Predicting cognitive-behavioral therapy outcomes in obsessive-compulsive disorder from inhibitory control neural activity: A mega-analysis and machine learning study from the ENIGMA-OCD consortium

ObjectiveCognitive behavioral therapy (CBT) is an effective first-line treatment for obsessive-compulsive disorder (OCD), yet it remains difficult to predict who will respond to this intervention. This study investigates associations between neural activity during inhibitory control tasks and CBT outcomes, and whether task-based fMRI data could serve as a predictive marker of individual CBT response. MethodsUsing fMRI data from individuals performing an inhibitory control task across five samples (n=130, age range=8-57, 54% female) of the ENIGMA-OCD consortium, univariate associations were analyzed between activity during response inhibition and error processing and three CBT outcomes: response, remission, and pre-post treatment change in symptom severity. Random forest and support vector machine models using leave-one-sample-out cross-validation were used for prediction of CBT response and remission from fMRI activity and clinical data. ResultsRemission after CBT was associated with weaker activity in default mode regions during response inhibition and in the right supramarginal gyrus during error processing. Greater symptom reduction was linked to weaker pre-treatment activity across frontoparietal, dorsal attention, visual, and subcortical regions during response inhibition, but to stronger default mode activity during error processing. Despite these robust group-level effects, machine learning models failed to predict individual outcomes above chance level with either neuroimaging or clinical data. ConclusionWeaker activity during response inhibition in a widespread network, as well as stronger activity in default mode regions during error processing before treatment, appear beneficial to CBT response. However, these findings cannot yet be translated into individually predictive markers of CBT outcome.