Identifying subgroups of urge suppression in Obsessive-Compulsive Disorder using machine learning.
Journal:
Journal of psychiatric research
PMID:
39004004
Abstract
Obsessive-compulsive disorder (OCD) is phenomenologically heterogeneous. While predominant models suggest fear and harm prevention drive compulsions, many patients also experience uncomfortable sensory-based urges ("sensory phenomena") that may be associated with heightened interoceptive sensitivity. Using an urge-to-blink eyeblink suppression paradigm to model sensory-based urges, we previously found that OCD patients as a group had more eyeblink suppression failures and greater activation of sensorimotor-interoceptive regions than controls. However, conventional approaches assuming OCD homogeneity may obscure important within-group variability, impeding precision treatment development. This study investigated the heterogeneity of urge suppression failure in OCD and examined relationships with clinical characteristics and neural activation. Eighty-two patients with OCD and 38 controls underwent an fMRI task presenting 60-s blocks of eyeblink suppression alternating with free-blinking blocks. Latent profile analysis identified OCD subgroups based on number of erroneous blinks during suppression. Subgroups were compared on behavior, clinical characteristics, and brain activation during task. Three patient subgroups were identified. Despite similar overall OCD severity, the subgroup with the most erroneous eyeblinks had the highest sensory phenomena severity, interoceptive sensitivity, and subjective urge intensity. Compared to other subgroups, this subgroup exhibited more neural activity in somatosensory and interoceptive regions during the early phase (first 30 s) of blink suppression and reduced activity in the middle frontal gyrus during the late phase (second 30 s) as the suppression period elapsed. Heterogeneity of urge suppression in OCD was associated with clinical characteristics and brain function. Our results reveal potential treatment targets that could inform personalized medicine.