Distinct electroencephalogram microstate in patients with methamphetamine use disorder and obsessive-compulsive disorder

Comprehensive Summary

This study investigated whether resting-state EEG microstates differ between individuals with methamphetamine use disorder (MUD), obsessive-compulsive disorder (OCD), and healthy controls to clarify shared and disorder-specific neurodynamic mechanisms underlying compulsive behavior. Using resting EEG data from 127 participants, the authors found that MUD and OCD exhibit distinct microstate profiles, particularly involving microstate A, with MUD patients showing greater time coverage and occurrence than OCD patients. Importantly, microstate dynamics were linked to clinical symptoms in a disorder-specific manner: in OCD, compulsion severity was negatively associated with microstate D duration and B→D transitions and positively associated with microstate C occurrence, whereas in MUD, craving severity was positively correlated with the occurrence and time coverage of microstate D. These findings suggest that while OCD and MUD share compulsive features, they are characterized by distinct large-scale brain network dynamics, with microstates C and D emerging as potential neurophysiological markers of symptom severity and microstate C showing promise as a biomarker specific to OCD.

Outcomes and Implications

The use of EEG microstate analysis in this study shows how advanced brain-recording technology can reveal brief, large-scale patterns of brain activity linked to psychiatric symptoms. Because EEG microstates capture brain dynamics on a millisecond timescale, they can detect differences in neural network functioning that are not visible with slower imaging methods. This approach supports biologically based models of mental illness, such as the Research Domain Criteria, by focusing on underlying brain processes rather than diagnoses alone. Importantly, EEG is noninvasive, relatively low-cost, and widely accessible, making microstate analysis a promising tool for identifying objective biomarkers and improving diagnosis, symptom monitoring, and personalized treatment in clinical psychiatry.