, 1 female)
PET
Delusions (untreated with psychotropics)— Neurobehavioral Rating Scale
R PFC hypometabolism, R lat OFC hypermetabolism
Nakano et al., 2006 (mild, moderate, and severe probable AD)
SPECT
Delusions—NPI
Hypoperfusion R PFC, R ACC, inf/ midtemporal (Continued )
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FUNCTIONAL NEUROIMAGING STUDIES OF PSYCHOSIS IN DEMENTIA (Continued ) STUDY
MODALITY
PSYCHOSIS
FINDINGS cortices and R parietal
Shinno et al., 2007 (mild probable AD)
MRS
Delusions (untreated with psychotropics)— BEHAVE-AD
Decreased NAA/Cr and increased mI/ Cr ratios in ACC
Lopez et al., 2001 (mild and moderate probable AD, n = 2)
PET
Hallucinations
L dorsolateral PFC, L medial temp cortex, R parietal cortex hypoperfusion
Grady et al., 1990 (probable AD)
PET
Delusions ⫹ hallucinations “psychotic behaviors”—chart review
Bilat parietotemporal glucose hypometabolism
Kotrla et al., 1995 (mild or moderate probable AD)
SPECT
Delusions ⫹ hallucinations
Bilat parietal hypoperfusion
Sultzer et al., 1995
PET
Delusions ⫹ hallucinations
Bilat frontal glucose hypometabolism
Mega et al., 2000 (probable or possible AD)
SPECT
Delusions ⫹ hallucinations (NPI)
Bilat dorsolateral frontal, left ACC, L ventral striatal, L pulvinar, L dorsolateral parietal hypoperfusion
Moran et al., 2007 (probable AD)
SPECT
Delusions ⫹ hallucinations (BEHAVE-AD)
♀: R inferolateral PFC and inferotemporal hypoperfusion ♂: R striatum hyperperfusion
ACC, anterior cingulate cortex; ant, anterior; bilat, bilateral; CT, computed tomography; inf, inferior; IPG, inferior parietal gyrus; L, left; lat, lateral; med, medial; mI, myoinositol; MRI, magnetic resonance imaging; MRS, magnetic resonance spectroscopy; NAA, N-acetyl aspartate; Cr, creatinine; OFC, orbitofrontal cortex; PCC, posterior cingulate cortex; PET, positron emission tomography; post, posterior; R, right; SPECT, single photon emission computed tomography; STG, superior temporal gyrus.
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(MRS) study further implicates frontal regions, specifically the anterior cingulate cortex (ACC) (Shinno et al., 2007), in delusional thinking and activity disturbances. However, the temporal lobes are also implicated in conjunction with the frontal lobes (Lopez et al., 2001; Mentis et al., 1995; Nakano et al., 2006) and independent from the frontal lobes (Hirono et al., 1998a; Ponton et al., 1995; Starkstein et al., 1994). The parietal lobes have also been implicated (Mentis et al., 1995; Nakano et al., 2006) but only in one study in isolation from frontal and temporal regions (Fukuhara et al., 2001). Although the aforementioned studies addressed delusions in AD, delusions often occur in conjunction with hallucinations and other behaviors. Unfortunately, this comorbidity has not been consistently reported. Other studies have investigated delusions and hallucinations together. Whereas the frontal (Mega et al., 2000; Moran et al., 2007; Sultzer et al., 1995) and temporal (Grady et al., 1990; Moran et al., 2007) lobes have been implicated, the parietal lobes play a more significant role in this group (Grady et al., 1990; Kotrla et al., 1995; Mega et al., 2000) than in delusions alone. Only one small study addressed hallucinations without delusions and frontal and temporal regions were implicated (Lopez et al., 2001). These neuroimaging results are largely consistent with the neuropathological findings discussed earlier. Specifically, the presence of delusions in AD appears to be associated with frontal and temporal lobe pathology, while the presence of hallucinations further involves the parietal cortices. These findings are also consistent with the neuroimaging literature in schizophrenia, which suggest an alteration in the relation between frontal and temporal activation (for review, see Gur, Keshavan, & Lawrie, 2007). Consistencies notwithstanding, there are many inconsistencies in the literature that must be addressed. The differences between studies are instructive, especially as they relate to the variability in methodology. With respect to dementia, diagnoses range from presenile dementia to pathologically confirmed AD. In earlier studies, samples likely included a mix of dementia of multiple etiologies including AD, LBD, frontotemporal dementia (FTD), mixed, and vascular dementia. Further, the severity of dementia varies between studies; some studies include a narrow range of severity while others include a broad range, resulting in interand intragroup differences. In addition, groups with AD⫹P and AD⫺P are not always of the same stage of dementia. Age of onset, patient age, duration of dementia, and handedness are not consistently reported in all studies. Gender is not always evenly represented between groups, and
