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Abstract
We propose a novel optimization framework to predict clinical severity from resting
state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term
decomposes the correlation matrices into a sparse set of representative subnetworks
that define a network manifold. These subnetworks are modeled as rank-one outer-products
which correspond to the elemental patterns of co-activation across the brain; the
subnetworks are combined via patient-specific non-negative coefficients. The second
term is a linear regression model that uses the patient-specific coefficients to predict
a measure of clinical severity. We validate our framework on two separate datasets
in a ten fold cross validation setting. The first is a cohort of fifty-eight patients
diagnosed with Autism Spectrum Disorder (ASD). The second dataset consists of sixty
three patients from a publicly available ASD database. Our method outperforms standard
semi-supervised frameworks, which employ conventional graph theoretic and statistical
representation learning techniques to relate the rs-fMRI correlations to behavior.
In contrast, our joint network optimization framework exploits the structure of the
rs-fMRI correlation matrices to simultaneously capture group level effects and patient
heterogeneity. Finally, we demonstrate that our proposed framework robustly identifies
clinically relevant networks characteristic of ASD.