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Human Connectome Module Pattern Detection Using a New Multi-graph MinMax Cut Model

  • De Wang
  • Yang Wang
  • Feiping Nie
  • Jingwen Yan
  • Weidong Cai
  • Andrew J. Saykin
  • Li Shen
  • Heng Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

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.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • De Wang
    • 1
  • Yang Wang
    • 2
  • Feiping Nie
    • 1
  • Jingwen Yan
    • 2
  • Weidong Cai
    • 3
  • Andrew J. Saykin
    • 2
  • Li Shen
    • 2
  • Heng Huang
    • 1
  1. 1.Computer Science and EngineeringUniversity of Texas at ArlingtonUSA
  2. 2.Radiology and Imaging SciencesIndiana University School of MedicineUSA
  3. 3.BMIT Research Group, School of ITUniversity of SydneyAustralia

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