Machine learning-based source-level EEG functional network biomarkers of antidepressant treatment response in major depressive disorder.

Abstract

Major depressive disorder (MDD) is highly prevalent, yet antidepressant selection remains largely trial and error because of limited predictive biomarkers. Resting-state electroencephalography (EEG) combined with graph theory-based functional networks offers a noninvasive approach to probing brain dysconnectivity. This study aimed to delineate source-level functional network differences between antidepressant responders and non-responders, and to evaluate whether machine learning models using nodal network features can discriminate treatment outcome. Resting-state EEG was recorded at baseline from 47 patients with MDD and 41 healthy controls (HCs). After 12 weeks, patients were classified as responders (n = 20) or non-responders (n = 27) based on a ≥ 50% reduction in Hamilton Depression Rating Scale scores. Source-level networks were assessed using path length (PL), strength, clustering coefficient (CC), and eigenvector centrality (EC). Machine learning with nested cross-validation classified responders and non-responders using nodal features. At the global level, non-responders showed lower strength and CC and higher PL in the high beta band than responders. Responders showed higher theta-band CC than HCs. A support vector machine achieved 82.98% accuracy (95% CI, 72.34%-93.62%; p < 0.001), 85.19% sensitivity (95% CI, 70.37%-96.30%; p < 0.001), and 80.00% specificity (95% CI, 60.00%-95.00%; p < 0.001). In responders, theta-band EC in the precuneus and pars orbitalis correlated significantly with psychological assessment scores. Source-level functional network analysis combined with machine learning may identify pretreatment EEG features associated with subsequent antidepressant response.