Mechanisms of interactive specialization and emergence of functional brain circuits supporting cognitive development in children

From genetic disposition to academic achievement: The mediating role of non-cognitive skills across development

Genetic effects on academic achievement are likely to capture environmental, developmental, and psychological processes. How these processes contribute to translating genetic dispositions into observed academic achievement remains critically under-investigated. Here, we examined the role of non-cognitive skills-e.g., motivation, attitudes and self-regulation-in mediating education-associated genetic effects on academic achievement across development. Data were collected from 5,016 children enrolled in the Twins Early Development Study at ages 7, 9, 12, and 16, as well as their parents and teachers. We found that non-cognitive skills mediated polygenic score effects on academic achievement across development, and longitudinally, accounting for up to 64% of the total effects. Within-family analyses highlighted the contribution of non-cognitive skills beyond genetic, environmental and demographic factors that are shared between siblings, accounting for up to 83% of the total mediation effect, likely reflecting evocative/active gene-environment correlation. Our results underscore the role of non-cognitive skills in academic development in how children evoke and select experiences that align with their genetic propensity.

Brain markers of subtraction and multiplication skills in childhood: task-based functional connectivity and individualized structural similarity

Arithmetic, a high-order cognitive ability, show marked individual difference over development. Despite recent advancements in neuroimaging techniques have enabled the identification of brain markers for individual differences in high-order cognitive abilities, it remains largely unknown about the brain markers for arithmetic. This study used a data-driven connectome-based prediction model to identify brain markers of arithmetic skills from arithmetic-state functional connectivity and individualized structural similarity in 132 children aged 8 to 15 years. We found that both subtraction-state functional connectivity and individualized SS successfully predicted subtraction and multiplication skills but multiplication-state functional connectivity failed to predict either skill. Among the four successful prediction models, most predictive connections were located in frontal-parietal, default-mode, and secondary visual networks. Further computational lesion analyses revealed the essential structural role of frontal-parietal network in predicting subtraction and the essential functional roles of secondary visual, language, and ventral multimodal networks in predicting multiplication. Finally, a few shared nodes but largely nonoverlapping functional and structural connections were found to predict subtraction and multiplication skills. Altogether, our findings provide new insights into the brain markers of arithmetic skills in children and highlight the importance of studying different connectivity modalities and different arithmetic domains to advance our understanding of children's arithmetic skills.

Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth

The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.

Development of human visual cortical function: A scoping review of task- and naturalistic-fMRI studies through the interactive specialization and maturational frameworks

Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies.

Unveiling the cognitive network organization through cognitive performance

The evaluation of cognitive functions interactions has become increasingly implemented in the cognition exploration. In the present study, we propose to examine the organization of the cognitive network in healthy participants through the analysis of behavioral performances in several cognitive domains. Specifically, we aim to explore cognitive interactions profiles, in terms of cognitive network, and as a function of participants' handedness. To this end, we proposed several behavioral tasks evaluating language, memory, executive functions, and social cognition performances in 175 young healthy right-handed and left-handed participants and we analyzed cognitive scores, from a network perspective, using graph theory. Our results highlight the existence of intricate interactions between cognitive functions both within and beyond the same cognitive domain. Language functions are interrelated with executive functions and memory in healthy cognitive functioning and assume a central role in the cognitive network. Interestingly, for similar high performance, our findings unveiled differential organizations within the cognitive network between right-handed and left-handed participants, with variations observed both at a global and nodal level. This original integrative network approach to the study of cognition provides new insights into cognitive interactions and modulations. It allows a more global understanding and consideration of cognitive functioning, from which complex behaviors emerge.

Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2

The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2  years of age. Our findings highlight network-level neural substrates underlying early cognitive development.

Structural neural connectivity correlates with pre-reading abilities in preschool children

Pre-reading abilities are predictive of later reading ability and can be assessed before reading begins. However, the neural correlates of pre-reading abilities in young children are not fully understood. To address this, we examined 246 datasets collected in an accelerated longitudinal design from 81 children aged 2-6 years (age = 4.6 ± 0.98 years, 47 males). Children completed pre-reading assessments (NEPSY-II Phonological Processing and Speeded Naming) and underwent a diffusion magnetic resonance imaging (MRI) scan to assess white matter connectivity. We defined a core neural network of reading and language regions based on prior literature, and structural connections within this network were assessed using graph theory analysis. Linear mixed models accounting for repeated measures were used to test associations between children's pre-reading performance and graph theory measures for the whole bilateral reading network and each hemisphere separately. Phonological Processing scores were positively associated with global efficiency, local efficiency, and clustering coefficient in the bilateral and right hemisphere networks, as well as local efficiency and clustering coefficient in the left hemisphere network. Our findings provide further evidence that structural neural correlates of Phonological Processing emerge in early childhood, before and during early reading instruction.