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sample sizes vary considerably between studies. These methodological differences would be expected to contribute to the variability in specific brain regions implicated in the studies. With respect to psychosis, there are also differences between groups. First, some studies investigated delusions or hallucinations alone, while others grouped these together as psychosis. Agitation is included with psychosis in some but not all. Even within the delusional subgroup, some studies specify misidentification or paranoid syndromes while others group them together. Duration of psychotic symptoms, whether persistent or episodic, is not consistently reported, nor is its relation to onset of dementia. Comorbid psychiatric or neurological illness and medication differences between groups are often overlooked, and whereas some studies have included unmedicated patients, most did not. Finally, different methodologies are used to diagnose psychosis, including chart review, clinical interview, semistructured interview, and structured rating scales including the NPI, BEHAVE-AD, Neurobehavioral Rating Scale, and Geriatric Mental State Schedule; these may emphasize different aspects of psychosis. With respect to neuroimaging, different modalities and timing of scans are important to address. Structural images have shown atrophy, while functional images did not necessarily account for this variable, as atrophy corrections were inconsistently performed. Although regionof-interest analysis casts a specific net to address specific brain regions, whole-brain analysis using voxel-based morphometry can identify involvement of interconnected brain regions, an important consideration; this can also be studied using diffusion tensor imaging (DTI). Finally, whether psychotic symptoms, especially hallucinations, are present during the scan is an important albeit difficult variable to control between groups.
CONCLUSION Integrating the epidemiological, neuropathological, genetic, and neuroimaging literature thus far allows for only a superficial understanding of mechanisms involved in the presentation of behavioral disturbances, including psychosis, in AD. The literature suggests but does not show conclusively that the deposition of plaques and tangles in genetically predisposed individuals with susceptible alleles of serotonergic, muscarinic, or APOE ε4 genes results in the disruption of frontal, temporal,
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and parietal circuits, culminating in delusions and hallucinations in the context of AD. The absence of a uniform, valid, and reliable standard of quantifying and qualifying AD⫹P has been a major limitation in this field. Future studies should also consider the stage and age of onset of dementia and the duration of psychosis in the design and analyses and should accurately report demographic data in order to allow for comparisons of the literature. Considering the wealth of mechanistic data that has already been derived despite these limitations, we conclude that future therapeutic advances for AD⫺P are within the reach of well-designed neuroimaging studies based in a solid diagnostic framework that include the characterization of delusions and/or hallucinations and integrates genetic information of candidate genes involved in dementia and psychosis. REFERENCES Assal, F., Alarcon, M., Solomon, E. C., Masterman, D., Geschwind, D. H., & Cummings, J. L. (2004). Association of the serotonin transporter and receptor gene polymorphisms in neuropsychiatric symptoms in Alzheimer disease. Archives of Neurology, 61(8), 1249–1253. Assal, F., & Cummings, J. L. (2002). Neuropsychiatric symptoms in the dementias. Current Opinion in Neurology, 15(4), 445–450. Avramopoulos, D., Fallin, M. D., & Bassett, S. S. (2005). Linkage to chromosome 14q in Alzheimer’s disease (AD) patients without psychotic symptoms. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics, 132(1), 9–13. Bacanu, S. A., Devlin, B., Chowdari, K. V., DeKosky, S. T., Nimgaonkar, V. L., & Sweet, R. A. (2005). Heritability of psychosis in Alzheimer disease. American Journal of Geriatric Psychiatry, 13(7), 624–627. Binetti, G., Padovani, A., Magni, E., Bianchetti, A., Scuratti, A., Lenzi, G. L., et al. (1995). Delusions and dementia: Clinical and CT correlates. Acta Neurologica Scandinavica, 91(4), 271–275. Chang, J. B., Wang, P. N., Chen, W. T., Liu, C. Y., Hong, C. J., Lin, K. N., et al. (2004). ApoE epsilon4 allele is associated with incidental hallucinations and delusions in patients with AD. Neurology, 63(6), 1105–1107. Cook, S. E., Miyahara, S., Bacanu, S. A., Perez-Madrinan, G., Lopez, O. L., Kaufer, D. I., et al. (2003). Psychotic symptoms in Alzheimer disease: Evidence for subtypes. American Journal of Geriatric Psychiatry, 11(4), 406–413. Craig, D., Hart, D. J., McCool, K., McIlroy, S. P., & Passmore, A. P. (2004). The interleukin 1beta gene promoter polymorphism (-511) acts as a risk factor for psychosis in Alzheimer’s dementia. Annals of Neurology, 56(1), 121–124. Farber, N. B., Rubin, E. H., Newcomer, J. W., Kinscherf, D. A., Miller J. P., Morris, J., C, et al. (2000). Increased neocortical neurofibrillary tangle density in subjects with Alzheimer disease and psychosis. Archives of General Psychiatry, 57(12), 1165–1173. Förstl, H., Besthorn, C., Burns, A., Geiger-Kabisch, C., Levy, R., & Sattel, A. (1994a). Delusional misidentification in Alzheimer’s disease: A summary of clinical and biological aspects. Psychopathology, 27(3–5), 194–199.
