Progress in neuro-psychopharmacology & biological psychiatry · 2026
Severity-dependent alterations of EEG microstate dynamics in obsessive-compulsive disorder.
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Zhang Xinyue, Zhu Rongrong, Guo Qihui, Wang Dongmei, Zhang Xiang-Yang
Аннотация
BACKGROUND: While elevated symptom severity in obsessive-compulsive disorder (OCD) is associated with a profound clinical burden and escalating psychiatric risks, the underlying large-scale network dynamics remain poorly understood. Electroencephalogram (EEG) microstate analysis offers a window into these rapid network fluctuations, though studies in this area remain limited.
METHODS: Resting-state 64-channel EEG was recorded from a total sample of 120 participants. After quality control, we analyzed resting-state EEG data from 101 participants, comprising 65 OCD patients (35 males; mean age = 28.24 ± 8.74 years) and 36 healthy controls (19 males; mean age = 24.67 ± 13.56 years). Patients were stratified into severe (n = 30) and non-severe (n = 35) groups based on Yale-Brown Obsessive-Compulsive Scale (YBOCS) scores. Then, we compared microstate parameters across groups and employed machine learning (ML) to identify severity-related features.
RESULTS: Distinct microstate patterns emerged based on severity. Non-severe patients showed altered microstates B and C, whereas severe patients exhibited significant deficits in microstate D and elevated microstate A. In the combined OCD cohort and the severe group, YBOCS scores had no significant relationship with microstate parameters, while YBOCS-compulsion scores negatively correlated with coverage A and TP B → A in the non-severe group. Moreover, the ML model successfully distinguished severe from non-severe cases (Balanced Accuracy = 69.76%; AUC = 0.78).
CONCLUSION: Our findings indicate that OCD severity maps onto distinct, severity-dependent microstate profiles rather than a linear network disruption. The successful classification of severity subgroups via ML further underscores the neurophysiological heterogeneity of the disorder, suggesting that precision psychiatry strategies should be tailored to the specific network status associated with different disease stages.
METHODS: Resting-state 64-channel EEG was recorded from a total sample of 120 participants. After quality control, we analyzed resting-state EEG data from 101 participants, comprising 65 OCD patients (35 males; mean age = 28.24 ± 8.74 years) and 36 healthy controls (19 males; mean age = 24.67 ± 13.56 years). Patients were stratified into severe (n = 30) and non-severe (n = 35) groups based on Yale-Brown Obsessive-Compulsive Scale (YBOCS) scores. Then, we compared microstate parameters across groups and employed machine learning (ML) to identify severity-related features.
RESULTS: Distinct microstate patterns emerged based on severity. Non-severe patients showed altered microstates B and C, whereas severe patients exhibited significant deficits in microstate D and elevated microstate A. In the combined OCD cohort and the severe group, YBOCS scores had no significant relationship with microstate parameters, while YBOCS-compulsion scores negatively correlated with coverage A and TP B → A in the non-severe group. Moreover, the ML model successfully distinguished severe from non-severe cases (Balanced Accuracy = 69.76%; AUC = 0.78).
CONCLUSION: Our findings indicate that OCD severity maps onto distinct, severity-dependent microstate profiles rather than a linear network disruption. The successful classification of severity subgroups via ML further underscores the neurophysiological heterogeneity of the disorder, suggesting that precision psychiatry strategies should be tailored to the specific network status associated with different disease stages.