Profiling intra- and inter-individual differences in brain development across early adolescence

As we move toward population-level developmental neuroscience, understanding intra- and inter-individual variability in brain maturation and sources of neurodevelopmental heterogeneity becomes paramount. Large-scale, longitudinal neuroimaging studies have uncovered group-level neurodevelopmental trajectories, and while recent work has begun to untangle intra- and inter-individual differences, they remain largely unclear. Here, we aim to quantify both intra- and inter-individual variability across facets of neurodevelopment across early adolescence (ages 8.92 to 13.83 years) in the Adolescent Brain Cognitive Development (ABCD) Study and examine inter-individual variability as a function of age, sex, and puberty. Our results provide novel insight into differences in annualized percent change in macrostructure, microstructure, and functional brain development from ages 9-13 years old. These findings reveal moderate age-related intra-individual change, but age-related differences in inter-individual variability only in a few measures of cortical macro- and microstructure development. Greater inter-individual variability in brain development were seen in mid-pubertal individuals, except for a few aspects of white matter development that were more variable between prepubertal individuals in some tracts. Although both sexes contributed to inter-individual differences in macrostructure and functional development in a few regions of the brain, we found limited support for hypotheses regarding greater male-than-female variability. This work highlights pockets of individual variability across facets of early adolescent brain development, while also highlighting regional differences in heterogeneity to facilitate future investigations in quantifying and probing nuances in normative development, and deviations therefrom.

Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity

Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.

Challenges and Solutions to the Measurement of Neurocognitive Mechanisms in Developmental Settings

Identifying early neurocognitive mechanisms that confer risk for mental health problems is one important avenue as we seek to develop successful early interventions. Currently, however, we have limited understanding of the neurocognitive mechanisms involved in shaping mental health trajectories from childhood through young adulthood, and this constrains our ability to develop effective clinical interventions. In particular, there is an urgent need to develop more sensitive, reliable, and scalable measures of individual differences for use in developmental settings. In this review, we outline methodological shortcomings that explain why widely used task-based measures of neurocognition currently tell us little about mental health risk. We discuss specific challenges that arise when studying neurocognitive mechanisms in developmental settings, and we share suggestions for overcoming them. We also propose a novel experimental approach-which we refer to as "cognitive microscopy"-that involves adaptive design optimization, temporally sensitive task administration, and multilevel modeling. This approach addresses some of the methodological shortcomings outlined above and provides measures of stability, variability, and developmental change in neurocognitive mechanisms within a multivariate framework.

Toward computational neuroconstructivism: a framework for developmental systems neuroscience

Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.

Irritability in early to middle childhood: Cross-sectional and longitudinal associations with resting state amygdala and ventral striatum connectivity

BACKGROUND

Irritability is a common symptom that may affect children's brain development. This study aims to (1) characterize age-dependent and age-independent neural correlates of irritability in a sample of 4-8 year old children, and (2) examine early irritability as a predictor of change in brain connectivity over time.

METHODS

Typically developing children, ages 4-8 years, with varying levels of irritability were included. Resting state fMRI and parent-rated irritability (via Child Behavior Checklist; CBCL) were collected at up to three time points, resulting in a cross-sectional sample at baseline (N = 176, M = 6.27, SD = 1.49), and two subsamples consisting of children who were either 4 or 6 years old at baseline that were followed longitudinally for two additional timepoints, one- and two-years post-baseline. That is, a "younger" cohort (age 4 at baseline, n = 34, M age = 4.44, SD = 0.25) and an "older" cohort (age 6 at baseline, n = 29, M age = 6.50, SD = 0.30). Across our exploratory analyses, we examined how irritability related to seed-based intrinsic connectivity via whole-brain connectivity ANCOVAs using the left and right amygdala, and left and right ventral striatum as seed regions.

