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Merenstein JL, Bennett IJ. Bridging patterns of neurocognitive aging across the older adult lifespan. Neurosci Biobehav Rev 2022; 135:104594. [PMID: 35227712 PMCID: PMC9888009 DOI: 10.1016/j.neubiorev.2022.104594] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/27/2022] [Accepted: 02/23/2022] [Indexed: 02/02/2023]
Abstract
Magnetic resonance imaging (MRI) studies of brain and neurocognitive aging rarely include oldest-old adults (ages 80 +). But predictions of neurocognitive aging theories derived from MRI findings in younger-old adults (ages ~55-80) may not generalize into advanced age, particularly given the increased prevalence of cognitive impairment/dementia in the oldest-old. Here, we reviewed the MRI literature in oldest-old adults and interpreted findings within the context of regional variation, compensation, brain maintenance, and reserve theories. Structural MRI studies revealed regional variation in brain aging as larger age effects on medial temporal and posterior regions for oldest-old than younger-old adults. They also revealed that brain maintenance explained preserved cognitive functioning into the tenth decade of life. Very few functional MRI studies examined compensatory activity in oldest-old adults who perform as well as younger groups, although there was evidence that higher brain reserve in oldest-old adults may mediate effects of brain aging on cognition. Despite some continuity, different cognitive and neural profiles across the older adult lifespan should be addressed in modern neurocognitive aging theories.
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Woodworth DC, Scambray KA, Corrada MM, Kawas CH, Sajjadi SA. Neuroimaging in the Oldest-Old: A Review of the Literature. J Alzheimers Dis 2021; 82:129-147. [PMID: 33998539 DOI: 10.3233/jad-201578] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The oldest-old, those 85 years and older, are the fastest growing segment of the population and present with the highest prevalence of dementia. Given the importance of neuroimaging measures to understand aging and dementia, the objective of this study was to review neuroimaging studies performed in oldest-old participants. We used PubMed, Google Scholar, and Web of Science search engines to identify in vivo CT, MRI, and PET neuroimaging studies either performed in the oldest-old or that addressed the oldest-old as a distinct group in analyses. We identified 60 studies and summarized the main group characteristics and findings. Generally, oldest-old participants presented with greater atrophy compared to younger old participants, with most studies reporting a relatively stable constant decline in brain volumes over time. Oldest-old participants with greater global atrophy and atrophy in key brain structures such as the medial temporal lobe were more likely to have dementia or cognitive impairment. The oldest-old presented with a high burden of white matter lesions, which were associated with various lifestyle factors and some cognitive measures. Amyloid burden as assessed by PET, while high in the oldest-old compared to younger age groups, was still predictive of transition from normal to impaired cognition, especially when other adverse neuroimaging measures (atrophy and white matter lesions) were also present. While this review highlights past neuroimaging research in the oldest-old, it also highlights the dearth of studies in this important population. It is imperative to perform more neuroimaging studies in the oldest-old to better understand aging and dementia.
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Affiliation(s)
- Davis C Woodworth
- Department of Neurology, University of California, Irvine, CA, USA.,Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Kiana A Scambray
- Department of Neurology, University of California, Irvine, CA, USA.,Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - María M Corrada
- Department of Neurology, University of California, Irvine, CA, USA.,Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA.,Department of Epidemiology, University of California, Irvine, CA, USA
| | - Claudia H Kawas
- Department of Neurology, University of California, Irvine, CA, USA.,Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA.,Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - S Ahmad Sajjadi
- Department of Neurology, University of California, Irvine, CA, USA.,Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
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Combining Cognitive Markers to Identify Individuals at Increased Dementia Risk: Influence of Modifying Factors and Time to Diagnosis. J Int Neuropsychol Soc 2020; 26:785-797. [PMID: 32207675 DOI: 10.1017/s1355617720000272] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE We investigated the extent to which combining cognitive markers increases the predictive value for future dementia, when compared to individual markers. Furthermore, we examined whether predictivity of markers differed depending on a range of modifying factors and time to diagnosis. METHOD Neuropsychological assessment was performed for 2357 participants (60+ years) without dementia from the population-based Swedish National Study on Aging and Care in Kungsholmen. In the main sample analyses, the outcome was dementia at 6 years. In the time-to-diagnosis analyses, a subsample of 407 participants underwent cognitive testing 12, 6, and 3 years before diagnosis, with dementia diagnosis at the 12-year follow-up. RESULTS Category fluency was the strongest individual predictor of dementia 6 years before diagnosis [area under the curve (AUC) = .903]. The final model included tests of verbal fluency, episodic memory, and perceptual speed (AUC = .913); these three domains were found to be the most predictive across a range of different subgroups. Twelve years before diagnosis, pattern comparison (perceptual speed) was the strongest individual predictor (AUC = .686). However, models 12 years before diagnosis did not show significantly increased predictivity above that of the covariates. CONCLUSIONS This study shows that combining markers from different cognitive domains leads to increased accuracy in predicting future dementia 6 years later. Markers from the verbal fluency, episodic memory, and perceptual speed domains consistently showed high predictivity across subgroups stratified by age, sex, education, apolipoprotein E ϵ4 status, and dementia type. Predictivity increased closer to diagnosis and showed highest accuracy up to 6 years before a dementia diagnosis. (JINS, 2020, 00, 1-13).
