Qiu Pintao, Dai Jinxiao, Wang Ting, Li Hangcheng, Ma Cunbin, Xi Xugang
HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China.
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China.
Brain Sci. 2022 Dec 7;12(12):1680. doi: 10.3390/brainsci12121680.
Major depressive disorder (MDD) is a common mental illness. This study used electroencephalography (EEG) to explore the effects of music therapy on brain networks in MDD patients and to elucidate changes in functional brain connectivity in subjects before and after musical stimulation. EEG signals were collected from eight MDD patients and eight healthy controls. The phase locking value was adopted to calculate the EEG correlation of different channels in different frequency bands. Correlation matrices and network topologies were studied to analyze changes in functional connectivity between brain regions. The results of the experimental analysis found that the connectivity of the delta and beta bands decreased, while the connectivity of the alpha band increased. Regarding the characteristics of the EEG functional network, the average clustering coefficient, characteristic path length and degree of each node in the delta band decreased significantly after musical stimulation, while the characteristic path length in the beta band increased significantly. Characterized by the average clustering coefficient and characteristic path length, the classification of depression and healthy controls reached 93.75% using a support vector machine.
重度抑郁症（MDD）是一种常见的精神疾病。本研究采用脑电图（EEG）来探究音乐疗法对MDD患者脑网络的影响，并阐明音乐刺激前后受试者大脑功能连接的变化。从8名MDD患者和8名健康对照者身上采集EEG信号。采用锁相值来计算不同频段不同通道的EEG相关性。研究相关矩阵和网络拓扑结构以分析脑区之间功能连接的变化。实验分析结果发现，δ波和β波频段的连接性降低，而α波频段的连接性增加。关于EEG功能网络的特征，音乐刺激后δ波频段的平均聚类系数、特征路径长度和每个节点的度显著降低，而β波频段的特征路径长度显著增加。以平均聚类系数和特征路径长度为特征，使用支持向量机对抑郁症患者和健康对照者的分类准确率达到93.75%。
