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Resting-state EEG networks characterized by intramodular and global hyperconnectivity in depressive sample

Published online by Cambridge University Press:  01 September 2022

A. Komarova ,
A. Kiselnikov ,
M. Yurlova ,
E. Slovenko ,
I. Tan ,
D. Mitiureva ,
P. Kabanova ,
E. Terlichenko ,
V. Zubko  and
E. Shcherbakova
A. Komarova*
Affiliation:
Lomonosov Moscow State University, Psychology, Moscow, Russian Federation
A. Kiselnikov
Affiliation:
Lomonosov Moscow State University, Psychology, Moscow, Russian Federation
M. Yurlova
Affiliation:
Lomonosov Moscow State University, Psychology, Moscow, Russian Federation
E. Slovenko
Affiliation:
Lomonosov Moscow State University, Psychology, Moscow, Russian Federation
I. Tan
Affiliation:
Lomonosov Moscow State University, Psychology, Moscow, Russian Federation
D. Mitiureva
Affiliation:
Lomonosov Moscow State University, Psychology, Moscow, Russian Federation
P. Kabanova
Affiliation:
Lomonosov Moscow State University, Psychology, Moscow, Russian Federation
E. Terlichenko
Affiliation:
Lomonosov Moscow State University, Psychology, Moscow, Russian Federation
V. Zubko
Affiliation:
Lomonosov Moscow State University, Psychology, Moscow, Russian Federation
E. Shcherbakova
Affiliation:
Lomonosov Moscow State University, Psychology, Moscow, Russian Federation
*
*Corresponding author.

Abstract

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Introduction

Depression is characterized by a pattern of specific changes in the network organization of brain functioning.

Objectives

We researched a graph structure specificity in a depressive student sample by analyzing resting-state EEG. All possible combinations of graph metrics, frequency bands, and sensors/sources levels of networks were examined.

Methods

We recorded resting-state EEG in fourteen participants with high Beck Depression Inventory score (24.4 ± 9.7; 20.4 ± 1.5 y.o.; 14 females; 1 left-handed) and fourteen participants with a low score (6.8 ± 3.7; 21.3 ± 2.0 y.o.; 8 females; 1 left-handed). We applied weighted phase-lag index (wPLI) to construct functional networks at sensors and sources levels and computed characteristic path length (CPL), clustering coefficient (CC), index of modularity (Q), small-world index (SWI) in 4-8, 8-13, 13-30, and 4-30 Hz frequency bands. We used Mann-Whitney U-test (p < 0.05) to investigate between-group differences in the graph metrics.

Results

The depressive sample was characterized by increased CC and Q in the 4-30 Hz band networks and decreased CPL in the beta-band network (sensors-level for CPL and CC, and sources-level for Q).

Conclusions

Elevated CC and Q may relate to an increase of intramodular connectivity, and CPL reduction reflects the global connectivity increasing. We hypothesize that intramodular hyperconnectivity could explain the rise of global functional connectivity in participants with depressive symptoms. Funding: This research has been supported by the Interdisciplinary Scientific and Educational School of Lomonosov Moscow State University ‘Brain, Cognitive Systems, Artificial Intelligence’.

Disclosure

No significant relationships.

Keywords

DepressionResting-state EEGConnectivity
Type
Abstract
Information
European Psychiatry , Volume 65 , Special Issue S1: Abstracts of the 30th European Congress of Psychiatry , June 2022 , pp. S213 - S214
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the European Psychiatric Association
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