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Tang X, Holland D, Dale AM, Miller MI. APOE Affects the Volume and Shape of the Amygdala and the Hippocampus in Mild Cognitive Impairment and Alzheimer's Disease: Age Matters. J Alzheimers Dis 2016; 47:645-60. [PMID: 26401700 DOI: 10.3233/jad-150262] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper examines how age intervenes in the effects of APOE ɛ4 allele on the volume and shape morphometrics of the hippocampus and the amygdala in mild cognitive impairment (MCI) and Alzheimer's disease. We evaluate the structural morphological differences between ɛ4 carriers and non-carriers in two age-dependent subgroups; younger than 75 years (Young-Old) and older than 80 years (Very-Old). While we show that the four structures of interest atrophy significantly in the ɛ4 carriers, relative to the non-carriers, of the Young-Old group, this effect is not observed in their Very-Old counterparts. The structures in the right hemisphere are found to be more affected by the APOE genotype than those in the left hemisphere and we identify the relevant regions in which significant atrophy occurs to be parts of the basolateral, centromedial, and lateral nucleus subregions of the amygdala and the CA1 and subiculum subregions of the hippocampus. We also observe that the APOE genotype only affects MCI patients that deteriorated to dementia within 3 years while leaving their "non-converting" counterparts unaffected.
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Affiliation(s)
- Xiaoying Tang
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Dominic Holland
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Anders M Dale
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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Abstract
Two new sets of criteria for Alzheimer’s disease (AD) are now in play, including one set released in 2014, and a proposal for a “new lexicon” for how to describe the disease spectrum. A 2012 Canadian consensus conference said that to then, none of the new criteria or terminology would change primary care practice; that is still likely to be so. For dementia consultants, however, the new criteria pose challenges and offer opportunities. In general, the new criteria see an expanded role for bio-markers. Even so, the evidence base for this remains incomplete. Our understanding of the neuropathological criteria for dementia changed as the evidence base included more community cases. This is likely to inform the experience with biomarkers. At present, each of the criteria specifies an exclusive research role. Still, wider uptake is likely, especially in the United States. Geriatricians should be aware of the fundamental change in the terminology now being employed: AD diagnosis no longer obliges a diagnosis of dementia. Until more data emerge—something to which geriatricians can contribute—there is reason to be cautious in the adoption of the new criteria, as they are likely to be least applicable to older adults.
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Affiliation(s)
- Pierre Molin
- Department of Medicine, Divisions of Geriatric Medicine and of Neurology, Dalhousie University, Halifax, NS;; Département de médecine, Division de gériatrie, Université Laval, Québec, QC, Canada
| | - Kenneth Rockwood
- Department of Medicine, Divisions of Geriatric Medicine and of Neurology, Dalhousie University, Halifax, NS
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Abstract
Deux nouvelles séries de critères pour le diagnostic de la maladie d’Alzheimer sont maintenant en vigueur, incluant une série publiée en 2014. Un « nouveau lexique » conceptualisant la maladie a également été proposé. En 2012, la Conférence consensuelle canadienne affirmait que, pour l’instant, ni les nouveaux critères ni la nouvelle terminologie ne modifiaient la pratique en première ligne. Néanmoins, pour les consultants spécialisés en démence, l’avènement de ces critères ouvre la porte à de nombreux défis et occasions. En général, les nouveaux critères accordent une place grandissante aux biomarqueurs. Toutefois, les évidences qui sous-tendent leur utilisation demeurent incomplètes. L’étude de sujets provenant de la communauté ayant raffiné notre compréhension des critères neuropathologiques des démences, il est probable que notre expérience avec les biomarqueurs en bénéficierait également. Pour l’instant, ces critères sont réservés à la recherche. Cependant, leur adoption à plus large échelle est pressentie, particulièrement aux États-Unis. Les gériatres canadiens doivent être conscients de la terminologie maintenant utilisée et du changement fondamental qui en découle : un diagnostic de maladie d’Alzheimer ne requiert plus un diagnostic de démence. Dans l’attente de nouvelles données – auxquelles les gériatres peuvent contribuer – il y a lieu de faire preuve de prudence dans l’adoption des nouveaux critères, car ils sont susceptibles de moins bien s’appliquer aux personnes âgées.
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Affiliation(s)
- Pierre Molin
- Department of Medicine, Divisions of Geriatric Medicine and of Neurology, Dalhousie University, Halifax, NS;; Département de médecine, Division de gériatrie, Université Laval, Québec, QC
| | - Kenneth Rockwood
- Department of Medicine, Divisions of Geriatric Medicine and of Neurology, Dalhousie University, Halifax, NS
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7
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Montagne A, Nation DA, Pa J, Sweeney MD, Toga AW, Zlokovic BV. Brain imaging of neurovascular dysfunction in Alzheimer's disease. Acta Neuropathol 2016; 131:687-707. [PMID: 27038189 PMCID: PMC5283382 DOI: 10.1007/s00401-016-1570-0] [Citation(s) in RCA: 143] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Revised: 03/21/2016] [Accepted: 03/23/2016] [Indexed: 11/29/2022]
Abstract
Neurovascular dysfunction, including blood-brain barrier (BBB) breakdown and cerebral blood flow (CBF) dysregulation and reduction, are increasingly recognized to contribute to Alzheimer's disease (AD). The spatial and temporal relationships between different pathophysiological events during preclinical stages of AD, including cerebrovascular dysfunction and pathology, amyloid and tau pathology, and brain structural and functional changes remain, however, still unclear. Recent advances in neuroimaging techniques, i.e., magnetic resonance imaging (MRI) and positron emission tomography (PET), offer new possibilities to understand how the human brain works in health and disease. This includes methods to detect subtle regional changes in the cerebrovascular system integrity. Here, we focus on the neurovascular imaging techniques to evaluate regional BBB permeability (dynamic contrast-enhanced MRI), regional CBF changes (arterial spin labeling- and functional-MRI), vascular pathology (structural MRI), and cerebral metabolism (PET) in the living human brain, and examine how they can inform about neurovascular dysfunction and vascular pathophysiology in dementia and AD. Altogether, these neuroimaging approaches will continue to elucidate the spatio-temporal progression of vascular and neurodegenerative processes in dementia and AD and how they relate to each other.
