Spatiotemporal complexity patterns of resting-state bioelectrical activity explain fluid intelligence: Sex matters.
fluid intelligence
frontoparietal network
multivariate multiscale sample entropy (mMSE)
rsEEG
sex
spatiotemporal complexity patterns
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
21
07
2019
revised:
12
07
2020
accepted:
27
07
2020
pubmed:
19
8
2020
medline:
15
12
2021
entrez:
19
8
2020
Statut:
ppublish
Résumé
Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting-state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6-min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo-parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro-temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing.
Identifiants
pubmed: 32808732
doi: 10.1002/hbm.25162
pmc: PMC7643359
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4846-4865Subventions
Organisme : Narodowe Centrum Nauki
ID : 2016/20/W/NZ4/00354
Organisme : Narodowe Centrum Nauki
ID : 2019/33/B/HS6/00321
Organisme : Narodowe Centrum Nauki
ID : 2015/18/E/HS6/00399
Informations de copyright
© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
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