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Disrupted pathways from frontal-parietal cortex to basal ganglia and cerebellum in patients with unmedicated obsessive compulsive disorder as observed by whole-brain resting-state effective connectivity analysis - a small sample pilot study.

Authors: Liu W, Hua M, Qin J, Tang Q, Han Y, Tian H, Lian D, Zhang Z, Wang W, Wang C, Chen C, Jiang D, Li G, Lin X, Zhuo C
Journal: Brain imaging and behavior
Functional disruptions Microcircuits Obsessive compulsive disorder Unmedicated Basal Ganglia Brain Brain Mapping Cerebellum Humans Magnetic Resonance Imaging Neural Pathways Obsessive-Compulsive Disorder Parietal Lobe Pilot Projects

Abstract

OBJECTIVE: To date, a systematic characterization of abnormalities in resting-state effective connectivity (rsEC) in obsessive-compulsive disorder (OCD) is lacking. The present study aimed to systematically characterize whole-brain rsEC in OCD patients as compared to healthy controls. METHODS: Using resting-state fMRI data of 50 unmedicated patients with OCD and 50 healthy participants, we constructed whole-brain rsEC networks using Granger causality analysis followed by univariate and multivariate comparisons between patients and controls. Similar analyses were performed for resting-state functional connectivity (rsFC) networks to examine how rsFC and rsEC differentially capture abnormal brain connectivity in OCD. RESULTS: Univariate comparisons identified 10 rsEC networks that were significantly disrupted in patients, and which were mainly associated with frontal-parietal cortex, basal ganglia, and cerebellum. Conversely, abnormal rsFC networks were widely distributed throughout the whole brain. Multivariate pattern analysis revealed a classification accuracy as high as 80.5% for distinguishing patients from controls using combined whole-brain rsEC and rsFC. CONCLUSIONS: The results of the present study suggest disrupted communication of information from frontal-parietal cortex to basal ganglia and cerebellum in OCD patients. Using combined whole-brain rsEC and rsFC, multivariate pattern analysis revealed a classification accuracy as high as 80.5% for distinguishing patients from controls. The alterations observed in OCD patients could aid in identifying treatment mechanisms for OCD.