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Index
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AD. See Alzheimer’s disease Addiction, 39 FDG and, 34 ADHD. See Attention deficit hyperactivity disorder Aerobic fitness, 116 –117 fractional anisotropy and, 117 tissue density and, 117 AFNI. See Analysis of Functional NeuroImages Aging age-related cognitive decline and microstructural, 113 –115 diffusion tensor imaging distinguishing between normal aging and pathological, 113 schizophrenia and, 208 –209 Aging, cerebral, 109 –113 interventions that may modify, 116–117 Aging, cognitive, 132 age-related morphometric changes to, relationship of, 107–109 Aging, normal, 172 amyloid imaging and, 47 diffusion tensor imaging distinguishing between pathological aging and, 113 magnetic resonance spectroscopy and, 7– 8 magnetization transfer ratio and, 115 –116 mild cognitive impairment and, distinguishing between, 9 structural MRI studies of, 102 volumetric studies of, 102 –107, 173 Agitation, 233 AIR. See Automated Image Registration Alcohol abuse, 45 Alzheimer’s disease (AD), 104, 105, 171 amyloid imaging and, 47 brain atrophy and, 189
definitive diagnosis of, 46 dementia v., 172, 196 early imaging manifestations of, 176 –178 FDG and, 34 functional neuroimaging, 240 –243 magnetic resonance spectroscopy and, 10 memory and, verbal, 10 misidentification delusions in, 233 –234 probable, 188, 190 serotonin and, 43 structural imaging analysis of, 186 –191 subtypes, 235 vascular dementia and, 10 –11 visuoconstructional performance and, 10 Alzheimer’s disease, late-onset (LOAD), 238 –240 Alzheimer’s disease, psychosis of (AD+P), 233 –236 epidemiology, 236 genetics and, 238 –240 literature inconsistencies, 244 –245 magnetic resonance imaging and, 244 misidentification/hallucination factor, 235 neuroimaging, 240 –245 neuropathology, 236 –238 persecutory delusion factor, 235 postmortem studies, 237–238 quantifying, 235 –236, 246 standard for, 246 structural neuroimaging, 240, 241 symptoms, duration of psychotic, 245 symptoms excluded from, 235 symptoms of, 234 –235 Amyloid imaging, 34, 46 – 47 agents, 26 –27 aging and, normal, 47
255
256
Index
Alzheimer’s disease and, 47 depression and, late-life, 47 Lewy body dementia and, 47 mild cognitive impairment and, 47 Parkinson’s disease and, 47 Analysis of Functional NeuroImages (AFNI), 90 Analyze 7.5, 88, 89 Aniplatelet agents, 160 Antidepressant pharmacotherapy, 154 Antidepressant treatment, 8 –9, 160 WMH and, 160 Antioxidants, 160 Arterial spin labeling (ASL), 77–78, 191 Arterial spin labeling, continuous (CASL), 77–78, 191, 193 Atrial fibrillation, 151 Attention deficit hyperactivity disorder (ADHD), 39, 40 DAT and, 44 Automated Image Registration (AIR), 91 Becquerel, 19 BEHAVE-AD. See Behavioral Pathology in Alzheimer’s Disease Behavioral Pathology in Alzheimer’s Disease (BEHAVE-AD), 235 BGO. See Bismuth germinate BioImage Suite, 91 Biomedical Imaging Resource at Mayo Clinic, 88 Bismuth germinate (BGO), 21 Blood flow, 29 – 33, 125 continuous flow through radiation monitoring system, 30 medication/treatment and schizophrenia, 216, 224 neuronal activation and, 30 PET measurement of, 29, 30 resting, 30 SPECT measurement of, 29 Blood flow, cerebral (CBF), 80, 191, 216 Blood flow, regional cerebral (rCBF), 127–128 determinants of, 192 maps, 193, 195 Blood oxygen level-dependent functional MRI (BOLD fMRI), 30, 73, 126, 211 basis of, 75 –77 limitation to, 77
