EEG Microstate Temporal Dynamics Predict Depressive Symptoms in College Students

Overview

Journal Brain Topogr

Specialty Neurology

Date 2022 Jul 5

PMID 35790705

Authors

Xiaorong Qin

Jingyi Xiong

Ruifang Cui

Guimin Zou

Changquan Long

Xu Lei

Affiliations

Soon will be listed here.

Abstract

Previous studies on resting-state electroencephalographic responses in patients with depressive disorders have identified electroencephalogram (EEG) parameters as potential biomarkers for the early detection and diagnosis of depressive disorders. However, these studies did not investigate the relationship between resting-state EEG microstates and the early detection of depressive symptoms in preclinical individuals. To explore the possible association between resting-state EEG microstate temporal dynamics and depressive symptoms among college students, EEG microstate analysis was performed on eyes-closed resting-state EEG data for approximately 5 min from 34 undergraduates with high intensity of depressive symptoms and 34 age- and sex-matched controls with low intensity of depressive symptoms. Five microstate classes (A-E) were identified to best explain the datasets of both groups. Compared to controls, the mean duration, occurrence, and coverage of microstate class B increased significantly, whereas the occurrence and coverage of microstate classes D and E decreased significantly in individuals with high intensity of depressive symptoms. Additionally, the presence of microstate class B was positively correlated with participants' Beck Depression Inventory-II (BDI-II) scores, and the presence of microstate classes D and E were negatively correlated with their BDI-II scores. Further, individuals with high intensity of depressive symptoms had higher transition probabilities of A→B, B→A, B→C, B→D, and C→B, with lower transition probabilities of A→D, A→E, D→A, D→E, E→A, E→C, and E→D than controls. These results highlight resting-state EEG microstate temporal dynamics as potential biomarkers for the early detection and timely treatment of depression in college students.

Citing Articles

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms.

Xue S, Shen X, Zhang D, Sang Z, Long Q, Song S Brain Topogr. 2024; 38(1):12.

PMID: 39499403 DOI: 10.1007/s10548-024-01082-y.

Loneliness and brain rhythmic activity in resting state: an exploratory report.

Hu X, Wang X, Long C, Lei X Soc Cogn Affect Neurosci. 2024; 19(1).

PMID: 39096513 PMC: 11374414. DOI: 10.1093/scan/nsae052.

Open access EEG dataset of repeated measurements from a single subject for microstate analysis.

Liu Q, Jia S, Tu N, Zhao T, Lyu Q, Liu Y Sci Data. 2024; 11(1):379.

PMID: 38615072 PMC: 11016104. DOI: 10.1038/s41597-024-03241-z.

Abnormalities of electroencephalography microstates in patients with depression and their association with cognitive function.

Peng R, Fan Y, Li J, Zhu F, Tian Q, Zhang X World J Psychiatry. 2024; 14(1):128-140.

PMID: 38327889 PMC: 10845229. DOI: 10.5498/wjp.v14.i1.128.

EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training.

Zhu M, Gong Q Front Neurosci. 2023; 17:1254423.

PMID: 38148944 PMC: 10750374. DOI: 10.3389/fnins.2023.1254423.

References

Abdallah C, Sanacora G, Duman R, Krystal J . The neurobiology of depression, ketamine and rapid-acting antidepressants: Is it glutamate inhibition or activation?. Pharmacol Ther. 2018; 190:148-158. PMC: 6165688. DOI: 10.1016/j.pharmthera.2018.05.010. View

Andreou C, Faber P, Leicht G, Schoettle D, Polomac N, Hanganu-Opatz I . Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG microstates. Schizophr Res. 2014; 152(2-3):513-20. DOI: 10.1016/j.schres.2013.12.008. View

Atluri S, Wong W, Moreno S, Blumberger D, Daskalakis Z, Farzan F . Selective modulation of brain network dynamics by seizure therapy in treatment-resistant depression. Neuroimage Clin. 2018; 20:1176-1190. PMC: 6214861. DOI: 10.1016/j.nicl.2018.10.015. View

Auerbach R, Mortier P, Bruffaerts R, Alonso J, Benjet C, Cuijpers P . WHO World Mental Health Surveys International College Student Project: Prevalence and distribution of mental disorders. J Abnorm Psychol. 2018; 127(7):623-638. PMC: 6193834. DOI: 10.1037/abn0000362. View

