The Adolescent Brain Cognitive Development (ABCD) Study is the largest U.S.-based neuroimaging initiative of adolescent brain maturation. Diffusion MRI (dMRI) provides unique insights into white matter organization, yet applying advanced processing pipelines and managing technical variability across scanning environments remains challenging at scale. To address these issues, we present ABCD-BIDS Community Collection (ABCC) release 3.1.0, including a curated resource of more than 24,000 fully processed ABCD dMRI datasets. ABCC provides fully processed images, nuanced image quality metrics, advanced microstructural measures, and person-specific bundle tractography. Evaluating these rich data revealed that measures of diffusion restriction and non-Gaussianity—in particular the intracellular volume fraction from NODDI and return-to-origin probability from MAP-MRI—were highly sensitive to neurodevelopment and robust to variation in image quality. Additionally, harmonization of microstructural features markedly improved the cross-vendor generalizability of developmental effects. Together, ABCC accelerates reproducible, rigorous research on adolescent white matter development.

Fluency in mental arithmetic is regarded as a foundational math skill typically measured as a single construct with pencil and paper-based assessments. Here we introduce a novel tablet-based paradigm that allows for the rapid assessment of single-digit fluency while also capturing trial to trial information. We administered our tablet-based assessment of single digit arithmetic across a large (n=914), diverse cohort of 3rd- 7th grade students (ages 7-13 years). The tablet-based paradigm enabled us to analyze performance across individual items, allowing us to capture established effects, such as operation and operand distance. We also distinguished problems that are common in arithmetic tests from those that are generally excluded from such assessments. Fluency with problems commonly included in arithmetic assessments also proved to be a stronger predictor of achievement on state-mandated standardized tests than traditional aggregate raw scores. This fluency across problem sets also partially mediated the relationship between parental income and mathematics achievement. Finally, we explore how speed-accuracy tradeoffs differ across these two problem types and find that they become more similar as a function of both age and overall math achievement. We propose that assessing single-digit arithmetic with this novel tablet-based paradigm reliably replicates known effects from laboratory based studies in an efficient 3-minute session. This form of assessment is vital for large-scale datasets looking to explore the development of mathematical cognition.

Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to assess physical properties of brain connections. Here, we present an integrative ecosystem of software that performs all steps of tractometry: post-processing of dMRI data, delineation of major white matter pathways, and modeling of the tissue properties within them. This ecosystem also provides a set of interoperable and extensible tools for visualization and interpretation of the results that extract insights from these measurements. These include novel machine learning and statistical analysis methods adapted to the characteristic structure of tract-based data. We benchmark the performance of these statistical analysis methods in different datasets and analysis tasks, including hypothesis testing on group differences and predictive analysis of subject age. We also demonstrate that computational advances implemented in the software offer orders of magnitude of acceleration. Taken together, these open-source software tools-freely available at https://tractometry.org-provide a transformative environment for the analysis of dMRI data.

Past studies leveraging cross-sectional data have raised questions surrounding the relationship between diffusion properties of the white matter and academic skills. Some studies have suggested that white matter properties serve as static predictors of academic skills, whereas other studies have observed no such relationship. However, longitudinal studies have suggested that within-individual changes in the white matter are linked to learning gains over time. In the present study, we look to replicate and extend the previous longitudinal results linking longitudinal changes in the white matter properties of the left arcuate fasciculus to individual differences in reading development. To do so, we analyzed diffusion MRI data, along with reading and mathematics scores in a longitudinal sample of 340 students as they progressed from first grade into fourth grade. Longitudinal growth models revealed that year-to-year within-individual changes in reading scores, but not mathematics, were related to the development of the left arcuate fasciculus. These findings provide further evidence linking the dynamics of white matter development and learning in a unique sample and highlight the importance of longitudinal designs.

Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual’s environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual’s educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come.

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual’s educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

Past studies leveraging cross-sectional data have raised questions surrounding the relationship between diffusion properties of the white matter and academic skills. Some studies have suggested that white matter properties serve as static predictors of academic skills, whereas other studies have observed no such relationship. On the other hand, longitudinal studies have suggested that within-individual changes in the white matter are linked to learning gains over time. In the present study, we look to replicate and extend the previous longitudinal results linking longitudinal changes in the white matter properties of the left arcuate fasciculus to individual differences in reading development. To do so, we analyzed diffusion MRI data, along with reading and mathematics scores in a longitudinal sample of 340 students as they progressed from 1st grade into 4th grade. Longitudinal growth models revealed that year-to-year within-individual changes in reading scores, but not math, were related to the development of the left arcuate fasciculus. These findings provide further evidence linking the dynamics of white matter development and learning in a unique sample and highlight the importance of longitudinal designs. ### Competing Interest Statement The authors have declared no competing interest.