Blood oxygen level-dependent hemodynamic response function (BOLD HRF), 77 in aging, 79 BOLD HRF. See Blood oxygen leveldependent hemodynamic response function Bolus injection tracer kinetic modeling study, 35 BPSD. See Dementia, behavioral and psychiatric symptoms of BrainSuite2, 91 BrainVisa/Anatomist, 91–92 Calcium channel blockers, 160 Capgras delusion, 234 Cardiovascular Health Study-Cognition Study (CHS-CS), 172 CASL. See Arterial spin labeling, continuous CBF. See Blood flow, cerebral Cell membrane integrity, 7 CERAD. See Consortium to Establish a Registry for Alzheimer’s Disease Cerebral atrophy, 47 Cerebral ventricular expansion, 182 –186. See also Ventricular-tobrain ratio depression and, 183 diabetes and, 183 as function of changing cognitive status, 186 hypertension and, 183 by progression group, 185 Cerebrospinal fluid (CSF), 48, 74 Cerebrovascular disease (CVD) hypertension and, 192 late-life depression and, 149 –150, 156 –157 pharmacotherapy resistance and, 150 signal hyperintensities and, 154 Chemical shift effect, 5 Choline uptake, 7– 8 CHS-CS. See Cardiovascular Health Study-Cognition Study Ci. See Curie CIND. See Cognitively impaired nondemented Citalopram, 153 Cocaine, 44 Coffey classification system, 155
Index
Cognition age-associated variability in, 107–108 microstructural aging and decline in, 113 –115 schizophrenia and, 212 –213 Cognitively impaired nondemented (CIND), 9 Confocal microscopy, 49 Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), 235 CSF. See Cerebrospinal fluid Cumulative Illness Rating ScaleGeriatrics, 150 Curie (Ci), 19 CVD. See Cerebrovascular disease DA. See Dopamine DAT. See Dopamine transporters Data Format Working Group in the Neuroimaging Informatics Technology Initiative, 88 Debabeler, 92 DED. See Depression with executive dysfunction Defluorination, 41 Dementia. See also Lewy body dementia AD and, 172, 196 frontotemporal, 11, 244 vascular, 10 –11 Dementia, behavioral and psychiatric symptoms of (BPSD), 233, 235 Dephosphorylation, 33 Depression, 42 atherosclerotic changes in development of, 149 cerebral ventricular expansion and, 183 early-onset, 149 FDG and, 34 historical overview of, 149 –151 monoamine hypothesis of, 26 poststroke, 151–152 risk of, 150 –151 serotonin and, 43 serotonin transporters and, 45 Depression, geriatric, 149 –150 cerebrovascular risk factors and, 153 –154 genetic contributions to, 158 –159 neuroimaging correlates of, 154 –156
257
Depression, late-life (LLD), 149 amyloid imaging and, 47 cerebrovascular disease and, 149 –150, 156 –157 early-onset v., 149 fMRI in, 80 – 82 magnetic resonance spectroscopy and, 8 –9 pathophysiology of, 157–158 signal hyperintensities and, 154 Depression, vascular, 150 brain stimulation methods and, 160 clinically defined, 150 electroconvulsive therapy and, 160 future directions for, 159 –161 MRI-defined, 150 rTMS and, 160 subcortical ischemic, 155 Depression with executive dysfunction (DED), 152 –154 Diabetes cerebral ventricular expansion and, 183 mellitus, 151 DICOM. See Digital Imaging and Communications in Medicine Diffusion anisotropy, 111 Diffusion spectrum imaging (DSI), 74 Diffusion tensor imaging (DTI), 110 –113, 156, 211 interpretation issues of, 115 schizophrenia and, 214 Diffusion-weighted imaging, 73, 74 Digital Imaging and Communications in Medicine (DICOM), 87 Donepezil treatment, 10 Dopamine (DA), 34 endogenous, 38 hypothesis of schizophrenia, 26 Dopamine receptors, 37– 40, 239 age-related decline in, 37, 39 antipsychotic medication effects of, 37 concentration of, 37 extrastriatal binding, 39 Positron emission tomography imaging of, 38 radiotracers, 37 Dopaminergic activity, 38 Dopamine transporters (DAT), 43 – 46 ADHD and, 44 binding declining with age, 45
258
Index