RESULTS

Cross-sectionally, higher levels of irritability were associated with greater amygdala connectivity with the posterior cingulate, controlling for child age. No age-dependent effects were observed in the cross-sectional analyses. Longitudinal analyses in the younger cohort revealed that early higher vs. lower levels of irritability, controlling for later irritability, were associated with decreases in amygdala and ventral striatum connectivity with multiple frontal and parietal regions over time. There were no significant findings in the older cohort.

CONCLUSIONS

Findings suggest that irritability is related to altered neural connectivity during rest regardless of age in early to middle childhood and that early childhood irritability may be linked to altered changes in neural connectivity over time. Understanding how childhood irritability interacts with neural processes can inform pathophysiological models of pediatric irritability and the development of targeted mechanistic interventions.

Defining brain-based OCD patient profiles using task-based fMRI and unsupervised machine learning

While much research has highlighted phenotypic heterogeneity in obsessive compulsive disorder (OCD), less work has focused on heterogeneity in neural activity. Conventional neuroimaging approaches rely on group averages that assume homogenous patient populations. If subgroups are present, these approaches can increase variability and can lead to discrepancies in the literature. They can also obscure differences between various subgroups. To address this issue, we used unsupervised machine learning to identify subgroup clusters of patients with OCD who were assessed by task-based fMRI. We predominantly focused on activation of cognitive control and performance monitoring neurocircuits, including three large-scale brain networks that have been implicated in OCD (the frontoparietal network, cingulo-opercular network, and default mode network). Participants were patients with OCD (n = 128) that included both adults (ages 24-45) and adolescents (ages 12-17), as well as unaffected controls (n = 64). Neural assessments included tests of cognitive interference and error processing. We found three patient clusters, reflecting a "normative" cluster that shared a brain activation pattern with unaffected controls (65.9% of clinical participants), as well as an "interference hyperactivity" cluster (15.2% of clinical participants) and an "error hyperactivity" cluster (18.9% of clinical participants). We also related these clusters to demographic and clinical correlates. After post-hoc correction for false discovery rates, the interference hyperactivity cluster showed significantly longer reaction times than the other patient clusters, but no other between-cluster differences in covariates were detected. These findings increase precision in patient characterization, reframe prior neurobehavioral research in OCD, and provide a starting point for neuroimaging-guided treatment selection.

How retrieval practice and semantic generation affect subsequently studied material: an analysis of item-level effects

The forward testing effect (FTE) refers to the finding that retrieval practice of previously studied material can facilitate recall of newly studied (critical) material. Such interim retrieval practice can also lead to a differential FTE, i.e., a more pronounced FTE for items at early than later serial positions in the critical material. The present study examined whether this differential FTE also holds with interim semantic generation of extra-list items, and whether it is influenced by study material. Consistent with prior work, the results of two experiments showed that both interim retrieval practice and interim semantic generation induce the general (list-level) FTE when unrelated study lists are applied, whereas retrieval practice only creates the effect with categorised study lists. Critically, however, the differential FTE was present in response to retrieval practice but absent in response to semantic generation. This pattern held regardless of which material was studied, thus experimentally dissociating the general (list-level) from the differential (item-level) FTE. The findings may suggest that retrieval practice, but not semantic generation, induces a reset of the encoding process which promotes attentional encoding such that a more pronounced FTE arises for early than middle and late serial positions in the critical list.

Processing efficiency in pediatric cancer survivors: A review and operationalization for outcomes research and clinical utility

OBJECTIVE

Childhood cancer and cancer-related treatments disrupt brain development and maturation, placing survivors at risk for cognitive late effects. Given that assessment tools vary widely across researchers and clinicians, it has been daunting to identify distinct patterns in outcomes across diverse cancer types and to implement systematic neurocognitive screening tools. This review aims to operationalize processing efficiency skill impairment-or inefficient neural processing as measured by working memory and processing speed abilities-as a worthwhile avenue for continued study within the context of childhood cancer.

METHODS

A comprehensive literature review was conducted to examine the existing research on cognitive late effects and biopsychosocial risk factors in order to conceptualize processing efficiency skill trends in childhood cancer survivors.

