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Title:: Magnetoencephalography study of depression based on multiscale Jensen Shannon divergence

作者:: 陈涛1; 姚文坡1; 白登选2; 闫伟3; 王俊1; （1.南京邮电大学地理与生物信息学院，南京 210003；2.南京邮电大学通信工程学院，南京 210003；3.南京医科大学附属脑科医院,南京 210003）

Author(s):: CHEN Tao1; YAO Wenpo1; BAI Dengxuan2; YAN Wei3; WANG Jun1; （1.School of Geographic and Bioinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210003，China;2.School of Communication Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003;3.Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210003）

Keywords:: Multi-scale; Jensen Shannon divergence; Depression; Magnetoencephalography; Nonlinear analysis

摘要:: 为探讨脑磁图在抑郁症诊断及治疗中的作用，本研究首先使用詹森香农分叉算法和多尺度詹森香农分叉算法在负性、正性和中性情绪刺激下，对抑郁症组和健康对照组的脑磁图信号进行分析，并采用SPSS软件对实验结果进行独立样本t检验，计算三种情绪刺激下区分度最好的通道和多尺度因子；最后，采用误差棒图分析三种情绪刺激下，抑郁症组和健康对照组的脑磁图信号。结果表明，詹森香农分叉算法和多尺度詹森香农分叉算法均可区分抑郁症组和健康对照组的脑磁图信号，两者脑磁图信号存在一定差异。在三种情绪刺激下，抑郁症组在大多数通道的统计复杂度比健康对照组高，且在中性情绪刺激下，区分度最高。该算法可为抑郁症的辅助诊断和治疗提供帮助。

Abstract:: To explore the role of magnetoencephalogram in the diagnosis and treatment of depression, the Jensen Shannon divergence(JSD) algorithm and the multi-scale JSD algorithm were used to study the magnetoencephalography signals of the depression control group and the healthy control group under the stimulation of negative, positive and neutral emotions, and the independent sample t test was carried out on the experimental results by SPSS statistical software, and the channels and multi-scale factors with the best discrimination under the three emotional stimuli were calculated. Finally, the error bar chart method was used to analyze the magnetoencephalography signals of the depression control group and the healthy control group under three emotional stimuli. The results showed that both the JSD algorithm and the multi-scale JSD algorithm can distinguish the magnetoencephalography signals of the depression control group and the healthy control group, and the magnetoencephalography signals of the two groups are different. Under the three emotional stimuli, the statistical complexity of most channels in the depression control group was higher than that of the healthy control group, the difference in the frontal region was more obvious, and the sensitivity under neutral emotional stimulation was greater. This algorithm can provide effective tools for the diagnosis and treatment of depression.