Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Data mining: concepts and techniques
Data mining: concepts and techniques
Case study: reconstruction, visualization and quantification of neuronal fiber pathways
Proceedings of the conference on Visualization '01
Adaptively Resizing Populations: An Algorithm and Analysis
Proceedings of the 5th International Conference on Genetic Algorithms
GCA: A Coclustering Algorithm for Thalamo-Cortico-Thalamic Connectivity Analysis
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coclustering for cross-subject fiber tract analysis through diffusion tensor imaging
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
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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.