RESULTS

While a frequently reported pattern of neurobiological (white matter) and cognitive (working memory and processing speed) disruption is consistent with processing efficiency skill impairment, these weaknesses have not yet been fully operationalized in this population. We offer a theoretical model that highlights the impacts of a host of biological and environmental factors on the underlying neurobiological substrates of cancer survivors that precede and may even predict long-term cognitive outcomes and functional abilities following treatment.

CONCLUSION

The unified construct of processing efficiency may be useful in assessing and communicating neurocognitive skills in both outcomes research and clinical practice. Deficits in processing efficiency may serve as a possible indicator of cognitive late effects and functional outcomes due to the unique relationship between processing efficiency skills and neurobiological disruption following cancer treatment. Continued research along these lines is crucial for advancing childhood cancer outcomes research and improving quality of life for survivors.

Reflections on the physical, executive developmental and systems applied framework in child neuropsychological rehabilitation

This paper describes the influence of the Physical, Executive, Developmental and Systems (PEDS) framework on the delivery of community-based child neuropsychological rehabilitation and how it has been enhanced by the proliferation of neuroscientific, neuropsychological and psychosocial research and evidence-base in childhood brain injury and rehabilitation over the past decade. The paper signposts to some of the key models, theories and concepts currently shaping service delivery. Application of the PEDS framework in a clinical case is described.

Predicting children's math skills from task-based and resting-state functional brain connectivity

A critical goal of cognitive neuroscience is to predict behavior from neural structure and function, thereby providing crucial insights into who might benefit from clinical and/or educational interventions. Across development, the strength of functional connectivity among a distributed set of brain regions is associated with children's math skills. Therefore, in the present study we use connectome-based predictive modeling to investigate whether functional connectivity during numerical processing and at rest "predicts" children's math skills (N = 31, Mage = 9.21 years, 14 Female). Overall, we found that functional connectivity during symbolic number comparison and rest, but not during nonsymbolic number comparison, predicts children's math skills. Each task revealed a largely distinct set of predictive connections distributed across canonical brain networks and major brain lobes. Most of these predictive connections were negatively correlated with children's math skills so that weaker connectivity predicted better math skills. Notably, these predictive connections were largely nonoverlapping across task states, suggesting children's math abilities may depend on state-dependent patterns of network segregation and/or regional specialization. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

The neural correlates of working memory training in typically developing children

Working memory training improves children's cognitive performance on untrained tasks; however, little is known about the underlying neural mechanisms. This was investigated in 32 typically developing children aged 10-14 years (19 girls and 13 boys) using a randomized controlled design and multi-modal magnetic resonance imaging (Devon, UK; 2015-2016). Training improved working memory performance and increased intrinsic functional connectivity between the bilateral intraparietal sulci. Furthermore, improvements in working memory were associated with greater recruitment of the left middle frontal gyrus on a complex span task. Repeated engagement of fronto-parietal regions during training may increase their activity and functional connectivity over time, affording greater working memory performance. The plausibility of generalizable cognitive benefits from a neurobiological perspective and implications for neurodevelopmental theory are discussed.

From Choice to Performance in Secondary Schools: Evidence from a Disadvantaged Setting in Italy

This study employs detailed micro-data to uncover the factors influencing secondary school choice and performance and, ultimately, dropout risks within a multidimensional framework. The findings reveal that young people’s choice of a comprehensive secondary school, characterised by a higher dropout rate, is highly influenced by future expectations and family background. Further, teachers’ role, learning methods and technology positively drive performance. Perceived cognitive skills only affect students’ performance given their choice. Besides, an ANOVA analysis assesses that the interaction between cognitive and non-cognitive skills impacts performance. Peer study is pivotal for success in individuals with perceived cognitive difficulties.

The selective contributions of right cerebellar lobules to reading

The engagement of the cerebellum during reading tasks is not unprecedented. However, it is still unclear which regions in the cerebellum are specifically involved in reading and how the cerebellum processes different languages. With functional magnetic resonance imaging, we compared the cerebellar neural activity in Chinese child learners of English between reading and non-reading tasks to identify functionally specialized areas for reading, and between Chinese characters and English words in a passive viewing paradigm to detect regions sensitive to different scripts. Two posterior subregions of right lobule VI, as well as right lobule VIIIA, demonstrated greater activation to viewing Chinese characters and English words compared to the non-reading tasks. However, we did not find any cerebellar regions that were differentially responsive to Chinese versus English print. Instead, we observed that functional connectivity between the two above-mentioned cerebellar regions (lobules VI and VIIIA) and the left inferior parietal lobule was significantly greater in English reading compared to Chinese reading. Overall, these results indicate that the posterior parts of right lobule VI and the right lobule VIIIA could be reading-specific regions, and deepen our understanding of how the cerebellum contributes to reading.

