GCA: A Coclustering Algorithm for Thalamo-Cortico-Thalamic Connectivity Analysis

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 analysis of this connectivity is critically important for brain diagnosis and surgery planning. While existing work either focuses on fiber tracking analysis or on thalamic nuclei segmentation, to our best knowledge, no techniques yet exist for performing in-vivo analysis of thalamo-cortico-thalamic connectivity. In this paper, (i) we propose a new partitioning paradigm, called coclustering, to model this problem. 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; (ii) we develop the first coclustering algorithm, genetic coclustering algorithm (GCA), to solve the coclustering problem; and (iii) we apply GCA to perform in-vivo analysis of the thalamo-cortico-thalamic connectivity and produce a strikingly clear 3D visualization of the seven thalamic nuclei groups as well as their connectivities to the corresponding cortical regions of a human brain