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Research area 08 - HUMANITIES AND SOCIAL SCIENCES, 08-555 - Developmental psychology, pedagogical psychology, correctional psychology, psychology of education

Keywordsmental-attentional capacity, giftedness, children, cognitive abilities, intelligence, cognitive development, ultrasonography, eye-tracking, fMRI, neuroimaging

Annotation
Cognitive capital reflects the potential of a nation in terms of the skills, intelligence and well being of its people. Now more than ever before strong nations value intelligence and cognitive abilities to be competitive in an international marketplace that demands productivity, creativity and innovation. Initiatives such as the Center for Gifted Education “Sirius” (sochisirius.ru) are important because they aim to identify and nurture the talents of cognitively gifted students ages 10-17 years. The main focus of our 2017-2019 project was to test new, objective and contemporary methods of identifying cognitively gifted children earlier, when children enter school, ages 7-10 years. Specifically, research suggests that 1-10% of the general population is cognitively gifted (e.g., Lupart & Pyryt 1996). Traditional methods for measuring intelligence (e.g., intelligence quotient tests) and domain specific olympiads have several limitations as they rely heavily on formalized schooling. As a result of our 2017-2019 project we showed that measures of parametric mental-attentional capacity are objective, contemporary and culture fair because the obtained scores from children in Russia match theoretical predictions (e.g., Pascual-Leone, 1970) and empirical findings obtained across many countries (Arsalidou & Im-Bolter, 2017). Thus, our research shows that these tests are suitable for assessing cognitive abilities in young school aged children in Russia. Moreover, we were able to obtain and replicate the same established rates of cognitive giftedness as those reported in the literature (i.e., 1-10%). We have identified about 60 children in grades 1, 2, 3, and 4 who significantly outperformed their peers. These children score 2 units or higher on mental-attentional capacity measures than their same age peers (i.e., they perform similarly to children 4 years or older than themselves). Cognitively gifted children, and a matched control group completed a head and neck ultrasound examination and a survey on school engagement. Some of the children were also invited to play cognitive games while we recorded eye-movements and functional magnetic resonance imaging (MRI). Results form our 2017-2019 project show that cognitively gifted children are significantly more engaged in school and particularly exhibit significantly higher scores in behavioral and cognitive engagement. Moreover, significant differences were observed in terms of blood flow volume in the left hemisphere with cognitively gifted children showing higher values. Importantly, our eye-tracking data show that higher scores on mental-attentional capacity tasks are related not only to performance accuracy but also to the speed with which individuals can process information. Speed of processing is linked both to electrical impulses and maturation of white matter tracts (i.e., myelination) in the brain. Specifically, as white matter tracts mature they become more conductive (i.e., electrical signals travel faster: Susuki, 2010). Research shows that there is a relation between white matter tracts and reaction time (e.g., Turken et al., 2008), however, the nature of this relation across development remains unclear. No study to date has combined data from electrical activity and fiber density in white matter tracts to examine neurocognitive profiles in cognitively gifted children. To better understand the neurocognitive factors that underlie high cognitive performance, the continuation of this study 2020-2021 will employ neuroimaging techniques that examine electrical activity using Electroencephalography (EEG) and myelination of white matter tracts using diffusion tensor imaging (DTI; a scanning sequence recorded using MRI). DTI uses principles of Brownian motion of water molecules to tract white matter (i.e., myelinated paths) in the brain (e.g., Merboldt et al., 1985). This method generates fractional anisotropy (FA) values for every voxel that can be correlated with other variables. The degree of FA is related to the degree of myelination, therefore FA can be considered as an indicator of brain maturation (Kochunov et al., 2011). Myelination patterns have a complex, cyclic expression across development (e.g., Yakovlev and Lecours, 1967). To our knowledge no study to date has compared DTI indices with performance indices of mental-attentional capacity in cognitively gifted children. EEG uses electrodes placed to the surface of the scalp to measure electrical activity associated with brain areas under the scalp. Electrical impulses are generated when brain cells are communicating, thus EEG can show with high accuracy when activity occurs in the brain (i.e., high temporal resolution). Lastly, in addition to standard statistical analyses protocols we will employ machine learning algorithms to develop models of parameters that better predict high cognitive abilities. Overall, the 2020-2021 is organized in three main studies (A) DTI study - to evaluate white matter fiber tracts, (B) EEG study - to evaluate electrophysiological signals in resting state and during a task and (C) Machine learning study – to combine neurocognitive data and build models for detecting cognitively gifted children. Participants: We will invite children who we already identified as cognitively gifted (N = 40-60) and a matched control group (N = 40-60) to participate in the studies. We expect a rate of 40% participation, therefore to increase our sample, we will continue to foster our relationships with schools, and we will continue testing children to identify new participants who are cognitively gifted, using measures of mental-attentional capacity. This is also important for our long term objecting to increase the group of children who we identify as gifted for a longitudinal investigation. Novelty: This will be the first study in Russia and worldwide to model with machine learning approaches neurocognitive parameters associated with mental-attentional capacity, EEG and DTI to predict cognitively gifted performance. Team: This project brings together a strong multidisciplinary team with expertise in cognitive development, biology, machine learning and neuroimaging, with experience in working productively together. Significance: Our long-term objective is to establish a multidimensional framework for detecting neurocognitive giftedness in children that will lead to improved teaching practices and improved learning outcomes for children. This project contributes to one of Russia’s National Priorities - Quality Education. Ultimately, our goal is to work with cognitively gifted children longitudinally and evaluate their neurocognitive transformation across the lifespan.