Parietal and hippocampal hyper-connectivity is associated with low math achievement in adolescence - A preliminary study

Mathematical cognition requires coordinated activity across multiple brain regions, leading to the emergence of resting-state functional connectivity as a method for studying the neural basis of differences in mathematical achievement. Hyper-connectivity of the intraparietal sulcus (IPS), a key locus of mathematical and numerical processing, has been associated with poor mathematical skills in childhood, whereas greater connectivity has been related to better performance in adulthood. No studies to date have considered its role in adolescence. Further, hippocampal connectivity can predict mathematical learning, yet no studies have considered its contributions to contemporaneous measures of math achievement. Here, we used seed-based resting-state fMRI analyses to examine IPS and hippocampal intrinsic functional connectivity relations to math achievement in a group of 31 adolescents (mean age = 16.42 years, range 15-17), whose math performance spanned the 1% to 99% percentile. After controlling for IQ, IPS connectivity was negatively related to math achievement, akin to findings in children. However, the specific temporo-occipital regions were more akin to the posterior loci implicated in adults. Hippocampal connectivity with frontal regions was also negatively correlated with concurrent math measures, which contrasts with results from learning studies. Finally, hyper-connectivity was not a global feature of low math performance, as math performance did not modulate connectivity of Heschl's gyrus, a control seed not involved in math cognition. Our results provide preliminary evidence that adolescence is a transitional stage in which patterns found in childhood and adulthood can be observed; most notably, hyper-connectivity continues to be related to low math ability into this period.

Early Phonological Neural Specialization Predicts Later Growth in Word Reading Skills

According to the Interactive Specialization Theory, cognitive skill development is facilitated by a process of neural specialization. In line with this theory, the current study investigated whether neural specialization for phonological and semantic processing at 5-to-6 years old was predictive of growth in word reading skills 2 years later. Specifically, four regression models were estimated in which reading growth was predicted from: (1) an intercept-only model; (2) measures of semantic and phonological neural specialization; (3) performance on semantic and phonological behavioral tasks; or (4) a combination of neural specialization and behavioral performance. Results from the preregistered analyses revealed little evidence in favor of the hypothesis that early semantic and phonological skills are predictive of growth in reading. However, results from the exploratory analyses, which included a larger sample, added age at Time 1 as a covariate, and investigated relative growth in reading, demonstrated decisive evidence that variability in phonological processing is predictive of reading growth. The best fitting model included both measures of specialization within the posterior superior temporal gyrus (pSTG) and behavioral performance. This work provides important evidence in favor of the Interactive Specialization Theory and, more specifically, for the role of phonological neural specialization in the development of early word reading skills.

Interventions with Serious Games and Entertainment Games in Autism Spectrum Disorder: A Systematic Review

The use of serious games and entertainment games was compared as adjuvant tools for intervention in Autism Spectrum Disorder (ASD). A comprehensive search was performed in the MEDLINE, PsycINFO, Scopus, and Web of Science databases. From 295 studies, 53 studies were selected and included in this review. Overall, studies showed improvement after intervention, regardless of the type of video games, mostly for social skills and behavior. However, these changes should be regarded with caution, as they are limited to the tests applied. Furthermore, neither the entertainment nor the serious approach had a therapeutic impact on emotional resilience, representing the current gap in the field. Thus, even considering the limitations, our study is important because it shows that both categories have strengths.

