Abstract
Introduction: 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). Methods: 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. Results: 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. Discussion: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals’ brain-aging patterns relative to this large consortium.
Original language | English (US) |
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Pages (from-to) | 89-102 |
Number of pages | 14 |
Journal | Alzheimer's and Dementia |
Volume | 17 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2021 |
Keywords
- Alzheimer's disease pathology
- Dementia
- MRI
- Machine Learning
- Neuroimaging
- PET
- beta-amyloid
- brain aging
- brain signatures
- cognitive testing
- harmonized neuroimaging cohorts
- preclinical Alzheimer's disease
- small vessel ischemic disease
- tau
ASJC Scopus subject areas
- Epidemiology
- Health Policy
- Developmental Neuroscience
- Clinical Neurology
- Geriatrics and Gerontology
- Cellular and Molecular Neuroscience
- Psychiatry and Mental health