Mathematical knowledge is essential to function properly in our modern society. It is therefore crucial for teachers and clinicians to be able to assess learners’ mathematical abilities with validity, reliability, and speed. Here we describe a new tablet-based assessment app designed to measure math fluency, the Stanford Mental Arithmetic Response Time Evaluation (SMARTE) tool. SMARTE consists of three 2-minutes tasks (non-symbolic dot enumeration, math fluency, and math recall). We analyzed data from 6,855 participants (3,262 female, mean age: 12.97 years) who completed SMARTE in year 3 of the Adolescent Brain Cognitive DevelopmentSM study. Because of the COVID-19 crisis, 2,682 participants were tested online while 1,159 participants were tested in person as the original protocol called for. Our analyses revealed that the SMARTE score and sub-scores highly correlated with each other. Overall fluency metrics revealed more problems per unit time were completed in the laboratory setting, yet a host of experimental contrasts that manipulated the cognitive demands across problem types produced equivalent effects across both settings, suggesting these effects are quite robust across variations in study setting.

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5 - 21 years old) and showed that maturation of the optic radiation speci fi cally accounts for developmental changes of alpha frequency. Behavioral analyses also con fi rmed that variations of alpha frequency are related to maturational changes in visual perception. The present fi ndings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.

Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual’s environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual’s educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come.

Understanding the cognitive processes central to mathematical development is crucial to addressing systemic inequities in math achievement. We investigate the "Groupitizing" ability in 1209 third to eighth graders (mean age at first timepoint = 10.48, 586 girls, 39.16% Asian, 28.88% Hispanic/Latino, 18.51% White), a process that captures the ability to use grouping cues to access the exact value of a set. Groupitizing improves each year from late childhood to early adolescence (d = 3.29), is a central predictor of math achievement (beta weight = .30), is linked to conceptual processes in mathematics (minimum d = 0.69), and helps explain the dynamic between the ongoing development of non-symbolic number concepts, systemic educational inequities in school associated with SES, and mathematics achievement (minimum beta weight = .11) in ways that explicit symbolic measures may miss.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

Abstract“Ubiquitous AI”—embodied in cloud computing web services, coupled with sensors in phones and the physical world—is becoming infrastructural to cultural practices. It creates a surveillance society. We review the capabilities of four core surveillance technologies, all making headway into universities and PreK-12 schools: (1) location tracking, (2) facial identification, (3) automated speech recognition, and (4) social media mining. We pose primary issues educational research should investigate on cultural practices with these technologies. We interweave three priority themes: (1) how these technologies are shaping human development and learning; (2) current algorithmic biases and access inequities; and (3) the need for learners’ critical consciousness concerning their data privacy. We close with calls to action—research, policy and law, and practice.

Groupitizing – the ability to take advantage of grouping cues to rapidly enumerate sets that otherwise require serial counting – is linked to conceptual aspects of numbers (accessing the cardinality of subgroups) and math (combining the subgroups values) that rapidly emerge during the first years of schooling (Starkey & McCandliss, 2014). Little else is known about its broader role in mathematical development. This study followed the development of groupitizing skill from late childhood through early adolescence (N = 1,209), revealing a pattern of progressive development over these critical years for math achievement. Individual differences were highly predictive of global math achievement from 3rd to 8th grade, above and beyond socioeconomic and cognitive (domain-general and math-specific) predictors. Experimental manipulations of item grouping cues (number of subgroups, numerical composition of subgroups) lead to similar effects that manipulations of operands have on symbolic mathematical reasoning, corroborating the view that groupitizing draws upon the same conceptual processes as symbolic math even in the absence of well-learned symbolic retrieval cues. Finally, we show that groupitizing provides new cognitive insights into the nature of the socioeconomic status achievement gap, which cannot be fully explained by familiarity with specific symbolic math facts learned in school but rather suggest inequities in educational opportunities that promote flexible mastery of conceptual processes. Taken together, groupitizing – as a non-symbolic assessment of conceptual processes in mathematics – could be a critical tool in implicitly assessing math ability.

