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Human Connectome Module Pattern Detection Using A New Multi-Graph MinMax Cut Model
- Source :
- Scopus-Elsevier, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 ISBN: 9783319104423, MICCAI (3)
- Publication Year :
- 2014
-
Abstract
- Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.
- Subjects :
- Male
Computer science
Models, Neurological
Module pattern
Machine learning
computer.software_genre
Sensitivity and Specificity
Article
Pattern Recognition, Automated
Young Adult
Imaging, Three-Dimensional
Artificial Intelligence
Image Interpretation, Computer-Assisted
Connectome
Humans
Computer Simulation
Computational neuroscience
Models, Statistical
business.industry
Brain
Reproducibility of Results
Human Connectome
Minimax
Image Enhancement
Diffusion Tensor Imaging
Subtraction Technique
Graph (abstract data type)
Artificial intelligence
business
computer
Algorithms
Diffusion MRI
Network analysis
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-319-10442-3
- ISBNs :
- 9783319104423
- Database :
- OpenAIRE
- Journal :
- Scopus-Elsevier, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 ISBN: 9783319104423, MICCAI (3)
- Accession number :
- edsair.doi.dedup.....1bf4ce00dccfac343160d3b5cb3f40e5