Abnormal Properties of Cortical Functional Brain Network in Major Depressive Disorder: Graph Theory Analysis Based on Electroencephalography-Source Estimates. (1st December 2022)

• Record Type: Journal Article
• Authors: Teng, Chaolin; Wang, Mengwei; Wang, Wei; Ma, Jin; Jia, Min; Wu, Min; Luo, Yuanyuan; Wang, Yu; Zhang, Yiyang; Xu, Jin
• Is Part Of: Neuroscience. Volume 506(2022)
• Page Start: 80
• Page End: 90
• Publication Date: 2022-12-01
• Subjects: Major depressive disorder (MDD) -- Electroencephalography (EEG) -- Functional brain networks -- sLORETA -- Correntropy coefficient; BAs Brodmann areas -- CEC correntropy coefficient -- EEG electroencephalography -- FC functional connectivity -- fMRI functional magnetic resonance imaging -- MDD major depressive disorder -- MEG magnetoencephalography -- ROIs regions of interest; Neurochemistry -- Periodicals; Neurophysiology -- Periodicals; Neurology -- Periodicals; Neurochimie -- Périodiques; Neurophysiologie -- Périodiques; Neurochemistry; Neurophysiology; Electronic journals; Periodicals; Electronic journals; 612.8
• DOI: 10.1016/j.neuroscience.2022.10.010
• Languages: English
• ISSNs: 0306-4522

Abstract

Highlights: The whole cortical functional brain networks were investigated in MDD patients based on EEG-sources. MDD caused enhanced global functional connectivity in α band. Increased global and local clustering coefficients were both found in MDD patients in α and β bands. Abstract: Studies of scalp electroencephalography (EEG) had shown altered topological organization of functional brain networks in patients with major depressive disorder (MDD). However, most previous EEG-based network analyses were performed at sensor level, while the interpretation of obtained results was not straightforward due to volume conduction effect. To reduce the impact of this defect, the whole cortical functional brain networks of MDD patients were studied during resting state based on EEG-source estimates in this paper. First, scalp EEG signals were recorded from 19 patients with MDD and 20 normal controls under resting eyes-closed state, and cortical neural signals were estimated by using sLORETA method. Then, the correntropy coefficient of wavelet packet coefficients was performed to calculate functional connectivity (FC) matrices in four different frequency bands: δ, θ, α, β, respectively. Afterwards, topological properties of brain networks were analyzed by graph theory approaches. The results showed that the global FC strength of MDD patients was significantly higher than that of healthy subjects in α band. Also, it was found that MDD patients have abnormally increased clustering coefficient and local efficiency in both α and β bands compared to normal people. Furthermore, patients with MDD exhibited increased nodal clustering coefficients in the left lingual gryus and left precuneus in α band. In addition, β band global clustering coefficient was positively correlated with the scores of depression severity. Therefore, the findings indicated the cortical functional brain networks in MDD patients were disruptions, which suggested it would be one of potential causes of depression.