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

Yitong XUE; Kuiliang LI; Jingxuan ZHANG; Zhengzhi FENG

doi:10.16016/j.1000-5404.202106083

您当前的位置：

首页 >

文章列表页 >

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

Neurosciences | 更新时间：2026-01-09

- Characteristics of resting-state electroencephalogram microstates in individuals with different levels of depressive symptoms
- Journal of Third Military Medical University Vol. 43, Issue 24, Pages: 2609-2617(2021)
- 作者机构：
  
  400038 重庆，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
- 作者简介：
- 基金信息：
  
  the General Program of National Natural Science Foundation of China(81971278);the Humanity and Social Science Research Fund of Army Medical University(2019XRW10)
- DOI：10.16016/j.1000-5404.202106083
  CLC： R195.4;R741.044;R749.4
- Received：09 June 2021，
  
  Revised：2021-07-04，
  
  Published：30 December 2021
- 稿件说明：
移动端阅览
Yitong XUE, Kuiliang LI, Jingxuan ZHANG, et al. Characteristics of resting-state electroencephalogram microstates in individuals with different levels of depressive symptoms[J]. Journal of Third Military Medical University, 2021, 43(24): 2609-2617.
DOI：

Yitong XUE, Kuiliang LI, Jingxuan ZHANG, et al. Characteristics of resting-state electroencephalogram microstates in individuals with different levels of depressive symptoms[J]. Journal of Third Military Medical University, 2021, 43(24): 2609-2617. DOI： 10.16016/j.1000-5404.202106083.

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(

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(

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(

0.001)。⑤不同类型微状态之间的转换率存在差异(

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。

Abstract

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 Chong

qing. According to the results of Self-Rating Depression Scale (SDS)

they were divided into following 3 groups: non-depressive group (

=107)

mild depressive group (

=18) and moderate to severe depressive group (

=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

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%. ②The mean duration of microstate A in the mild depressive group was lower than that in the non-depressive and moderate to severe depressive group (

0.001). ③EEG microstate A had higher occurrence frequency than the other 3 types of microstates in the non-depressive and moderatet to severe depressive group (

0.001). ④The mild depressive group presented lower coverage rate of microstate A than the other 2 groups (

0.001). ⑤There were differences in conversion rates among different types of microstates (

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

Keywords

references

LEHMANN D, OZAKI H, PAL I. EEG alpha map series: brain micro-states by space-oriented adaptive segmentation[J]. Electroencephalogr Clin Neurophysiol, 1987, 67(3): 271-288. DOI: 10.1016/0013-4694(87)90025-3.

MICHEL C M, KOENIG T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review[J]. Neuroimage, 2018, 180(Pt B): 577-593. DOI: 10.1016/j.neuroimage.2017.11.062.

VELLANTE F, FERRI F, BARONI G, et al . Euthymic bipolar disorder patients and EEG microstates: a neural signature of their abnormal self experience?[J ] . J Affect Disord, 2020, 272: 326-334. DOI: 10.1016/j.jad.2020.03.175.

BRÉCHET L, BRUNET D, BIROT G, et al . Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI[J ] . Neuroimage, 2019, 194: 82-92. DOI: 10.1016/j.neuroimage.2019.03.029.

CUSTO A, VAN DE VILLE D, WELLS W M, et al . Electroencephalographic resting-state networks: source localization of microstates[J ] . Brain Connect, 2017, 7(10): 671-682. DOI: 10.1089/brain.2016.0476.

KAISER R H, ANDREWS-HANNA J R, WAGER T D, et al . Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity[J ] . JAMA Psychiatry, 2015, 72(6): 603-611. DOI: 10.1001/jamapsychiatry.2015.0071.

KHANNA A, PASCUAL-LEONE A, MICHEL C M, et al . Microstates in resting-state EEG: current status and future directions[J ] . Neurosci Biobehav Rev, 2015, 49: 105-113. DOI: 10.1016/j.neubiorev.2014.12.010.

GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017[J]. Lancet, 2018, 392(10159): 1789-1858. DOI: 10.1016/S0140-6736(18)32279-7.

MURPHY M, WHITTON A E, DECCY S, et al . Abnormalities in electroencephalographic microstates are state and trait markers of major depressive disorder[J ] . Neuropsychopharmacology, 2020, 45(12): 2030-2037. DOI: 10.1038/s41386-020-0749-1.

