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Relationship of Depression, Anxiety, and Rumination Scores with EEG Connectivity of Resting State Networks

The aim of the research was to study the effect of depression, anxiety, and rumination scores on the balance of activity of the default mode network and attention networks revealed in the resting state EEG records. Forty-five healthy volunteers (24 men aged from 18 to 25 years) participated in the r...

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Bibliographic Details
Published in:Human physiology 2021, Vol.47 (2), p.123-127
Main Authors: Bocharov, A. V., Knyazev, G. G., Savostyanov, A. N., Saprygin, A. E., Proshina, E. A., Tamozhnikov, S. S.
Format: Article
Language:English
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Summary:The aim of the research was to study the effect of depression, anxiety, and rumination scores on the balance of activity of the default mode network and attention networks revealed in the resting state EEG records. Forty-five healthy volunteers (24 men aged from 18 to 25 years) participated in the resting state EEG recording. The participants filled in the Beck Depression Inventory-II (BDI II), Ruminative Responses Scale, and Eysenck Personality Profiler. The connectivity measures of resting state networks were calculated in EEG data. The networks were detected by the “seed” method. The effects of depressive symptoms, anxiety, and rumination on the connectivity of the networks were analyzed by the regression method. The depressive symptom scores and the rumination scores were correlated with the dominance of the default mode network over attention networks in the right temporal cortex. The depression scores and the anxiety scores were correlated with the dominance of attention networks over the default mode network in the anterior cingulate cortex. It could be suggested that rumination processes are specific for depressive symptoms and are reflected in the dominance of the default mode network in brain structures associated with the processing of emotional introspection. Common to depressive and anxious symptoms is a state of alertness, which is reflected in the dominance of attention networks in brain structures associated with decision-making.
ISSN:0362-1197
1608-3164
DOI:10.1134/S0362119721010023