Predicting cognitive aging through brain structural covariance networks: A decade of longitudinal insights using source-based morphometry

Cognitive aging presents significant challenges to public health as the global population ages. While functional connectivity changes in aging have been extensively studied, the predictive value of structural covariance networks remains understudied. This longitudinal study investigated whether baseline structural covariance networks could predict cognitive decline over a 10-year period using Source-Based Morphometry (SBM). Thirty-seven participants (23 males; mean age 54.97 ± 1.14 years) underwent structural magnetic resonance imaging (T3) and cognitive assessments at baseline (T3) and follow-up (T4). SBM analysis identified twelve independent components (ICs) representing distinct structural covariance networks. After controlling for demographics and APOE genotype, IC1 strongly predicted working memory (β = -3.12, p< 0.001), while IC2 predicted global cognitive function (β = 0.37, p= 0.047). Brain-cognition relationships were significantly moderated by baseline cognitive performance, with key interactions observed for working memory and IC1 (β = 0.50, p< 0.001), executive function and IC7 (β = -0.25, p< 0.001), and processing speed and IC8 (β = 0.28, p= 0.003). Sex-specific effects emerged for IC8 in relation to verbal memory (β = 1.99, p= 0.007) and IC10 in relation to processing speed (β = 2.17, p= 0.022). APOE genotype demonstrated pronounced moderation effects between IC8 and processing speed (β = -7.68, p< 0.001) and for IC2 and global cognitive function (β = 0.37, p= 0.018). These findings demonstrate that structural covariance networks can serve as predictive markers for cognitive aging trajectories, potentially informing early intervention strategies for preserving cognitive health.