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.

METHOD: Resting-state EEG from 30 DSM-IV MDD participants and 28 healthy controls was analyzed using a 19-channel 10-20 montage. After MNE-Python preprocessing, including filtering, ICA-based artifact removal, current source density (CSD) transformation, and symmetric orthogonalization, coherence, PLV, PLI, and MI were computed across theta, alpha, and beta bands. Edge-wise differences were tested using Brunner-Munzel with Holm correction, confirmed with Westfall-Young maxT correction, and summarized using Hedges' g and graph measures.

FINDING: MDD showed frequency- and metric-dependent sensor-level connectivity alterations. Theta coherence, PLV, and PLI were dominated by lower connectivity across frontal-to-posterior, fronto-central, temporal/posterior-temporal, parieto-occipital, and central-to-occipital sensor pairs, whereas theta MI showed few increased frontal/fronto-temporal effects. Alpha findings were more selective, with mainly lower coherence and PLV across frontopolar/frontal-to-central, fronto-midline, and posterior links, while alpha MI showed a limited mixed pattern. In beta, coherence, PLV, and PLI showed sparse robust reductions, whereas beta MI showed widespread increased statistical dependence. Graph-theoretical summaries at 20% proportional density showed mainly lower mean weighted strength and global efficiency in theta- and alpha-band networks.

CONCLUSION

These findings indicate frequency- and metric-dependent resting-state EEG sensor-level connectivity alterations in MDD. The dominant pattern was lower theta/alpha connectivity across distributed sensor pairs, alongside increased beta MI-based dependence. The results provide corrected, effect-size-informed evidence of altered inter-sensor coordination without establishing dysfunction of specific cortical regions or named networks. This framework may support future hypothesis-driven research in independent, clinically annotated, higher-density EEG or multimodal datasets.