J 2019

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

DAMBORSKÁ, Alena, Miralena, I. TOMESCU, Eliška HONZÍRKOVÁ, Elis BARTEČKŮ, Jana HOŘÍNKOVÁ et. al.

Basic information

Original name

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

Authors

DAMBORSKÁ, Alena (203 Czech Republic, guarantor, belonging to the institution), Miralena, I. TOMESCU (756 Switzerland), Eliška HONZÍRKOVÁ (203 Czech Republic, belonging to the institution), Elis BARTEČKŮ (203 Czech Republic, belonging to the institution), Jana HOŘÍNKOVÁ (203 Czech Republic, belonging to the institution), Sylvie FEDOROVÁ (203 Czech Republic, belonging to the institution), Šimon ONDRUŠ (203 Czech Republic, belonging to the institution) and Christoph M. MICHEL (756 Switzerland)

Edition

Frontiers in Psychiatry, Lausanne, Frontiers, 2019, 1664-0640

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30215 Psychiatry

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 2.849

RIV identification code

RIV/00216224:14110/19:00110571

Organization unit

Faculty of Medicine

UT WoS

000480255000001

Keywords in English

EEG microstates; large-scale brain networks; resting state; dynamic brain activity; major depressive disorder; bipolar disorder

Tags

Tags

International impact, Reviewed
Změněno: 11/5/2020 09:59, Mgr. Tereza Miškechová

Abstract

V originále

Background: 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. Methods: 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. Results: 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-angstrom sberg Depression Rating Scale correlated with higher occurrence of microstate A (Spearman's rank correlation, r = 0.70, p < 0.01). Conclusion: 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.