The Brain Chart of Aging: Machine‐learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans
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- Mohamad Habes
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Raymond Pomponio
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Haochang Shou
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Jimit Doshi
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Guray Erus
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Ilya Nasrallah
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Lenore J. Launer
- Laboratory of Epidemiology and Population Sciences National Institute on Aging Bethesda Maryland USA
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- Tanweer Rashid
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Murat Bilgel
- Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore Maryland USA
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- Yong Fan
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Jon B. Toledo
- Department of Pathology and Laboratory Medicine Institute on Aging Center for Neurodegenerative Disease Research University of Pennsylvania School of Medicine Philadelphia Pennsylvania USA
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- Kristine Yaffe
- Departments of Neurology Psychiatry and Epidemiology and Biostatistics University of California San Francisco San Francisco California USA
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- Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Mark Espeland
- Department of Biostatistics and Data Science Wake Forest School of Medicine Winston‐Salem North Carolina USA
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- Colin Masters
- Florey Institute of Neuroscience and Mental Health University of Melbourne Melbourne Australia
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- Paul Maruff
- Florey Institute of Neuroscience and Mental Health University of Melbourne Melbourne Australia
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- Jurgen Fripp
- CSIRO Health and Biosecurity Australian e‐Health Research Centre CSIRO Australia
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- Henry Völzk
- Institute for Community Medicine University of Greifswald Greifswald Germany
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- Sterling C. Johnson
- Wisconsin Alzheimer's Institute University of Wisconsin School of Medicine and Public Health Madison Wisconsin USA
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- John C. Morris
- Department of Neurology Washington University in St. Louis St. Louis Missouri USA
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- Marilyn S. Albert
- Department of Neurology Johns Hopkins University School of Medicine Baltimore Maryland USA
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- Michael I. Miller
- Department of Biomedical Engineering Johns Hopkins University Baltimore Maryland USA
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- R. Nick Bryan
- Department of Diagnostic Medicine University of Texas Austin Texas USA
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- Hans J. Grabe
- Department of Psychiatry and Psychotherapy University of Greifswald Germany
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- Susan M. Resnick
- Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore Maryland USA
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- David A. Wolk
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
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- Christos Davatzikos
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
説明
<jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Three brain signatures were calculated: Brain‐age, AD‐like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>WMHs were associated with advanced brain aging, AD‐like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10‐year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD‐like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>A Brain Chart quantifying brain‐aging trajectories was established, enabling the systematic evaluation of individuals’ brain‐aging patterns relative to this large consortium.</jats:p></jats:sec>
収録刊行物
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- Alzheimer's & Dementia
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Alzheimer's & Dementia 17 (1), 89-102, 2020-09-13
Wiley
