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Abstract #1719

Learning Subnetwork Biomarkers via Hypergraph for Classification of Autism Disease

Chen Zu1, Yue Gao2, Brent Munsell3, Minjeong Kim1, Ziwen Peng4, Yingying Zhu1, Wei Gao5, Daoqiang Zhang6, Dinggang Shen1, and Guorong Wu1

1University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 2School of Software, Tsinghua University, Beijing, China, 3Department of Computer Science, College of Charleston, Charleston, SC, USA, 4Centre for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China, 5Biomedical Imaging Research Institute (BIRI), Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA, 6Department of Computer Science and Technology, Nanjing University of Aeronautics and As-tronautics, Nanjing, China

Most brain network connectivity models consider correlations between discrete-time series signals that only connect two brain regions. Here we propose a method to explore subnetwork biomarkers that are significantly distinguishable between two clinical cohorts. We construct a hypergraph by exhaustively inspecting all possible subnetworks for all subjects. The objective function of hypergraph learning is to jointly optimize the weights for all hyperedges. We deploy our method to find high order childhood autism biomarkers from rs-fMRI images. Promising results have been obtained from comprehensive evaluation on the discriminative power in diagnosis of Autism.

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