Graph theory analysis of directed functional brain networks in major depressive disorder based on EEG signal

Analysis of functional and structural brain networks has suggested that major depressive disorder (MDD) is associated with a disruption in brain networks. This paper aims to investigate the abnormalities of brain networks in MDD.

To this aim, we constructed weighted directed functional networks based on electroencephalography (EEG) signals of 26 MDD patients and 23 normal (N) subjects. The nodes of networks were 19 EEG electrodes, and the edges were phase transfer entropy (PTE) between each pair of electrodes. PTE is a model-free, phase-based effective connectivity measure that is relatively robust to noise and linear mixing. Since the correct instantaneous phase of a signal is computed for narrow frequency bands, the networks were analyzed in eight frequency sub-bands including delta, theta, alpha1, alpha2, beta1, beta2, beta3, and beta4. To assess the alteration in the topology of brain networks in MDD patients, graph theory metrics consisting of global efficiency, local efficiency, node betweenness centrality, node degree, and node strength were analyzed by statistical tests and classification. Furthermore, directed differential connectivity graphs (dDCGs) for the MDD and N groups were studied.

These analyses revealed a higher node degree and strength in the dDCGs of the MDD group than the normal group. It was also found that MDD brain networks have a more randomized structure than the N group. Moreover, our results indicated that the out-degree of networks classified MDD and N subjects with an accuracy of 92%; thus, our method can be considered as a powerful tool for depression detection.

Our analysis may provide new insights into developing biomarkers for depression detection based on brain networks.