Factor structure and psychometric properties of the Perceived Stress Scale in Russian adolescents

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 13-17 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 helplessness, 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.

Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model

This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG study, affirming that gender prediction can be attained with noteworthy accuracy. The best performing model achieved an accuracy of 85% and an ROC AUC of 89%, surpassing all prior benchmarks set using EEG data and rivaling the top-tier results derived from fMRI studies. A comparative analysis of LightGBM and Deep Convolutional Neural Network (DCNN) models revealed DCNN's superior performance, attributed to its ability to learn complex spatial-temporal patterns in the EEG data and handle large volumes of data effectively. Despite this, interpretability remained a challenge for the DCNN model. The LightGBM interpretability analysis revealed that the most important EEG features for accurate sex prediction were related to left fronto-central and parietal EEG connectivity. We also showed the role of both low (delta and theta) and high (beta and gamma) activity in the accurate sex prediction. These results, however, have to be approached with caution, because it was obtained from a dataset comprised largely of participants with various mental health conditions, which limits the generalizability of the results and necessitates further validation in future studies. . Overall, the study illuminates the potential of interpretable machine learning for sex prediction, alongside highlighting the importance of considering individual differences in prediction sex from brain activity.

The Interaction between Congruency and Numerical Ratio Effects in the Nonsymbolic Comparison Test

The nonsymbolic comparison task is used to investigate the precision of the Approximate Number Sense, the ability to process discrete numerosity without counting and symbols. There is an ongoing debate regarding the extent to which the ANS is influenced by the processing of non-numerical visual cues. To address this question, we assessed the congruency effect in a nonsymbolic comparison task, examining its variability across different stimulus presentation formats and numerical proportions. Additionally, we examined the variability of the numerical ratio effect with the format and congruency. Utilizing generalized linear mixed-effects models with a sample of 290 students (89% female, mean age 19.33 years), we estimated the congruency effect and numerical ratio effect for separated and intermixed formats of stimulus presentation, and for small and large numerical proportions. The findings indicated that the congruency effect increased in large numerical proportion conditions, but this pattern was observed only in the separated format. In the intermixed format, the congruency effect was insignificant for both types of numerical proportion. Notably, the numerical ratio effect varied for congruent and incongruent trials in different formats. The results may suggest that the processing of visual non-numerical parameters may be crucial when numerosity processing becomes noisier, specifically when numerical proportion becomes larger. The implications of these findings for refining the ANS theory are discussed.

Attitudes towards genetic testing: The role of genetic literacy, motivated cognition, and socio-demographic characteristics

Understanding reasons for why people choose to have or not to have a genetic test is essential given the ever-increasing use of genetic technologies in everyday life. The present study explored the multiple drivers of people's attitudes towards genetic testing. Using the International Genetic Literacy and Attitudes Survey (iGLAS), we collected data on: (1) willingness to undergo testing; (2) genetic literacy; (3) motivated cognition; and (4) demographic and cultural characteristics. The 37 variables were explored in the largest to-date sample of 4311 participants from diverse demographic and cultural backgrounds. The results showed that 82% of participants were willing to undergo genetic testing for improved treatment; and over 73%-for research. The 35 predictor variables together explained only a small proportion of variance: 7%-in the willingness to test for Treatment; and 6%-for Research. The strongest predictors of willingness to undergo genetic testing were genetic knowledge and deterministic beliefs. Concerns about data misuse and about finding out unwanted health-related information were weakly negatively associated with willingness to undergo genetic testing. We also found some differences in factors linked to attitudes towards genetic testing across the countries included in this study. Our study demonstrates that decision-making regarding genetic testing is influenced by a large number of potentially interacting factors. Further research into these factors may help consumers to make decisions regarding genetic testing that are right for their specific circumstances.

The Psychological Science Accelerator's COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data.

Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset

Introduction

Brain age prediction has been shown to be clinically relevant, with errors in its prediction associated with various psychiatric and neurological conditions. While the prediction from structural and functional magnetic resonance imaging data has been feasible with high accuracy, whether the same results can be achieved with electroencephalography is unclear.

Methods

The current study aimed to create a new deep learning solution for brain age prediction using raw resting-state scalp EEG. To this end, we utilized the TD-BRAIN dataset, including 1,274 subjects (both healthy controls and individuals with various psychiatric disorders, with a total of 1,335 recording sessions). To achieve the best age prediction, we used data augmentation techniques to increase the diversity of the training set and developed a deep convolutional neural network model.

Results

The model's training was done with 10-fold cross-subject cross-validation, with the EEG recordings of the subjects used for training not considered to test the model. In training, using the relative rather than the absolute loss function led to a better mean absolute error of 5.96 years in cross-validation. We found that the best performance could be achieved when both eyes-open and eyes-closed states are used simultaneously. The frontocentral electrodes played the most important role in age prediction.

Discussion

The architecture and training method of the proposed deep convolutional neural networks (DCNN) improve state-of-the-art metrics in the age prediction task using raw resting-state EEG data by 13%. Given that brain age prediction might be a potential biomarker of numerous brain diseases, inexpensive and precise EEG-based estimation of brain age will be in demand for clinical practice.

The thresholding problem and variability in the EEG graph network parameters

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.

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 12 May 2020. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.c.4878591.v1.

#EEGManyLabs: Investigating the replicability of influential EEG experiments

There is growing awareness across the neuroscience community that the replicability of findings about the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardised analysis pipelines. Inspired by recent efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.

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

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.

