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

Lin, Chia-Wen; Lu, Shiyong; Liang, Xuwei; Hua, Jing

doi:10.1109/icdmw.2006.85

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper extends [19] with a comprehensive study of the effects of various operators and parameters on the convergence performance of the GCA algorithm (Section 5). First, the image acquisition and data preprocessing procedure is described in Section 5.1.…”

Section: Introductionmentioning

confidence: 86%

“…Second, the evaluation of the effects of K-means, selection, mutation, population size, and mutation probability on the convergence performance of the GCA algorithm is reported in Section 5.2. Finally, a 3-D visualization of the thalamic nuclei as well as their connectivities to the corresponding cortical regions is presented in 5.3; this visualization adds more detailed annotations to the one presented in [19]. We also extend [19] with a description of the background and related work in Section 2.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cocluster analysis of thalamo-cortical fibre tracts extracted from diffusion tensor MRI

Lin

Liang

et al. 2008

IJDMB

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Cocluster analysis of thalamo-cortical fibre tracts extracted from diffusion tensor MRI

Lin

Liang

et al. 2008

IJDMB

Self Cite

View full text Add to dashboard Cite

show abstract

Coclustering Based Parcellation of Human Brain Cortex Using Diffusion Tensor MRI

Lin

et al.

Bioinformatics Research and Applications

View full text Add to dashboard Cite

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

Abstract: 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

Cited by 2 publications

References 18 publications

Cocluster analysis of thalamo-cortical fibre tracts extracted from diffusion tensor MRI

Cocluster analysis of thalamo-cortical fibre tracts extracted from diffusion tensor MRI

Coclustering Based Parcellation of Human Brain Cortex Using Diffusion Tensor MRI

Contact Info

Product

Resources

About