Linear and nonlinear profiles of weak behavioral and neural differentiation between numerical operations in children with math learning difficulties

Mathematical knowledge is constructed hierarchically during development from a basic understanding of addition and subtraction, two foundational and inter-related, but semantically distinct, numerical operations. Early in development, children show remarkable variability in their numerical problem-solving skills and difficulties in solving even simple addition and subtraction problems are a hallmark of math learning difficulties. Here, we use novel quantitative analyses to investigate whether less distinct representations are associated with poor problem-solving abilities in children during the early stages of math-skill acquisition. Crucially, we leverage dimensional and categorical analyses to identify linear and nonlinear neurobehavioral profiles of individual differences in math skills. Behaviorally, performance on the two different numerical operations was less differentiated in children with low math abilities, and lower problem-solving efficiency stemmed from weak evidence-accumulation during problem-solving. Children with low numerical abilities also showed less differentiated neural representations between addition and subtraction operations in multiple cortical areas, including the fusiform gyrus, intraparietal sulcus, anterior temporal cortex and insula. Furthermore, analysis of multi-regional neural representation patterns revealed significantly higher network similarity and aberrant integration of representations within a fusiform gyrus-intraparietal sulcus pathway important for manipulation of numerical quantity. These findings identify the lack of distinct neural representations as a novel neurobiological feature of individual differences in children's numerical problem-solving abilities, and an early developmental biomarker of low math skills. More generally, our approach combining dimensional and categorical analyses overcomes pitfalls associated with the use of arbitrary cutoffs for probing neurobehavioral profiles of individual differences in math abilities.

Gray matter volume in left intraparietal sulcus predicts longitudinal gains in subtraction skill in elementary school

Although behavioral studies show large improvements in arithmetic skills in elementary school, we do not know how brain structure supports math gains in typically developing children. While some correlational studies have investigated the concurrent association between math performance and brain structure, such as gray matter volume (GMV), longitudinal studies are needed to infer if there is a causal relation. Although discrepancies in the literature on the relation between GMV and math performance have been attributed to the different demands on quantity vs. retrieval mechanisms, no study has experimentally tested this assumption. We defined regions of interests (ROIs) associated with quantity representations in the bilateral intraparietal sulcus (IPS) and associated with the storage of arithmetic facts in long-term memory in the left middle and superior temporal gyri (MTG/STG), and studied associations between GMV in these ROIs and children's performance on operations having greater demands on quantity vs. retrieval mechanisms, namely subtraction vs. multiplication. The aims of this study were threefold: First, to study concurrent associations between GMV and math performance, second, to investigate the role of GMV at the first time-point (T1) in predicting longitudinal gains in math skill to the second time-point (T2), and third, to study whether changes in GMV over time were associated with gains in math skill. Results showed no concurrent association between GMV in IPS and math performance, but a concurrent association between GMV in left MTG/STG and multiplication skill at T1. This association showed that the higher the GMV in this ROI, the higher the children's multiplication skill. Results also revealed that GMV in left IPS and left MTG/STG predicted longitudinal gains in subtraction skill only for younger children (approximately 10 years old). Whereas higher levels of GMV in left IPS at T1 predicted larger subtraction gains, higher levels of GMV in left MTG/STG predicted smaller gains. GMV in left MTG/STG did not predict longitudinal gains in multiplication skill. No significant association was found between changes in GMV over time and longitudinal gains in math. Our findings support the early importance of brain structure in the IPS for mathematical skills that rely on quantity mechanisms.

Emerging neurodevelopmental perspectives on mathematical learning

Strong foundational skills in mathematical problem solving, acquired in early childhood, are critical not only for success in the science, technology, engineering, and mathematical (STEM) fields but also for quantitative reasoning in everyday life. The acquisition of mathematical skills relies on protracted interactive specialization of functional brain networks across development. Using a systems neuroscience approach, this review synthesizes emerging perspectives on neurodevelopmental pathways of mathematical learning, highlighting the functional brain architecture that supports these processes and sources of heterogeneity in mathematical skill acquisition. We identify the core neural building blocks of numerical cognition, anchored in the posterior parietal and ventral temporal-occipital cortices, and describe how memory and cognitive control systems, anchored in the medial temporal lobe and prefrontal cortex, help scaffold mathematical skill development. We highlight how interactive specialization of functional circuits influences mathematical learning across different stages of development. Functional and structural brain integrity and plasticity associated with math learning can be examined using an individual differences approach to better understand sources of heterogeneity in learning, including cognitive, affective, motivational, and sociocultural factors. Our review emphasizes the dynamic role of neurodevelopmental processes in mathematical learning and cognitive development more generally.

