Human connectome module pattern detection using a new multi-graph MinMax cut model

dc.contributor.authorWang, De
dc.contributor.authorWang, Yang
dc.contributor.authorNie, Feiping
dc.contributor.authorCai, Weidong
dc.contributor.authorSaykin, Andrew J.
dc.contributor.authorShen, Li
dc.contributor.authorHuang, Heng
dc.contributor.departmentDepartment of Radiology and Imaging Sciences, IU School of Medicineen_US
dc.date.accessioned2016-06-30T16:39:52Z
dc.date.available2016-06-30T16:39:52Z
dc.date.issued2014
dc.description.abstractMany 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.en_US
dc.eprint.versionAuthor's manuscripten_US
dc.identifier.citationWang, D., Wang, Y., Nie, F., Yan, J., Cai, W., Saykin, A. J., … Huang, H. (2014). Human Connectome Module Pattern Detection Using A New Multi-Graph MinMax Cut Model. Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17(0 3), 313–320.en_US
dc.identifier.urihttps://hdl.handle.net/1805/10272
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relation.journalMedical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Interventionen_US
dc.rightsPublisher Policyen_US
dc.sourcePMCen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectConnectomeen_US
dc.subjectDiffusion Tensor Imagingen_US
dc.subjectImage Enhancementen_US
dc.subjectImage Interpretation, Computer-Assisteden_US
dc.subjectImaging, Three-Dimensionalen_US
dc.subjectPattern Recognition, Automateden_US
dc.subjectSensitivity and Specificityen_US
dc.titleHuman connectome module pattern detection using a new multi-graph MinMax cut modelen_US
dc.typeArticleen_US
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