Wenya Liu1, Chi Zhang2, Xiaoyu Wang2, Jing Xu3, Yi Chang4, Tapani Ristaniemi5, Fengyu Cong6. 1. School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland. 2. School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China. 3. Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, 116011 Dalian, China. Electronic address: xujing_doc@aliyun.com. 4. Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, 116011 Dalian, China. Electronic address: changee99@gmail.com. 5. Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland. 6. School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland; School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China; Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, 116024 Dalian, China. Electronic address: cong@dlut.edu.cn.
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 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. 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.
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 MDDpatients exposed to conditions of music perception using ongoing electroencephalogram (EEG). METHODS: 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. RESULTS: During music perception, MDDpatients 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 MDDpatients 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:MDDpatients 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.
Authors: Mariella Gregorich; Federico Melograna; Martina Sunqvist; Stefan Michiels; Kristel Van Steen; Georg Heinze Journal: BMC Med Res Methodol Date: 2022-03-06 Impact factor: 4.615