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Affiliation(s)
- Axel Montagne
- Zilkha Neurogenetic Institute and Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Daniel A Nation
- Department of Psychology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Judy Pa
- Department of Neurology, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, 90089, USA
| | - Melanie D Sweeney
- Zilkha Neurogenetic Institute and Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Arthur W Toga
- Department of Neurology, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, 90089, USA
| | - Berislav V Zlokovic
- Zilkha Neurogenetic Institute and Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
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Jack CR, Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, Dale AM, Carmichael O, Ching C, DeCarli C, Desikan RS, Fennema-Notestine C, Fjell AM, Fletcher E, Fox NC, Gunter J, Gutman BA, Holland D, Hua X, Insel P, Kantarci K, Killiany RJ, Krueger G, Leung KK, Mackin S, Maillard P, Malone IB, Mattsson N, McEvoy L, Modat M, Mueller S, Nosheny R, Ourselin S, Schuff N, Senjem ML, Simonson A, Thompson PM, Rettmann D, Vemuri P, Walhovd K, Zhao Y, Zuk S, Weiner M. Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2. Alzheimers Dement 2016; 11:740-56. [PMID: 26194310 DOI: 10.1016/j.jalz.2015.05.002] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 04/28/2015] [Accepted: 05/05/2015] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. METHODS We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. RESULTS Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. DISCUSSION Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.
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Affiliation(s)
| | - Josephine Barnes
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | | | | | - James Brewer
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Shona Clegg
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Anders M Dale
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Owen Carmichael
- Department of Neurology, University of California at Davis, Davis, CA, USA
| | - Christopher Ching
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Charles DeCarli
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Rahul S Desikan
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA
| | - Christine Fennema-Notestine
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California at San Diego, La Jolla, CA, USA
| | - Anders M Fjell
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Evan Fletcher
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Nick C Fox
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jeff Gunter
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Boris A Gutman
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Dominic Holland
- Department of Neuroscience, University of California at San Diego, La Jolla, CA, USA
| | - Xue Hua
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Philip Insel
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Ron J Killiany
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | | | - Kelvin K Leung
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Scott Mackin
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA
| | - Pauline Maillard
- Department of Neurology, University of California at Davis, Davis, CA, USA; Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Ian B Malone
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Niklas Mattsson
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
| | - Linda McEvoy
- Department of Radiology, University of California at San Diego, La Jolla, CA, USA
| | - Marc Modat
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Susanne Mueller
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Rachel Nosheny
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | - Sebastien Ourselin
- Department of Neurodegenerative Disease, Dementia Research Centre, Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Norbert Schuff
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA
| | | | - Alix Simonson
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Department of Neurology, Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Marina del Rey, CA, USA
| | - Dan Rettmann
- MR Applications and Workflow, GE Healthcare, Rochester, MN, USA
| | | | | | | | - Samantha Zuk
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Weiner
- Department of Radiology and Biomedical Imaging, Center for Imaging of Neurodegenerative Diseases, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Psychiatry, University of California at San Francisco, San Francisco, CA, USA; Department of Radiology, University of California at San Francisco, San Francisco, CA, USA; Department of Medicine, University of California at San Francisco, San Francisco, CA, USA; Department of Neurology, University of California at San Francisco, San Francisco, CA, USA
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9
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Korolev IO, Symonds LL, Bozoki AC. Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. PLoS One 2016; 11:e0138866. [PMID: 26901338 PMCID: PMC4762666 DOI: 10.1371/journal.pone.0138866] [Citation(s) in RCA: 133] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Accepted: 09/04/2015] [Indexed: 01/21/2023] Open
Abstract
Background Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level. Methods Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. Results Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. Conclusions We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.
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Affiliation(s)
- Igor O. Korolev
- Neuroscience Program, Michigan State University, East Lansing, Michigan, United States of America
- College of Osteopathic Medicine, Michigan State University, East Lansing, Michigan, United States of America
- * E-mail:
| | - Laura L. Symonds
- Neuroscience Program, Michigan State University, East Lansing, Michigan, United States of America
| | - Andrea C. Bozoki
- Neuroscience Program, Michigan State University, East Lansing, Michigan, United States of America
- Department of Neurology, Michigan State University, East Lansing, Michigan, United States of America
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Coen RF, Robertson DA, Kenny RA, King-Kallimanis BL. Strengths and Limitations of the MoCA for Assessing Cognitive Functioning: Findings From a Large Representative Sample of Irish Older Adults. J Geriatr Psychiatry Neurol 2016; 29:18-24. [PMID: 26251108 DOI: 10.1177/0891988715598236] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 05/19/2015] [Indexed: 11/15/2022]
Abstract
BACKGROUND The Montreal Cognitive Assessment (MoCA) is a very widely used test for mild cognitive impairment. Differing recommendations have been made regarding its utility in providing a profile of performance across several cognitive domains. OBJECTIVES To examine the factor structure of the MoCA in a nationally representative population study of older Irish adults and evaluate its utility in providing domain-specific information. METHODS A cross-sectional analysis of wave 1 data from the Irish Longitudinal Study on Ageing was undertaken. Data from a subset of 2342 participants assessed using the MoCA were analyzed using both confirmatory factor analytic (CFA) and exploratory factor analytic (EFA) methods. RESULTS Mean age was 72.64 (range 65 to 98), 53% female. The CFA provided evidence of adequate overall model fit for a previously proposed 6-factor model. In contrast, EFA yielded a 3-factor solution and test items cross-loaded onto a number of factors with no clear pattern of underlying cognitive domains. Using EFA to explore the 6-factor model yielded good fit, but again test items cross-loaded onto a number of factors with no clear pattern evident. CONCLUSION Lack of concordance between the CFA and EFA findings demonstrates that the correspondence between individual tests and their assumed cognitive domains is not robust, reflecting at least in part a current lack of consensus on how core cognitive constructs are defined and on what subcomponents can be subsumed under different cognitive domains. The MoCA should not be viewed as a substitute for more in-depth neuropsychological assessment when domain-specific information is required.
