Interpretable Local-to-Global Estimation of Brain Aging Speed From Morphological Changes Using Longitudinal Structural MRI Data.

Accurate characterization of brain aging is essential for understanding cognitive decline and assessing the risk of neurodegenerative disease. Brain age estimated from cross-sectional magnetic resonance imaging (MRI) data provides a snapshot of brain health relative to chronological age, and the derived brain age delta has emerged as a promising biomarker. However, brain age delta reflects cumulative effects at a single time point and fails to capture ongoing aging dynamics. Longitudinal approaches address this limitation by estimating age differences between scan pairs to derive aging speed. Nevertheless, existing methods primarily rely on intensity or texture differences between images, overlook the spatial heterogeneity of aging processes, and provide limited interpretability. To overcome these limitations, we propose a novel framework that estimates brain aging speed from longitudinal deformation fields obtained via diffeomorphic image registration. Instead of solely generating a single global estimate, our method produces patch-wise local aging predictions and adaptively integrates them into a unified global prediction, improving both predictive performance and interpretability. Evaluated on large-scale datasets, our approach achieves superior accuracy compared with existing methods and enhances the identification of abnormal aging patterns in diseased populations. Code is available at https://github.com/Kateridge/MorphologyAgingSpeed.