International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 pp 169-176 | Cite as

Identifying Connectome Module Patterns via New Balanced Multi-graph Normalized Cut

  • Hongchang Gao
  • Chengtao Cai
  • Jingwen Yan
  • Lin Yan
  • Joaquin Goni Cortes
  • Yang Wang
  • Feiping Nie
  • John West
  • Andrew Saykin
  • Li Shen
  • Heng Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9350)

Abstract

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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hongchang Gao
    • 1
  • Chengtao Cai
    • 2
  • Jingwen Yan
    • 2
  • Lin Yan
    • 3
  • Joaquin Goni Cortes
    • 2
  • Yang Wang
    • 2
  • Feiping Nie
    • 1
  • John West
    • 2
  • Andrew Saykin
    • 2
  • Li Shen
    • 2
  • Heng Huang
    • 1
  1. 1.Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA
  3. 3.Department of Electronic Engineering, School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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