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Affiliation(s)
- Robert F Coen
- Mercer's Institute for Research on Ageing, St James's Hospital, Dublin, Ireland
| | - Deirdre A Robertson
- TILDA (The Irish Longitudinal Study on Ageing), Lincoln Gate, Trinity College Dublin, Dublin, Ireland
| | - Rose Anne Kenny
- TILDA (The Irish Longitudinal Study on Ageing), Lincoln Gate, Trinity College Dublin, Dublin, Ireland
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11
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Nowrangi MA, Okonkwo O, Lyketsos C, Oishi K, Mori S, Albert M, Mielke MM. Atlas-based diffusion tensor imaging correlates of executive function. J Alzheimers Dis 2015; 44:585-98. [PMID: 25318544 DOI: 10.3233/jad-141937] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Impairment in executive function (EF) is commonly found in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Atlas-based diffusion tensor imaging (DTI) methods may be useful in relating regional integrity to EF measures in MCI and AD. Sixty-six participants (25 normal controls, 22 MCI, and 19 AD) received DTI scans and clinical evaluation. DTI scans were applied to a pre-segmented atlas and fractional anisotropy (FA) and mean diffusivity (MD) were calculated. ANOVA was used to assess group differences in frontal, parietal, and cerebellar regions. For regions differing between groups (p < 0.01), linear regression examined the relationship between EF scores and regional FA and MD. Anisotropy and diffusivity in frontal and parietal lobe white matter structures were associated with EF scores in MCI and only frontal lobe structures in AD. EF was more strongly associated with FA than MD. The relationship between EF and anisotropy and diffusivity was strongest in MCI. These results suggest that regional white matter integrity is compromised in MCI and AD and that FA may be a better correlate of EF than MD.
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Affiliation(s)
- Milap A Nowrangi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Ozioma Okonkwo
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Constantine Lyketsos
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Kenichi Oishi
- Department of Radiology Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Susumu Mori
- Department of Radiology Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Michelle M Mielke
- Department of Health Sciences Research, Division of Epidemiology and Department of Neurology, Mayo Clinic, Rochester, MN, USA
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Green RC, Harvey D, Jack CR, Jagust W, Luthman J, Morris JC, Petersen RC, Saykin AJ, Shaw L, Shen L, Schwarz A, Toga AW, Trojanowski JQ. 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimers Dement 2015; 11:e1-120. [PMID: 26073027 PMCID: PMC5469297 DOI: 10.1016/j.jalz.2014.11.001] [Citation(s) in RCA: 203] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/18/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jesse Cedarbaum
- Neurology Early Clinical Development, Biogen Idec, Cambridge, MA, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Johan Luthman
- Neuroscience Clinical Development, Neuroscience & General Medicine Product Creation Unit, Eisai Inc., Philadelphia, PA, USA
| | - John C Morris
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adam Schwarz
- Tailored Therapeutics, Eli Lilly and Company, Indianapolis, IN, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Mak E, Su L, Williams GB, Watson R, Firbank M, Blamire AM, O'Brien JT. Longitudinal assessment of global and regional atrophy rates in Alzheimer's disease and dementia with Lewy bodies. NEUROIMAGE-CLINICAL 2015; 7:456-62. [PMID: 25685712 PMCID: PMC4325088 DOI: 10.1016/j.nicl.2015.01.017] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Revised: 01/13/2015] [Accepted: 01/30/2015] [Indexed: 11/29/2022]
Abstract
BACKGROUND & OBJECTIVE Percent whole brain volume change (PBVC) measured from serial MRI scans is widely accepted as a sensitive marker of disease progression in Alzheimer's disease (AD). However, the utility of PBVC in the differential diagnosis of dementia remains to be established. We compared PBVC in AD and dementia with Lewy bodies (DLB), and investigated associations with clinical measures. METHODS 72 participants (14 DLBs, 25 ADs, and 33 healthy controls (HCs)) underwent clinical assessment and 3 Tesla T1-weighted MRI at baseline and repeated at 12 months. We used FSL-SIENA to estimate PBVC for each subject. Voxelwise analyses and ANCOVA compared PBVC between DLB and AD, while correlational tests examined associations of PBVC with clinical measures. RESULTS AD had significantly greater atrophy over 1 year (1.8%) compared to DLB (1.0%; p = 0.01) and HC (0.9%; p < 0.01) in widespread regions of the brain including periventricular areas. PBVC was not significantly different between DLB and HC (p = 0.95). There were no differences in cognitive decline between DLB and AD. In the combined dementia group (AD and DLB), younger age was associated with higher atrophy rates (r = 0.49, p < 0.01). CONCLUSIONS AD showed a faster rate of global brain atrophy compared to DLB, which had similar rates of atrophy to HC. Among dementia subjects, younger age was associated with accelerated atrophy, reflecting more aggressive disease in younger people. PBVC could aid in differentiating between DLB and AD, however its utility as an outcome marker in DLB is limited.