DAMBORSKÁ A, TOMESCU M I, HONZÍRKOVÁ E, et al . EEG resting-state large-scale brain network dynamics are related to depressive symptoms[J ] . Front Psychiatry, 2019, 10: 548. DOI: 10.3389/fpsyt.2019.00548.

DAMBORSKÁ A, PIGUET C, AUBRY J M, et al . Altered electroencephalographic resting-state large-scale brain network dynamics in euthymic bipolar disorder patients[J ] . Front Psychiatry, 2019, 10: 826. DOI: 10.3389/fpsyt.2019.00826.

STUART A L, PASCO J A, JACKA F N, et al . Comparison of self-report and structured clinical interview in the identification of depression[J ] . Compr Psychiatry, 2014, 55(4): 866-869. DOI: 10.1016/j.comppsych.2013.12.019.

田银娣, 王怡恺, 李静, 等. 焦虑和抑郁量表在肝硬化患者临床应用中的信效度评价[J]. 实用肝脏病杂志, 2019, 22(1): 105-108.

TIAN Y D, WANG Y K, LI J, et al . Evaluation of reliability and validity of self-rating anxiety scale and self-rating depression scale in patients with liver cirrhosis[J ] . J Pract Hepatol, 2019, 22(1): 105-108.

孙振晓, 刘化学, 焦林瑛, 等. 医院焦虑抑郁量表的信度及效度研究[J]. 中华临床医师杂志(电子版), 2017, 11(2): 198-201.

SUN Z X, LIU H X, JIAO L Y, et al . Reliability and validity of hospital anxiety and depression scale[J ] . Chin JClin Electron Ed, 2017, 11(2): 19 8-201.

茆广绪, 杜振芳, 叶炜杰, 等. 基于结构方程模型的家庭支持、抑郁对围绝经期妇女生活质量的影响[J]. 中国卫生统计, 2016, 33(3): 396-399.

MAO G X, DU Z F, YE W J, et al . The structural equation modeling analysis of the quality of life's influencing factors among perimenopause women[J ] . Chin J Heal Stat, 2016, 33(3): 396-399.

王征宇, 迟玉芬. 抑郁自评量表(SDS)[J]. 上海精神医学, 1984(2): 71-72.

WANG Z Y, CHI Y F. Self-rating depression scale (SDS)[J]. Shanghai Arch Psychiatry, 1984(2): 71-72.

KHANNA A, PASCUAL-LEONE A, FARZAN F. Reliability of resting-state microstate features inelectroencephalo-graphy[J]. PLoS ONE, 2014, 9(12): e114163. DOI: 10.1371/journal.pone.0114163.

KOENIG T, LEHMANN D, MERLO M C, et al . A deviant EEG brain microstate in acute, neuroleptic-naive schizophrenics at rest[J ] . Eur Arch Psychiatry Clin Neurosci, 1999, 249(4): 205-211. DOI: 10.1007/s004060050088.

LIU J Y, XU J, ZOU G Y, et al . Reliability and individual specificity of EEG microstate characteristics[J ] . Brain Topogr, 2020, 33(4): 438-449. DOI: 10.1007/s10548-020-00777-2.

STRIK W K, CHIARAMONTI R, MUSCAS G C, et al . Decreased EEG microstate duration and anteriorisation of the brain electrical fields in mild and moderate dementia of the Alzheimer type[J ] . Psychiatry Res, 1997, 75(3): 183-191. DOI: 10.1016/s0925-4927(97)00054-1.

STEVENS A, LUTZENBERGER W, BARTELS D M, et al . Increased duration and altered topography of EEG microstates during cognitive tasks in chronic schizophrenia[J ] . Psychiatry Res, 1997, 66(1): 45-57. DOI: 10.1016/s0165-1781(96)02938-1.

NEWSON J J, THIAGARAJAN T C. EEG frequency bands in psychiatric disorders: a review of resting state studies[J]. Front Hum Neurosci, 2018, 12: 521. DOI: 10.3389/fnhum.2018.00521.

DELL'ACQUA C, GHIASI S, MESSEROTTI BENVENUTI S, et al . Increased functional connectivity within alpha and Theta frequency bands in dysphoria: a resting-state EEG study[J ] . J Affect Disord, 2021, 281: 199-207. DOI: 10.1016/j.jad.2020.12.015.

MOHAMMADI Y, MORADI M H. Prediction of depression severity scores based on functional connectivity and complexity of the EEG signal[J]. Clin EEG Neurosci, 2021, 52(1): 52-60. DOI: 10.1177/1550059420965431.

