Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics

• Published in: NeuroImage (Orlando, Fla.) Vol. 100; pp. 75 - 90
• Main Authors: Jin, Yan, Shi, Yonggang, Zhan, Liang, Gutman, Boris A., de Zubicaray, Greig I., McMahon, Katie L., Wright, Margaret J., Toga, Arthur W., Thompson, Paul M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.10.2014; Elsevier Limited
• Subjects: Adult; Algorithms; Atlases as Topic; Brain Mapping - methods; Diffusion; Diffusion Tensor Imaging - methods; Female; Fiber clustering; Genetic heritability; Genetics; HARDI; Humans; Label fusion; Male; Methods; Nerve Fibers, Myelinated; Population; Studies; Tractography; White Matter - anatomy & histology; Young Adult; Genetic heritability; Fiber clustering; HARDI; Label fusion; Tractography
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.04.048

To understand factors that affect brain connectivity and integrity, it is beneficial to automatically cluster white matter (WM) fibers into anatomically recognizable tracts. Whole brain tractography, based on diffusion-weighted MRI, generates vast sets of fibers throughout the brain; clustering them into consistent and recognizable bundles can be difficult as there are wide individual variations in the trajectory and shape of WM pathways. Here we introduce a novel automated tract clustering algorithm based on label fusion – a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so our top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. We fuse clustering results from different atlases, using a mean distance fusion scheme. We reliably extracted the major tracts from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins. To compute population statistics, we use a pointwise correspondence method to match, compare, and average WM tracts across subjects. We illustrate our method in a genetic study of white matter tract heritability in twins. •Developed a workflow to extract fiber tracts through whole-brain tractography•Extended the label fusion scheme to fiber clustering•Designed a pointwise fiber matching algorithm to facilitate population studies•Demonstrated a heritability population study with the proposed workflow•Provided a practical tool for future population studies (ex. Alzheimer's disease)