Comprehensive Summary
This study investigates the use of EEG microstate analysis combined with machine learning to enhance the diagnosis and subclassification of obsessive-compulsive disorder (OCD). OCD is a chronic mental health disorder characterized by recurring thoughts and repetitive behaviors, which significantly impact patients' quality of life. The study compares EEG features between OCD patients and healthy controls, focusing on EEG microstates that reflect the temporal dynamics of brain networks. The authors report significant changes in these microstate features in OCD patients, correlating with the severity of obsessive thoughts and anxiety symptoms. Three machine learning models—random forest, SVM, and XGBoost—were employed to subclassify OCD severity. Although these models showed limited classification ability, the research highlights the potential of combining EEG microstates with machine learning for future OCD diagnosis and management.
Outcomes and Implications
The study underscores the challenges in managing OCD due to the low response rate to pharmacological treatments and the lack of reliable biological markers. By characterizing pathological features of OCD and exploring EEG microstates as potential biomarkers, the research contributes to a deeper understanding of OCD pathophysiology. The findings suggest that EEG microstates, in conjunction with machine learning, could aid in the early diagnosis and subclassification of OCD, potentially leading to more personalized treatment approaches. This could improve the quality of life for patients by enabling more targeted interventions based on the severity of symptoms.