cocaine and, 44 methamphetamine and, 44 radioligands, 44 radiotracers, 44 DPTools, 92 –93 DSI. See Diffusion spectrum imaging DTI. See Diffusion tensor imaging DTIQuery/CINCH, 92 DWMH. See White matter hyperintensities, deep Eating disorders, 42 Echo planar imaging, 73 Echo time (TE), 73 Ecstasy, 45 – 46 ECT. See Electroconvulsive therapy EEG. See Electroencephalography Electroconvulsive therapy (ECT), 8 signal hyperintensities and, 154 vascular depression and, 160 Electroencephalography (EEG), 125 schizophrenia and, 224 –225 Eluant, 21 Emission imaging, 17–23 artifacts arising from subject motion, 49 challenges, 47– 49 data acquisition for, 23 –26 data analysis for, 23 –26 data analysis for, progressive, 27 frame-by-frame registration, 49 goal of, 19 initial focus of, 26 limited resolution of, 48 methodology developments, 50 protocol, 23 protocol, fully dynamic, 25 –26 resolution correction scheme, 48 spatial mapping errors, 48 EOS. See Schizophrenia, early-onset Escitalopram treatment, 155 FA. See Fractional anisotropy Fazekas classification system, 150 FDG. See 2-Fluoro-2-deoxy-D-glucose Fiber tractography, 214 FLAIR. See Fluid-attenuated inversionrecovery transfer imaging Fluid-attenuated inversion-recovery transfer imaging (FLAIR), 73 2-Fluoro-2-deoxy-D-glucose (FDG), 33 addiction and, 34 Alzheimer’s disease and, 34
depression and, 34 phosphorylation rate of, 33 schizophrenia and, 34 simplified analysis of, 34 transport rate of, 33 Fluoxetine, 46 FMRLAB, 92 Fractional anisotropy (FA), 111–112 aerobic fitness and, 117 information processing speed and, 114 mean diffusivity and, relationship between, 114 visual attention and, 114 voxelwise analysis of, 114 FreeSurfer, 93 Frontotemporal dementia (FTD), 244 magnetic resonance spectroscopy and, 11 FSL, 93 FTD. See Frontotemporal dementia Functional magnetic resonance imaging (fMRI), 74 –75, 125 –126, 191–196, 211. See also Blood oxygen leveldependent functional MRI block design, 76 Depression in, late-life, 80 – 82 event-related, 76 –77 influence of brain morphometric changes on, 78 –79 methodological challenges of, 78 schizophrenia and, 211, 215, 216 Gamma camera, 20 –21 heads, 21 Genetics, 238 –240 Glucose analogue, 33 cerebral metabolic rate of, 34 phosphorylation rate of, 33 transport rate of, 33 utilization, 33 – 34 HAROLD model. See Hemispheric asymmetry reduction in older adults Head immobilization system, 23 Hemispheric asymmetry reduction in older adults (HAROLD model), 133, 136 Hexokinase, 33 Hormone replacement therapy, 116 –117 5-HT. See Serotonin HTN. See Hypertension
Index
Human brain mapping, 75 Hypercholesterolemia, 160 Hypertension (HTN), 33, 109, 156, 160, 192 –194 Cerebral ventricular expansion and, 183 cerebrovascular disease and, 192 Idiopathic amnestic disorder (MCI-A), 179 –181 abnormalities, 181 Image coordinate systems, 88 format, 86 – 88 header structure, 87 orientation, 88 – 89 postprocessing, 85 preprocessing, 85 processing, 85 reconstruction methods, 86 ImageJ, 93 –94 Imaging gradients, 72 frequency-encoding, 72 phase encoding, 72 readout, 72 slice-selection, 72 Information processing speed cerebrovascular risk factors and, 153 –154 fractional anisotropy and, 114 Insight Toolkit (ITK), 94 ITK. See Insight Toolkit ITK-SNAP, 94 JPRESS. See Two-dimensional J-coupled point-resolved spectroscopy K-space, 86 Lauterbur, Paul C., 71 LBD. See Lewy body dementia LC. See Lumped constant Lewy body dementia (LBD), 234, 236, 244 amyloid imaging and, 47 magnetic resonance spectroscopy and, 11 Ligand-binding interactions, 34 – 37 Line of response (LOR), 20 LLD. See Depression, late-life LOAD. See Alzheimer’s disease, lateonset
