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Identifying Connectome Module Patterns via New Balanced Multi-Graph Normalized Cut
- Source :
- Lecture Notes in Computer Science ISBN: 9783319245706, MICCAI (2)
- Publication Year :
- 2015
-
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.
- Subjects :
- Brain network
Brain Diseases
Computational neuroscience
Computer science
Brain
Reproducibility of Results
Human Connectome
computer.software_genre
Sensitivity and Specificity
Graph
Article
Pattern Recognition, Automated
Connectome
Graph (abstract data type)
Humans
Data mining
Nerve Net
computer
Biological network
Algorithms
Clustering coefficient
Subjects
Details
- ISBN :
- 978-3-319-24570-6
- ISBNs :
- 9783319245706
- Volume :
- 9350
- Database :
- OpenAIRE
- Journal :
- Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
- Accession number :
- edsair.doi.dedup.....ea75fc2c391c3d85060417f06624e3a9