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Affiliation(s)
- Elijah Mak
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Rosie Watson
- Department of Aged Care, The Royal Melbourne Hospital, Melbourne, Australia ; Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle, UK
| | - Michael Firbank
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle, UK
| | - Andrew M Blamire
- Institute of Cellular Medicine & Newcastle Magnetic Resonance Centre, Newcastle University, Newcastle, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK
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Clark LR, Nation DA, Wierenga CE, Bangen KJ, Dev SI, Shin DD, Delano-Wood L, Liu TT, Rissman RA, Bondi MW. Elevated cerebrovascular resistance index is associated with cognitive dysfunction in the very-old. ALZHEIMERS RESEARCH & THERAPY 2015; 7:3. [PMID: 27391477 PMCID: PMC4942967 DOI: 10.1186/s13195-014-0080-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Accepted: 10/29/2014] [Indexed: 11/10/2022]
Abstract
INTRODUCTION Age-related vascular changes, including blood pressure elevation and cerebral blood flow (CBF) reduction, are associated with cognitive decline and Alzheimer's disease (AD). Evidence suggests that the relationship between blood pressure and dementia risk varies between younger and older samples within the elderly population. METHODS We examined the relationship between mean arterial pressure (MAP), CBF, and cognition in young-old (60 to 75 years of age) versus very-old (80+ years of age) adults. Fifty-eight non-demented older adults completed an arterial spin labeling MRI scan, and an index of cerebrovascular resistance (CVRi) was estimated for each participant by calculating the ratio of MAP and CBF. RESULTS Results demonstrated a similar negative relationship between MAP and CBF across both age groups. However, very-old participants exhibited elevated CVRi and reduced CBF compared to young-old participants in regions implicated in AD and cerebral small vessel disease. Furthermore, significant age by CVRi interactions revealed that elevated CVRi in the thalamus was inversely related to verbal fluency performance in the very-old group. CONCLUSIONS Findings support CVRi as a potential vascular biomarker and suggest that regionally-specific vascular changes may contribute to cognitive decline, particularly in the very-old.
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Affiliation(s)
- Lindsay R Clark
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA. .,Wisconsin Alzheimer's Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
| | - Daniel A Nation
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Christina E Wierenga
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA.,Department of Veterans Affairs, San Diego Healthcare System, San Diego, CA, USA
| | - Katherine J Bangen
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Sheena I Dev
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - David D Shin
- Center for Functional MRI, University of California San Diego, San Diego, CA, USA
| | - Lisa Delano-Wood
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA.,Department of Veterans Affairs, San Diego Healthcare System, San Diego, CA, USA
| | - Thomas T Liu
- Center for Functional MRI, University of California San Diego, San Diego, CA, USA
| | - Robert A Rissman
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
| | - Mark W Bondi
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA. .,Department of Veterans Affairs, San Diego Healthcare System, San Diego, CA, USA.
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15
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Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, DeKosky ST, Gauthier S, Selkoe D, Bateman R, Cappa S, Crutch S, Engelborghs S, Frisoni GB, Fox NC, Galasko D, Habert MO, Jicha GA, Nordberg A, Pasquier F, Rabinovici G, Robert P, Rowe C, Salloway S, Sarazin M, Epelbaum S, de Souza LC, Vellas B, Visser PJ, Schneider L, Stern Y, Scheltens P, Cummings JL. Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. Lancet Neurol 2014; 13:614-29. [PMID: 24849862 DOI: 10.1016/s1474-4422(14)70090-0] [Citation(s) in RCA: 2188] [Impact Index Per Article: 218.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
In the past 8 years, both the International Working Group (IWG) and the US National Institute on Aging-Alzheimer's Association have contributed criteria for the diagnosis of Alzheimer's disease (AD) that better define clinical phenotypes and integrate biomarkers into the diagnostic process, covering the full staging of the disease. This Position Paper considers the strengths and limitations of the IWG research diagnostic criteria and proposes advances to improve the diagnostic framework. On the basis of these refinements, the diagnosis of AD can be simplified, requiring the presence of an appropriate clinical AD phenotype (typical or atypical) and a pathophysiological biomarker consistent with the presence of Alzheimer's pathology. We propose that downstream topographical biomarkers of the disease, such as volumetric MRI and fluorodeoxyglucose PET, might better serve in the measurement and monitoring of the course of disease. This paper also elaborates on the specific diagnostic criteria for atypical forms of AD, for mixed AD, and for the preclinical states of AD.
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Affiliation(s)
- Bruno Dubois
- Centre des Maladies Cognitives et Comportementales, Institut du Cerveau et de la Moelle épinière, Paris, France; Université Pierre et Marie Curie-Paris 6, AP-HP, Hôpital de la Salpêtrière, Paris, France.
