Multi-Metric Quantitative MRI Identifies Spatially Distinct Age-Related Brain Differences in Female Bonnet Macaques

Abstract

This study employs a data-driven, voxelwise analysis of high-resolution ex vivo quantitative MRI (qMRI) to examine age-related differences in brain morphometry and microstructure in female bonnet macaques. A binary classifier differentiated mid- and late-age groups, achieving the highest accuracy when integrating all MRI metrics rather than using diffusion or relaxometry alone. Diffusion-only and relaxometry-only classifiers revealed distinct, minimally overlapping spatial patterns, while the multi-metric approach captured a broader range of age-related differences. Tensor-based morphometry (TBM) differences were most pronounced in the neocortex, whereas the thalamus showed the highest classification accuracy despite minimal morphometric differences, suggesting unique tissue composition alterations. These findings highlight the complementary nature of diffusion, relaxometry, and morphometry qMRI metrics in aging research. Our results support the use of multi-parametric qMRI to identify age-vulnerable brain regions and highlight its potential for improving qMRI biomarkers in larger, longitudinal aging studies.