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

Inna Feklicheva,Svetlana Korobova,Ekaterina Maslennikova,Timofey Adamovich,Ilya Zakharov,Victoria Ismatullina,Nadezda Chipeeva,Sergey Malykh

doi:10.3390/brainsci11010094

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.

Highlights

Individual differences in intelligence play a prominent role in human life
If neurons or brain regions are represented as vertices of a network, and synapses, neural connections, and/or temporal correlations of activity that may occur between pairs of brain regions—as the edges, the graph can be analyzed with specific metrics, characterizing its topological properties [12]
The current study aims to investigate whether both verbal and non-verbal individual differences characteristics are associated with large-scale topological properties of the brain networks and local functional connectivity characteristics according to the network neuroscience approach

Summary

Introduction

Individual differences in intelligence play a prominent role in human life. It largely determines success in learning and in real-life outcomes, such as success in occupational or even marital status [1,2,3,4]. The use of a network neuroscience approach allows us to study the interaction of brain regions on the scale of the whole brain [9,10] and to identify global patterns of the brain networks activity underlying individual differences in cognitive abilities [11]. These global connectivity patterns can be revealed using mathematical graph theory. These metrics can be used to describe fundamental processes within the brain: integration of brain networks

Methods

Results

Discussion

Conclusion