Brain Network Analysis Based on Frontal Cortical EEG Sources in Major Depressive Disorder (MDD)

Studies on functional connectivity and brain networks have suggested that major depressive disorder (MDD) affects brain networks. This paper investigates the abnormalities in MDD brain networks. To this goal, we constructed brain networks based on electroencephalographic (EEG) source space in 32 patients and 32 healthy controls. The studied sources that are located in the prefrontal cortex, including the subgenual (SGPFC), medial (MPFC), and dorsolateral (DLPFC) prefrontal cortex, are extracted by eLORETA (exact low resolution brain electromagnetic tomography). The effective connectivity between extracted sources is assessed by transfer entropy (TE). After constructing 6*6 connectivity graphs between 6 sources (3 in the right and 3 in the left hemisphere), the topological characteristics of these graphs is quantified by graph theory measures. The applied measures include density, global efficiency, local efficiency, node betweenness centrality (BC), and node degree. The graph measures are statistically compared between two groups. The results show statistically significant differences in degree and BC between depressed and normal groups. Moreover, depressed patients revealed lower prefrontal connectivity at the delta frequency band. The obtained results indicate that degree and BC of networks based on TE might have the potential to apply as depression biomarkers.