The ability to predict an individual’s potential response to treatment would permit clinicians to more prudently allocate resources that support cognitive behavioral therapy for obsessive–compulsive disorder (OCD), an often stressful and time-consuming treatment. The current study lays important groundwork for an exciting advance toward personalized medicine in psychiatry that up to this point has eluded the field. This study marks a success in using multivariate pattern recognition to identify neurobiological predictors of treatment response. In addition, it advances knowledge of the neurophysiology of OCD and of mechanistic processes involved in the therapeutic response, which could be used to refine existing treatments or to develop novel treatments based on identified potential brain targets.Abstract
Cognitive behavioral therapy (CBT) is an effective treatment for many with obsessive–compulsive disorder (OCD). However, response varies considerably among individuals. Attaining a means to predict an individual’s potential response would permit clinicians to more prudently allocate resources for this often stressful and time-consuming treatment. We collected resting-state functional magnetic resonance imaging from adults with OCD before and after 4 weeks of intensive daily CBT. We leveraged machine learning with cross-validation to assess the power of functional connectivity (FC) patterns to predict individual posttreatment OCD symptom severity. Pretreatment FC patterns within the default mode network and visual network significantly predicted posttreatment OCD severity, explaining up to 67% of the variance. These networks were stronger predictors than pretreatment clinical scores. Results have clinical implications for developing personalized medicine approaches to identifying individual OCD patients who will maximally benefit from intensive CBT.