259
Logan analysis, 36, 41– 42 LONI Pipeline Processing Environment, 94 LOR. See Line of response LOS. See Schizophrenia, late-onset LSO. See Lutetium oxyorthosilicate Lumped constant (LC), 33 Lutetium oxyorthosilicate (LSO), 21 Magnetic resonance imaging (MRI), 25, 32, 211. See also Blood oxygen level-dependent functional MRI; Functional magnetic resonance imaging Alzheimer’s disease and, 240, 244 analysis categories, 173 coregistration with PET, 48 defined vascular depression, 150 magnet strength, 73 perfusion, 77–78 physics of, 71–73 quality control steps, 48 schizophrenia and, 213 structural, 73 –74, 102, 172 Magnetic resonance spectroscopy (MRS). See also Two-dimensional magnetic resonance spectroscopy aging and, normal, 7– 8 Alzheimer’s disease and, 10 basic principles of, 5 – 6 cognitively impaired nondemented and, 9 depression and, late-life, 8 –9 frontotemporal dementia and, 11 Lewy body dementia and, 11 limitations of, 12 mild cognitive impairment and, 9 nuclear isotopes used in, 6 –7 phosphorus, 6 poor resolution of, 12 as predictor of developing dementia, 9 proton, 7 strengths of, 12 vascular dementia and, 10 –11 Magnetization longitudinal net, 72 of protons, 72 transfer imaging, 73, 74, 115 –116 transverse, 72, 73 Magnetization transfer ratio (MTR), 115 aging and, 115 –116 Mansfield, Peter, 71
260
Index
McConnell Brain Imaging Centre of the Montreal Neurological Institute, 88 MCI. See Mild cognitive impairment MCI-A. See Idiopathic amnestic disorder MCI-MCD. See Mild cognitive impairment multiple cognitive domain type MD. See Mean diffusivity Mean diffusivity (MD), 111–112 fractional anisotropy and, relationship between, 114 Medical comorbidity, 150 –151 Medical Image Processing, Analysis and Visualization (MIFAV), 95 MedINRIA, 94 –95 Memory attentional control processes and, 128 decline in, 126 distracting information and, 132 dysfunction, 179, 212 executive control processes and, 128 nondeclarative, 126 –127 prospective, 126 semantic, 126 source, 126 theories on deficits in, 127 Memory, encoding, 134 –141 deep, 134 divided attention and, 134, 138 shallow, 134 Memory, episodic, 126 age-related declines in, 108 cerebrovascular risk factors and, 153 –154 testing, 127 Memory, verbal, 29 Alzheimer’s disease and, 10 Memory, working maintenance of information in, 132 microstructural white matter reductions and deficits in, 114 processing components within, 132 studies of, 131–134 Memory retrieval, 134 –141 divided attention and, 138 –139 Methamphetamine, 44 Methyphenidate, 44 Microvascular remodeling, 192 MIFAV. See Medical Image Processing, Analysis and Visualization
Mild cognitive impairment (MCI), 104, 171. See also Idiopathic amnestic disorder amyloid imaging and, 47 magnetic resonance spectroscopy and, 9 memory dysfunction and, 179 paradoxical nonlinear activation patterns in, 75 structural abnormalities and, 179 structural imaging analysis of, 178 –182 Mild cognitive impairment multiple cognitive domain type (MCIMCD), 179 –181 abnormalities, 181 MINC, 88 Mini Mental State Examination, 180 Mirror drawing task, 108 Mirror sign, 234 Monoamine hypothesis, 26 Monoamine transporters, 43 – 46 radiotracers, 44 Mood disorder, 39 MRI. See Magnetic resonance imaging MRIcro, 95 MRS. See Magnetic resonance spectroscopy MTR. See Magnetization transfer ratio Multitracer, 95 National Electrical Manufacturers Association (NEMA), 87 National Institute of Mental Health (NIMH) Division of Adult Translational Research, 2 Geriatrics Research Branch, 2 mission of, 1 Strategic Plan, 1–2 NEMA. See National Electrical Manufacturers Association NET. See Norepinephrine transporters Neuregulin-1 (NRG1), 239 Neurofibrillary tangles, 46 Neuroimaging. See specific techniques Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC), 98 NeuroLens, 96 Neuronal viability, 7, 8 Neurotransmission, alterations in, 18 NIfTI, 88
