Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive-compulsive disorder

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.

Trial registration: ClinicalTrials.gov NCT01368510.

Keywords: CBT; OCD; functional connectivity; machine learning; resting state.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

(A) The average resting-state activity within ROIs from eight functional brain networks defined by Power et al. (24) was used to create a mean BOLD time course. A pairwise Pearson correlation of these time courses resulted in a functional connectivity (FC) matrix specific to each network. (B) The lower diagonal of each participant’s network-specific FC matrix was concatenated with the participant’s pretreatment YBOCS score and a binary variable indicating whether or not the participant was on medication to create a feature set for that participant. (C) A LASSO regression model was trained on n − 10 participants’ feature sets and their associated posttreatment YBOCS values and used to predict each of the left-out participant’s posttreatment YBOCS scores. Left-out participants are denoted as shaded feature sets (only three shown here due to space constraints). This process was repeated until all participants had been left out in a fold of the cross-validation and had been assigned a predicted posttreatment YBOCS (Y^). We correlated the array of predicted values (Y^) with the actual values (Y), yielding Pearson’s R and R2, a measure of our model’s feature-dependent ability to capture the behavioral variance across participants. Note that due to our participant sample size (n = 42), one fold of the cross-validation left out two participants, exemplified in fold 5.

Fig. 2.

Scatterplots depicting the relationship between the array of predicted posttreatment YBOCS values with the actual posttreatment YBOCS values when the LASSO cross-validation model was relying on feature sets that included pretreatment functional connectivity from the default mode network (Left) and the visual network (Right).