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We propose a novel optimization framework to predict clinical severity fromresting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. Thefirst term decomposes the correlation matrices into a sparse set ofrepresentative subnetworks that define a network manifold. These subnetworksare modeled as rank-one outer-products which correspond to the elementalpatterns of co-activation across the brain; the subnetworks are combined viapatient-specific non-negative coefficients. The second term is a linearregression model that uses the patient-specific coefficients to predict ameasure of clinical severity. We validate our framework on two separatedatasets in a ten fold cross validation setting. The first is a cohort offifty-eight patients diagnosed with Autism Spectrum Disorder (ASD). The seconddataset consists of sixty three patients from a publicly available ASDdatabase. Our method outperforms standard semi-supervised frameworks, whichemploy conventional graph theoretic and statistical representation learningtechniques to relate the rs-fMRI correlations to behavior. In contrast, ourjoint network optimization framework exploits the structure of the rs-fMRIcorrelation matrices to simultaneously capture group level effects and patientheterogeneity. Finally, we demonstrate that our proposed framework robustlyidentifies clinically relevant networks characteristic of ASD.

A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data