Expected results
We anticipate that we will have three main sets of results from the (A) DTI-study, (B) EEG-study, and (C) Machine-learning study. (A) DTI-study: The development and maturation of white matter tracts in the human brain is a process that follows complex topographical and temporal patterns (Yakovlev & Lecours, 1967; Hermoye et al., 2006; Asato et al. 2010). Research suggests that white matter maturation in typically developing children predicts the reaction time to visual (Dockstader et a., 2012) and cognitive tasks (Schmithorst et al., 2005; Mabbott et al., 2006, Turken et al., 2008. Olson et al., 2009). One study reports that white matter values significantly correlated with mathematical giftedness and intelligence scores in adolescents ages 11-15 years (Navas-Sanchez et al., 2014). We are aware of no study that examined DTI parameters associated with cognitively gifted children (e.g., ages 7-10 years). Particularly, expected results include relations among specific white matter tracts (e.g., acruate fasciculus, superior longitudinal fasciculus, corpus callosum) and performance on mental-attentional capacity tasks in children. Results will have theoretical and practical significance as they can inform developmental cognitive theories on the brain representation of cognitive processes and practically this knowledge will be beneficial for parents, teachers and policy makers (e.g., stakeholders of the sochisirius program) who aim to constructively support cognitively gifted children. (B) EEG study: Electroencephalography (EEG) measures brain oscillations and electrical activity associated with communicating neurons. EEG is a classic method in neuroimaging and it has been used extensively to study the relation between electrical activity and cognitive abilities (e.g., Corsi-Cabrera et al., 1989; Klimesch, 1999). The majority of EEG work focuses on children with neurodevelopmental disorders such as epilepsy (e.g., Gordon, 2000; Asano et al., 2013), autism spectrum disorders (Monteiro et al., 2017 review) and attention deficit hyperactivity disorder in children (e.g., Barry et al., 2003; Shi et al., 2012). Notably, the relation of EEG signal and cognitive abilities in typical developing children has been established (Heinrich et al., 2009; Uhlhaas et al., 2009) and best practices and applications of EEG to study cognitive development have been documented (Bell & Cuevas, 2012), however, little is known about EEG correlates of gifted children. Few studies used EEG to examine children older than 11 years-old who were either highly intelligent or gifted in math using eye-open resting state task (Alexander et al., 1996), tasks that involving generating hypothesis (Jin et al. 2006), and auditory stimuli (Liu et al., 2008). No study to date has used EEG to examine brain responses to mental-attentional capacity tasks in young children 7-10 years. We will examine EEG activity during a mental-attentional capacity task and during resting state. This will allow us to examine whether brain oscillations recorded using EEG can serve as predictors of cognitive performance in young school age children (7-10 years). Results will have significant theoretical and practical applications. Theoretically results will provide knowledge on the EEG profile of young children who are cognitively gifted and identify the EEG factors that are common and distinct in their development. Practically, this information can inform teaching practice and public policy associated with the learning opportunities offered to children. (C) Machine learning study: The literature on machine learning and neuroimaging is increasing exponentially. Although there has been great progress in using machine learning methods and medical imaging for predicting all sorts of abnormalities such as cancer (Greenspan et al., 2016; Erickson et al., 2017, for reviews), the work on machine learning and neurocognitive abilities is very limited, particularly in children. Specifically, resting-state MRI have used supervised and unsupervised machine learning applications to discover principal modes of variation across space, time or population (Khosla et al., 2018 for review), however these studies focus on adults. Currently, there are worldwide efforts to predict intelligence from neurocognitive parameter, which was promoted by the Medical Image Computing and Computer Assisted Intervention Society (http://www.miccai.org/). This particular study is focused on fluid intelligence scores (i.e., ability to think flexibly), an ability that relates to executive function skills, which can improve with training. Our group has contributed to this initiative to examine the relation between structural MRI data and fluid intelligence and has received the fifth place in the competition. Specifically, our approach was based on the use of convolutional neural networks with three-dimensional convolutions with 3D MRI data. Details are published in the competition article (Pominova et al., 2019) and reported at MICCAI 2019. Critically, this worldwide effort is in progress and remains unresolved. Our study will focus on mental attentional capacity, which quantitatively assesses the number of items a child can process simultaneously. In other words, this capacity that is minimally biased by schooling, because all training needed to complete the task is given to all children. Thus, this study has a practical and cultural advantage for objective assessment of cognitive performance. Overall, results from our 2020-2021 continuation, will have significant scientific and social impact in many areas. Specifically, in the academic sphere results from this project will have an impact in the fields of psychology, cognitive neuroscience and medicine. In the social sphere, our results will have an impact in education, and economics (i.e., development of human capital). Importantly, it is fundamental for parents, educators and clinicians to have access to new methods and techniques for improving their practice and parenting decisions. Thus, in addition to publishing this work in peer-reviewed scientific journals we will make our research available to the community through public presentations, school presentations and appropriate websites. Examples include: Scientific Journals: NeuroImage: (Impact Factor: 5.812) Child Development (Impact Factor: 5.024) Human Brain Mapping: (Impact Factor: 4.927) Developmental Cognitive Neuroscience (Impact Factor: 4.920) Conferences: Meeting of the Society for Research in Child Development (SRCD: http://www.srcd.org/) Meeting of the Cognitive Development Society (https://cogdevsoc.org/) Meeting of Cognitive Science (http://cogconf.ru/default.aspx?l=r) Meeting of Virtual Laboratory of Cognitive Science (http://virtualcoglab.ru/index.html)

