Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning

  • Chen Zu
  • Yue Gao
  • Brent Munsell
  • Minjeong Kim
  • Ziwen Peng
  • Jessica R. Cohen
  • Daoqiang Zhang
  • Guorong Wu
Original Research


The functional brain network has gained increased attention in the neuroscience community because of its ability to reveal the underlying architecture of human brain. In general, majority work of functional network connectivity is built based on the correlations between discrete-time-series signals that link only two different brain regions. However, these simple region-to-region connectivity models do not capture complex connectivity patterns between three or more brain regions that form a connectivity subnetwork, or subnetwork for short. To overcome this current limitation, a hypergraph learning-based method is proposed to identify subnetwork differences between two different cohorts. To achieve our goal, a hypergraph is constructed, where each vertex represents a subject and also a hyperedge encodes a subnetwork with similar functional connectivity patterns between different subjects. Unlike previous learning-based methods, our approach is designed to jointly optimize the weights for all hyperedges such that the learned representation is in consensus with the distribution of phenotype data, i.e. clinical labels. In order to suppress the spurious subnetwork biomarkers, we further enforce a sparsity constraint on the hyperedge weights, where a larger hyperedge weight indicates the subnetwork with the capability of identifying the disorder condition. We apply our hypergraph learning-based method to identify subnetwork biomarkers in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). A comprehensive quantitative and qualitative analysis is performed, and the results show that our approach can correctly classify ASD and ADHD subjects from normal controls with 87.65 and 65.08% accuracies, respectively.


Hypergraph learning Brain network Biomarker Autism spectrum disorder Attention deficit hyperactivity disorder 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11682_2018_9899_MOESM1_ESM.docx (36 kb)
ESM 1 (DOCX 36 kb)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Chen Zu
    • 1
    • 2
  • Yue Gao
    • 3
  • Brent Munsell
    • 4
  • Minjeong Kim
    • 5
  • Ziwen Peng
    • 6
  • Jessica R. Cohen
    • 7
  • Daoqiang Zhang
    • 1
  • Guorong Wu
    • 2
  1. 1.Department of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.School of SoftwareTsinghua UniversityBeijingChina
  4. 4.Department of Computer ScienceCollege of CharlestonCharlestonUSA
  5. 5.Department of Computer ScienceUniversity of North CarolinaGreensboroUSA
  6. 6.Centre for Studies of Psychological Application, School of PsychologySouth China Normal UniversityGuangzhouChina
  7. 7.Department of Psychology and NeuroscienceUniversity of North Carolina at Chapel HillChapel HillUSA

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