Name
Abstract | ||
|---|---|---|
Functional connectivity analysis yields powerful insights into our understanding of the human brain. Group-wise functional community detection aims to partition the brain into clusters, or communities, in which functional activity is inter-regionally correlated in a common manner across a group of subjects. In this article, we show how to use multiple-view spectral clustering to perform group-wise functional community detection. In a series of experiments on 291 subjects from the Human Connectome Project, we compare three versions of multiple-view spectral clustering: MVSC (uniform weights), MVSCW (weights based on subject-specific embedding quality), and AASC (weights optimized along with the embedding) with the competing technique of Joint Diagonalization of Laplacians (JDL). Results show that multiple-view spectral clustering not only yields group-wise functional communities that are more consistent than JDL when using randomly selected subsets of individual brains, but it is several orders of magnitude faster than JDL. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Cluster (physics),Spectral clustering,Embedding,Human Connectome Project,Pattern recognition,Computer science,Artificial intelligence,Partition (number theory),Machine learning |
DocType | Volume | Citations |
Journal | abs/1611.06981 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nathan D. Cahill | 1 | 134 | 19.33 |
| Harmeet Singh | 2 | 26 | 4.77 |
| Chao Zhang | 3 | 351 | 63.97 |
| Daryl A. Corcoran | 4 | 0 | 0.34 |
| Alison M. Prengaman | 5 | 0 | 0.34 |
| Paul Wenger | 6 | 35 | 9.91 |
| John F. Hamilton | 7 | 0 | 0.34 |
| Peter Bajorski | 8 | 0 | 0.34 |
| Andrew Michael | 9 | 25 | 3.19 |