Abstract #4493

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

Zhou Lan¹, Yuqian Chen¹, Jarrett Rushmore², Leo Zekelman³, Nikos Makris⁴, Yogesh Rathi¹, Alexandra J. Golby¹, Fan Zhang⁵, and Lauren J. O’Donnell¹

¹Brigham and Women's Hospital, Boston, MA, United States, ²Boston University School of Medicine, Boston, MA, United States, ³Harvard Medical School, Boston, MA, United States, ⁴Massachusetts General Hospital, Boston, MA, United States, ⁵University of Electronic Science and Technology of China, Chengdu, China

Synopsis

Keywords: DWI/DTI/DKI, Brain Connectivity

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.

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