CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
DTI Fiber Clustering in the Whole Brain
VIS '04 Proceedings of the conference on Visualization '04
Cocluster analysis of thalamo-cortical fibre tracts extracted from diffusion tensor MRI
International Journal of Data Mining and Bioinformatics
Coclustering based parcellation of human brain cortex using diffusion tensor MRI
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
White matter tract clustering and correspondence in populations
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Automated atlas-based clustering of white matter fiber tracts from DTMRI
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
| Hi-index | 0.00 |
One of the fundamental goals of computational neuroscience is the study of anatomical features that reflect the functional organization of the brain. The study of physical associations between neuronal structures and the examination of brain activity in vivo have given rise to the concept of anatomical and functional connectivity, which has been invaluable for our understanding of brain mechanisms and their plasticity during development. However, at present, there is no robust and accurate computational framework for the quantitative assessment of cortical connectivity patterns. In this paper, we present a quantitative analysis and modeling tool that is able to characterize anatomical connectivity patterns based on a newly developed coclustering algorithm, termed the business model-based coclustering algorithm (BCA). We apply BCA to diffusion tensor imaging (DTI) data in order to provide an automated and reproducible assessment of the connectivity patterns between different cortical areas in human brains. BCA not only partitions the cortical mantel into well-defined clusters, but also maximizes the connectivity strength between these clusters. Moreover, BCA is computationally robust and allows both outlier detection as well as parameter-independent determination of the number of clusters. Our coclustering results have showed good performance of BCA in identifying major white matter fiber bundles in human brains and facilitate the detection of abnormal connectivity patterns in patients suffering from various neurological diseases.