Treating Mild to Moderate Depression With Transcutaneous Electrical Cranial-Auricular Vagus Nerve Stimulation: A...

Transcutaneous electrical cranial-auricular acupoints stimulation (TECAS) has been recognized as a promising therapeutic approach for depression. However, the efficacy of TECAS varies among individuals, and it remains unclear which populations are more sensitive to this treatment. This study aims to investigate the impact of TECAS on brain functional networks by analyzing electroencephalogram (EEG) data, distinguishing between responders and non-responders. We included 57 patients with mild to moderate depression and collected EEG data at baseline and after 8 weeks of TECAS treatment. Our analysis focused on identifying baseline network characteristics correlating with positive TECAS responses. The results indicate that patients with higher network integration and synchrony, particularly those with elevated delta frequency band network topology parameters, showed better outcomes with TECAS. Additionally, using a nonlinear regression model, we predicted the effectiveness of TECAS with a correlation coefficient of 0.52 and an RMSE of 17.3 %. Machine learning techniques were further employed to identify responders and non-responders at baseline, with the XGBoost classifier achieving the highest accuracy of 82.91 %. These findings suggest that specific EEG network features can serve as predictors for the efficacy of TECAS in treating depression.