Index
NITRC. See Neuroimaging Informatics Tools and Resources Clearinghouse NMR. See Nuclear magnetic resonance Norepinephrine transporters (NET), 43 – 46 reference region identification, 44 signal-to-noise issues, 44 NRG1. See Neuregulin-1 Nuclear magnetic resonance (NMR), 71 Obsessive compulsive disorder (OCD), 45 OsiriX, 96 Paraview, 96 Parkinson’s disease, 40 amyloid imaging and, 47 Paroxetine, 46 Partial Least Squares (PLS), 96 –97 Partial least squares method, 140 PASL. See Pulsed arterial spin labeling (PASL) Patlak graphical analysis, 34, 36 Paxil, 46 PD. See Proton density Periventricular hyperintensities (PVHs), 157 PET. See Positron emission tomography Phantom boarder syndrome, 234 Pharmacotherapy resistance, 150 Photomultiplier tubes (PMTs), 20 Placebo, 75 Plasma radioactivity, 25 PLS. See Partial Least Squares PMTs. See Photomultiplier tubes Positron emission tomography (PET), 17, 21–23, 125, 211 blood flow measurement with, 29, 30 coregistration with MRI, 48 data acquisition, 23 imaging of dopamine receptor, 38 quality control, 48 quantitative properties of, 30 radionuclides used in, 22 –23 radiotracers, 24 schizophrenia and, 215, 216 Single photon emission computed tomography and, fundamental difference between, 20 spatial resolution in, 22, 39 studies of neuroreceptor binding, 34 – 37
261
Postmortem findings, 101–102, 110 Alzheimer’s disease, psychosis of, 237–238 Precession, 71 Protein deposits, abnormal, 46 Proton density (PD), 115 Protons, 71–72 magnetization of, 72 Prozac, 46 Psychosis. See Alzheimer’s disease, psychosis of “Psychosis-modifier genes,” 239 Pulsed arterial spin labeling (PASL), 77–78 PVHs. See Periventricular hyperintensities Q-ball imaging, 74 Quantitative tracer kinetic studies, 25 Radiation dosimetry, 19 Radioactive indicator molecules, 17 Radioactivity, 19 plasma, 25 Radioligands affinity, 37 alternate measures, 36 DAT, 44 free from nonspecific binding, 35 initial evaluation of neuroreceptor, 35 irreversible binding of, 35 reversibly binding, 35 SERT, 44 Radionuclides half-life of, 19 obtained through radioactive decay, 19 production of, 18 –19 used in PET, 22 –23 used in SPECT, 21 Radio pulse, 72 Radiotracers, 17 agonist, 34 – 35 amphetamine-induced reduction in binding of, 40 antagonist, 34 to assess energy utilization of brain tissue, 18 for assessments of cerebral perfusion, 18 bidirectional blood brain barrier transport of, 35
262
Index
for brain imaging, 18 –19 DAT, 44 delivery of, 23 dopamine receptor, 37 dosimetry of, 19 internalization, 49 monoamine, 44 PET, 24 preparing, 30 SPECT, 22 synthesizing, 18 rCBF. See Blood flow, regional cerebral Reference tissue modeling, 36 Relaxation time (T1), 72 Repetition time (TR), 72 –73 Repetitive transcranial magnetic stimulation (rTMS), 160 Retrogenesis, 175 Rivastigmine, 10 rTMS. See Repetitive transcranial magnetic stimulation Schizophrenia, 39, 40 aging process and, 208 –209 blood flow medication/treatment and, 216, 224 brain activation and age or duration of illness in, 217–223 cognition and, 212 –213 diffusion tensor imaging and, 214 dopamine hypothesis of, 26 FDG and, 34 functional magnetic resonance imaging and, 211, 215, 216 functional neuroimaging and, 215 –216, 224 –225 literature limitations, 225 –226 magnetic resonance imaging and, 213 onset age of, typical, 211–212 positron emission tomography and, 215, 216 prevalence of, 209 serotonin and, 43 structural neuroimaging and, 213 –215 Schizophrenia, early-onset (EOS), 209 Schizophrenia, geriatric double stigma of, 210 future directions of, 226 –227 memory dysfunction and, 212 verbal learning and, 212
