不同抑郁程度个体的静息态脑电微状态特点研究  

Characteristics of resting-state electroencephalogram microstates in individuals with different levels of depressive symptoms

作　　者：薛奕童 李奎良 张晶轩[1] 冯正直[1] XUE Yitong;LI Kuiliang;ZHANG Jingxuan;FENG Zhengzhi(Department of Developmental Psychology for Army man,Faculty of Medical Psychology,Army Medical University(Third Military Medical University),Chongqing,400038,China)

机构地区：[1]陆军军医大学(第三军医大学)医学心理系军人发展心理学教研室,重庆400038

出　　处：《第三军医大学学报》2021年第24期2609-2617,共9页Journal of Third Military Medical University

基　　金：国家自然科学基金面上项目(81971278);陆军军医大学人文社科基金一般项目(2019XRW10)。

摘　　要：目的探索不同抑郁程度的个体在静息状态下脑电微状态的特征,为筛查和识别抑郁症状提供客观的生物标记。方法 2020年7-10月在重庆市内公开招募了129名18~40岁的健康志愿者,采用抑郁自评量表(Self-Rating Depression Scale,SDS)对其进行测查。按量表反映的症状分数,将所有志愿者分为无抑郁组(107人)、轻度抑郁组(18人)和中重度抑郁组(4人),分别采集8 min静息态脑电信号,通过MATLAB、EEGLAB等工具对脑电数据进行预处理和微状态分析,得到4类(A、B、C、D)微状态参数,包括全局解释方差(global explanation variance,GEV)、持续时间、出现频率、覆盖率和转换率等,利用SPSS 24.0对各项参数进行统计学分析。结果 (1)17.05%的个体存在抑郁症状。静息状态下健康志愿者脑电微状态的平均GEV为76.49%。(2)轻度抑郁组微状态A的平均持续时间低于无抑郁组和中重度抑郁组(P<0.001)。(3)无抑郁组及中重度抑郁组个体微状态A出现频率高于其他三类微状态(P<0.001)。(4)轻度抑郁组微状态A的覆盖率低于无抑郁组和中重度抑郁组(P<0.001)。(5)不同类型微状态之间的转换率存在差异(P<0.05)。结论微状态A持续时间可能是抑郁症状发生发展的预警信号,是抑郁症临床前期的状态特征;微状态A和C的转换率下降可能提示抑郁症状发生,微状态C和D的转换率改变可能是抑郁症状发展和转归过程的关键特征。Objective To explore the characteristics of electroencephalogram(EEG) microstates of individuals with different levels of depressive symptoms in resting state so as to provide objective biomarkers in screening and identification of depressive symptoms in the population.Methods A total of 129 healthy volunteers aged 18 to 40 years were recruited from July to October 2020 in Chongqing.According to the results of Self-Rating Depression Scale(SDS),they were divided into following 3 groups:non-depressive group(n=107),mild depressive group(n=18) and moderate to severe depressive group(n=4).EEG signals of subjects in 8-min resting state were collected,and then were preprocessed and analyzed by MATLAB,EEGLAB and other tools to obtain 4 types(A,B,C and D) of microstate and their parameters,including global explanation variance(GEV),duration,occurrence frequency,coverage rate and conversion rate.SPSS24.0 was used for statistical analysis of each parameter.Results(1)Among the subjects,17.05% showed depressive symptoms.The average GEV of resting EEG microstates in healthy volunteers was 76.49%.(2)The mean duration of microstate A in the mild depressive group was lower than that in the non-depressive and moderate to severe depressive group(P<0.001).(3)EEG microstate A had higher occurrence frequency than the other 3 types of microstates in the non-depressive and moderatet to severe depressive group(P<0.001).(4)The mild depressive group presented lower coverage rate of microstate A than the other 2 groups(P<0.001).(5)There were differences in conversion rates among different types of microstates(P<0.05).Conclusion The duration of microstate A may be an early warning signal of the occurrence and development of depressive symptoms,which characterizes the preclinical state of depression.The declined conversion rate of microstates A and C may indicate the occurrence of depressive symptoms,and the alteration in the conversion rate of microstates C and D may be a key feature of the development and progression of depressive symptoms.

关 键 词：抑郁自评量表 抑郁症状 脑电图 微状态 

分 类 号：R195.4[医药卫生—卫生统计学] R741.044[医药卫生—卫生事业管理]