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

TL;DR

This paper introduces a joint network optimization framework that predicts clinical severity from resting state fMRI data by decomposing brain connectivity into subnetworks and using these to model individual patient severity, outperforming standard methods.

Contribution

The novel framework simultaneously captures group-level brain network structures and individual differences, improving clinical severity prediction from rs-fMRI data.

Findings

Outperforms standard semi-supervised methods in ASD severity prediction.

Robustly identifies clinically relevant brain networks associated with ASD.

Validated on two separate ASD datasets with cross-validation.

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.