Working memory processes and intrinsic motivation: An EEG study.

Processes typically encompassed by working memory (WM) include encoding, retention, and retrieval of information. Previous research has demonstrated that motivation can influence WM performance, although the specific WM processes affected by motivation are not yet fully understood. In this study, we investigated the effects of motivation on different WM processes, examining how task difficulty modulates these effects. We hypothesized that motivation level and personality traits of the participants (N = 48, 32 females; mean age = 21) would modulate the parietal alpha and frontal theta electroencephalography (EEG) correlates of WM encoding, retention, and retrieval phases of the Sternberg task. This effect was expected to be more pronounced under conditions of very high task difficulty. We found that increasing difficulty led to reduced accuracy and increased response time, but no significant relationship was found between motivation and accuracy. However, EEG data revealed that motivation influenced WM processes, as indicated by changes in alpha and theta oscillations. Specifically, higher levels of the Resilience trait-associated with mental toughness, hardiness, self-efficacy, achievement motivation, and low anxiety-were related to increased alpha desynchronization during encoding and retrieval. Increased scores of Subjective Motivation to perform well in the task were related to enhanced frontal midline theta during retention. Additionally, these effects were significantly stronger under conditions of high difficulty. These findings provide insights into the specific WM processes that are influenced by motivation, and underscore the importance of considering both task difficulty and intrinsic motivation in WM research.

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.

An Independent Evaluation of the Psychometric Properties of the Russian Version of the Pediatric Daytime Sleepiness Scale (PDSS).

Background: The quality of sleep significantly impacts children's day-to-day performance, with at least 20% reporting issues with sleepiness. Valid tools for assessing the quality of sleep are needed. Objective: In this study, we assessed the psychometric properties of the Russian version of the Pediatric Daytime Sleepiness Scale (PDSS). The initial adaptation of the PDSS was conducted on a sample from the Arctic regions of Russia. This location may have influenced the scale's generalizability due to variations in natural daylight across different areas of the country. Design: To rectify this, we gathered a comprehensive, geographically diverse sample from Russia. This combined dataset comprised 3772 participants between 10 to 18 years of age, from nine different regions of Russia. Results: We confirmed the unifactorial structure of the PDSS, which showed no regional effects. The psychometric analysis indicated that one item from the 8item PDSS could be removed, thereby improving the scale's model fit. We also observed gender and age impacts on sleep quality: boys reported fewer sleep-related issues than girls, and younger children reported fewer problems than older children. Conclusion: This study validates the usefulness and reliability of the Russian version of the PDSS, thereby enhancing its general applicability. Furthermore, we replicated previously reported age and sex effects on the sleep quality of school-aged children.

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.

Parietal Alpha Oscillations: Cognitive Load and Mental Toughness.

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.

Apoptotic Features in Non-Apoptotic Processes.

Apoptosis is the most thoroughly studied type of regulated cell death. Certain events, such as externalization of phosphatidylserine (PS) into the outer leaflet of plasma membrane, mitochondrial outer membrane permeabilization, caspase cascade activation, DNA fragmentation and blebbing, are widely considered to be hallmarks of apoptosis as well as being traditionally viewed as irreversible. This review shows that under particular circumstances these events can also participate in physiological processes not associated with initiation of apoptosis, such as cell differentiation, division, and motility, as well as non-apoptotic types of cell death. Moreover, these events may often be reversible. This review focuses on three processes: phosphatidylserine externalization, blebbing, and activation of apoptotic caspases. Mitochondrial outer membrane permeabilization and DNA fragmentation are not discussed.

The Place of Gender Stereotypes in the Network of Cognitive Abilities, Self-Perceived Ability and Intrinsic Value of School in School Children Depending on Sex and Preferences in STEM.

Adolescents face many barriers on the path towards a STEM profession, especially girls. We examine the gender stereotypes, cognitive abilities, self-perceived ability and intrinsic values of 546 Russian school children from 12 to 17 years old by sex and STEM preferences. In our sample, STEM students compared to no-STEM have higher cognitive abilities, intrinsic motivation towards math and science, are more confident in their math abilities and perceive math as being easier. Boys scored higher in science, math and overall academic self-efficacy, intrinsic learning motivation and math's importance for future careers. Meanwhile, girls displayed higher levels of gender stereotypes related to STEM and lower self-efficacy in math. A network analysis was conducted to identify the structure of psychological traits and the position of the stem-related stereotypes among them. The analysis arrived at substantially different results when adolescents were grouped by sex or preference towards STEM. It also demonstrated that gender stereotypes are connected with cognitive abilities, with a stronger link in the no-STEM group. Such stereotypes play a more important role for girls than boys and, jointly with the general self-efficacy of cognitive and academic abilities, are associated with the factors that distinguish groups of adolescents in their future careers.

