Divergent Multimodal Age-Association Profiles Across the Human Brain

Chronological age accounts for much of the variance in structural brain measures, yet aging is spatially distributed, modality-dependent and often nonlinear, features that conventional T1-only brain-age models overlook. We introduce a population-calibrated, multimodal framework in 2,796 adults that integrates gray-matter volume with diffusion-derived metrics to fit linear and quadratic age-association profiles across cortical and subcortical regions. We define the Age of Structural Transition (AST) as the vertex of the preferred trajectory, marking maximal macrostructural or microstructural refinement before age-related reduction. AST maps show early peaks in sensory-motor cortex and later peaks in association networks. Trajectory-based clustering uncovers distributed co-aging networks that group distant regions by shared multimodal trends rather than anatomy. A regionally stacked predictor improves age estimation (mean absolute error 3.45 years) and yields interpretable spatial importance maps. Sliding-window analyses show that brain-age-gap associations with phenotypes are age-dependent, weak in younger adults and amplified later in life. Researchers mapped when and where the adult brain reaches peak structural refinement, finding that macrostructural and microstructural aging unfold on different clocks across distributed brain networks.