ESTIMATING FIBER ORIENTATION DISTRIBUTION WITH APPLICATION TO STUDY BRAIN LATERALIZATION USING HCP D-MRI DATA

• 成果类型: Article
• 署名作者: Hwang, Seungyong; Lee, Thomas C. M.; Paul, Debashis; Peng, Jie
• 署名单位: University of California System; University of California Davis
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1781
• 发表日期: 2024
• 页码: 100-124
• 关键词: Adaptive Estimation; spatial statistics; diffusion mri; in-vivo; robust; segmentation; optimization; handedness; asymmetry; MODEL

摘要

Diffusion -weighted magnetic resonance imaging (D-MRI) is an in vivo and noninvasive imaging technology for characterizing tissue microstructure in biological samples. A major application of D-MRI is for white matter fiber tract reconstruction in brains. It begins by estimating the water molecule movements (serving as proxies for fiber directions) in the brain voxels and then combines the results to form fiber tracts. The voxel-level fiber direction information can be modeled by a fiber orientation distribution (FOD) function, and in this paper, we propose a computationally scalable FOD estimator, the blockwise James-Stein (BJS) estimator. We then apply BJS to the D-MRI data from the Human Connectome Project (HCP) to study brain lateralization, an important topic in neuroscience. Specifically, we focus on the association between lateralization of the superior longitudinal fasciculus (SLF)-a major association tract and handedness. For each subject from the HCP data, we extract voxel-level directional information by BJS and then reconstruct the SLF in each brain hemisphere through a tractography algorithm. Finally, we derive a lateralization score that quantifies hemispheric asymmetry of the reconstructed SLF. We then relate this lateralization score to gender and handedness through an ANOVA model, where significant handedness effects are found. The results indicate that the SLF lateralization is likely to be different in right-handed and left-handed individuals. Codes and example scripts for both synthetic experiments and HCP data application can be found at https://github.com/vic-dragon/BJS.