Abacus-based mental calculation (AMC) is a widely used educational tool for enhancing math learning, offering an accessible and cost-effective method for classroom implementation. Despite its universal appeal, the neurocognitive mechanisms that drive the efficacy of AMC training remain poorly understood. Notably, although abacus training relies heavily on the rapid recall of number positions and sequences, the role of memory systems in driving long-term AMC learning remains unknown. Here, we sought to address this gap by investigating the role of the medial temporal lobe (MTL) memory system in predicting long-term AMC training gains in second-grade children, who were longitudinally assessed up to fifth grade. Leveraging multimodal neuroimaging data, we tested the hypothesis that MTL systems, known for their involvement in associative memory, are instrumental in facilitating AMC-induced improvements in math skills. We found that gray matter volume in bilateral MTL, along with functional connectivity between the MTL and frontal and ventral temporal-occipital cortices, significantly predicted learning gains. Intriguingly, greater gray matter volume but weaker connectivity of the posterior parietal cortex predicted better learning outcomes, offering a more nuanced view of brain systems at play in AMC training. Our findings not only underscore the critical role of the MTL memory system in AMC training but also illuminate the neurobiological factors contributing to individual differences in cognitive skill acquisition. A video abstract of this article can be viewed at https://youtu.be/StVooNRc7T8. RESEARCH HIGHLIGHTS: We investigated the role of medial temporal lobe (MTL) memory system in driving children's math learning following abacus-based mental calculation (AMC) training. AMC training improved math skills in elementary school children across their second and fifth grade. MTL structural integrity and functional connectivity with prefrontal and ventral temporal-occipital cortices predicted long-term AMC training-related gains.

IntroductionAlthough memory challenges in autistic individuals have been characterized recently, the functional connectivity of the hippocampus and ventral temporal lobe, two structures important for episodic and semantic memory functions, are poorly understood in autistic individuals. Moreover, age-related differences in the functional connectivity associated with these two memory networks are unrevealed.MethodsThe current study investigated age-related differences in intrinsic connectivity of the hippocampal and ventral temporal lobe (vTL) memory networks in well-matched ASD (n = 73; age range: 10.23–55.40 years old) and Non-ASD groups (n = 74; age range: 10.46–56.20 years old) from the open dataset ABIDE-I. Both theory-driven ROI-to-ROI approach and exploratory seed-based whole-brain approach were used.Results and discussionOur findings revealed reduced connectivity in ASD compared to Non-ASD peers, as well as an age-related reduction in the connectivity of hippocampal and vTL networks with triple networks, namely, the default mode network (DMN), the central executive network (CEN), and the salience network (SN), potentially underpinning their challenges in memory, language, and social functions. However, we did not observe reliable differences in age-related effects between the ASD and Non-ASD groups. Our study underscores the importance of understanding memory network dysfunctions in ASD across the lifespan to inform educational and clinical practices.

