General Microstructure Factor Analysis of Diffusion MRI in Gray-Matter Predicts Cognitive Scores

TL;DR

This study uses diffusion MRI and PCA to identify global gray-matter microstructure factors that predict cognitive performance, extending understanding beyond white matter analysis.

Contribution

It introduces a PCA-based method to derive general gray-matter microstructure factors from NODDI parameters that relate to cognition.

Findings

Isotropic volume fraction factor correlates with reading and vocabulary scores.

Global microstructure factors provide complementary markers to region-specific analyses.

Microstructure factors may serve as exploratory biomarkers for cognition.

Abstract

Diffusion magnetic resonance imaging (MRI) has revealed important insights into white matter microstructure, but its application to gray matter remains comparatively less explored. Here, we investigate whether global patterns of gray-matter microstructure can be captured through neurite orientation dispersion and density imaging (NODDI) and whether such patterns are predictive of cognitive performance. Using diffusion MRI and behavioral data from the Human Connectome Project Young Adult study, we derive region averaged NODDI parameters and apply principal component analysis (PCA) to construct general gray-matter microstructure factors. We find that the factor derived from isotropic volume fraction explained substantial inter-individual variability and was significantly correlated with specific cognitive scores collected from the NIH Toolbox. In particular, the isotropic volume fraction factor is linked to reading and vocabulary performance and to cognitive fluidity. Our findings demonstrate that PCA-based global indicators of gray-matter microstructure provide complementary markers of structure-function relationships, extending beyond region-specific analyses. Our results suggest that general microstructure factors may serve as population-level exploratory biomarkers for studying cognition and cortical organization.