Details

Autor(en) / Beteiligte

• 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.

Titel

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

Ist Teil von

• NeuroImage (Orlando, Fla.), 2014-10, Vol.100, p.75-90

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2014

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• 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)