Anatomical hubs from spectral clustering of structural connectomes
Files
(Restricted Access)
Date
2016
Authors
Mitra, J.
Ghose, S.
Bourgeat, P.
Fripp, J.
Mathias, J.
Rose, S.
Salvado, O.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2016, vol.2016-June, pp.894-897
Statement of Responsibility
J. Mitra, S. Ghose, P. Bourgeat, J. Fripp, J.L. Mathias, S. Rose, O. Salvado
Conference Name
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016) (13 Apr 2016 - 16 Apr 2016 : Prague, Czech Republic)
Abstract
The analysis of the structural brain networks have recently gathered extensive interest due to its crucial role in unveiling the fundamental principles of the brain. The uniformity of structural networks inferred from diffusion tensor imaging across different individuals is however, unknown. This paper presents a method to infer group-wise consistent structural clusters from the connectome Laplacian graph. The spectral clustering of the cortical networks from diffusion tensor imaging was applied on 146 healthy subjects using 3 random groups, and on groups based on gender and age, to determine the optimal number of clusters. The results show six consistent sub-networks of structural connections that was validated using known cluster validity indices, showing highly reproducible clusters for random groups and groups based on gender; while, cluster differences were observed between younger and older groups in areas related to memory.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© 2016 IEEE