Annotation of the results obtained in 2021
Cognitive development refers to the growth of children's ability to think, reason and learn. Mental functions related with thinking, reasoning and learning improve with age. Cognitive psychologists are interested in predicting when and what exactly changes with age. Neuroscientists are interested in identifying the biological factors that contribute to brain maturation. Understanding relations among cognition, development and neuroscience gave rise to the field of developmental cognitive neuroscience. Although many studies have examined altered brain and cognitive correlates in children with neurodevelopmental disorders, the fundamental normative trajectory of development is not fully documented. Further, children with outstanding cognitive abilities remain an interesting puzzle. Our research focuses on brain and cognitive correlates in typically developing children. A main goal of our study was to identify neurocognitive interrelations as a function of age focusing on children with advanced cognitive abilities. Theoretically, our project is framed on the theory of constructive operators. The theory of constructive operators proposes a domain general framework that characterizes mental attention as the maturational construct that drives development. The model of endogenous mental attention follows a flashlight model such that mental attention is nested within working memory, working memory is nested within the field of activated schemes, which are in turn nested within the repertoire of schemes. Mental attention mimics the brightest part of the beam of light, metaphorically illustrating the mental attention as a core resource or ‘energy’ that can be used for problem solving. Many tasks of mental attention have been designed and have been extensively used by developmental psychologists and educators. These tasks offer multiple levels of difficulty and invariant executive goals, allowing individuals of different cognitive abilities to complete the same tasks. In our project we use measures of mental attentional capacity to study brain and behavior in children and adults using technologies such as eye-tracking and magnetic resonance imaging. We use classic statistical approaches and machine learning methods to evaluate data from the literature and data we collect in schools, online or in the laboratory. We are thankful to all teachers and educators for their valuable contribution. We are particularly grateful to parents and all the children for their participation in our studies. Without them our research would not be possible. In what follows we summarize some of the research questions we addressed and fascinating results we obtained. What are the characteristics of cognitively gifted children? Educational practice values the integral development of individuals and the betterment of society as a whole. Both individual development and betterment of society rely to a large extent on intelligence. Intelligence is fundamental for discovery and innovation and the educational sector has a vested interest in improved methods of assessment and identification of highly performing children. A lively debate continues on which methods are better suited for these assessments as researchers have been moving away from classic intelligence tests and efforts persist in identifying what distinguishes cognitively gifted children. In a systematic review, we synthesize literature to address these questions. A total of 115 articles conducted between 1930 and 2020 survived our criteria for the review and were included in further methodological evaluation. About 45% of articles used the Wechsler Intelligence Scale Test. 39% of the studies examined cognitive abilities. Eight out of ten studies that examine information processing speed reported significant differences in at least one in favor of gifted children. Twenty two out of 24 studies measuring accuracy showed significant differences in at least one task. Eighteen studies examined psychophysiological data using electroencephalography (EEG). Specifically, seven out of nine studies that examined event related potentials and cognitive functions show significant differences between gifted and control children in at least one event related potential component. Four out of four studies that examined EEG oscillations related to cognitive function show significant differences between gifted and control children at least in one frequency band. Motivation, self-efficacy, self-esteem and social characteristics were investigated in 14 studies that compared cognitively gifted children with a control group. All studies with motivation show that cognitively gifted children have increased scores on intrinsic and extrinsic motivation. Cognitively gifted children also show higher scores on tasks assessing academic self-efficacy, self-esteem, perceived competence compared to the control group. No significant differences were observed in social self-efficacy, however, cognitively gifted children showed higher social psychological adaptation. No differences were observed on life satisfaction and tasks of overexcitability. However, studies examining the big 5 personality characteristics found that cognitively gifted adolescents were significantly more Open to new experiences, although they were not different from the control group on Extraversion, Agreeableness and Conscientiousness. How are white matter tracts in the brain related to age and cognition? Cognitive development occurs concurrently with biological