Journal
ALZHEIMERS & DEMENTIA
Volume 17, Issue 1, Pages 89-102Publisher
WILEY
DOI: 10.1002/alz.12178
Keywords
Alzheimer's disease pathology; beta-amyloid; brain aging; brain signatures; cognitive testing; Dementia; harmonized neuroimaging cohorts; Machine Learning; MRI; Neuroimaging; PET; preclinical Alzheimer's disease; small vessel ischemic disease; tau
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The relationship between white matter hyperintensities and various factors such as brain aging, AD neuropathology, cognitive decline, and Aβ positivity after age 65 have been observed in a large MRI database. The Brain Chart established can quantify brain-aging trajectories and provide systematic evaluation of brain-aging patterns relative to a large consortium. Brain aging, AD-like atrophy, and WMHs were shown to be better predictors of cognition than chronological age in MCI/AD subjects.
IntroductionRelationships 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 approximate to 10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (A beta) 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.
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