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

Wang De, Yang Wang, Feiping Nie, Jingwen Yan, Weidong Cai, Andrew Saykin, Li Shen, Heng Huang

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

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.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages313-320
Number of pages8
Volume17
EditionPt 3
StatePublished - 2014
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)

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  • Cite this

    De, W., Wang, Y., Nie, F., Yan, J., Cai, W., Saykin, A., Shen, L., & Huang, H. (2014). Human connectome module pattern detection using a new multi-graph MinMax cut model. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 17, pp. 313-320)