development and corresponds with dramatic changes in brain structure and function. White matter makes up about 50% of the human brain and supports communication through neurons. Myelination of cerebral white matter fiber tracts changes with individual differences such as age. These fiber tracts allow communication for distal and proximal connections in the brain. Progressive myelination of white matter pathways throughout infancy, childhood adolescence and into adulthood is concurrent with pronounced changes in cognitive abilities. Critically, fewer studies examine typical maturation of white matter tracts to cognitive function. We performed an extensive selective review that focuses on the most popular methods for assessing myelination, identifies anatomical considerations of nine fiber tracts, and presents current knowledge on developmental trajectories in relation to individual differences associated with age, gender, and cognition. Specifically, we cover eight major association tracts: superior longitudinal fasciculus, arcuate fasciculus, cingulum, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, uncinate fasciculus, middle longitudinal fasciculus, frontal aslant tract; and one major commissural fiber system, the corpus callosum, which play a critical role in the interhemispheric integration and transfer of the information. Our findings suggest that there are at least six classic and neuroimaging methods used to assess myelination. These include DTI, myelin water imaging, magnetization transfer imaging, g-ration imaging and myelin mapping. Although classic in vitro studies allow for more precise assessment of myelin, in vivo studies with neuroimaging offer indirect approaches for measuring myelination metrics. The most popular of these methods is diffusion tensor imaging (DTI). DTI studies show that there is no period of development when white matter microstructure in the brain is static. Notably various fiber tracts mature at different rates. Normative values across studies differ and this may be due to methodological approaches such as preprocessing, statistical analyses and age group limits. More research is needed to understand as dynamic non-linear maturation models may be better in explaining development. Overall, our results suggest that myelination follows a complex developmental trajectory that varies by age, fiber tract and hemisphere. Effects of gender were also identified although differences may be confounded by methodological factors (e.g., not controlling for brain volume) or sociocultural and learning opportunities. These results provide an impetus for future studies to examine overarching patterns using multivariate nonparametric and meta-analytical methods. The study of the individual differences in cerebral white matter myelination is very important for developmental biology and neuroscience. Practically, understanding how and when myelination changes and its relation to cognitive performance can inform education practice and clinical interventions. Can we use machine learning to predict accuracy in cognitive tasks? Predicting accuracy in cognitively demanding tasks has potential applications educational technologies and labor market. Task difficulty is associated with increases in reaction time and eye-movements however no study to date has used machine learning to predict performance on tasks with multiple levels of difficulty. We have examined data from young adult participants who completed two visual spatial tasks of mental attentional capacity while their eye movements, accuracy and reaction time was being recorded. We used a common machine learning pipeline to train and test various models. The tested models that considered all data from all tasks and models that tested parts of the data in the prediction (e.g., only eye-movements or only reaction time). Comparable results were obtained from all models, with the XGBoost regression model yielding the most accurate prediction of accuracy. Models with all data showed the highest prediction power. All key features (e.g., reaction time, eye-movements, difficulty level) showed robust prediction of accuracy, albeit reaction time was the strongest. We further analyzed the data to identify which specific features of reaction time and eye-movements contributed more to the prediction of accuracy. This is called analysis of feature importances. The standard deviation of reaction time showed the biggest importance score for models, the model that shows the highest prediction of accuracy. When eye-movements were considered on their own, the mean fixation time and the standard deviation of saccade duration showed the highest importance scores. Our findings can inform theories of cognition and vision science. Practically, our results are useful in designing decision support systems to forecast performance that can benefit edtech technologies. Results associated with this project have been presented at many local and international conferences, published in peer-reviewed journals, and advanced awareness on developmental cognitive neuroscience research in open seminars for schools. Concluding, we are pleased with the progress of our project and will continue our research on better understanding neurocognitive abilities across development. Again, we take the opportunity to thank the children who participated in our studies, their parents, teachers, psychologists and Principals at their schools. We invite readers to visit our laboratory page for more information: https://social.hse.ru/neuropsy

