Multi-Metric EEG Connectivity and Graph Analysis Reveal Altered Resting-State Networks in Major Depressive Disorder.
- 1Department of Computer Science and Engineering, Varendra University, Rajshahi, Bangladesh.
- 2Center For Interdisciplinary Research, Varendra University, Rajshahi, Bangladesh.
- 3Professor Emeritus of Mathematical Sciences, Ball State University, Muncie, Indiana, USA.
Abstract
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.
Related
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BibTeX
@article{rose2026multimetric,
title = {{Multi-Metric EEG Connectivity and Graph Analysis Reveal Altered Resting-State Networks in Major Depressive Disorder.}},
author = {Sushmit Jahan Rose and A H M Rahmatullah Imon and Rushdania Bushra and Nafis Ahmed and Moha Tasneem Habib Warid},
journal = {Brain and behavior},
year = {2026},
month = oct,
doi = {10.1002/brb3.71842},
url = {https://europepmc.org/article/MED/42902871},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi-Metric EEG Connectivity and Graph Analysis Reveal Altered Resting-State Networks in Major Depressive Disorder.
AU - Sushmit Jahan Rose
AU - A H M Rahmatullah Imon
AU - Rushdania Bushra
AU - Nafis Ahmed
AU - Moha Tasneem Habib Warid
PY - 2026
DA - 2026/10/01
JO - Brain and behavior
DO - 10.1002/brb3.71842
UR - https://europepmc.org/article/MED/42902871
AB - 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.
SN - 2162-3279
ER -