Background Adolescent depression is a significant public health concern; however, access to effective mental health care is limited. Digital therapeutics (DTx) can improve access to evidence-based interventions; however, their efficacy in adolescents is sparsely documented. Objective This study aims to examine the efficacy of a mobile app DTx versus an active control as an adjunct treatment for adolescent depression symptoms. Methods An internet-based open-label randomized control trial was conducted nationwide with a partial crossover design, and 168 adolescents aged 13 to 21 years with symptoms of depression were recruited between November 2020 and September 2021. Participants were randomized (1:1) to the cognitive behavioral therapy–based treatment app (Spark) or to a psychoeducational control app (control), which they would use for a duration of 5 weeks. The primary outcome was a between-group (Spark vs control) difference in the change in depression symptoms from baseline to postintervention, as measured by the Patient Health Questionnaire-8 (PHQ-8) using a linear mixed-effects analysis. The PHQ-8 ranges from 0 to 24, with scores of 5 to 9 indicating mild depression symptoms, scores of 10 to 14 indicating moderate symptoms, scores of 15 to 19 indicating moderately severe symptoms, and scores of 20 to 24 indicating severe symptoms. A minimal clinically important difference (5-point reduction between baseline and postintervention) in the Spark arm and group differences in remission and treatment response rates based on the PHQ-8 at postintervention were also investigated. Results A total of 160 participants were randomized, 80 in the Spark arm (mean age 16.89, SD 2.5 y) and 80 in the control arm (mean age 16.79, SD 2.59 y). Data from 121 participants (Spark: n=63; control: n=58) with moderate to severe (PHQ-8≥10) symptoms at baseline were included in the primary analyses following a modified intention-to-treat principle. A linear mixed-effect analysis revealed a nonsignificant difference between the study arms in depression symptom change over the intervention period. The Spark arm met a minimal clinically important difference threshold (mean −5.08, 95% CI −6.72 to −3.42). The remission rate in the Spark arm was significantly higher than that in the control arm (11/63, 17% vs 2/58, 3%; χ21=6.2; P=.01; false discovery rate–adjusted P=.03). The treatment response rates were not significantly different between the study arms (P=.07; false discovery rate–adjusted P=.16). Post hoc analyses including participants with mild to severe (PHQ-8 score ≥5) symptoms at baseline revealed promising evidence that Spark is effective in those with mild to severe symptoms. Conclusions There is initial evidence that a self-guided, cognitive behavioral therapy–based DTx intervention may effectively treat mild to severe depression symptoms in adolescents. DTx may improve access to mental health care for adolescents or serve as an important adjunct to the standard of care. Trial Registration ClinicalTrials.gov NCT04524598; https://clinicaltrials.gov/study/NCT04524598

Emerging research suggests that episodic memory challenges are commonly encountered by autistic individuals; however, the specific nature of these memory challenges remains elusive. Here, we address critical gaps in the literature by examining pattern separation memory, the ability to store distinct memories of similar stimuli, and its links to the core autistic trait of repetitive, restricted interests and behaviors. Utilizing a large sample of over 120 autistic children and well-matched non-autistic peers, we found that autistic children showed significantly reduced performance on pattern separation memory. A clustering analysis identified three distinct pattern separation memory profiles in autism, each characterized by reduced or increased generalization abilities. Importantly, pattern separation memory was negatively correlated with the severity of repetitive, restricted interest and behavior symptoms in autism. These findings offer new evidence for challenges in pattern separation memory in autism and emphasize the need to consider these challenges when assessing and supporting autistic individuals in educational and clinical settings. Lay abstract Memory challenges remain understudied in childhood autism. Our study investigates one specific aspect of memory function, known as pattern separation memory, in autistic children. Pattern separation memory refers to the critical ability to store unique memories of similar stimuli; however, its role in childhood autism remains largely uncharted. Our study first uncovered that the pattern separation memory was significantly reduced in autistic children, and then showed that reduced memory performance was linked to their symptoms of repetitive, restricted interest and behavior. We also identified distinct subgroups with profiles of reduced and increased generalization for pattern separation memory. More than 72% of autistic children showed a tendency to reduce memory generalization, focusing heavily on unique details of objects for memorization. This focus made it challenging for them to identify commonalities across similar entities. Interestingly, a smaller proportion of autistic children displayed an opposite pattern of increased generalization, marked by challenges in differentiating between similar yet distinct objects. Our findings advance the understanding of memory function in autism and have practical implications for devising personalized learning strategies that align with the unique memory patterns exhibited by autistic children. This study will be of broad interest to researchers in psychology, psychiatry, and brain development as well as teachers, parents, clinicians, and the wider public.

Math learning difficulty (MLD) is a learning disorder characterized by persistent impairments in the understanding and application of numbers independent of intelligence or schooling. The current study aims to review existing neuroimaging studies to characterize the neurobiological basis in MLD for their quantity and arithmetic dysfunctions. We identified a total of 24 studies with 728 participants through the literature. Using the activation likelihood estimate (ALE) method, we found that the most consistent neurobiological dysfunction in MLD was observed in the right intraparietal sulcus (IPS) with distinct patterns of the anterior and posterior aspects. Meanwhile, neurobiological dysfunctions were also observed in a distributed network including the fusiform gyrus, inferior temporal gyrus, insula, prefrontal cortex, anterior cingulate cortex, and claustrum. Our results suggest a core dysfunction in the right anterior IPS and left fusiform gyrus with atypically upregulated functions in brain regions for attention, working memory, visual processing, and motivation, serving as the neurobiological basis of MLD.

