Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24571-3_21 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Data mining,Computational neuroscience,Brain network,Graph,Normalization (statistics),Connectome,Biological network,Computer science,Human Connectome,Clustering coefficient | Conference | 9350 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
9 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongchang Gao | 1 | 54 | 8.32 |
| Chengtao Cai | 2 | 0 | 0.68 |
| Jingwen Yan | 3 | 171 | 25.64 |
| Lin Yan | 4 | 0 | 0.68 |
| Joaquin Goni Cortes | 5 | 0 | 0.34 |
| Yang Wang | 6 | 12 | 1.80 |
| Feiping Nie | 7 | 7061 | 309.42 |
| John D West | 8 | 50 | 7.13 |
| Saykin Andrew J | 9 | 631 | 66.57 |
| Li Shen | 10 | 863 | 102.99 |
| Heng Huang | 11 | 3080 | 203.21 |