Cognitive effort is intrinsically linked to task difficulty, intelligence, and mental toughness. Intelligence reflects an individual’s cognitive aptitude, whereas mental toughness (MT) reflects an individual’s resilience in pursuing success. Research shows that parietal alpha oscillations are associated with changes in task difficulty. Critically, it remains unclear whether parietal alpha oscillations are modulated by intelligence and MT as a personality trait. We examined event-related (de)synchronization (ERD/ERS) of alpha oscillations associated with encoding, retention, and recognition in the Sternberg task in relation to intelligence and mental toughness. Eighty participants completed the Sternberg task with 3, 4, 5 and 6 digits, Raven Standard Progressive Matrices test and an MT questionnaire. A positive dependence on difficulty was observed for all studied oscillatory effects (t = −8.497, p < 0.001; t = 2.806, p < 0.005; t = −2.103, p < 0.05). The influence of Raven intelligence was observed for encoding-related alpha ERD (t = −2.02, p = 0.049). The influence of MT was observed only for difficult conditions in recognition-related alpha ERD (t = −3.282, p < 0.005). Findings indicate that the modulation of alpha rhythm related to encoding, retention and recognition may be interpreted as correlates of cognitive effort modulation. Specifically, results suggest that effort related to encoding depends on intelligence, whereas recognition-related effort level depends on mental toughness.

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.

Aim. The purpose of the article is to study the correlation between physical activity and functional connectivity (FC) of the brain based on EEG data. Materials and methods. The study sample included 43 healthy persons aged from 17 to 35 years (26 women). The participants were divided into two groups. The first group (21 persons) was engaged in physical activity for more than 3 hours a week, the second (22 persons) group was not engaged in physical activity. In all participants, 10-minute EEG recording at rest was performed. To assess the differences in the global characteristics of functional connectivity, such graph metrics as the characteristic path length, clustering coefficient, small world index, and modularity were chosen. Results. Significant differences between the two groups in terms of the cluster coefficient were obtained using the Wilcoxon test (W = 201, p < 0.001). To compare the intergroup differences, the DOT (double one-sided test) procedure was used, which allowed assessing the equivalence of groups based on a pre-selected effect size. When comparing the two groups, statistically significant differences are observed for two one-sided Student’s tests, while the effect size exceeds the pre-selected effect size (d = 0.05), both for the upper and lower reference values, which indicates not only statistical significance, but also the inequality of the samples. Conclusion. Young people who regularly engage in physical activity for more than 3 hours per week have higher functional connectivity of the brain than those of the same age who do not engage in physical activity, which is expressed in the clustering coefficient. In general, the results of this study show that physical activity increases functional connectivity of the brain in the alpha range. The connectivity increases due to the emergence of new functional clusters within existing associations of brain regions.