A brain functional network framework based on EEG connectivity for major depressive disorder recognition.
- 1School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China.
- 2School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, China.
Abstract
Introduction
When the brain is regarded as a complex dynamic system, its abnormalities often show the overall network reorganization. Traditional analysis for major depressive disorder (MDD) based on single-channel features of electroencephalogram (EEG) mainly focuses on signal changes, while ignoring inter-regional functional connectivity and network topology. As a consequence, it is challenging to reveal the systemic abnormal mechanisms.Methods
To address this problem, this study built a framework of multi-frequency brain functional network (BFN) construction and recognition based on resting-state EEG. Phase-locking value (PLV) and Pearson correlation coefficient (PCC) were separately utilized to characterize phase synchronization and amplitude correlation. Abnormal BFNs in MDD were systematically evaluated from both connectivity patterns and node topology.Results
Results suggest that MDD patients exhibit increased connectivity in the left frontal, central, and parietal regions in the low-frequency bands. In the mid-frequency bands, increased fronto-central connectivity and reduced posterior connectivity are observed. In the high-frequency band, the connectivity pattern is characterized by increased fronto-temporal and centro-temporal connectivity, together with reduced connectivity in the posterior regions. At the node level, the most prominent alterations are concentrated in the parietal region, particularly in the α band, where altered betweenness centrality (BC) is consistently identified across both PLV and PCC networks. On this basis, the support vector machine (SVM) classifier was employed to evaluate the discriminative capability of PLV-Edge, PCC-Edge, PLV-Node, and PCC-Node features for MDD identification. Among the four feature types, PLV-Node features achieve the highest mean classification balanced accuracy of 81.44%, and PCC-Node features achieve a mean balanced accuracy of 79.14%.Discussion
All four feature types demonstrate discriminative potential for distinguishing patients with MDD from healthy controls (HCs). These findings suggest that functional connectivity-based brain network features may provide meaningful information for the auxiliary identification of MDD.
Related
- Alterations in local neural activity and functional connectivity density in first-episode and recurrent major depressive disorder: a resting-state fMRI study
- Resting-state functional connectivity of hippocampal subregions in predicting escitalopram treatment outcome in major depressive disorder
- Network-level convergence of functional and structural alterations in first-episode major depressive disorder
- Regional and network-level resting state functional connectivity markers of pharmacological treatment resistance in obsessive-compulsive disorder
- Multi-Metric EEG Connectivity and Graph Analysis Reveal Altered Resting-State Networks in Major Depressive Disorder.
BibTeX
@article{fang2026brain,
title = {{A brain functional network framework based on EEG connectivity for major depressive disorder recognition.}},
author = {Min Fang and Tian-Ao Cao and Jionghang Wu and Huixin Liu and Jijun Tong and Zhengkui Chen},
journal = {Frontiers in human neuroscience},
year = {2026},
month = oct,
doi = {10.3389/fnhum.2026.1930925},
url = {https://europepmc.org/article/MED/42908938},
note = {CC BY},
}
RIS
TY - JOUR
TI - A brain functional network framework based on EEG connectivity for major depressive disorder recognition.
AU - Min Fang
AU - Tian-Ao Cao
AU - Jionghang Wu
AU - Huixin Liu
AU - Jijun Tong
AU - Zhengkui Chen
PY - 2026
DA - 2026/10/07
JO - Frontiers in human neuroscience
DO - 10.3389/fnhum.2026.1930925
UR - https://europepmc.org/article/MED/42908938
AB - Introduction When the brain is regarded as a complex dynamic system, its abnormalities often show the overall network reorganization. Traditional analysis for major depressive disorder (MDD) based on single-channel features of electroencephalogram (EEG) mainly focuses on signal changes, while ignoring inter-regional functional connectivity and network topology. As a consequence, it is challenging to reveal the systemic abnormal mechanisms.Methods To address this problem, this study built a framework of multi-frequency brain functional network (BFN) construction and recognition based on resting-state EEG. Phase-locking value (PLV) and Pearson correlation coefficient (PCC) were separately utilized to characterize phase synchronization and amplitude correlation. Abnormal BFNs in MDD were systematically evaluated from both connectivity patterns and node topology.Results Results suggest that MDD patients exhibit increased connectivity in the left frontal, central, and parietal regions in the low-frequency bands. In the mid-frequency bands, increased fronto-central connectivity and reduced posterior connectivity are observed. In the high-frequency band, the connectivity pattern is characterized by increased fronto-temporal and centro-temporal connectivity, together with reduced connectivity in the posterior regions. At the node level, the most prominent alterations are concentrated in the parietal region, particularly in the α band, where altered betweenness centrality (BC) is consistently identified across both PLV and PCC networks. On this basis, the support vector machine (SVM) classifier was employed to evaluate the discriminative capability of PLV-Edge, PCC-Edge, PLV-Node, and PCC-Node features for MDD identification. Among the four feature types, PLV-Node features achieve the highest mean classification balanced accuracy of 81.44%, and PCC-Node features achieve a mean balanced accuracy of 79.14%.Discussion All four feature types demonstrate discriminative potential for distinguishing patients with MDD from healthy controls (HCs). These findings suggest that functional connectivity-based brain network features may provide meaningful information for the auxiliary identification of MDD.
SN - 1662-5161
ER -