Chapter title |
Identifying Connectome Module Patterns via New Balanced Multi-graph Normalized Cut
|
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Chapter number | 21 |
Book title |
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, October 2015
|
DOI | 10.1007/978-3-319-24571-3_21 |
Pubmed ID | |
Book ISBNs |
978-3-31-924570-6, 978-3-31-924571-3
|
Authors |
Hongchang Gao, Chengtao Cai, Jingwen Yan, Lin Yan, Joaquin Goni Cortes, Yang Wang, Feiping Nie, John West, Andrew Saykin, Li Shen, Heng Huang |
Abstract |
Computational tools for the analysis of complex biological networks are lacking in human connectome research. Especially, how to discover the brain network patterns shared by a group of subjects is a challenging computational neuroscience problem. Although some single graph clustering methods can be extended to solve the multi-graph cases, the discovered network patterns are often imbalanced, e.g. isolated points. To address these problems, we propose a novel indicator constrained and balanced multi-graph normalized cut method to identify the connectome module patterns from the connectivity brain networks of the targeted subject group. We evaluated our method by analyzing the weighted fiber connectivity networks. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 3 | 33% |
Student > Doctoral Student | 2 | 22% |
Student > Ph. D. Student | 1 | 11% |
Other | 1 | 11% |
Professor > Associate Professor | 1 | 11% |
Other | 1 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 5 | 56% |
Neuroscience | 1 | 11% |
Medicine and Dentistry | 1 | 11% |
Unknown | 2 | 22% |