Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study

Area of Science:

• Neuroscience
• Psychiatry
• Biomedical Engineering

Background:

• Electroencephalogram (EEG)-based brain network analysis offers insights into brain function.
• Sensor-level EEG analysis can be limited by volume conduction, necessitating source-level approaches.
• Source-level network analysis provides a valuable alternative for understanding regional brain characteristics.

Purpose of the Study:

• To investigate EEG-based source-level brain network characteristics in major depressive disorder (MDD).
• To explore potential network biomarkers for MDD using source-level EEG analysis.

Main Methods:

• Resting-state EEG data were collected from 87 individuals with MDD and 58 healthy controls.
• Cortical source signals were estimated, and network measures (global and nodal indices) were calculated across six frequency bands.
• Correlation analyses examined relationships between network indices and depression/anxiety symptom scales.

Main Results:

• MDD exhibited decreased global network strength, clustering coefficient (CC) in theta and alpha bands, and efficiency in the alpha band.
• Path length (PL) was enhanced in the alpha band for MDD patients.
• Nodal CCs in the alpha band were reduced in MDD and negatively correlated with depression and anxiety scores, with region-dependent changes in eigenvector centrality.

Conclusions:

• Altered EEG-based source-level brain network indices in MDD may indicate disturbances in emotional processing.
• Source-level network measures show promise as potential biomarkers for understanding regional brain pathology in MDD.