Multi-Metric EEG Connectivity and Graph Analysis Reveal Altered Resting-State Networks in Major Depressive Disorder

Purpose

EEG functional-connectivity studies in major depressive disorder (MDD) have produced inconsistent findings, partly because different estimators capture distinct aspects of neural coupling. We implemented a multi-metric resting-state EEG approach combining linear, phase-based, nonlinear, and graph-theoretical measures to characterize frequency-dependent sensor-level connectivity alterations and identify sensor-level network patterns relevant to depression-related dysconnectivity.