Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics - 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. (NeuroImage (Orlando, Fla.), 2014)
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)