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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 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 (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PublisherSpringer Verlag
Pages313-320
Number of pages8
EditionPART 3
ISBN (Print)9783319104423
DOIs
StatePublished - Jan 1 2014
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
Duration: Sep 14 2014Sep 18 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8675 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
CountryUnited States
CityBoston, MA
Period9/14/149/18/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Wang, D., Wang, Y., Nie, F., Yan, J., Cai, W., Saykin, A. J., 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 2014 - 17th International Conference, Proceedings (PART 3 ed., pp. 313-320). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8675 LNCS, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-319-10443-0_40