Amb motiu del tancament d'estiu, la validació de documents es reprendrà a partir del 28 d'agost de 2026. Disculpeu les molèsties.
Con motivo del cierre de verano, la validación de documentos se reanudará a partir del 28 de agosto de 2026. Disculpad las molestias
Due to the summer closure, document validation will resume starting August 28, 2026. We apologize for any inconvenience.

Files

1-s2.0-S0165032725011711-main.pdf (2.77 MB)

Document type

Article

Version

Published version

Publication date

2025-06-18

Publication license

cc by (c) Mortel, Laurens A. van de et al, 2024
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/222391

Development and validation of a machine learning model to predict cognitive behavioral therapy outcome in obsessive-compulsive disorder using clinical and neuroimaging data

Journal Title

Authors

Director/Tutor

Journal ISSN

Volume Title

Related resource

https://doi.org/10.1016/j.jad.2025.119729

Abstract

Background: Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. In this study, we aimed to develop predictive models using clinical and neuroimaging data from the multicenter Enhancing Neuro-Imaging and Genetics through MetaAnalysis (ENIGMA)-OCD consortium. Methods: Baseline clinical and resting-state functional magnetic imaging (rs-fMRI) data from 159 adult patients aged 18-60 years (88 female) with OCD who received CBT at four treatment/neuroimaging sites were included. Fractional amplitude of low frequency fluctuations, regional homogeneity and atlas-based functional connectivity were computed. Clinical CBT response and remission were predicted using support vector machine and random forest classifiers on clinical data only, rs-fMRI data only, and the combination of both clinical and rs-fMRI data. Results: The use of only clinical data yielded an area under the ROC curve (AUC) of 0.69 for predicting remission (p symbolscript 0.001). Lower baseline symptom severity, younger age, an absence of cleaning obsessions, unmedicated status, and higher education had the highest model impact in predicting remission. The best predictive perfor-mance using only rs-fMRI was obtained with regional homogeneity for remission (AUC symbolscript 0.59). Predicting response with rs-fMRI generally did not exceed chance level. Conclusions: Machine learning models based on clinical data may thus hold promise in predicting remission after CBT for OCD, but the predictive power of multicenter rs-fMRI data is limited.

Subject

Teràpia cognitiva, Conducta compulsiva

Subject (English)

Cognitive therapy, Compulsive behavior

Citation

Collections

Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

Citation

MORTEL, Laurens A. van de, et al. Development and validation of a machine learning model to predict cognitive behavioral therapy outcome in obsessive-compulsive disorder using clinical and neuroimaging data. Journal of Affective Disorders. 2025. Vol. 389, num. 119729. ISSN 1573-2517. [consulted: 15 of August of 2026]. Available at: https://hdl.handle.net/2445/222391

Export metadata

JSON - METS

Share record