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Functional connectivity of major depression disorder using ongoing EEG during music perception
Clinical Neurophysiology ( IF 4.7 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.clinph.2020.06.031
Wenya Liu 1 , Chi Zhang 2 , Xiaoyu Wang 2 , Jing Xu 3 , Yi Chang 3 , Tapani Ristaniemi 4 , Fengyu Cong 5
Affiliation  

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.

中文翻译:

在音乐感知过程中使用持续脑电图对重度抑郁症的功能连接

目的 重性抑郁症 (MDD) 的功能连接 (FC) 在自然和连续刺激条件下尚未得到很好的研究。在这项研究中,我们使用正在进行的脑电图 (EEG) 调查了暴露于音乐感知条件的 MDD 患者的频率特异性 FC。方法 首先,我们应用相位滞后指数 (PLI) 方法来计算连接矩阵和基于图论的方法来测量跨不同频段的大脑网络的拓扑结构。然后,采用分类方法来识别最有辨别力的频带用于诊断MDD。结果 在音乐感知过程中,MDD 患者在 delta 波段表现出连接模式降低,但在 beta 波段表现出增加的连接模式。健康人表现出左半球优势现象,但 MDD 患者并没有表现出这种偏侧效应。支持向量机(SVM)在 beta 频段取得了最佳分类性能,准确率为 89.7%,灵敏度为 89.4%,特异性为 89.9%。结论 MDD 患者在 delta 和 beta 带中表现出改变的 FC,并且 beta 带在 MDD 的诊断中显示出优越性。意义我们的研究为MDD的诊断提供了有希望的参考,并为理解音乐感知过程中MDD大脑网络的拓扑结构提供了新的视角。结论 MDD 患者在 delta 和 beta 带中表现出改变的 FC,并且 beta 带在 MDD 的诊断中显示出优越性。意义我们的研究为MDD的诊断提供了有希望的参考,并为理解音乐感知过程中MDD大脑网络的拓扑结构提供了新的视角。结论 MDD 患者在 delta 和 beta 带中表现出改变的 FC,并且 beta 带在 MDD 的诊断中显示出优越性。意义我们的研究为MDD的诊断提供了有希望的参考,并为理解音乐感知过程中MDD大脑网络的拓扑结构提供了新的视角。
更新日期:2020-10-01
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