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

Year of publication

2020

Authors

Liu, Wenya; Zhang, Chi; Wang, Xiaoyu; Xu, Jing; Chang, Yi; Ristaniemi, Tapani; Cong, Fengyu

Abstract

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.
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Organizations and authors

University of Jyväskylä

Cong Fengyu

Ristaniemi Tapani Orcid -palvelun logo

Liu Wenya

Publication type

Publication format

Article

Parent publication type

Journal

Article type

Original article

Audience

Scientific

Peer-reviewed

Peer-Reviewed

MINEDU's publication type classification code

A1 Journal article (refereed), original research

Publication channel information

Publisher

Elsevier BV

Volume

131

Issue

10

Pages

2413-2422

​Publication forum

53610

​Publication forum level

2

Open access

Open access in the publisher’s service

No

Self-archived

Yes

Other information

Fields of science

Computer and information sciences

Keywords

[object Object],[object Object],[object Object],[object Object]

Internationality of the publisher

International

Language

English

International co-publication

Yes

Co-publication with a company

No

DOI

10.1016/j.clinph.2020.06.031

The publication is included in the Ministry of Education and Culture’s Publication data collection

Yes