Evaluating clinical and neuroimaging predictors for cognitive-behavioral therapy outcome in obsessive-compulsive disorder.
Authors
Affiliations (12)
Affiliations (12)
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany. [email protected].
- Hertie Institute for AI in Brain Health, Eberhard Karls Universität Tübingen, Tübingen, Germany. [email protected].
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin (corporate member of Freie Universität at Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health), Berlin, Germany. [email protected].
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.
- Department of Medicine, MSB Medical School Berlin, Berlin, Germany.
- Department of Psychology, MSB Medical School Berlin, Berlin, Germany.
- Department of Psychology, Universität Hamburg, Hamburg, Germany.
- Department of Educational Sciences and Psychology, TU Dortmund University, Dortmund, Germany.
- Hertie Institute for AI in Brain Health, Eberhard Karls Universität Tübingen, Tübingen, Germany.
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin (corporate member of Freie Universität at Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health), Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Berlin, Germany.
- Modelling of Cognitive Processes, Technical University of Berlin, Berlin, Germany.
Abstract
Cognitive-behavioral therapy (CBT) is the first-line treatment for obsessive-compulsive disorder (OCD), yet a significant number of patients do not achieve remission or substantial symptom relief. This study aims to enhance the prediction of CBT outcomes in OCD by integrating demographic, clinical, and neuroimaging data using machine learning (ML) models. We conduct a comprehensive analysis on a well-characterized clinical sample, employing a rigorous validation scheme to avoid data leakage, and comparing multiple ML algorithms to minimize bias. Out of four different ML models trained on demographic and clinical data, structural MRI, and resting-state MRI functional connectivity data, no model was able to predict CBT success significantly above chance level in the present sample. Although clinical and demographic data enabled 64%-66% accuracy for predicting remission, this did not reach statistical significance after permutation testing. Pre-treatment symptom severity emerged numerically as the most promising predictor of remission, aligning with previous studies, but did not pass the significance threshold in the present study. Despite efforts to identify neuroimaging predictors, neither functional nor structural MRI features significantly contributed to the prediction models. These findings suggest that robust, individualized brain-based predictions for mental health outcomes remain challenging with the available data and sample size.