Wang, DeWang, YangNie, FeipingCai, WeidongSaykin, Andrew J.Shen, LiHuang, Heng2016-06-302016-06-302014Wang, 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.https://hdl.handle.net/1805/10272Many 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-USPublisher PolicyArtificial IntelligenceConnectomeDiffusion Tensor ImagingImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalPattern Recognition, AutomatedSensitivity and SpecificityArticle