Schizophrenia, late-onset (LOS), 209 course of, 211–213 electrophysiology of, 224 –225 executive dysfunction in, 212 features, 211–213 Scintillation detectors, 20 Sensory functions, studies of, 130 –131 Serotonin (5-HT), 34 endogenous, 42 Serotonin receptors, 40 – 43 Alzheimer’s disease and, 43 antidepressant response, 41 depression and, 43 distribution of, 40, 42 schizophrenia and, 43 subtypes, 41 Serotonin reuptake inhibitors, selective (SSRIs), 46 Serotonin transporters (SERT), 43 – 46, 239 analysis, 45 depression and, 45 distribution of, 45 dysfunction, 45 OCD and, 45 radioligands, 44 SERT. See Serotonin transporters Sertraline, 46 SHs. See Signal hyperintensities Siemens HRRT, 22 Signal hyperintensities (SHs) antidepressant pharmacotherapy and, 154 cerebrovascular disease and, 154 electroconvulsive therapy and, 154 escitalopram treatment and, 155 executive dysfunction and, 154 late-life depression and, 154 Simplified kinetic method (SKM), 34 Simplified reference tissue model (SRTM), 36, 41– 42 Single photon emission computed tomography (SPECT), 17, 20 –21 activation studies, 33 blood flow measurement with, 29 collimator choice, 21 perfusion imaging, 29 positron emission tomography and, fundamental difference between, 20 radionuclides used in, 21 radiotracers, 22
Index
spatial resolution of, 21 split-dose technique, 33 studies of neuroreceptor binding, 34 – 37 Skills, executive age-related declines in, 109 microstructural white matter reductions and deficits in, 114 Skills acquisition, age-related declines in, 108 SKM. See Simplified kinetic method SnPM. See Statistical non-Parametric Mapping SPECT. See Single photon emission computed tomography SPM. See Statistical Parametric Mapping SPM5. See Statistical parametric mapping framework SRTM. See Simplified reference tissue model SSRIs. See Serotonin reuptake inhibitors, selective Statistical non-Parametric Mapping (SnPM), 97 Statistical Parametric Mapping (SPM), 97 Statistical parametric mapping framework (SPM5), 31, 33 T1. See Relaxation time TE. See Echo time Television sign, 234 3D Slicer, 90 Tissue density, 117 disruption, 112 TR. See Repetition time 2D-COSY. See Two-dimensional chemical shift correlated magnetic resonance spectroscopy 2D-MRS. See Two-dimensional magnetic resonance spectroscopy Two-dimensional chemical shift correlated magnetic resonance
263
spectroscopy (2D-COSY), 12 –13 advantages of, 13 Two-dimensional J-coupled pointresolved spectroscopy (JPRESS), 12 Two-dimensional J-resolved spectroscopy, 12 Two-dimensional magnetic resonance spectroscopy (2D-MRS), 12 –13 advantages of, 12 Vascular dementia (VaD), 10 –11 Vascular hypertrophy, 192 VBM. See Voxel-based morphometry VBR. See Ventricular-to-brain ratio Venlafaxine treatment, 9 Ventricular-to-brain ratio (VBR), 183 –184 Visual attention, fractional anisotropy and, 114 Visualization Toolkit (VTK), 98 Visuoconstructional performance, 10 VoxBo, 97–98 Voxel-based morphometry (VBM), 173 advantage of, 107 subprofile scaling model applied to, 106 VTK. See Visualization Toolkit Waikato Environment for Knowledge Analysis, 177 White matter hyperintensities (WMH), 74, 109 –110, 156 –157 antidepressants and, 160 postmortem studies of, 110 quantifying, 109 –110 severity of, 109 White matter hyperintensities, deep (DWMH), 157 WMH. See White matter hyperintensities Xenon inhalation technique, 80 Zoloft, 46
