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Automated Clustering of White Matter Fibers in Diffusion MRI and Voxelwise Spectral Diffusional Connectivity


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 I propose a novel automated tract clustering algorithm based on label fusion - a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so this top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. Then, I fuse clustering results from different atlases, using a mean distance fusion scheme. To compute population statistics, I develop a point-wise correspondence method to match, compare, and average WM tracts across subjects.

The complete workflow is demonstrated in two large-scale population studies. In one study, the major 17 tracts were extracted from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins to show the 3-D genetic heritability profile for each tract. In the other study, the fornix tracts of 210 participants were segmented from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study its relationship with cognitive decline.

Besides studying the local connectivity with fiber clustering, I also investigate whole brain connectivity from a different perspective. Many connectivity studies parcellate the brain into regions and count fibers extracted between them. The resulting network analyses require validation of the tractography, as well as region and parameter selection. I propose a mathematical formulation based on studying the eigenvalues of the Laplacian matrix of the diffusion tensor field at the voxel level. This voxelwise matrix has over a million parameters, but I derive the Kirchhoff complexity and eigen-spectrum through elegant mathematical theorems, without heavy computation. These novel measures were used to estimate the voxelwise connectivity in multiple biomedical applications such as Alzheimer's disease and intelligence prediction.

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