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Plain English: In this work, we collected Resting-State fMRI data from patients with OCD before they engaged in 4 weeks of Cognitive Behavioral Therapy (CBT). We calculated functional connectivity within the Default Mode and Visual Networks and trained a machine-learning classifier to learn how those patterns relate to a patient’s success in the CBT. The classifier was able to learn so well from these patterns that it could predict a patient’s OCD symptom severity (as measured by YBOCS) after CBT– effectively predicting their symptoms 4 weeks in the future. Such insights could help guide treatment options.

Citation: Reggente, N., Moody, T. D., Morfini, F., Sheen, C., Rissman, J., O’Neill, J., & Feusner, J. D. (2018). Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive–compulsive disorder. Proceedings of the National Academy of Sciences, 201716686. https://doi.org/10.1073/pnas.1716686115

PNAS Hosting: http://www.pnas.org/content/early/2018/02/06/1716686115