Context-dependent memory effects in two immersive virtual reality environments: On Mars and underwater

The context-dependent memory effect, in which memory for an item is better when the retrieval context matches the original learning context, has proved to be difficult to reproduce in a laboratory setting. In an effort to identify a set of features that generate a robust context-dependent memory effect, we developed a paradigm in virtual reality using two semantically distinct virtual contexts: underwater and Mars environments, each with a separate body of knowledge (schema) associated with it. We show that items are better recalled when retrieved in the same context as the study context; we also show that the size of the effect is larger for items deemed context-relevant at encoding, suggesting that context-dependent memory effects may depend on items being integrated into an active schema.

Hemodynamic Response Function in Brain White Matter in a Resting State

The hemodynamic response function (HRF) characterizes temporal variations of blood oxygenation level-dependent (BOLD) signals. Although a variety of HRF models have been proposed for gray matter responses to functional demands, few studies have investigated HRF profiles in white matter particularly under resting conditions. In the present work we quantified the nature of the HRFs that are embedded in resting state BOLD signals in white matter, and which modulate the temporal fluctuations of baseline signals. We demonstrate that resting state HRFs in white matter could be derived by referencing to intrinsic avalanches in gray matter activities, and the derived white matter HRFs had reduced peak amplitudes and delayed peak times as compared with those in gray matter. Distributions of the time delays and correlation profiles in white matter depend on gray matter activities as well as white matter tract distributions, indicating that resting state BOLD signals in white matter encode neural activities associated with those of gray matter. This is the first investigation of derivations and characterizations of resting state HRFs in white matter and their relations to gray matter activities. Findings from this work have important implications for analysis of BOLD signals in the brain.

Network topology of symbolic and nonsymbolic number comparison

Studies of brain activity during number processing suggest symbolic and nonsymbolic numerical stimuli (e.g., Arabic digits and dot arrays) engage both shared and distinct neural mechanisms. However, the extent to which number format influences large-scale functional network organization is unknown. In this study, using 7 Tesla MRI, we adopted a network neuroscience approach to characterize the whole-brain functional architecture supporting symbolic and nonsymbolic number comparison in 33 adults. Results showed the degree of global modularity was similar for both formats. The symbolic format, however, elicited stronger community membership among auditory regions, whereas for nonsymbolic, stronger membership was observed within and between cingulo-opercular/salience network and basal ganglia communities. The right posterior inferior temporal gyrus, left intraparietal sulcus, and two regions in the right ventromedial occipital cortex demonstrated robust differences between formats in terms of their community membership, supporting prior findings that these areas are differentially engaged based on number format. Furthermore, a unified fronto-parietal/dorsal attention community in the nonsymbolic condition was fractionated into two components in the symbolic condition. Taken together, these results reveal a pattern of overlapping and distinct network architectures for symbolic and nonsymbolic number processing.

Gender differences in the development of semantic and spatial processing of numbers

This study recruited kindergarteners and first graders to investigate gender and grade differences in semantic and spatial processing of number magnitude. Results based on the Bayesian statistics showed that (1) there was extreme evidence in favour of grade differences in both semantic processing and spatial processing; (2) there were no gender differences in semantic processing; and (3) boys developed earlier than girls in spatial processing of numbers, especially for the more difficult task. These results are discussed in terms of gender differences in cognitive mechanisms underlying semantic and spatial processing of number magnitude.

Developing, mature, and unique functions of the child's brain in reading and mathematics

Cognitive development research shows that children use basic "child-unique" strategies for reading and mathematics. This suggests that children's neural processes will differ qualitatively from those of adults during this developmental period. The goals of the current study were to 1) establish whether a within-subjects neural dissociation between reading and mathematics exists in early childhood as it does in adulthood, and 2) use a novel, developmental intersubject correlation method to test for "child-unique", developing, and adult-like patterns of neural activation within those networks. Across multiple tasks, children's reading and mathematics activity converged in prefrontal cortex, but dissociated in temporal and parietal cortices, showing similarities to the adult pattern of dissociation. "Child-unique" patterns of neural activity were observed in multiple regions, including the anterior temporal lobe and inferior frontal gyri, and showed "child-unique" profiles of functional connectivity to prefrontal cortex. This provides a new demonstration that "children are not just little adults" - the developing brain is not only quantitatively different from adults, it is also qualitatively different.