As the central relay station of the human brain, the thalamus modulates sensory signals to and from the cerebral cortex. The reciprocal connectivity between the cerebral cortex and the thalamus is believed to play an essential role in consciousness and various neurological disorders. Thus, in-vivo analysis of thalamo-cortical connectivity is important for our understanding of normal and pathological brain processes. In this paper: We propose a new partitioning paradigm, called coclustering, in order to segment the thalamus into thalamic nuclei based on their cortical projections. In contrast to the traditional clustering paradigm, a coclustering procedure not only simultaneously partitions cortical voxels and thalamic voxels into groups, but also identifies the corresponding strong connectivities between the two classes of groups. We develop the first coclustering algorithm, Genetic Coclustering Algorithm (GCA), to solve the coclustering problem. We apply GCA to segment the thalamus into thalamic nuclei and visualise main thalamo-cortical fibre tracts.

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Abstract-Reliable shape modeling and clustering of white matter fiber tracts is essential for clinical and anatomical studies that use diffusion tensor imaging (DTI) tractography techniques. In this work we present a novel scheme to model the shape of white matter fiber tracts reconstructed from DTI and cluster them into bundles using Fourier descriptors. We characterize a tract's shape by using Fourier descriptors which are effective in capturing shape properties of fiber tracts. Fourier descriptors derived from different shape signatures are analyzed. Clustering is then performed on these multidimensional features in conjunction with mass centers using a k-means like threshold based approach. The advantage of this method lies in the fact that Fourier descriptors achieve spatial independent representation and normalization of white matter fiber tracts which makes it useful for tract comparison across subjects. It also eliminates the need to find matching correspondences between two randomly organized tracts from whole brain tracking. Several issues related to tract shape representation and normalization are also discussed. Real DTI datasets are used to test this technique. Experiment results show that this technique can effectively separate multiple fascicles into plausible bundles.

The reciprocal connectivity between the cerebral cortex and the thalamus in a human brain is involved in consciousness and related to various brain disorders, thus, in-vivo

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