BACKGROUND:Memory impairments have profound implications for social communication and educational outcomes in children with autism spectrum disorder (ASD). However, the precise nature of memory dysfunction in children with ASD and the underlying neural circuit mechanisms remain poorly understood. The default mode network (DMN) is a brain network that is associated with memory and cognitive function, and DMN dysfunction is among the most replicable and robust brain signatures of ASD. METHODS:We used a comprehensive battery of standardized episodic memory assessments and functional circuit analyses in 25 8- to 12-year-old children with ASD and 29 matched typically developing control children. RESULTS:Memory performance was reduced in children with ASD compared with control children. General and face memory emerged as distinct dimensions of memory difficulties in ASD. Importantly, findings of diminished episodic memory in children with ASD were replicated in 2 independent data sets. Analysis of intrinsic functional circuits associated with the DMN revealed that general and face memory deficits were associated with distinct, hyperconnected circuits: Aberrant hippocampal connectivity predicted diminished general memory while aberrant posterior cingulate cortex connectivity predicted diminished face memory. Notably, aberrant hippocampal-posterior cingulate cortex circuitry was a common feature of diminished general and face memory in ASD. CONCLUSIONS:Our results represent a comprehensive appraisal of episodic memory function in children with ASD and identify extensive and replicable patterns of memory reductions in children with ASD that are linked to dysfunction of distinct DMN-related circuits. These findings highlight a role for DMN dysfunction in ASD that extends beyond face memory to general memory function.

Children with autism spectrum disorders (ASDs) often display atypical learning styles; however, little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. While learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.

The development of mathematical skills in early childhood relies on number sense, the foundational ability to discriminate among quantities. Number sense in early childhood is predictive of academic and professional success, and deficits in number sense are thought to underlie lifelong impairments in mathematical abilities. Despite its importance, the brain circuit mechanisms that support number sense learning remain poorly understood. Here, we designed a theoretically motivated training program to determine brain circuit mechanisms underlying foundational number sense learning in female and male elementary school-age children (7–10 years). Our 4 week integrative number sense training program gradually strengthened the understanding of the relations between symbolic (Arabic numerals) and nonsymbolic (sets of items) representations of quantity. We found that our number sense training program improved symbolic quantity discrimination ability in children across a wide range of math abilities including children with learning difficulties. Crucially, the strength of pretraining functional connectivity between the hippocampus and intraparietal sulcus, brain regions implicated in associative learning and quantity discrimination, respectively, predicted individual differences in number sense learning across typically developing children and children with learning difficulties. Reverse meta-analysis of interregional coactivations across 14,371 fMRI studies and 89 cognitive functions confirmed a reliable role for hippocampal–intraparietal sulcus circuits in learning. Our study identifies a canonical hippocampal–parietal circuit for learning that plays a foundational role in children's cognitive skill acquisition. Findings provide important insights into neurobiological circuit markers of individual differences in children's learning and delineate a robust target for effective cognitive interventions. SIGNIFICANCE STATEMENT Mathematical skill development relies on number sense, the ability to discriminate among quantities. Here, we develop a theoretically motivated training program and investigate brain circuits that predict number sense learning in children during a period important for acquisition of foundational cognitive skills. Our integrated number sense training program was effective in children across a wide a range of math abilities, including children with learning difficulties. We identify hippocampal–parietal circuits that predict individual differences in learning gains. Our study identifies a brain circuit critical for the acquisition of foundational cognitive skills, which will be useful for developing effective interventions to remediate learning disabilities.

