期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 115, 期 9, 页码 2222-2227出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1716686115
关键词
OCD; CBT; resting state; functional connectivity; machine learning
资金
- National Institute of Mental Health [R01MH085900]
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.
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