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

摘要

Objective. Analysis of functional and structural brain networks has suggested that major depressivedisorder (MDD) is associated with a disruption in brain networks. This paper aims to investigatethe abnormalities of brain networks in MDD. Approach. To this aim, we constructed weighteddirected functional networks based on electroencephalography (EEG) signals of 26 MDD patientsand 23 normal (N) subjects. The nodes of networks were 19 EEG electrodes, and the edges werephase transfer entropy (PTE) between each pair of electrodes. PTE is a model-free, phase-basedeffective connectivity measure that is relatively robust to noise and linear mixing. Since the correctinstantaneous phase of a signal is computed for narrow frequency bands, the networks wereanalyzed 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, graphtheory metrics consisting of global efficiency, local efficiency, node betweenness centrality, nodedegree, and node strength were analyzed by statistical tests and classification. Furthermore,directed differential connectivity graphs (dDCGs) for the MDD and N groups were studied. Mainresults. These analyses revealed a higher node degree and strength in the dDCGs of the MDDgroup than the normal group. It was also found that MDD brain networks have a morerandomized structure than the N group. Moreover, our results indicated that the out-degree ofnetworks classified MDD and N subjects with an accuracy of 92%; thus, our method can beconsidered as a powerful tool for depression detection. Significance. Our analysis may provide newinsights into developing biomarkers for depression detection based on brain networks.