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Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEGстатья
Информация о цитировании статьи получена из
Web of Science,
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 24 февраля 2021 г.
- Авторы: Feklicheva I., Zakharov I., Chipeeva N., Maslennikova E., Korobova S., Adamovich T., Ismatullina V., Malykh S.
- Журнал: BRAIN SCIENCES
- Том: 11
- Год издания: 2021
- Номер статьи: 94
- DOI: 10.3390/brainsci11010094
- Аннотация: The present study investigates the relationship between individual differences in verbaland non-verbal cognitive abilities and resting-state EEG network characteristics. We used a networkneuroscience approach to analyze both large-scale topological characteristics of the whole brain aswell as local brain network characteristics. The characteristic path length, modularity, and clustercoefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) werecalculated to estimate large-scale topological integration and segregation properties of the brainnetworks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength werecalculated as local network characteristics. We showed that global network integration measuresin the alpha band were positively correlated with non-verbal intelligence, especially with the moredifficult part of the test (Raven’s total scores and E series), and the ability to operate with verbalinformation (the “Conclusions” verbal subtest). At the same time, individual differences in nonverbal intelligence (Raven’s total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.
- Добавил в систему: Малых Сергей Борисович