| | - Howard H Feldman
- Division of Neurology, University of British Columbia and Vancouver Coastal Health, Vancouver, BC, Canada
| | - Claudia Jacova
- UBC Division of Neurology, S152 UBC Hospital, BC, Canada
| | - Harald Hampel
- Centre des Maladies Cognitives et Comportementales, Institut du Cerveau et de la Moelle épinière, Paris, France; Université Pierre et Marie Curie-Paris 6, AP-HP, Hôpital de la Salpêtrière, Paris, France
| | - José Luis Molinuevo
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, IDIBAPS Hospital Clinici Universitari, Barcelona, Spain; BarcelonaBeta Brain Research Centre, Fundació Pasqual Maragall, Barcelona, Spain
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Steven T DeKosky
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
| | - Serge Gauthier
- McGill Center for Studies in Aging, Douglas Hospital, Montreal, Quebec, QC, Canada
| | - Dennis Selkoe
- Harvard Medical School Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
| | - Randall Bateman
- Washington University School of Medicine, St Louis, Missouri, MO, USA
| | - Stefano Cappa
- Vita-Salute San Raffaele University, Milan, Italy; Department of Clinical Neurosciences, Cognitive Neurorehabilitation, Milan, Italy
| | - Sebastian Crutch
- Dementia Research Centre, Department of Neurodegeneration, Institute of Neurology, University College London, London, UK; Dementia Research Centre, National Hospital, London, UK
| | - Sebastiaan Engelborghs
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA), Middelheim and Hoge Beuken, Antwerp, Belgium; Reference Centre for Biological Markers of Dementia, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Giovanni B Frisoni
- Hopitaux Universitaires et Université de Genève, Geneva, Switzerland; IRCCS Fatebenefratelli, Brescia, Italy; HUG Belle-Idée, bâtiment les Voirons, Chêne-Bourg, France
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegeneration, Institute of Neurology, University College London, London, UK
| | - Douglas Galasko
- Department of Neurosciences, -University of California, San Diego, CA, USA
| | - Marie-Odile Habert
- INSERM UMR, Paris, France; AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de Médecine Nucléaire, Paris, France
| | - Gregory A Jicha
- University of Kentucky Alzheimer's Disease Center, Lexington, KY, USA
| | - Agneta Nordberg
- Karolinska Institutet, Karolinska University Hospital Huddinge, Alzheimer Neurobiology Center, Stockholm, Sweden
| | - Florence Pasquier
- Université Lille Nord de France, Lille, France; CHRU, Clinique Neurologique, Hôpital Roger Salengro, Lille, France
| | - Gil Rabinovici
- UCSF Memory & Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Philippe Robert
- EA CoBTeK and Memory Center, CHU University of Nice, UNSA, Hôpital de Cimiez 4 av Victoria, Nice, France
| | - Christopher Rowe
- FRACP, Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, Melbourne, VIC, Australia
| | - Stephen Salloway
- Neurology and the Memory and Aging Program, Butler Hospital, Department of Neurology and Psychiatry, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Marie Sarazin
- Neurologie de la Mémoire et du Langage, Centre Hospitalier Sainte-Anne, Paris Cedex, France; Université Paris 5, Paris, France
| | - Stéphane Epelbaum
- Centre des Maladies Cognitives et Comportementales, Institut du Cerveau et de la Moelle épinière, Paris, France; Université Pierre et Marie Curie-Paris 6, AP-HP, Hôpital de la Salpêtrière, Paris, France
| | - Leonardo C de Souza
- Centre des Maladies Cognitives et Comportementales, Institut du Cerveau et de la Moelle épinière, Paris, France; Université Pierre et Marie Curie-Paris 6, AP-HP, Hôpital de la Salpêtrière, Paris, France; Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Bruno Vellas
- Gerontopole, Pavillon Junod, University Toulouse 3, Toulouse, France
| | - Pieter J Visser
- Department of Psychiatry and Neuropsychology, Alzheimer Centre Limburg, School of Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, Netherlands; Department of Neurology and Alzheimer Center, Amsterdam, Netherlands
| | - Lon Schneider
- Department of Psychiatry, Neurology, and Gerontology, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Yaakov Stern
- Cognitive Neuroscience Division of the Taub Institute, Presbyterian Hospital, New York, NY, USA
| | - Philip Scheltens
- Alzheimer Centrum Vrije Universiteit Medical Center, VU University, Amsterdam, Netherlands
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Bangen KJ, Nation DA, Clark LR, Harmell AL, Wierenga CE, Dev SI, Delano-Wood L, Zlatar ZZ, Salmon DP, Liu TT, Bondi MW. Interactive effects of vascular risk burden and advanced age on cerebral blood flow. Front Aging Neurosci 2014; 6:159. [PMID: 25071567 PMCID: PMC4083452 DOI: 10.3389/fnagi.2014.00159] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2014] [Accepted: 06/19/2014] [Indexed: 01/24/2023] Open
Abstract
Vascular risk factors and cerebral blood flow (CBF) reduction have been linked to increased risk of cognitive impairment and Alzheimer's disease (AD); however the possible moderating effects of age and vascular risk burden on CBF in late life remain understudied. We examined the relationships among elevated vascular risk burden, age, CBF, and cognition. Seventy-one non-demented older adults completed an arterial spin labeling MR scan, neuropsychological assessment, and medical history interview. Relationships among vascular risk burden, age, and CBF were examined in a priori regions of interest (ROIs) previously implicated in aging and AD. Interaction effects indicated that, among older adults with elevated vascular risk burden (i.e., multiple vascular risk factors), advancing age was significantly associated with reduced cortical CBF whereas there was no such relationship for those with low vascular risk burden (i.e., no or one vascular risk factor). This pattern was observed in cortical ROIs including medial temporal (hippocampus, parahippocampal gyrus, uncus), inferior parietal (supramarginal gyrus, inferior parietal lobule, angular gyrus), and frontal (anterior cingulate, middle frontal gyrus, medial frontal gyrus) cortices. Furthermore, among those with elevated vascular risk, reduced CBF was associated with poorer cognitive performance. Such findings suggest that older adults with elevated vascular risk burden may be particularly vulnerable to cognitive change as a function of CBF reductions. Findings support the use of CBF as a potential biomarker in preclinical AD and suggest that vascular risk burden and regionally-specific CBF changes may contribute to differential age-related cognitive declines.
