Alpha band resting-state EEG connectivity is associated with non-verbal intelligence I. Zakharov, A. Tabueva, T. Adamovich [et al.]
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ArticleContent type: Текст Media type: электронный Subject(s): нейронная эффективность | состояние покоя | интеллект | альфа-диапазон | электроэнцефалографияGenre/Form: статьи в журналах Online resources: Click here to access online
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Frontiers in human neuroscience Vol. 14. P. 10 (1-10)Abstract: The aim of the present study was to investigate whether EEG resting state connectivity
correlates with intelligence. One-hundred and sixty five participants took part in
the study. Six minutes of eyes closed EEG resting state was recorded for each
participant. Graph theoretical connectivity metrics were calculated separately for two
well-established synchronization measures [weighted Phase Lag Index (wPLI) and
Imaginary Coherence (iMCOH)] and for sensor- and source EEG space. Non-verbal
intelligence was measured with Raven’s Progressive Matrices. In line with the Neural
Efficiency Hypothesis, path lengths characteristics of the brain networks (Average and
Characteristic Path lengths, Diameter and Closeness Centrality) within alpha band range
were significantly correlated with non-verbal intelligence for sensor space but no for
source space. According to our results, variance in non-verbal intelligence measure can
be mainly explained by the graph metrics built from the networks that include both weak
and strong connections between the nodes.
Библиогр.: с. 10 (8-10)
The aim of the present study was to investigate whether EEG resting state connectivity
correlates with intelligence. One-hundred and sixty five participants took part in
the study. Six minutes of eyes closed EEG resting state was recorded for each
participant. Graph theoretical connectivity metrics were calculated separately for two
well-established synchronization measures [weighted Phase Lag Index (wPLI) and
Imaginary Coherence (iMCOH)] and for sensor- and source EEG space. Non-verbal
intelligence was measured with Raven’s Progressive Matrices. In line with the Neural
Efficiency Hypothesis, path lengths characteristics of the brain networks (Average and
Characteristic Path lengths, Diameter and Closeness Centrality) within alpha band range
were significantly correlated with non-verbal intelligence for sensor space but no for
source space. According to our results, variance in non-verbal intelligence measure can
be mainly explained by the graph metrics built from the networks that include both weak
and strong connections between the nodes.
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