Alpha Band Resting-State EEG Connectivity Is Associated With Non-verbal Intelligence

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.

CORRELATION BETWEEN PHYSICAL ACTIVITY AND FUNCTIONAL CONNECTIVITY OF THE BRAIN

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.

Western Eurasian ancestry in modern Siberians based on mitogenomic data

Background

Although the genetic heritage of aboriginal Siberians is mostly of eastern Asian ancestry, a substantial western Eurasian component is observed in the majority of northern Asian populations. Traces of at least two migrations into southern Siberia, one from eastern Europe and the other from western Asia/the Caucasus have been detected previously in mitochondrial gene pools of modern Siberians.

Results

We report here 166 new complete mitochondrial DNA (mtDNA) sequences that allow us to expand and re-analyze the available data sets of western Eurasian lineages found in northern Asian populations, define the phylogenetic status of Siberian-specific subclades and search for links between mtDNA haplotypes/subclades and events of human migrations. From a survey of 158 western Eurasian mtDNA genomes found in Siberia we estimate that nearly 40% of them most likely have western Asian and another 29% European ancestry. It is striking that 65 of northern Asian mitogenomes, i.e. ~41%, fall into 19 branches and subclades which can be considered as Siberian-specific being found so far only in Siberian populations. From the coalescence analysis it is evident that the sequence divergence of Siberian-specific subclades was relatively small, corresponding to only 0.6-9.5 kya (using the complete mtDNA rate) and 1–6 kya (coding region rate).

Conclusions

The phylogeographic analysis implies that the western Eurasian founders, giving rise to Siberian specific subclades, may trace their ancestry only to the early and mid-Holocene, though some of genetic lineages may trace their ancestry back to the end of Last Glacial Maximum (LGM). We have not found the modern northern Asians to have western Eurasian genetic components of sufficient antiquity to indicate traces of pre-LGM expansions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-014-0217-9) contains supplementary material, which is available to authorized users.

Ancient links between Siberians and Native Americans revealed by subtyping the Y chromosome haplogroup Q1a

To investigate the structure of Y chromosome haplogroups R-M207 and Q-M242 in human populations of North Asia, we have performed high-resolution genotyping using both single nucleotide polymorphisms and short tandem repeat (STR)-based approaches of 121 M207- and M242-derived samples from 885 males of 16 ethnic groups of Siberia and East Asia. As a result, the following Y chromosome haplogroups were revealed: R1b1b1-M73 (2.0%), R1b1b2-M269 (0.7%), R2-M124 (1.1%), Q1a*-MEH2 (0.5%), Q1a2-M25 (0.1%), Q1a3*-M346 (9.2%) and Q1a3a-M3 (0.2%). Despite the low coalescence age of haplogroup Q1a3*-M346, which is estimated in South Siberia as about 4.5±1.5 thousand years ago (Ka), divergence time between these Q1a3*-M346 haplotypes and Amerindian-specific haplogroup Q1a3a-M3 is equal to 13.8±3.9 Ka, pointing to a relatively recent entry date to America. In addition, unique cluster of haplotypes belonging to Q1a*-MEH2 was found in Koryaks inhabiting the Sea of Okhotsk coast (at a frequency of 10.3%). Although the level of STR diversity associated with Q1a*-MEH2 is very low, this lineage appears to be closest to the extinct Palaeo-Eskimo individuals belonging to the Saqqaq culture arisen in the New World Arctic about 5.5 Ka. This finding suggests that Q1a*-MEH2 likely traces a population migration originating in Northeast Siberia across the Bering Strait.

Evidence for biogeographic patterning of mitochondrial DNA sequences in Eastern horse populations

Equine mitochondrial DNA (mtDNA) phylogeny reconstruction reveals a complex pattern of variation unlike that seen in other large domesticates. It is likely that this pattern reflects a process of multiple and repeated, although not necessarily independent, domestication events. Until now, no clear geographic affiliation of clades has been apparent. In this study, amova analyses have revealed a significant non-random distribution of the diversity among equine populations when seven newly sequenced Eurasian populations were examined in the context of previously published sequences. The association of Eastern mtDNA types in haplogroup F was highly significant using Fisher's exact test of independence (P = 0.00000). For the first time, clear biogeographic partitioning has been detected in equine mtDNA sequence.

History and integrity of thoroughbred dam lines revealed in equine mtDNA variation

Mitochondrial DNA (mtDNA) D-loop sequences (381 bp) from 100 thoroughbreds in 19 of the most common matrilineal female families were used to reconstruct a founder female population for the thoroughbred ( approximately 1650-1750 AD). Seventeen haplotypes were found to have contributed to the 19 female lineages. In order to place the reconstructed founder population in wider historical context, we examined, using both single strand conformation polymorphism and direct sequence analysis, variation in a 343 bp mtDNA fragment in that population and 13 other horse populations of disparate provenance. Interpopulation diversity analyses revealed no significant difference in variation between the thoroughbred founder population and the 13 other diverse horse populations and suggested a non-random partitioning of diversity among geographically diverse horse populations. Within thoroughbreds, almost half of the female families, which are each considered from pedigrees to have matrilines converging to one of >30 historically recognized female ancestors, contained sequences which were inconsistent with common descent. Examination of the anomalies in the context of pedigree records suggests the majority might be best explained as confusion of mares at the foundation stages, although some could have some bearing on more recent (19th century - 1980) lineages. We have used this combination of molecular and historical information to identify some of the founder dams and to make new interpretations about the early history of the thoroughbred.