4.6 Article

Task-positive and task-negative networks and their relation to depression: EEG beamformer analysis

Journal

BEHAVIOURAL BRAIN RESEARCH
Volume 306, Issue -, Pages 160-169

Publisher

ELSEVIER
DOI: 10.1016/j.bbr.2016.03.033

Keywords

Depression; EEG; Resting-state networks; Default mode network; Beamformer; Functional connectivity

Funding

  1. Russian Foundation for Basic Research (RFBR) [14-06-00039]
  2. Russian Science Foundation (RSF) [14-15-00202]
  3. Russian Science Foundation [14-15-00202] Funding Source: Russian Science Foundation

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Major Depressive Disorder (MDD) has been associated with predominance of the default-mode network (DMN) over the task-positive network (TPN), which is considered a neurobiological base for ruminative responding. It is not known whether this predominance is a signature of the full-blown MDD or it already exists at preclinical stages. Besides, all relevant evidence has been obtained using fMRI, which allows for a precise spatial characterization of resting state networks (RSNs), but their neural correlates remain elusive. Here we show that after leakage correction of beamformer-projected resting EEG time series, seed-based oscillatory-power envelope correlation analysis allows revealing RSNs with significant similarity to respective fMRI RSNs. In a non-clinical sample, depressive symptoms, as measured by the Beck Depression Inventory, are associated with predominance of DMN over TPN connectivity in the right insula and the right temporal lobe in the delta frequency band. These findings imply that in individuals with heightened level of depressive symptoms, emotional circuits are stronger connected with DMN than TPN and should be more easily engaged in self-referential rumination than in responding to environmental challenges. The study's findings are in agreement with fMRI evidence, thus confirming the neural base of the observed in fMRI research effects and showing that implicated in depression neural mechanism may already be in action even at preclinical stages. (C) 2016 Elsevier B.V. All rights reserved.

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