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

@article{1552236,
   author = {Damborská, Alena and Tomescu, Miralena, I. and Honzírková, Eliška and Bartečků, Elis and Hořínková, Jana and Fedorová, Sylvie and Ondruš, Šimon and Michel, Christoph M.},
   article_location = {Lausanne},
   article_number = {548},
   doi = {http://dx.doi.org/10.3389/fpsyt.2019.00548},
   keywords = {EEG microstates; large-scale brain networks; resting state; dynamic brain activity; major depressive disorder; bipolar disorder},
   language = {eng},
   issn = {1664-0640},
   journal = {Frontiers in Psychiatry},
   title = {EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms},
   url = {http://dx.doi.org/10.3389/fpsyt.2019.00548},
   volume = {10},
   year = {2019}
}

TY  - JOUR
ID  - 1552236
AU  - Damborská, Alena - Tomescu, Miralena, I. - Honzírková, Eliška - Bartečků, Elis - Hořínková, Jana - Fedorová, Sylvie - Ondruš, Šimon - Michel, Christoph M.
PY  - 2019
TI  - EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms
JF  - Frontiers in Psychiatry
VL  - 10
IS  - 548
SP  - 1-10
EP  - 1-10
PB  - Frontiers
SN  - 16640640
KW  - EEG microstates
KW  - large-scale brain networks
KW  - resting state
KW  - dynamic brain activity
KW  - major depressive disorder
KW  - bipolar disorder
UR  - http://dx.doi.org/10.3389/fpsyt.2019.00548
L2  - http://dx.doi.org/10.3389/fpsyt.2019.00548
N2  - 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.
ER  - 

DAMBORSKÁ, Alena, Miralena, I. TOMESCU, Eliška HONZÍRKOVÁ, Elis BARTEČKŮ, Jana HOŘÍNKOVÁ, Sylvie FEDOROVÁ, Šimon ONDRUŠ and Christoph M. MICHEL. EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms. \textit{Frontiers in Psychiatry}. Lausanne: Frontiers, 2019, vol.~10, No~548, p.~1-10. ISSN~1664-0640. Available from: https://dx.doi.org/10.3389/fpsyt.2019.00548.