Human connectome module pattern detection using a new multi-graph MinMax cut model. Med Image Comput Comput Assist Interv 2014;17(Pt 3):313-20
Date
10/17/2014Pubmed ID
25320814Pubmed Central ID
PMC4203411DOI
10.1007/978-3-319-10443-0_40Scopus ID
2-s2.0-84909629630 (requires institutional sign-in at Scopus site) 3 CitationsAbstract
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.
Author List
De W, Wang Y, Nie F, Yan J, Cai W, Saykin AJ, Shen L, Huang HAuthor
Yang Wang MD Professor in the Radiology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsArtificial Intelligence
Brain
Computer Simulation
Connectome
Diffusion Tensor Imaging
Humans
Image Enhancement
Image Interpretation, Computer-Assisted
Imaging, Three-Dimensional
Male
Models, Neurological
Models, Statistical
Pattern Recognition, Automated
Reproducibility of Results
Sensitivity and Specificity
Subtraction Technique
Young Adult