Fluency in mental arithmetic is regarded as a foundational math skill typically measured as a single construct with pencil and paper-based assessments. Here we introduce a novel tablet-based paradigm that allows for the rapid assessment of single-digit fluency while also capturing trial to trial information. We administered our tablet-based assessment of single digit arithmetic across a large (n=914), diverse cohort of 3rd- 7th grade students (ages 7-13 years). The tablet-based paradigm enabled us to analyze performance across individual items, allowing us to capture established effects, such as operation and operand distance. We also distinguished problems that are common in arithmetic tests from those that are generally excluded from such assessments. Fluency with problems commonly included in arithmetic assessments also proved to be a stronger predictor of achievement on state-mandated standardized tests than traditional aggregate raw scores. This fluency across problem sets also partially mediated the relationship between parental income and mathematics achievement. Finally, we explore how speed-accuracy tradeoffs differ across these two problem types and find that they become more similar as a function of both age and overall math achievement. We propose that assessing single-digit arithmetic with this novel tablet-based paradigm reliably replicates known effects from laboratory based studies in an efficient 3-minute session. This form of assessment is vital for large-scale datasets looking to explore the development of mathematical cognition.

In this paper we present PearProgram, a hybrid learning and research tool that helps introductory Computer Science (CS) students learn how to pair program, including in remote learning environments. Grounded in theory from the Learning Sciences, the tool -- a collaborative, online IDE -- has two primary goals: 1) to help introductory CS students achieve pair programming success; and 2) to research what factors contribute to pairs that have beneficial outcomes. We present our learnings from the use of PearProgram in three remote introductory CS courses: a CS1 course, and two large international courses, including one for high school students. Teacher and student users responded positively to PearProgram, and use of the tool was associated with beneficial learning outcomes in these online learning environments. Our research opens many future research directions for (remote) pair programming, and indicates practices that may prove useful for CS educators at all levels.

The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.

It is now well established that neurogenesis occurs throughout adulthood in select brain regions, but the functional significance of adult neurogenesis remains unclear. There is considerable evidence that steroid hormones modulate various stages of adult neurogenesis, and this review provides a focused summary of the effects of testosterone on adult neurogenesis. Initial evidence came from field studies with birds and wild rodent populations. Subsequent experiments with laboratory rodents have tested the effects of testosterone and its steroid metabolites upon adult neurogenesis, as well as the functional consequences of induced changes in neurogenesis. These experiments have provided clear evidence that testosterone increases adult neurogenesis within the dentate gyrus region of the hippocampus through an androgen-dependent pathway. Most evidence indicates that androgens selectively enhance the survival of newly generated neurons, while having little effect on cell proliferation. Whether this is a result of androgens acting directly on receptors of new neurons remains unclear, and indirect routes involving brain-derived neurotrophic factor (BDNF) and glucocorticoids may be involved. In vitro experiments suggest that testosterone has broad-ranging neuroprotective effects, which will be briefly reviewed. A better understanding of the effects of testosterone upon adult neurogenesis could shed light on neurological diseases that show sex differences.

Past research indicates that female meadow voles (Microtus pennsylvanicus) show decreased neurogenesis within the hippocampus during the breeding season relative to the non-breeding season, whereas male voles show no such seasonal changes. We expanded upon these results by quantifying a variety of endogenous cell proliferation and neurogenesis markers in wild-caught voles. Adult male and female voles were captured in the summer (breeding season) or fall (non-breeding season), and blood samples and brain tissue were collected. Four cellular markers (pHisH3, Ki67, DCX, and pyknosis) were labeled and then quantified using either fluorescent or light microscopy. The volume of the cell layers within the dentate gyrus (hilus and granule cell layer) was significantly larger in males than in females. In both sexes, all the cellular markers decreased significantly in the dentate gyrus during the breeding season relative to the non-breeding season, indicating decreased cell proliferation, neurogenesis, and pyknosis. Only the pHisH3 marker showed a sex difference, with females having a greater density of this cell proliferation marker than males. During the breeding season relative to the non-breeding season, males and females showed the predicted significant increases in testosterone and estradiol, respectively. Overall, these results suggest higher levels of neuronal turn-over during the non-breeding season relative to the breeding season, possibly due to seasonal changes in sex steroids.