Clustering of fiber tracts and quantitative analysis of white matter fiber bundles in the human brain

The human brain as the neural processing center consists of a highly complex microstructure that is primarily composed of interconnected nerve cells. While the nerve cells are arranged in functional units, dedicated to perform certain unique tasks (e.g., visual perception, motor control or speech), their axons form a complex network that interconnects nerve cells of the same area and enables long-range signal propagation between different functional units. With the advent of magnetic resonance imaging and, in particular, the introduction of diffusion weighted imaging researchers began to study the fiber network of the human brain, pursuing the goal to unravel its inherent complexity non-invasively. The employment of tractography facilitated this process and enabled the approximation of the structural organization of the fiber network with three-dimensional fiber tracts, which opened the unique opportunity to study the course and the integrity of the microstructure in unprecedented ways. For many medical applications, the automatic parcellation of fiber tracts into bundles that represent the underlying microstructure is of extraordinary importance, but often too time consuming and too complicated, thus limiting the usability of fiber tractography. In order to improve the applicability of fiber tractography, this thesis introduces novel concepts and strategies towards automatic parcellation of fiber tracts by using cluster analysis. With CATSER (cluster analysis through smartly extracted representatives), a new method for the automated clustering of fiber tracts is presented that exploits the intrinsic redundancy of the data to make cluster analysis applicable to large tractography datasets. In order to improve the grouping of tracts into bundles that represent the underlying microstructure more correctly, CATSER can be used together with a white matter atlas. By using parallel computing and random sampling as well as novel similarity measures, the cluster analysis of fiber tracts can be performed in reasonable time. To study properties of the introduced cluster analysis framework and to demonstrate its performance in a multiprocessing environment, various experiments were conducted. The fast and consistent extraction of fiber bundles with CATSER aids amultitude ofmedical applications. Group-based analyses methods that are used to study altered diffusion between groups of subjects (e.g., healthy volunteers vs. patients) or between subgroups of patients are particularly suited candidates to benefit from the additional anatomical information of fiber bundles. For this reason, this thesis introduces a novel fiber bundle-driven technique that takes advantage of fiber bundles to enhance quantitative group-based analyses.This new technique enables selective, voxel-wise analysis of local diffusivity properties for individual bundles and restricts the analysis to voxels that belong to the bundle of interest. Contributions of other regions or bundles that are not part of the analyzed bundle are thereby suppressed. To investigate the applicability of these new methods, diffusion-associated hemispheric differences were studied in selected fiber bundles of healthy volunteers and schizophrenic patients.