EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms

Overview

Journal Front Psychiatry

Specialty Psychiatry

Date 2019 Sep 3

PMID 31474881

Citations 47

Authors

Alena Damborska

Miralena I Tomescu

Eliska Honzirkova

Richard Bartecek

Jana Horinkova

Sylvie Fedorova

Simon Ondrus

Christoph M Michel

Affiliations

Soon will be listed here.

Abstract

The few previous studies on resting-state electroencephalography (EEG) microstates in depressive patients suggest altered temporal characteristics of microstates compared to those of healthy subjects. We tested whether resting-state microstate temporal characteristics could capture large-scale brain network dynamic activity relevant to depressive symptomatology. To evaluate a possible relationship between the resting-state large-scale brain network dynamics and depressive symptoms, we performed EEG microstate analysis in 19 patients with moderate to severe depression in bipolar affective disorder, depressive episode, and recurrent depressive disorder and in 19 healthy controls. Microstate analysis revealed six classes of microstates (A-F) in global clustering across all subjects. There were no between-group differences in the temporal characteristics of microstates. In the patient group, higher depressive symptomatology on the Montgomery-Åsberg Depression Rating Scale correlated with higher occurrence of microstate A (Spearman's rank correlation, r = 0.70, p < 0.01). Our results suggest that the observed interindividual differences in resting-state EEG microstate parameters could reflect altered large-scale brain network dynamics relevant to depressive symptomatology during depressive episodes. Replication in larger cohort is needed to assess the utility of the microstate analysis approach in an objective depression assessment at the individual level.

Citing Articles

Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness.

Li H, Dong L, Su W, Liu Y, Tang Z, Liao X Front Neurosci. 2025; 19:1492225.

PMID: 39975972 PMC: 11836006. DOI: 10.3389/fnins.2025.1492225.

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.

Mulc D, Vukojevic J, Kalafatic E, Cifrek M, Vidovic D, Jovic A Sensors (Basel). 2025; 25(2).

PMID: 39860780 PMC: 11769153. DOI: 10.3390/s25020409.

EEG microstate as a biomarker of post-stroke depression with acupuncture treatment.

Wei C, Yang Q, Chen J, Rao X, Li Q, Luo J Front Neurol. 2024; 15:1452243.

PMID: 39534268 PMC: 11554454. DOI: 10.3389/fneur.2024.1452243.

Valence-specific EEG microstate modulations during self-generated affective states.

Nazare K, Tomescu M Front Psychol. 2024; 15:1300416.

PMID: 38855303 PMC: 11160840. DOI: 10.3389/fpsyg.2024.1300416.

Clinical Implication of Maumgyeol Basic Biotypes-Electroencephalography- and Photoplethysmogram-Based Bwave State Inventory.

Kim Y, Hwang J, Lee J, Jang S, Im Y, Yoon S Psychiatry Investig. 2024; 21(5):528-538.

PMID: 38811002 PMC: 11136575. DOI: 10.30773/pi.2023.0381.

References

Ihl R, Brinkmeyer J . Differential diagnosis of aging, dementia of the Alzheimer type and depression with EEG-segmentation. Dement Geriatr Cogn Disord. 1999; 10(2):64-9. DOI: 10.1159/000017103. View

Jung T, Makeig S, Westerfield M, Townsend J, Courchesne E, Sejnowski T . Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clin Neurophysiol. 2000; 111(10):1745-58. DOI: 10.1016/s1388-2457(00)00386-2. View

Gusnard D, Raichle M . Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci. 2001; 2(10):685-94. DOI: 10.1038/35094500. View

Koenig T, Prichep L, Lehmann D, Sosa P, Braeker E, Kleinlogel H . Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. Neuroimage. 2002; 16(1):41-8. DOI: 10.1006/nimg.2002.1070. View

Andrade L, Caraveo-Anduaga J, Berglund P, Bijl R, de Graaf R, Vollebergh W . The epidemiology of major depressive episodes: results from the International Consortium of Psychiatric Epidemiology (ICPE) Surveys. Int J Methods Psychiatr Res. 2003; 12(1):3-21. PMC: 6878531. DOI: 10.1002/mpr.138. View

Greicius M, Flores B, Menon V, Glover G, Solvason H, Kenna H . Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry. 2007; 62(5):429-37. PMC: 2001244. DOI: 10.1016/j.biopsych.2006.09.020. View

Honey C, Kotter R, Breakspear M, Sporns O . Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci U S A. 2007; 104(24):10240-5. PMC: 1891224. DOI: 10.1073/pnas.0701519104. View

Williams J, Kobak K . Development and reliability of a structured interview guide for the Montgomery Asberg Depression Rating Scale (SIGMA). Br J Psychiatry. 2008; 192(1):52-8. DOI: 10.1192/bjp.bp.106.032532. View

Murray M, Brunet D, Michel C . Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr. 2008; 20(4):249-64. DOI: 10.1007/s10548-008-0054-5. View

10.

Price J, Drevets W . Neurocircuitry of mood disorders. Neuropsychopharmacology. 2009; 35(1):192-216. PMC: 3055427. DOI: 10.1038/npp.2009.104. View

11.

Chang C, Glover G . Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage. 2009; 50(1):81-98. PMC: 2827259. DOI: 10.1016/j.neuroimage.2009.12.011. View

12.

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

13.

Liu Z, Xu C, Xu Y, Wang Y, Zhao B, Lv Y . Decreased regional homogeneity in insula and cerebellum: a resting-state fMRI study in patients with major depression and subjects at high risk for major depression. Psychiatry Res. 2010; 182(3):211-5. DOI: 10.1016/j.pscychresns.2010.03.004. View

14.

Bressler S, Menon V . Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn Sci. 2010; 14(6):277-90. DOI: 10.1016/j.tics.2010.04.004. View

15.

Van De Ville D, Britz J, Michel C . EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proc Natl Acad Sci U S A. 2010; 107(42):18179-84. PMC: 2964192. DOI: 10.1073/pnas.1007841107. View

16.

Brunet D, Murray M, Michel C . Spatiotemporal analysis of multichannel EEG: CARTOOL. Comput Intell Neurosci. 2011; 2011:813870. PMC: 3022183. DOI: 10.1155/2011/813870. View

17.

Michel C, Murray M . Towards the utilization of EEG as a brain imaging tool. Neuroimage. 2012; 61(2):371-85. DOI: 10.1016/j.neuroimage.2011.12.039. View

18.

Hamilton J, Chen M, Gotlib I . Neural systems approaches to understanding major depressive disorder: an intrinsic functional organization perspective. Neurobiol Dis. 2013; 52:4-11. PMC: 3596788. DOI: 10.1016/j.nbd.2012.01.015. View

19.

Hutchison R, Womelsdorf T, Allen E, Bandettini P, Calhoun V, Corbetta M . Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage. 2013; 80:360-78. PMC: 3807588. DOI: 10.1016/j.neuroimage.2013.05.079. View

20.

Lehmann D, Ozaki H, Pal I . EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol. 1987; 67(3):271-88. DOI: 10.1016/0013-4694(87)90025-3. View