Browsing by Author "Nie, Feiping"

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    Human connectome module pattern detection using a new multi-graph MinMax cut model
    (Springer, 2014) Wang, De; Wang, Yang; Nie, Feiping; Cai, Weidong; Saykin, Andrew J.; Shen, Li; Huang, Heng; Department of Radiology and Imaging Sciences, IU School of Medicine
    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.
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    Identifying Connectome Module Patterns via New Balanced Multi-Graph Normalized Cut.
    (Springer, 2015-10) Gao, Hongchang; Cai, Chengtao; Yan, Jingwen; Yan, Lin; Cortes, Joaquin Goni; Wang, Yang; Nie, Feiping; West, John; Saykin, Andrew J.; Shen, Li; Huang, Heng; Department of Radiology and Imaging Sciences, IU School of Medicine
    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.