Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in
Nicco Reggente1, Teena D Moody2, Francesca Morfini2
1Department of Psychology, University of California, Los Angeles, CA 90095; nreggente@psych.ucla.edu.
Summary
Predicting treatment success for obsessive-compulsive disorder (OCD) is possible. Resting-state brain connectivity patterns before treatment can forecast how well individuals will respond to cognitive behavioral therapy (CBT).
Area of Science:
- Neuroscience
- Psychiatry
- Machine Learning
Background:
- Cognitive behavioral therapy (CBT) is a primary treatment for obsessive-compulsive disorder (OCD).
- Individual responses to CBT for OCD vary significantly, necessitating predictive tools for personalized treatment allocation.
- Predictive biomarkers are needed to optimize resource allocation for intensive, time-consuming OCD therapies.
Purpose of the Study:
- To investigate the predictive power of resting-state functional connectivity (FC) patterns for individual treatment response in OCD patients undergoing CBT.
- To determine if neuroimaging-based FC metrics can predict post-treatment symptom severity more effectively than clinical assessments.
Main Methods:
- Resting-state functional magnetic resonance imaging (fMRI) data were acquired from adults with OCD before and after 4 weeks of intensive daily CBT.
- Machine learning algorithms with cross-validation were employed to analyze pretreatment FC patterns.
- The predictive accuracy of FC patterns within specific brain networks (default mode network, visual network) for post-treatment OCD symptom severity was assessed.
Main Results:
- Pretreatment functional connectivity (FC) patterns within the default mode network and visual network significantly predicted post-treatment OCD symptom severity.
- These FC patterns explained up to 67% of the variance in treatment response.
- Neuroimaging-based FC predictors demonstrated superior predictive power compared to pretreatment clinical scores.
Conclusions:
- Resting-state FC patterns hold significant potential as biomarkers for predicting individual treatment outcomes in OCD.
- These findings support the development of personalized medicine approaches to identify OCD patients most likely to benefit from intensive CBT.
- Integrating neuroimaging data into clinical practice could enhance the efficacy and efficiency of OCD treatment.
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