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

Result code in 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>

Alternative codes found

RIV/00216224:14110/19:00110571

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30215 - Psychiatry

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

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

Name of the periodical

Frontiers in Psychiatry

ISSN

1664-0640

e-ISSN

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Volume of the periodical

10

Issue of the periodical within the volume

AUG 9

Country of publishing house

CH - SWITZERLAND

Number of pages

10

Pages from-to

548

UT code for WoS article

000480255000001

EID of the result in the Scopus database

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