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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F19%3A00071110" target="_blank" >RIV/65269705:_____/19:00071110 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14110/19:00110571

Výsledek na webu

<a href="https://www.frontiersin.org/articles/10.3389/fpsyt.2019.00548/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fpsyt.2019.00548/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fpsyt.2019.00548" target="_blank" >10.3389/fpsyt.2019.00548</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos;s rank correlation, r = 0.70, p &lt; 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos;s rank correlation, r = 0.70, p &lt; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30215 - Psychiatry

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Frontiers in Psychiatry

ISSN

1664-0640

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

AUG 9

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

10

Strana od-do

548

Kód UT WoS článku

000480255000001

EID výsledku v databázi Scopus

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