Fiber Microstructure Quantile (FMQ) Regression: A Novel Statistical Approach for Analyzing White Matter Bundles from Periphery to Core

Motivation: The study aims to address the limitations of traditional diffusion MRI analyses, which overlook regional microstructural variations within white matter tracts, limiting our understanding of their role in behavior. Goal(s): Develop and apply Fiber Microstructure Quantile (FMQ) Regression to investigate region-specific associations between white matter microstructure and cognitive/motor performance. Approach: FMQ Regression was used on dMRI data from 809 participants to analyze four white matter tracts. The method was compared with FA Mean and AFQ Regression approaches. Results: FMQ Regression revealed associations undetected by conventional methods, such as peripheral arcuate fasciculus correlating with language performance. Impact: This study provides a novel perspective on white matter microstructure and its functional implications, which can guide future research on brain development and interventions for neurological disorders.