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.

Graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary, and the effect of the threshold choice is not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graph’s global connectivity measures as a function of the chosen threshold which may influence the outcome of the study. The choice of the threshold could lead to different study conclusions; thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider the adoption of different analytic approaches. We also proposed some ways of improving the procedure of thresholding in functional connectivity research.

The procedure of thresholding for graph construction is one of the common steps in calculating networks of brain connections. However, this procedure can lead to incomparable results from different studies. In the present study we aim to test the effect of thresholding or algorithmic reduction of the number of connected nodes on the construction of a set of widely used connectivity graph metrics derived from EEG data. 164 people took part in our study. Participants were recruited via social networks. EEG was recorded during resting state. At the beginning of the procedure each participant was asked to relax and not to think about anything. Source reconstruction was performed using standard source localization pipeline from MNE-package. Desikan-Killiany Atlas was used for cortical parcellation with 34 ROI per hemisphere. Synchronization was estimated with weighted phase lag index in 4–30 Hz frequency range for eyes closed and eyes open separately. We have found that All metrics except average participation coefficient vary monotonously as a function of density level (moreover, we have found, that for Cluster Coefficient, more than 95% and for the Characteristic Path Length ∼50% of the variance is related to thresholding cut-off). The different data-driven approaches to the network construction leads to significant changes in the group-level graph metrics and can eliminate the variance in the data that can be crucial for individual differences studies.

The graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary and the effect of the threshold choice in not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graphs global connectivity measures as a function of the chosen threshold which may influence on the outcome of the study. The choice of the threshold could lead to the different study conclusions, thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider adoption of different analytic approaches.

This study investigates the psychometric properties of brief COPE in Russian schoolteachers. A total of 773 (91% female; M = 43, SD = 9.79) teachers participated in the study. Principal component analysis (PCA) and confirmatory factor analysis (CFA) were applied to assess the psychometric properties of the brief COPE. The Perceived Stress Scale (PSS) was used to assess the construct validity. The main result of the current research is a revised structure for the brief COPE consisting of six sub-scales: «socio-emotional support», «religion», «acceptance», «problem-focused coping», «avoidance», and «humor». The goodness-of-fit criteria were as follows: SRMR = 0.054, RMSEA = 0.064, CFI = 0.888, and TLI = 869. Overall, the Russian version of the brief COPE shows acceptable psychometric properties and may be applied by researchers, school administrators, and psychologists; however, the reliability of the “avoidance” scale is doubtful and must be considered before application.

This study involved a psychometric analysis of the 10-item Perceived Stress Scale (PSS-10). To investigate the Russian version of the PSS-10 for adolescents, 3530 adolescents aged 13 to 17 years were recruited. Confirmatory factor analysis revealed that the data corresponded to the expected two-factor configuration. Psychometric properties and factor structure were evaluated. As expected, the PSS-10 included two factors: perceived helplessness and perceived self-efficacy. Internal consistency demonstrated acceptable values (Cronbach’s alpha was 0.82 for perceived distress, 0.77 for perceived self-efficacy, and 0.80 for the overall PSS score). Measurement invariance across sexes was assessed, and configural and metric invariance were confirmed. The developed diagnostic tool can be used both in the school system to alleviate the negative consequences of academic stress in adolescents and, in the future, in other areas, particularly in clinical practice.

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The COVID-19 pandemic has had a dramatic impact on the mental state of teachers and students, who faced the necessity to teach and study online because of lockdown. The current study aimed to establish the association between attitudes toward the pandemic and the degree of stress, anxiety and depression among teachers and students. A total of 8051 participants constituted the sample. The Hospital Anxiety and Depression Scale (HADS) was used to assess depression and anxiety, and the PSS was used to assess stress. Principal component analysis was implemented to derive latent variables reflecting various attitudes toward the pandemic, and multinomial logistic models were implemented to establish the association between attitudes toward the pandemic and the degree of anxiety, stress and depression. The majority of participants, regardless of their social group, reported low levels of anxiety and depression and medium levels of stress. Overall, worry about COVID-19 itself is negatively associated with anxiety, stress and depression, whereas worry about the side effects of the pandemic has a positive association with those constructs. Additionally, social group moderates the relationship: specifically for teachers, the association between worry about COVID-19 and anxiety and depression tended to be positive. The theoretical explanation and practical implication of the findings are discussed.