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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