Publications

1. Buyanova I.S., Arsalidou M. Cerebral White Matter Myelination and Relations to Age, Gender, and Cognition: A Selective Review Frontiers in Human Neuroscience, Vol.15, 662031 (year - 2021) https://doi.org/10.3389/fnhum.2021.662031

2. Arsalidou M., Bachurina V., Sushchinskaya S., Sharaev M., Burnaev E. Predicting cognitive performance using eye- movements, reaction time and difficulty level Journal of Vision, Vol.21, 2551 (year - 2021) https://doi.org/10.1167/jov.21.9.2551

3. Bachurina V., Arsalidou M. Attentional strategies during mental arithmetic Journal of Vision, Vol.21, 2539 (year - 2021) https://doi.org/10.1167/jov.21.9.2539

Annotation of the results obtained in 2020
Everyday we mentally attend to information that needs to be remembered and processed, such as following directions and figuring out the price of an item. Processing demands can differ among tasks and the ability to process these tasks varies among individuals. Importantly, such abilities improve across childhood and adolescence. Although research shows that there is a relation between cognitive abilities and task demands, this relation is not very well understood. For example, most typically developing children undergo several stages of cognitive development, however, some children can solve some tasks as well or even better than some adults. Our project examines behavioural, physiological and brain metrics associated with factors that contribute to normative and advanced cognitive abilities. Our project is grounded on the theory of constructive operators as proposed by Juan Pascual-Leone in 1970. This theory defines mental attentional capacity as the analog of mental power that can be applied when holding and manipulating information in mind. This mental power increases gradually across childhood and adolescence in quantifiable stages, reaching seven units in mid- adolescence. Measures of mental attentional capacity have been extensively used in educational research and are found to correlate highly with intelligence tests and academic achievement. Our project uses higher order cognitive measures such as mental attentional capacity to study brain-behaviour relations using cutting edge technologies such as eye-tracking, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). We evaluate and analyze the data using standard statistical approaches as well as machine learning methods. Our research would not be possible without our research volunteers. We are particularly thankful to parents and all the children for their valuable contribution to science. Below we summarize some of the research questions and fascinating results we obtained. Which brain areas do children and adolescents use when solving cognitive tasks associated with rewards? Reward processing refers to a cognitive decision made when a reward or a prize is offered. Children experience reward regularly and rewards serve as motivators for behavioural and cognitive actions. Numerous fMRI studies have examined the brain areas that relate to reward processing and quantitative meta-analyses show that the prefrontal, insular, anterior cingulate cortices as well as subcortical regions in the basal ganglia and amygdala underlie these processes in adults. Research in children was inconclusive, therefore we performed a series of quantitative meta-analyses to identify overarching patterns in the data. We evaluated data from a total of 554 children,1059 adolescents, and 1831 adults from 70 articles. Main results reveal that: (a) children show brain activity in subcortical regions, yet lack implication of prefrontal regions associated with the executive system; (b) adolescents recruit analogous subcortical regions as children yet they also engage the anterior and posterior cingulate cortices, amygdala and middle frontal gyrus (i.e., dorsolateral prefrontal cortex), and; (c) multiple regions (i.e., posterior cingulate gyrus, basal ganglia, insula, and middle frontal gyrus) in adolescence appear to be hyperactive when compared to adults. Our data support the notion of reorganization of function over childhood and adolescence that can inform current theories of cognitive development. How is blink rate related to cognitive effort in children and adults? Cognitive effort a subjective phenomenon, generally defined as the amount of sustained mental activity, exerted during a cognitive task. A well-established eye movement index of cognitive effort is blink rate, the frequency with which we open and close our eyes. Critically, little is known about how blink rate relates to cognitive load across development. Children (age: 9.53±0.76 years) and adults (age: 23.25±3.6) completed visual-spatial tasks that assess mental attentional capacity while their eye movements were being recorded. This tasks have six levels of difficulty that reflect cognitive load. Results showed significant differences between children and adults showing children making less blinks than adults for all levels of difficulty. A significant effect was also observed as a function of difficulty, with blink rate decreasing as difficulty increased. Results show for the first time physiological effects of cognitive load and may have implications to psychological and educational practice. What are the optimal theoretical and empirical criteria for identifying cognitive over-performers using a classic task of mental attentional capacity? Tasks of mental attentional capacity have been extensively used in education, and correlate highly with students' scholastic performance in math and science. Scores of mental attentional capacity also correlate highly with intelligence scores. Research using intelligence tests shows that