{"id":2681,"date":"2019-10-31T22:05:41","date_gmt":"2019-11-01T02:05:41","guid":{"rendered":"https:\/\/engineering.jhu.edu\/nsa\/?p=2681"},"modified":"2020-10-05T11:27:53","modified_gmt":"2020-10-05T15:27:53","slug":"our-paper-on-predicting-clinical-severity-from-rsfmri-accepted-to-neuroimage","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/nsa\/2019\/10\/31\/our-paper-on-predicting-clinical-severity-from-rsfmri-accepted-to-neuroimage\/","title":{"rendered":"Our Paper on Predicting Clinical Severity from rsfMRI Accepted to NeuroImage!"},"content":{"rendered":"

Title:<\/strong> A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data<\/p>\n

Abstract: <\/strong>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 outerproducts 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.<\/p>\n","protected":false},"excerpt":{"rendered":"

Title: A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data Abstract: We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. 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