MILZ P, PASCUAL-MARQUI R D, ACHERMANN P, et al . The EEG microstate topography is predominantly determined by intracortical sources in the alpha band[J ] . Neuroimage, 2017, 162: 353-361. DOI: 10.1016/j.neuroimage.2017.08.058.

GSCHWIND M, HARDMEIER M, VAN DE VILLE D, et al . Fluctuations of spontaneous EEG topographies predict disease state in relapsing-remitting multiple sclerosis[J ] . Neuroimage Clin, 2016, 12: 466-477. DOI: 10.1016/j.nicl.2016.08.008.

MILZ P, FABER P L, LEHMANN D, et al . The functional significance of EEG microstates: Associations with modalities of thinking[J ] . Neuroimage, 2016, 125: 643-656. DOI: 10.1016/j.neuroimage.2015.08.023.

KADIER N, STEIN M, KOENIG T. EEG microstates and psychosocial stress during an exchange year[J]. Brain Topogr, 2021, 34(2): 117-120. DOI: 10.1007/s10548-020-00806-0.

PTAK R, SCHNIDER A, FELLRATH J. The dorsal frontoparietal network: a core system for emulated action[J]. Trends Cogn Sci, 2017, 21(8): 589-599. DOI: 10.1016/j.tics.2017.05.002.

STRIK W K, DIERKS T, BECKER T, et al . Larger topographical variance and decreased duration of brain electric micro-states in depression[J ] . J Neural Transm Gen Sect, 1995, 99(1/2/3): 213-222. DOI: 10.1007/BF01271480.

ZHANG Y, YU C, ZHOU Y, et al . Decreased gyrification in major depressive disorder[J ] . Neuroreport, 2009, 20(4): 378-380. DOI: 10.1097/wnr.0b013e3283249b34.

ZHANG S, LI C S. Functional connectivity mapping of the humanprecuneus by resting state fMRI[J]. Neuroimage, 2012, 59(4): 3548-3562. DOI: 10.1016/j.neuroimage.2011.11.023.

罗敏, 姜旸, 刘照月, 等. 新型冠状病毒流行下普通民众焦虑抑郁调查分析[J]. 中华灾害救援医学, 2020, 8(9): 481-484. DOI: 10.13919/j.issn.2095-6274.2020.09.001.

LUO M, JIANG Y, LIU Z Y, et al . Anxiety and depression analysis of the general public people during the COVID-19[J ] . Chin J Disaster Med, 2020, 8(9): 481-484. DOI: 10.13919/j.issn.2095-6274.2020.09.001.

马慧, 杨超, 刘娜, 等. 大学生抑郁症状与人格特征及应对方式的关系研究[J]. 中国健康教育, 2019, 35(2): 179-181, 191. DOI: 10.16168/j.cnki.issn.1002-9982.2019.02.018.

MA H, YANG C, LIU N, et al . Study on the relationships of depressive symptoms with personality traits and coping styles of undergraduates[J ] . Chin J Heal Educ, 2019, 35(2): 179-181, 191. DOI: 10.16168/j.cnki.issn.1002-9982.2019.02.018.

XU J, PAN Y, ZHOU S, et al . EEG microstates are correlated with brain functional networks during slow-wave sleep[J ] . Neuroimage, 2020, 215: 116786. DOI: 10.1016/j.neuroimage.2020.116786.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

The trajectory of depressive symptoms and its risk factors in the middle-aged and elderly population

Epilepsy secondary to focal cortical dysplasia: a clinical report of 38 children

Health literacy and depressive symptoms and their relationship in junior high school students in Chongqing

EEG findings in patients with Alzheimer’s disease and senile depressive pseudo-dementia

Retrospective analysis of 236 patients with sporadic Creutzfeldt-Jakob disease in China

Related Author

CHANG Song

HUANG Heqing

ZHU Ling

CUI Xiao

Jin ZHU

Jinhua CAI

Yue HU

Li JIANG

Related Institution

Department of Medical Psychology， First Affiliated Hospital， Army Medical University

Department of Neurosurgery, Children's Hospital of Chongqing Medical University

Department of Neurology, Children's Hospital of Chongqing Medical University

Department of Radiology, Children's Hospital of Chongqing Medical University

Department of Pathology, Children's Hospital of Chongqing Medical University

⁰