Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods.

ABSTRACT: Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden.

SUBMITTER: Eavani H 

PROVIDER: S-EPMC6162110 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature