Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

Abstract

Abstract Objective No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Magnetic resonance imaging (MRI) studies have provided evidence for structural abnormalities in distinct brain regions, but effect sizes are small and have limited clinical relevance. To investigate whether individual patients can be distinguished from healthy controls, we performed multivariate analysis of structural neuroimaging data from the ENIGMA-OCD consortium. Method We included 46 data sets with neuroimaging and clinical data from adult (≥18 years) and pediatric (<18 years) samples. T 1 images from 2,304 OCD patients and 2,068 healthy controls were analyzed using standardized processing to extract regional measures of cortical thickness, surface area and subcortical volume. Machine learning classification performance was tested using cross-validation, and possible effects of clinical variables were investigated by stratification. Results Classification performance for OCD versus controls using the complete sample with different classifiers and cross-validation strategies was poor (AUC—0.57 (standard deviation (SD)=0.02;P corr =0.19) to 0.62 (SD=0.03;P corr <.001)). When models were validated on completely new data from other sites, model performance did not exceed chance-level (AUC—0.51 (SD=0.11;P corr >.99) to 0.54 (SD=0.08;P corr >.99)). In contrast, good classification performance (>0.8 AUC) was achieved within subgroups of patients split according to their medication status. Conclusions Parcellated structural MRI data do not enable good distinction between patients with OCD and controls. However, classifying subgroups of patients based on medication status enables good identification at the individual subject level. This underlines the need for longitudinal studies on the short- and long-term effects of medication on brain structure.