Growth mindset, the belief that one's abilities can improve through cognitive effort, is an important psychological construct with broad implications for enabling children to reach their highest potential. However, surprisingly little is known about malleability of growth mindset in response to cognitive interventions in children and its neurobiological underpinnings. Here we address critical gaps in our knowledge by investigating behavioral and brain changes in growth mindset associated with a four-week training program designed to enhance foundational, academically relevant, cognitive skills in 7-10-year-old children. Cognitive training significantly enhanced children's growth mindset. Cross-lagged panel analysis of longitudinal pre- and post-training data revealed that growth mindset prior to training predicted cognitive abilities after training, providing support for the positive role of growth mindset in fostering academic achievement. We then examined training-induced changes in brain response and connectivity associated with problem solving in relation to changes in growth mindset. Children's gains in growth mindset were associated with increased neural response and functional connectivity of the dorsal anterior cingulate cortex, striatum, and hippocampus, brain regions crucial for cognitive control, motivation, and memory. Plasticity of cortico-striatal circuitry emerged as the strongest predictor of growth mindset gains. Taken together, our study demonstrates that children's growth mindset can be enhanced by cognitive training, and elucidates the potential neurobiological mechanisms underlying its malleability. Findings provide important insights into effective interventions that simultaneously promote growth mindset and learning during the early stages of cognitive development.

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.

The cortical organization of the semantic network has been examined extensively in neuropsychological and neuroimaging studies; however, after decades of research, several issues remain controversial. A comprehensive and systematic investigation is needed to characterize the consistent patterns of category-specific activations as well as to examine factors that contribute to the varying findings across numerous neuroimaging studies. In this study, we reviewed 113 published papers that reported category-specific activations for living or nonliving concepts from the past two decades. Using the Activation Likelihood Estimate (ALE) method, we characterized the brain regions associated with living and nonliving concepts and revealed how the observed patterns were heavily influenced by methodological factors including imaging mode, task demand, and stimuli modality. Our findings provided the most comprehensive summary of category-specific activations for living and nonliving concepts and critically revealed that these activation patterns are highly contextually dependent. This work advanced our knowledge about the organization of the cortical semantic network and provided important insights into theoretical accounts and future research directions.

Heterogeneity in cognitive and academic abilities is a prominent feature of autism spectrum disorder (ASD), yet little is known about its underlying causes. Here we combine functional brain imaging during numerical problem-solving with hierarchical drift-diffusion models of behavior and standardized measures of numerical abilities to investigate neural mechanisms underlying cognitive variability in children with ASD, and their IQ-matched Typically Developing (TD) peers. Although the two groups showed similar levels of brain activation, the relation to individual abilities differed markedly in ventral temporal-occipital, parietal and prefrontal regions important for numerical cognition: children with ASD showed a positive correlation between functional brain activation and numerical abilities, whereas TD children showed the opposite pattern. Despite similar accuracy and response times, decision thresholds were significantly higher in the ASD group, suggesting greater evidence required for problem-solving. Critically, the relationship between individual abilities and engagement of prefrontal control systems anchored in the anterior insula was differentially moderated by decision threshold in subgroups of children with ASD. Our findings uncover novel cognitive and neural sources of variability in academically-relevant cognitive skills in ASD and suggest that multilevel measures and latent decision-making dynamics can aid in characterization of cognitive variability and heterogeneity in neurodevelopmental disorders.

The cortical organization of the semantic network has been studied extensively in neuropsychological and neuroimaging studies. Recent theories have heavily relied on the observation of category-specific activations, i.e., the preferential activations in brain regions for specific semantic categories. With decades of research, a full understanding of the organization has not yet been reached, since little is known about the factors that contribute to the variances in observed activation patterns across numerous neuroimaging studies. In this study, we first reviewed 97 published papers that reported category-specific activations for living or nonliving concepts in the past two decades. Then, using the Activation Likelihood Estimate (ALE) method, we characterized the brain activation associated with living and nonliving concepts, revealing the influences of relevant factors (e.g., neuroimaging mode, task demands, and stimuli modality), and analyzing these findings in relation to theoretical accounts of cortical semantic networks.

