EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder

Background

Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological changes of whole-brain functional networks in patients with obsessive-compulsive disorders (OCD) through microstate analysis and further to explore its potential value as an auxiliary diagnostic index.

Methods

Forty-eight OCD patients (33 with more than moderate anxiety symptoms, 15 with mild anxiety symptoms) and 52 healthy controls (HCs) were recruited. Brain activities during eyes-closed period were collected using 64-channel electroencephalography. The differences in microstate features between OCD patients and HCs were compared, and the relationship between the microstate features and clinical symptoms were explored. Key microstate features were selected for machine learning modeling to achieve targeted classifications.

Results

The probability of transition from microstate B to C was significantly lower in OCD patients compared to HCs, and the obsessive thoughts factor scores were significantly correlated with the duration of microstate A, the occurrence of microstate B, and the transition probability from microstate C to B. The occurrence rate of microstate C was significantly negatively correlated with the Hamilton rating scale for anxiety (HAMA) scores. The AUC (Area Under the Receiver Operating Characteristic Curve) of the machine learning model in the test set classification between the above two groups and between OCD patients with more than moderate/mild anxiety symptoms could achieve 70.43% and 77.13%, respectively.

Conclusion

EEG microstate characteristics were altered in OCD patients, and these changes were closely associated with obsessive thoughts and anxiety symptoms. Besides, the machine learning classification model based on microstate features has limited ability to identify OCD, and further optimization on this classification approach is still needed in the future.