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.

Estimated resistance of the malaria mosquito Anopheles messeae s.l. to the insecticide malathion.

Resistance to agricultural pesticides is an important and insufficiently studied concern for pest and disease vector research. We determined the malathion resistance of species in the Anopheles maculipennis mosquito group in a habitat near Novosibirsk, Russia. Most of the 851 individuals we measured were members of the Anopheles messeae s.l. complex (An. messeae and An. daciae species). The LC50 value for malathion was 0.052 mg/L for the mixed specimens, and we failed to find any differences between species. The LC50 value was within the range of values for malathion resistance of Anopheles stephensi and Culex quinquefasciatus. As the main resistance mechanism to organophosphate and carbamate insecticides is a single mononucleotide substitution in the ace-1 gene, we searched for this mutation in An. messeae s.l. and An. beklemishevi by restriction analysis. This mutation was not found in 347 of the specimens. We sequenced the ace-1 gene fragment for 24 specimens from four species of the Anopheles maculipennis group, including An. messeae, An. daciae, An. atroparvus, and An. beklemishevi. These specimens harbored a nucleotide substitution in the triplet where a mutation can lead to insecticide resistance, but this substitution would make it difficult for the resistance to develop. Since the studied specimens belong to branches of the Palearctic portion of the Anopheles maculipennis group, we suspect that all other Palearctic species of this group would have difficulties harboring the ace-1 mutation that would lead to organophosphate and carbamate resistance.

Prevalence and genetic diversity of Wolbachia endosymbiont and mtDNA in Palearctic populations of Drosophila melanogaster.

BACKGROUND: Maternally inherited Wolbachia symbionts infect D. melanogaster populations worldwide. Infection rates vary greatly. Genetic diversity of Wolbachia in D. melanogaster can be subdivided into several closely related genotypes coinherited with certain mtDNA lineages. mtDNA haplotypes have the following global distribution pattern: mtDNA clade I is mostly found in North America, II and IV in Africa, III in Europe and Africa, V in Eurasia, VI is global but very rare, and VIII is found in Asia. The wMel Wolbachia genotype is predominant in D. melanogaster populations. However, according to the hypothesis of global Wolbachia replacement, the wMelCS genotype was predominant before the XX century when it was replaced by the wMel genotype. Here we analyse over 1500 fly isolates from the Palearctic region to evaluate the prevalence, genetic diversity and distribution pattrern of the Wolbachia symbiont, occurrence of mtDNA variants, and finally to discuss the Wolbachia genotype global replacement hypothesis. RESULTS: All studied Palearctic populations of D. melanogaster were infected with Wolbachia at a rate of 33-100%. We did not observe any significant correlation between infection rate and longitude or latitude. Five previously reported Wolbachia genotypes were found in Palearctic populations with a predominance of the wMel variant. The mtDNA haplotypes of the I_II_III clade and V clade were prevalent in Palearctic populations. To test the recent Wolbachia genotype replacement hypothesis, we examined three genomic regions of CS-like genotypes. Low genetic diversity was observed, only two haplotypes of the CS genotypes with a 'CCG' variant predominance were found. CONCLUSION: The results of our survey of Wolbachia infection prevalence and genotype diversity in Palearctic D. melanogaster populations confirm previous studies. Wolbachia is ubiquitous in the Palearctic region. The wMel genotype is dominant with local occurrence of rare genotypes. Together with variants of the V mtDNA clade, the variants of the 'III+' clade are dominant in both infected and uninfected flies of Palearctic populations. Based on our data on Wolbachia and mtDNA in different years in some Palearctic localities, we can conclude that flies that survive the winter make the predominant symbiont contribution to the subsequent generation. A comprehensive overview of mtDNA and Wolbachia infection of D. melanogaster populations worldwide does not support the recent global Wolbachia genotype replacement hypothesis. However, we cannot exclude wMelCS genotype rate fluctuations in the past.

