Power and temporal dynamics of alpha oscillations at rest differentiate cognitive performance involving sustained and phasic cognitive control.


Journal

NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515

Informations de publication

Date de publication:
03 2019
Historique:
received: 27 09 2018
revised: 09 11 2018
accepted: 01 12 2018
pubmed: 6 12 2018
medline: 18 12 2019
entrez: 6 12 2018
Statut: ppublish

Résumé

Resting state neuronal activity in EEG/MEG recordings is primarily characterized by the presence of alpha oscillations (approx. 8-12 Hz). However, their functional significance and link to cognitive task performance remains elusive. We investigated resting state neuronal activity and its relation to task performance by assessing traditional measures of alpha activity (power and individual alpha peak frequency) and dynamic properties of the signal measured by long-range temporal correlations (LRTC). Multichannel EEG was recorded at rest in 82 healthy male adults and compared to their cognitive performance, measured by tests involving executive functions, working memory, short- and long-term memory demands. Our results showed that attention-span scores positively correlated with alpha power at rest, with corresponding neuronal sources located primarily in the left-hemispheric anterior cingulate cortex, parietal regions, and bilateral suplementary motor areas. Furthermore, better working memory performance was related to increased LRTC of alpha oscillations at rest in the right hemispheric fronto-parietal, temporal, and occipital regions. Our findings suggest that resting state neuronal activity may reflect properties of brain networks that are functionally relevant for cognitive task performance. While alpha power measured at rest might relate to tasks that employ sustained inhibitory control, LRTC are suggested to reflect the capacity of neuronal networks to perform tasks that require phasic attention and quick adaptation to changing task demands.

Identifiants

pubmed: 30517844
pii: S1053-8119(18)32145-1
doi: 10.1016/j.neuroimage.2018.12.001
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

135-144

Informations de copyright

Copyright © 2018 Elsevier Inc. All rights reserved.

Auteurs

Keyvan Mahjoory (K)

Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute for Biomagnetism and Biosignal analysis, University of Muenster, Muenster, Germany.

Elena Cesnaite (E)

Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

Friederike U Hohlefeld (FU)

Berlin Mobile Brain Imaging Lab, Department of Biological Psychology and Neuroergonomics, Technical University Berlin, Berlin, Germany.

Arno Villringer (A)

Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany.

Vadim V Nikulin (VV)

Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia; Neurophysics Group, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany. Electronic address: nikulin@cbs.mpg.de.

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Classifications MeSH