Ayuso-Mateos J, Nuevo R, Verdes E, Naidoo N, Chatterji S . From depressive symptoms to depressive disorders: the relevance of thresholds. Br J Psychiatry. 2010; 196(5):365-71. DOI: 10.1192/bjp.bp.109.071191. View

Beiter R, Nash R, McCrady M, Rhoades D, Linscomb M, Clarahan M . The prevalence and correlates of depression, anxiety, and stress in a sample of college students. J Affect Disord. 2014; 173:90-6. DOI: 10.1016/j.jad.2014.10.054. View

Bochet A, Sperdin H, Rihs T, Kojovic N, Franchini M, Jan R . Early alterations of large-scale brain networks temporal dynamics in young children with autism. Commun Biol. 2021; 4(1):968. PMC: 8367954. DOI: 10.1038/s42003-021-02494-3. View

Brechet L, Brunet D, Birot G, Gruetter R, Michel C, Jorge J . Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI. Neuroimage. 2019; 194:82-92. DOI: 10.1016/j.neuroimage.2019.03.029. View

Britz J, Van De Ville D, Michel C . BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage. 2010; 52(4):1162-70. DOI: 10.1016/j.neuroimage.2010.02.052. View

10.

Chesney E, Goodwin G, Fazel S . Risks of all-cause and suicide mortality in mental disorders: a meta-review. World Psychiatry. 2014; 13(2):153-60. PMC: 4102288. DOI: 10.1002/wps.20128. View

11.

Damborska A, Tomescu M, Honzirkova E, Bartecek R, Horinkova J, Fedorova S . EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms. Front Psychiatry. 2019; 10:548. PMC: 6704975. DOI: 10.3389/fpsyt.2019.00548. View

12.

Delorme A, Makeig S . EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004; 134(1):9-21. DOI: 10.1016/j.jneumeth.2003.10.009. View

13.

Ding X, Yue X, Zheng R, Bi C, Li D, Yao G . Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data. J Affect Disord. 2019; 251:156-161. DOI: 10.1016/j.jad.2019.03.058. View

14.

Dyrbye L, Thomas M, Shanafelt T . Systematic review of depression, anxiety, and other indicators of psychological distress among U.S. and Canadian medical students. Acad Med. 2006; 81(4):354-73. DOI: 10.1097/00001888-200604000-00009. View

15.

Fischer A, Keller C, Etkin A . The Clinical Applicability of Functional Connectivity in Depression: Pathways Toward More Targeted Intervention. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018; 1(3):262-270. DOI: 10.1016/j.bpsc.2016.02.004. View

16.

Gao F, Jia H, Wu X, Yu D, Feng Y . Altered Resting-State EEG Microstate Parameters and Enhanced Spatial Complexity in Male Adolescent Patients with Mild Spastic Diplegia. Brain Topogr. 2016; 30(2):233-244. DOI: 10.1007/s10548-016-0520-4. View

17.

Gao L, Xie Y, Jia C, Wang W . Prevalence of depression among Chinese university students: a systematic review and meta-analysis. Sci Rep. 2020; 10(1):15897. PMC: 7522998. DOI: 10.1038/s41598-020-72998-1. View

18.

Gschwind M, Hardmeier M, Van De Ville D, Tomescu M, Penner I, Naegelin Y . Fluctuations of spontaneous EEG topographies predict disease state in relapsing-remitting multiple sclerosis. Neuroimage Clin. 2016; 12:466-77. PMC: 5011177. DOI: 10.1016/j.nicl.2016.08.008. View

19.

Hamilton J, Furman D, Chang C, Thomason M, Dennis E, Gotlib I . Default-mode and task-positive network activity in major depressive disorder: implications for adaptive and maladaptive rumination. Biol Psychiatry. 2011; 70(4):327-33. PMC: 3144981. DOI: 10.1016/j.biopsych.2011.02.003. View

20.

Ibrahim A, Kelly S, Adams C, Glazebrook C . A systematic review of studies of depression prevalence in university students. J Psychiatr Res. 2012; 47(3):391-400. DOI: 10.1016/j.jpsychires.2012.11.015. View