about 1-10% of individuals have outstanding cognitive abilities. Critically, theoretical and empirical criteria of assessing cognitive performance are understudied and less well understood. We use a classic measure of mental attentional capacity to assess normative performance, as well as theoretical and empirical criteria for identifying children with outstanding cognitive performance in children. Children in grades 1, 2, 3, and 4 (N = 277) completed a classic measure of mental attentional capacity, the Figural Intersection Task. Results show that normative scores from Russian speaking children closely followed theoretical expectations for all grades and were in agreement with past empirical data. Criteria for over-performance were set to be +2 and +3 above theoretical expectations and empirical scores for each age group. Percentages close to those obtained in the literature were obtained primarily using the stricter criterion. How does the integrity of white matter tracts in the brain relate to cognitive performance? DTI is a neuroimaging technique that is used to assess microstructure and orientation of myelinated white matter tracts in the brain. It is an indirect measure of white matter integrity. White matter density is related to high intelligence in children and adults, albeit gender may also influence this relation. Unlike intelligence tests that heavily rely on schooling opportunities and language background, measures of mental attentional capacity have been considered as culture fair. Functional neuroimaging studies show that prefrontal and parietal cortices play a key role in mental-attention tasks. However, relations among fiber density of white matter tracts and mental attention has not been previously examined. The present study investigates links among fractional anisotropy (FA) values in bilateral superior longitudinal fasciculus (SLF), arcuate fasciculus (AF), and corpus callosum (CC) and cognitive performance to a mental attentional capacity task with six levels of difficulty in children (9-12 years) and adolescents (13-15 years). Results show that adolescents show greater FA values in each white matter tract compared to younger children, which reflects protracted maturation of these fiber bundles. Mean reaction time for difficulty levels 3 and 4 was positively associated with FA values in the left AF and left SLF II, whereas for difficulty levels 1 and 2, the mean reaction demonstrated a negative correlation between FA in the splenium of the CC and a positive associated with FA in left SLF III. However, no significant relations were observed among mental attentional capacity scores and FA indices in SLF, AF, and CC. Adolescents demonstrated a negative correlation between the mean reaction time for difficulty levels 3 and 4 and the FA values in the left AF. Further, adolescents exhibited a right-hemispheric preference in the association between FA in the right SLF II and the mean accuracy for the levels 1-4 and mental attentional capacity score. Overall, results suggest that behavioral performance during different periods of cognitive development is sustained by different fiber tracts, thus reflecting dynamic redistribution of roles among these fiber tracts during the transition from childhood to adolescence. Although more research is need this is a promising direction for improved understanding of how cognitive development corresponds to maturation of brain structure. Can we distinguish a child from an adult using blood flow metrics recorded using ultrasonography? Mental functions are sustained by a constant blood supply to the brain. Ultrasonography uses echoes from high frequency sound waves to produce images of internal parts of the body. This technology is extensively used in clinical practice. Critically, little is known about normative development of ultrasound metrics considering also gender, and hemisphere. In addition, machine learning approaches examined brain maturation using various neuroimaging technologies, however no study to date has used ultrasonography to create models that distinguish the age of the individual. We recorded peak systolic, end diastolic, and time-averaged maximum velocities bilaterally in internal carotid, vertebral, and middle cerebral arteries in 821(6-25 years) participants. We highlight three main findings: (a) Results confirm age is highly correlated with blood flow velocities, however gender and hemispheric effects depend on blood vessel and age group. (b) Classification models demonstrated that ultrasonography indices can be used to distinguish children from adults with high accuracy. (c) Results show that the internal carotid artery, particularly in the left hemisphere, makes important contributions to models of both genders, whereas values in the vertebral arteries highly contributed to the model for females. These novel results and methods may benefit clinical evaluations and future research. Results associated with this project have been presented to local and international conferences, published in peer-reviewed Russian and international journals and have been featured in popular public websites. It is also very rewarding for us to disseminate our findings in schools and educational public seminars. Concluding, we are pleased with the progress of our project and the contributions we made to the literature. We will continue our research on better understanding neurocognitive abilities across development. Again, we take the opportunity to thank the children who participated in our studies, their parents, teachers, psychologists and Principals at their schools. More details for parents and educators can be found here: https://social.hse.ru/neuropsy

