Brain connectivity and prediction of relapse after cognitive-behavioral therapy in obsessive-compulsive disorder. - Feusner, Jamie; Moody, Teena; Lai, Tsz Man; Sheen, Courtney; Khalsa, Sahib; Brown, Jesse; Levitt, Jennifer; Alger, Jeffry; O'Neill, Joseph (Frontiers in Psychiatry, 2015)
Background: Intensive cognitive-behavioral therapy can effectively reduce symptoms in obsessive-compulsive disorder (OCD). However, many relapse after treatment. Few studies have investigated biological markers predictive of follow-up clinical status. The objective was to determine if brain network connectivity patterns prior to intensive cognitive-behavioral therapy predict worsening of clinic symptoms during follow-up. Methods: We acquired resting-state functional magnetic resonance imaging data from 17 adults with OCD prior to and following 4 weeks of intensive cognitive-behavioral therapy. Functional connectivity data were analyzed to yield graph theory metrics. We examined the relationship between pre-treatment connectome properties and OCD clinical symptoms before and after treatment and during a 12-month follow-up period. Results: Mean OCD symptom decrease was 40.4±16.4% pre- to post-treatment (64.7% responded; 58.8% remitted), but 35.3% experienced clinically significant worsening during follow-up. From pre- to post-treatment, small-worldness and clustering coefficient significantly increased. Decreases in modularity correlated with decreases in OCD symptoms. Higher pre-treatment small-world connectivity was significantly associated with worsening of OCD symptoms during the follow-up period. Psychometric and neurocognitive measures pre- and post-treatment were not significant predictors. Conclusions: This is the first graph theory connectivity study of the effects of cognitive-behavioral therapy in OCD, and the first to test associations with follow-up clinical status. Results show functional network efficiency as a biomarker of cognitive-behavioral therapy response and relapse in OCD. Cognitive-behavioral therapy increases network efficiency as it alleviates symptoms in most patients, but those entering therapy with already high network efficiency are at greater risk of relapse. Results have potential clinical implications for treatment selection.