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 Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China.
Clin Neurophysiol. 2020 Oct;131(10):2413-2422. doi: 10.1016/j.clinph.2020.06.031. Epub 2020 Jul 30.
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.
在自然和连续刺激条件下，尚未很好地研究重度抑郁症（MDD）的功能连接（FC）。在这项研究中，我们使用持续脑电图（EEG）研究了暴露于音乐感知条件下的 MDD 患者的频域特定 FC。
首先，我们应用相位滞后指数（PLI）方法计算连接矩阵和基于图论的方法来测量不同频带的脑网络拓扑。然后，采用分类方法来识别最具区分力的频带以诊断 MDD。
在音乐感知期间，MDD 患者在 delta 频带中表现出连接模式降低，但在 beta 频带中表现出连接模式增加。健康人表现出左半球优势现象，但 MDD 患者没有表现出这种偏侧化效应。支持向量机（SVM）在 beta 频带中实现了最佳的分类性能，准确率为 89.7%，灵敏度为 89.4%，特异性为 89.9%。
MDD 患者在 delta 和 beta 频带中表现出异常的 FC，并且 beta 频带在 MDD 的诊断中具有优势。
我们的研究为 MDD 的诊断提供了有前途的参考，并为理解音乐感知期间 MDD 大脑网络的拓扑结构提供了新的视角。