New literacy challenge for the twenty-first century: genetic knowledge is poor even among well educated.

We live in an age of rapidly advancing genetic research. This research is generating new knowledge that has implications for personal health and well-being. The present study assessed the level of genetic knowledge and personal engagement with genetics in a large sample (N = 5404) of participants. Participants received secondary education in 78 countries, with the largest samples from Russia, the UK and the USA. The results showed significant group differences in genetic knowledge between different countries, professions, education levels and religious affiliations. Overall, genetic knowledge was poor. The questions were designed to assess basic genetic literacy. However, only 1.2% of participants answered all 18 questions correctly, and the average score was 65.5%. Genetic knowledge was related to peoples' attitudes towards genetics. For example, those with greater genetic knowledge were on average more willing to use genetic knowledge for their personal health management. Based on the results, the paper proposes a number of immediate steps that societies can implement to empower the public to benefit from ever-advancing genetic knowledge.

Spiroplasma infection in Harmonia axyridis - Diversity and multiple infection.

The heritable endosymbiotic bacterium Spiroplasma is found in the harlequin ladybird Harmonia axyridis. The proportion of beetles infected with Spiroplasma in different native H. axyridis populations varies from 2% to 49%. We investigated the polymorphism of Spiroplasma strains in samples from individual beetles from Kyoto, Vladivostok, Troitsa Bay, Novosibirsk, and Gorno-Altaisk. To identify Spiroplasma strains, we analyzed nucleotide polymorphisms of the 16S rRNA gene and the ribosomal internal transcribed spacer (ITS1). The majority of infected beetles were infected with two or more Spiroplasma strains. We measured Spiroplasma density in beetles with different infection status using quantitative PCR. The abundance of Spiroplasma in samples with a single infection is an order of magnitude lower than in samples with multiple infections. Density dependent biological effects of Spiroplasma are discussed.

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.

Y-chromosome diversity in the Kalmyks at the ethnical and tribal levels.

The Mongolic-speaking Kalmyks currently inhabiting the steppes of the Volga region have Central Asian ancestry and are organized into the tribal groups. The genetic relationships among these tribes and their origin have remained obscure. We analyzed 17 short tandem repeat and 44 binary polymorphisms of Y-chromosome in 426 individuals mainly from three major tribes of the Kalmyks (the Torguuds, Dörwöds and Khoshuuds). Among these tribes, the Dörwöds and Torguuds, as well as the Kalmyks collectively as an ethnic group, showed relatively close genetic affinities to each other and to the Mongols and Altaian Kazakhs, whereas the Khoshuuds were clearly separated from all of them, gathering with the Manchu, Tibetans or Evenks (depending on the algorithm used to calculate genetic distances). The genetic results also indicate that paternal gene flow from East Europeans to the Kalmyks is very little, despite their cohabitation in the North Caspian Steppe during the last 380 years. The occurrence of unique cluster of N1c-Tat haplotypes in the Khoshuuds, which dates to about 340 years and is likely to have East European ancestry, is considered as a result of interethnic contacts occurred soon after the appearance of the Kalmyk tribes in the Volga-Ural region.

The Y-chromosome C3* star-cluster attributed to Genghis Khan's descendants is present at high frequency in the Kerey clan from Kazakhstan.

To verify the possibility that the Y-chromosome C3* star-cluster attributed to Genghis Khan and his patrilineal descendants is relatively frequent in the Kereys, who are the dominant clan in Kazakhstan and in Central Asia as a whole, polymorphism of the Y-chromosome was studied in Kazakhs, represented mostly by members of the Kerey clan. The Kereys showed the highest frequency (76.5%) of individuals carrying the Y-chromosome variant known as C3* star-cluster ascribed to the descendants of Genghis Khan. C3* star-cluster haplotypes were found in two subclans, Abakh-Kereys and Ashmaily-Kereys, diverged about 20-22 generations ago according to the historical data. Median network of the Kerey star-cluster haplotypes at 17 STR loci displays a bipartite structure, with two subclusters defined by the only difference at the DYS448 locus. Noteworthy is a strong correspondence of these subclusters with the Kerey subclans affiliation. The data obtained suggest that the Kerey clan appears to be the largest known clan in the world descending from a common Y-chromosome ancestor. Possible ways of Genghis Khan's relationship to the Kereys are discussed.