311. Altered resting-state sensorimotor network in patients with obsessive-compulsive disorder: an EEG study

Abstract

Abstract Background Dysfunction in the cortical-striatal-thalamo-cortical circuit is considered a core pathological mechanism of obsessive-compulsive disorder (OCD) and may contribute to abnormalities in the sensorimotor network (SMN). Although altered SMN patterns in OCD have been reported using resting-state fMRI, SMN alterations remain underexplored in resting-state EEG (rsEEG). Aims & Objectives This study aimed to identify frequency-specific SMN alterations in patients with OCD compared to healthy controls (HCs) using rsEEG. Method Eyes-closed rsEEG data were collected from 41 patients with OCD and 41 HCs. SMN was constructed by defining eight cortical regions as nodes and calculating functional connectivity (FC) using the weighted phase-lag index across six frequency bands. Group differences in FC and strength were assessed using a permutation test. Correlation analysis was conducted between significantly altered FC/strength values and Yale-Brown Obsessive Compulsive Scale (Y-BOCS). Machine learning-based classification was conducted to assess the potential of SMN features as biomarkers for OCD. Results In the theta band, FC between the left S1 and left SMA exhibited significant difference. In the high alpha band, FCs between the left S1 and right M1, and between the left S1 and right PMC, as well as local strength in the right PMC, showed significant differences. FCs between left S1 and right M1 in the high alpha band positively correlated with Y-BOCS. Classification analysis achieved the highest accuracy of 78.05% and AUC of 0.798. Discussion & Conclusions These findings suggest that rsEEG-derived alterations in the SMN may reflect underlying neurophysiological mechanisms of OCD and serve as candidate biomarkers for its diagnosis.