While predominant models of visual word form area (VWFA) function argue for its specific role in decoding written language, other accounts propose a more general role of VWFA in complex visual processing. However, a comprehensive examination of structural and functional VWFA circuits and their relationship to behavior has been missing. Here, using high-resolution multimodal imaging data from a large Human Connectome Project cohort ( N = 313), we demonstrate robust patterns of VWFA connectivity with both canonical language and attentional networks. Brain-behavior relationships revealed a striking pattern of double dissociation: structural connectivity of VWFA with lateral temporal language network predicted language, but not visuo-spatial attention abilities, while VWFA connectivity with dorsal fronto-parietal attention network predicted visuo-spatial attention, but not language abilities. Our findings support a multiplex model of VWFA function characterized by distinct circuits for integrating language and attention, and point to connectivity-constrained cognition as a key principle of human brain organization.

Autism spectrum disorders (ASD) represent a quintessential example of a clinical population with diverse symptom presentations and marked variation in cognitive abilities. However, the extensive literature lacks rigorous quantitative procedures for characterizing heterogeneity of cognitive abilities in these individuals. Here we employ novel clustering and cross-validation procedures to investigate the stability of heterogeneous patterns of cognitive abilities in reading and math in a relatively large sample (N = 114) of children with ASD and matched controls (N = 96). Our analysis revealed a unique profile of heterogeneity in ASD, consisting of a low-achieving subgroup with poor math skills compared with reading and a high-achieving subgroup who showed superior math skills compared with reading. Verbal and central executive working memory skills further differentiated these subgroups. Findings provide insights into distinct profiles of academic achievement in children with ASD, with implications for educational practice and intervention, and provide a novel framework for quantifying heterogeneity in the disorder.

Cognitive development is thought to depend on the refinement and specialization of functional circuits over time, yet little is known about how this process unfolds over the course of childhood. Here we investigated growth trajectories of functional brain circuits and tested an interactive specialization model of neurocognitive development which posits that the refinement of task-related functional networks is driven by a shared history of co-activation between cortical regions. We tested this model in a longitudinal cohort of 30 children with behavioral and task-related functional brain imaging data at multiple time points spanning childhood and adolescence, focusing on the maturation of parietal circuits associated with numerical problem solving and learning. Hierarchical linear modeling revealed selective strengthening as well as weakening of functional brain circuits. Connectivity between parietal and prefrontal cortex decreased over time, while connectivity within posterior brain regions, including intra-hemispheric and inter-hemispheric parietal connectivity, as well as parietal connectivity with ventral temporal occipital cortex regions implicated in quantity manipulation and numerical symbol recognition, increased over time. Our study provides insights into the longitudinal maturation of functional circuits in the human brain and the mechanisms by which interactive specialization shapes children's cognitive development and learning.

Positive attitude is thought to impact academic achievement and learning in children, but little is known about its underlying neurocognitive mechanisms. Using a large behavioral sample of 240 children, we found that positive attitude toward math uniquely predicted math achievement, even after we accounted for multiple other cognitive-affective factors. We then investigated the neural mechanisms underlying the link between positive attitude and academic achievement in two independent cohorts of children (discovery cohort: n = 47; replication cohort: n = 28) and tested competing hypotheses regarding the differential roles of affective-motivational and learning-memory systems. In both cohorts, we found that positive attitude was associated with increased engagement of the hippocampal learning-memory system. Structural equation modeling further revealed that, in both cohorts, increased hippocampal activity and more frequent use of efficient memory-based strategies mediated the relation between positive attitude and higher math achievement. Our study is the first to elucidate the neurocognitive mechanisms by which positive attitude influences learning and academic achievement.

How is knowledge about the meanings of words and objects represented in the human brain? Current theories embrace two radically different proposals: either distinct cortical systems have evolved to represent different kinds of things, or knowledge for all kinds is encoded within a single domain-general network. Neither view explains the full scope of relevant evidence from neuroimaging and neuropsychology. Here we propose that graded category-specificity emerges in some components of the semantic network through joint effects of learning and network connectivity. We test the proposal by measuring connectivity amongst cortical regions implicated in semantic representation, then simulating healthy and disordered semantic processing in a deep neural network whose architecture mirrors this structure. The resulting neuro-computational model explains the full complement of neuroimaging and patient evidence adduced in support of both domain-specific and domain-general approaches, reconciling long-standing disputes about the nature and origins of this uniquely human cognitive faculty.