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Affiliation(s)
- Katherine J Bangen
- Psychology Service, VA San Diego Healthcare System San Diego, CA, USA ; Department of Psychiatry, University of California, San Diego La Jolla, CA, USA
| | - Daniel A Nation
- Department of Psychology, University of Southern California Los Angeles, CA, USA
| | - Lindsay R Clark
- San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, CA, USA
| | - Alexandrea L Harmell
- San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, CA, USA
| | - Christina E Wierenga
- Department of Psychiatry, University of California, San Diego La Jolla, CA, USA ; Research Service, VA San Diego Healthcare System San Diego, CA, USA
| | - Sheena I Dev
- San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, CA, USA
| | - Lisa Delano-Wood
- Department of Psychiatry, University of California, San Diego La Jolla, CA, USA ; Research Service, VA San Diego Healthcare System San Diego, CA, USA
| | - Zvinka Z Zlatar
- Department of Psychiatry, University of California, San Diego La Jolla, CA, USA
| | - David P Salmon
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Thomas T Liu
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Mark W Bondi
- Psychology Service, VA San Diego Healthcare System San Diego, CA, USA ; Department of Psychiatry, University of California, San Diego La Jolla, CA, USA
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17
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Chang YL, Fennema-Notestine C, Holland D, McEvoy LK, Stricker NH, Salmon DP, Dale AM, Bondi MW. APOE interacts with age to modify rate of decline in cognitive and brain changes in Alzheimer's disease. Alzheimers Dement 2014; 10:336-48. [PMID: 23896613 PMCID: PMC3815680 DOI: 10.1016/j.jalz.2013.05.1763] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2012] [Revised: 03/29/2013] [Accepted: 05/02/2013] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To determine (1) whether age-standardized cognitive declines and brain morphometric change differ between Young-Old patients with Alzheimer's disease (YOAD) and Very-Old patients with Alzheimer's disease (VOAD), and (2) whether the apolipoprotein E (APOE) genotype modifies these neuropsychological and morphometric changes. METHODS Baseline and 12-month follow-up neuropsychological and morphometric measures were examined for healthy control subjects and patients with AD. The two AD groups were divided further into subgroups on the basis of the presence of at least one APOE ε4 allele. RESULTS The YOAD group showed more severe deficits and steeper declines in cognition than the VOAD group. Moreover, the presence of an APOE ε4 allele had a more deleterious effect on the YOAD group than the VOAD group on cognition and brain structure both cross-sectionally and longitudinally. CONCLUSIONS Results underscore the importance of integrating an individual's age and genetic susceptibility--and their interaction--when examining neuropsychological and neuroimaging changes in the early stages of Alzheimer's disease.
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Affiliation(s)
- Yu-Ling Chang
- Department of Psychology, National Taiwan University, Taipei, Taiwan
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California at San Diego, San Diego, CA, USA; Department of Radiology, University of California at San Diego, San Diego, CA, USA
| | - Dominic Holland
- Department of Neurosciences, University of California at San Diego, San Diego, CA, USA
| | - Linda K McEvoy
- Department of Radiology, University of California at San Diego, San Diego, CA, USA
| | - Nikki H Stricker
- Veterans Affairs Boston Healthcare System, Boston, MA, USA; Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - David P Salmon
- Department of Neurosciences, University of California at San Diego, San Diego, CA, USA
| | - Anders M Dale
- Department of Radiology, University of California at San Diego, San Diego, CA, USA; Department of Neurosciences, University of California at San Diego, San Diego, CA, USA
| | - Mark W Bondi
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California at San Diego, San Diego, CA, USA.
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Nowrangi MA, Lyketsos C, Rao V, Munro CA. Systematic review of neuroimaging correlates of executive functioning: converging evidence from different clinical populations. J Neuropsychiatry Clin Neurosci 2014; 26:114-25. [PMID: 24763759 PMCID: PMC5171230 DOI: 10.1176/appi.neuropsych.12070176] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Executive functioning (EF) is an important cognitive domain that is negatively affected in a number of neuropsychiatric conditions. Neuroimaging methods have led to insights into the anatomical and functional nature of EF. The authors conducted a systematic review of the recent cognitive and neuroimaging literature to investigate how the neuroimaging correlates of EF compare between different diagnostic groups. The authors found that the frontal, parietal, and cerebellar lobes were most frequently associated with EF when comparing results from different clinical populations; the occipital lobe was not correlated with EF in any group. These findings suggest that individual disease processes affect circuits within an identifiable distributed network rather than isolated regions.
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19
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Gawron N, Łojek E, Kijanowska-Haładyna B, Nestorowicz J, Harasim A, Pluta A, Sobańska M. Cognitive patterns of normal elderly subjects are consistent with frontal cortico-subcortical and fronto-parietal neuropsychological models of brain aging. APPLIED NEUROPSYCHOLOGY-ADULT 2013; 21:195-209. [PMID: 25084844 DOI: 10.1080/09084282.2013.789965] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Three neuropsychological theories have been developed according to a possible existence of a similar pattern of cognitive decline in elderly individuals and patients with brain damage. The respective neuropsychological theories attribute age-related deficits to: (a) dysfunction of the frontal lobes, (b) temporo-parietal dysfunction, or (c) decline of right-hemisphere functions. In the present study, we examined which of these theories best explains the cognitive patterns of normal elderly subjects older than 80 years of age (old elderly). Thirty normal old elderly subjects, 14 patients with subcortical vascular dementia, 14 with mild Alzheimer's disease, 15 with damage of the right hemisphere of the brain, and 20 young elderly controls participated. A test battery covering the main cognitive domains was administered to all participants. A hierarchical cluster analysis revealed five groups of individuals with different cognitive patterns across the whole sample. Old elderly subjects were assigned to four groups according to: (a) preserved overall cognitive performance, (b) processing speed decline, (c) attention decline, or (d) executive impairment. The results of the study are most congruent with models emphasizing frontal-lobe cortical-subcortical and fronto-parietal changes in old age. The results also indicate considerable heterogeneity in the cognitive patterns of normal old elderly adults.
