Functional connectivity of major depression disorder using ongoing EEG during music perception
Liu, W., Zhang, C., Wang, X., Xu, J., Chang, Y., Ristaniemi, T., & Cong, F. (2020). Functional connectivity of major depression disorder using ongoing EEG during music perception. Clinical Neurophysiology, 131(10), 2413-2422. https://doi.org/10.1016/j.clinph.2020.06.031
Published inClinical Neurophysiology
Xu, Jing |
© 2020 Elsevier
Objective 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). Methods First, we applied 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. Results 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%. Conclusions MDD patients exhibited an altered FC in delta and beta bands, and the beta band showed a superiority in the diagnosis of MDD. Significance 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. ...
ISSN Search the Publication Forum1388-2457
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Additional information about fundingThis work was supported by National Natural Science Foundation of China (Grant No.91748105 & Grant No.81471742); the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China; and the scholarships from China scholarship Council (No. 201706060263).
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