EEG-Based Effective Connectivity for Automated Detection of Major Depressive Disorder

Major depressive disorder (MDD) is a prevalent mental health condition with profound consequences on daily functioning and quality of life. Existing studies have focused on functional connectivity (FC), with limited investigation of effective connectivity (EC) and its frequency-specific manifestations within machine learning frameworks. New Method: This study proposes an automated MDD detection framework based on graph-theoretical features derived from two complementary connectivity measures: the phase slope index (PSI), representing directed EC, and the weighted phase lag index (WPLI), representing FC. Statistically selected features were extracted across five traditional frequency bands and evaluated using multiple machine learning classifiers. Results: PSI-based features showed a marginal advantage in accuracy and area under the curve (AUC) (63.4% and 0.63) compared with WPLI-based features (62.8% and 0.60). These small differences cannot be interpreted as a meaningful performance difference between the two connectivity measures. The delta and beta bands, in both connectivity methods, consistently yielded the most discriminative results across both connectivity methods, with the theta band additionally discriminative for FC. Comparison with existing methods: Most existing studies rely on FC measures, whereas the proposed framework incorporates directed EC and systematically evaluates the discriminative contribution of individual frequency bands through graph-theoretical analysis. Conclusions: Directed EC and FC achieved comparable classification performance, but frequency-specific graph-theoretical analysis of EC could provide additional interpretability by capturing the directionality of disrupted brain communication in MDD, supporting the development of more neurophysiologically meaningful diagnostic tools.