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.