Functional connectivity of major depression disorder using ongoing EEG during music perception

• Published in: Clinical neurophysiology 2020-10, Vol.131 (10), p.2413-2422
• Main Authors: Liu, Wenya, Zhang, Chi, Wang, Xiaoyu, Xu, Jing, Chang, Yi, Ristaniemi, Tapani, Cong, Fengyu
• Format: Article
• Language: English
• Subjects: Functional connectivity; Major depression disorder; Music perception; Naturalistic stimuli; Ongoing EEG
• ISSN: 1388-2457; 1872-8952
• DOI: 10.1016/j.clinph.2020.06.031

•Major depression causes altered connectivity in delta and beta bands during music perception.•Beta band connectivity is a promising biomarker for the diagnosis of major depression disorder.•Naturalistic music stimuli lead to frequency-specific functional connectivity. The functional connectivity (FC) of major depression disorder (MDD) has not been well studied under naturalistic and continuous stimuli conditions. In this study, we investigated the frequency-specific FC of MDD patients exposed to conditions of music perception using ongoing electroencephalogram (EEG). First, we applied the phase lag index (PLI) method to calculate the connectivity matrices and graph theory-based methods to measure the topology of brain networks across different frequency bands. Then, classification methods were adopted to identify the most discriminate frequency band for the diagnosis of MDD. During music perception, MDD patients exhibited a decreased connectivity pattern in the delta band but an increased connectivity pattern in the beta band. Healthy people showed a left hemisphere-dominant phenomenon, but MDD patients did not show such a lateralized effect. Support vector machine (SVM) achieved the best classification performance in the beta frequency band with an accuracy of 89.7%, sensitivity of 89.4% and specificity of 89.9%. MDD patients exhibited an altered FC in delta and beta bands, and the beta band showed a superiority in the diagnosis of MDD. Our study provided a promising reference for the diagnosis of MDD, and revealed a new perspective for understanding the topology of MDD brain networks during music perception.