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Updated: Feb 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
An MRI-based macro- and microstructural neuroimaging-wide association study of subsequent cognitive impairment
Tugce Duran1, Murat Bilgel1, Yang An1
1Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA.
Introduction:
This study longitudinally examined 154 magnetic resonance imaging (MRI) biomarkers in cognitively normal (CN) individuals to identify structural brain changes most strongly associated with subsequent cognitive impairment (SI).
Methods:
We analyzed 509 Baltimore Longitudinal Study of Aging participants (age ≥ 50, CN at baseline) with longitudinal cognitive and 3T MRI (T1/T2-weighted scans and diffusion tensor imaging) data. MRI biomarker associations with SI status were analyzed, adjusting for confounders and stratifying by sex and amyloid beta (Aβ) status.
Results:
Participants who developed SI over 4.6 years' follow-up showed greater longitudinal changes in white matter (WM) integrity, particularly in the corpus callosum, cingulum bundle, and inferior fronto-occipital fasciculus, compared to CN-stable. To a lesser degree, we also observed temporal thinning and atrophy linked to SI. These associations were amplified among males and amyloid-positive individuals.
Discussion:
Longitudinal alterations in WM microstructural integrity were most strongly associated with future impairment, highlighting the importance of early WM changes in the development of MCI and dementia.
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