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Affiliation(s)
- Natalia Gawron
- a Faculty of Psychology , University of Warsaw , Warsaw , Poland
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20
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Liu E, Morris JC, Petersen RC, Saykin AJ, Schmidt ME, Shaw L, Shen L, Siuciak JA, Soares H, Toga AW, Trojanowski JQ. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement 2013; 9:e111-94. [PMID: 23932184 DOI: 10.1016/j.jalz.2013.05.1769] [Citation(s) in RCA: 298] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/19/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA.
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Nation DA, Delano-Wood L, Bangen KJ, Wierenga CE, Jak AJ, Hansen LA, Galasko DR, Salmon DP, Bondi MW. Antemortem pulse pressure elevation predicts cerebrovascular disease in autopsy-confirmed Alzheimer's disease. J Alzheimers Dis 2012; 30:595-603. [PMID: 22451309 DOI: 10.3233/jad-2012-111697] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Elevated pulse pressure (PP) is associated with cognitive decline and increased risk of Alzheimer's disease (AD) in older adults, although the mechanisms behind these associations remain unclear. To address this question, we examined whether antemortem late-life PP elevation predicted vascular or AD pathology in autopsy-confirmed AD patients. Sixty-five elderly patients (mean age 74.2 years) clinically diagnosed with possible or probable AD underwent neuropsychological testing and blood pressure examinations. Postmortem histopathological measures of cerebrovascular disease (CVD) and AD neuropathology were later obtained on these same patients. We expected that antemortem PP elevation, but not standard blood pressure measures such as systolic or diastolic blood pressure, would predict the autopsy-based presence of CVD, and possibly AD pathology, in elderly AD patients. Results demonstrated that antemortem PP elevation was associated with the presence and severity of CVD at autopsy. For every 5 mmHg increase in antemortem PP there was an estimated 36% increase in the odds of having CVD at autopsy. Additionally, PP accounted for 12% of variance in CVD severity. No significant associations were present for cerebral amyloid angiopathy or Braak and Braak staging of the severity of AD pathology. Other standard blood pressure measures also did not significantly predict neuropathology. The association between antemortem PP and CVD at autopsy suggests that in older adults with AD, PP elevation may increase the risk of CVD. These findings may have treatment implications since some antihypertensive medications specifically address the pulsatile component of blood pressure (e.g., renin-angiotensin system inhibitors, calcium channel blockers).
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Affiliation(s)
- Daniel A Nation
- Veterans Affairs San Diego Healthcare System, San Diego, CA 92161, USA
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Rates of decline in Alzheimer disease decrease with age. PLoS One 2012; 7:e42325. [PMID: 22876315 PMCID: PMC3410919 DOI: 10.1371/journal.pone.0042325] [Citation(s) in RCA: 77] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2012] [Accepted: 07/05/2012] [Indexed: 12/22/2022] Open
Abstract
Age is the strongest risk factor for sporadic Alzheimer disease (AD), yet the effects of age on rates of clinical decline and brain atrophy in AD have been largely unexplored. Here, we examined longitudinal rates of change as a function of baseline age for measures of clinical decline and structural MRI-based regional brain atrophy, in cohorts of AD, mild cognitive impairment (MCI), and cognitively healthy (HC) individuals aged 65 to 90 years (total n = 723). The effect of age was modeled using mixed effects linear regression. There was pronounced reduction in rates of clinical decline and atrophy with age for AD and MCI individuals, whereas HCs showed increased rates of clinical decline and atrophy with age. This resulted in convergence in rates of change for HCs and patients with advancing age for several measures. Baseline cerebrospinal fluid densities of AD-relevant proteins, Aβ1–42, tau, and phospho-tau181p (ptau), showed a similar pattern of convergence with advanced age across cohorts, particularly for ptau. In contrast, baseline clinical measures did not differ by age, indicating uniformity of clinical severity at baseline. These results imply that the phenotypic expression of AD is relatively mild in individuals older than approximately 85 years, and this may affect the ability to distinguish AD from normal aging in the very old. Our findings show that inclusion of older individuals in clinical trials will substantially reduce the power to detect disease-modifying therapeutic effects, leading to dramatic increases in required clinical trial sample sizes with age of study sample.
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Abstract
The recently published revised National Institute on Aging/Alzheimer's Association clinical diagnostic criteria for Alzheimer's disease (AD) (Albert et al., 2011; Jack et al., 2011; McKhann et al., 2011; Sperling et al., 2011) have been hailed for incorporating a number of timely and important advances. They reflect new understanding that has been gained since the previous criteria were published in 1984 (McKhann et al., 1984). They include recognition of the state of mild cognitive impairment that is present before the threshold is crossed into dementia; they recognize the potential role of biomarkers in enhancing the specificity of diagnosis; they also address emerging work in the preclinical stage of AD that could help in understanding the sequence and stages of the core pathology before symptoms emerge. Among the previously listed diagnostic features that have disappeared was the requirement that onset of dementia occur before the age of 90 years. Meanwhile, the Neurocognitive Disorders Work Group for DSM-5 (the 5th edition of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders; American Psychiatric Association, 2010) is also doing away with the previous distinction between early-onset and late-onset dementia in AD, where an arbitrary division had been placed at age 65 (American Psychiatric Association, 2000). These changes are driven by the lack of biological data to support the age-based dichotomy, while recognizing the unique genetic characteristics of the relatively rare, autosomal dominantly inherited forms of AD which typically occur early. However, the disappearance of the age-based diagnostic dichotomy by no means implies that age is irrelevant to AD.
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