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Volume 11
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10.3390/brainsci11010094
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Article

Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity

by
Inna Feklicheva
1,*,
Ilya Zakharov
2,
Nadezda Chipeeva
1,
Ekaterina Maslennikova
3,
Svetlana Korobova
1,
Timofey Adamovich
2,
Victoria Ismatullina
2 and
Sergey Malykh
2
1
Laboratory of Molecular Genetic Research of Human Health and Development, Scientific and Educational Center “Biomedical Technologies”, Higher Medical and Biological School, South Ural State University, 454080 Chelyabinsk, Russia
2
Developmental Behavioral Genetics Lab, Psychological Institute of Russian Academy of Education, 125009 Moscow, Russia
3
Center of Interdisciplinary Research in Education, Russian Academy of Education, 199121 Moscow, Russia
*
Author to whom correspondence should be addressed.
Brain Sci. 2021, 11(1), 94; https://doi.org/10.3390/brainsci11010094
Submission received: 16 November 2020 / Revised: 4 January 2021 / Accepted: 11 January 2021 / Published: 13 January 2021
(This article belongs to the Special Issue Brain Connectivity Analysis from EEG Signals)
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Abstract

The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven’s total scores and E series), and the ability to operate with verbal information (the “Conclusions” verbal subtest). At the same time, individual differences in non-verbal 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.
Keywords: functional connectivity of the brain; EEG; nonverbal and verbal cognitive abilities functional connectivity of the brain; EEG; nonverbal and verbal cognitive abilities

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MDPI and ACS Style

Feklicheva, I.; Zakharov, I.; Chipeeva, N.; Maslennikova, E.; Korobova, S.; Adamovich, T.; Ismatullina, V.; Malykh, S. Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity. Brain Sci. 2021, 11, 94. https://doi.org/10.3390/brainsci11010094

AMA Style

Feklicheva I, Zakharov I, Chipeeva N, Maslennikova E, Korobova S, Adamovich T, Ismatullina V, Malykh S. Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity. Brain Sciences. 2021; 11(1):94. https://doi.org/10.3390/brainsci11010094

Chicago/Turabian Style

Feklicheva, Inna, Ilya Zakharov, Nadezda Chipeeva, Ekaterina Maslennikova, Svetlana Korobova, Timofey Adamovich, Victoria Ismatullina, and Sergey Malykh. 2021. "Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity" Brain Sciences 11, no. 1: 94. https://doi.org/10.3390/brainsci11010094

APA Style

Feklicheva, I., Zakharov, I., Chipeeva, N., Maslennikova, E., Korobova, S., Adamovich, T., Ismatullina, V., & Malykh, S. (2021). Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity. Brain Sciences, 11(1), 94. https://doi.org/10.3390/brainsci11010094

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