Publications

1. Khoroshkova, E., Sizova, V., Liashenko, A., Arsalidou, M. Theoretical and empirical criteria for selecting cognitive over-performers: Data from a primary school in Moscow Advances in Cognitive Research, Artificial Intelligence and Neuroinformatics – Proceedings of the 9th International Conference on Cognitive Sciences, Intercognsci-2020. Editors Boris M Velichkovsky, Pavel M. Balaban, Ivan I. Rusak, Vadim L. Ushakov., - (year - 2020)

2. Kouzalis A., Konopkina K., Arsalidou M. Functional neuroimaging of self-ratings associated with cognitive effort Proceedings of the 9th International Conference on Cognitive Sciences, Intercognsci-2020. Editors Boris M Velichkovsky, Pavel M. Balaban, Ivan I. Rusak, Vadim L. Ushakov., - (year - 2020)

3. Yaple, Z. A., Yu, R., Arsalidou, M. Spatial migration of human reward processing with functional development: Evidence from quantitative meta-analyses Human brain mapping, 41(14), 3993-4009 (year - 2020) https://doi.org/10.1002/hbm.25103

4. Arsalidou, M., Pascual-Leone, J., Johnson, J.M., Kotova, T. The constructive operators of the working mind: A developmental account of mental-attentional capacity The Russian Journal of Cognitive Science, vol. 6 (2), pp. 44 – 70 (year - 2019)

5. Bachurina, V., & Arsalidou, M. Effects of task complexity and working memory load on eye-tracking indices of cognitive effort in adults and children Journal of Vision, 20(11), 1069-1069. (year - 2020) https://doi.org/10.1167/jov.20.11.1069

6. Charkhabi, M., Kotov, A., Liashenko, A., Arsalidou, M. SCHOOL ENGAGEMENT AND MATH PERFORMANCE: RATINGS FROM STUDENTS AND TEACHERS IN RUSSIA. A. Shvarts (Ed), Technology and Psychology for Mathematics Education, pp. 250-250 (year - 2019)

7. Konopkina K., Matiulko I., Arsalidou M. MATHEMATICAL PROBLEM SOLVING: BEHAVIORAL AND NEUROIMAGING STUDIES A. Shvarts (Ed)Technology and Psychology for Mathematics Education, pp. 277-277 (year - 2019)

8. Matiulko I., Konopkina K., Kulikova S.P, Arsalidou M. FUNCTIONAL INVESTIGATION OF THE NETWORKS AND WHITE MATTER SUBSTRATES ASSOCIATED WITH THE PROCESSING OF MATHEMATICAL OPERATIONS A. Shvarts (Ed)Technology and Psychology for Mathematics Education, p. 280 (year - 2019)

9. - Emotions Help Engage School Students in Learning IQ: Research and Education Website, - (year - )

10. - Emotions to help engage school students in learning Еurekalert, - (year - )