Defining brain-based OCD patient profiles using task-based fMRI and unsupervised machine learning.

While much research has highlighted phenotypic heterogeneity in obsessive compulsive disorder (OCD), less work has focused on heterogeneity in neural activity. Conventional neuroimaging approaches rely on group averages that assume homogenous patient populations. If subgroups are present, these approaches can increase variability and can lead to discrepancies in the literature. They can also obscure differences between various subgroups. To address this issue, we used unsupervised machine learning to identify subgroup clusters of patients with OCD who were assessed by task-based fMRI. We predominantly focused on activation of cognitive control and performance monitoring neurocircuits, including three large-scale brain networks that have been implicated in OCD (the frontoparietal network, cingulo-opercular network, and default mode network). Participants were patients with OCD (n = 128) that included both adults (ages 24-45) and adolescents (ages 12-17), as well as unaffected controls (n = 64). Neural assessments included tests of cognitive interference and error processing. We found three patient clusters, reflecting a "normative" cluster that shared a brain activation pattern with unaffected controls (65.9% of clinical participants), as well as an "interference hyperactivity" cluster (15.2% of clinical participants) and an "error hyperactivity" cluster (18.9% of clinical participants). We also related these clusters to demographic and clinical correlates. After post-hoc correction for false discovery rates, the interference hyperactivity cluster showed significantly longer reaction times than the other patient clusters, but no other between-cluster differences in covariates were detected. These findings increase precision in patient characterization, reframe prior neurobehavioral research in OCD, and provide a starting point for neuroimaging-guided treatment selection.