Background: The National Institutes of Health (NIH) Toolbox Cognition Battery is increasingly being used as a standardized test to examine cognitive functioning in multicentric studies. This study examines the associations between the NIH Toolbox Cognition Battery composite scores with neuroimaging metrics using data from the Adolescent Brain Cognitive Development (ABCD) study to elucidate the neurobiological and neuroanatomical correlates of these cognitive scores. Methods: Neuroimaging data from 5290 children (mean age 9.9 years) were analyzed, assessing the correlation of the composite scores with Diffusion Tensor Imaging (DTI), structural Magnetic Resonance Imaging (sMRI), and resting-state functional connectivity (rs-fMRI). Results were adjusted for age, sex, race/ethnicity, head size, body mass index (BMI), and parental income and education. Results: Higher fluid cognition composite scores were linked to greater white matter (WM) microstructural integrity, lower cortical thickness, greater cortical surface area, and mixed associations with rs-fMRI. Conversely, crystallized cognition composite scores showed more complex associations, suggesting that higher scores correlated with lower WM microstructure integrity. Total cognition scores reflected patterns consistent with a combination of both fluid and crystallized cognition, but with diluted specific insights. Our findings highlight the complexity of the neuroimaging correlates of the NIH Toolbox composite scores. Conclusions: The results suggest that fluid cognition composite scores may serve as a marker for cognitive functioning, emphasizing neuroimaging’s clinical relevance in assessing cognitive performance in children. These insights can guide early interventions and personalized education strategies. Future ABCD follow-ups will further illuminate these associations into adolescence and adulthood.

BACKGROUND AND PURPOSE: Marijuana consumption by pregnant women has been steadily increasing over the past decades. Even though many pregnant women perceive marijuana consumption as safe during pregnancy it has been previously linked to poor maternal and neonatal outcomes. The specific long lasting neurodevelopmental alterations caused by prenatal marijuana exposure in children are still underexplored. Thus, this study aims to determine the effect of prenatal marijuana exposure on brain neurodevelopment at late childhood. MATERIALS AND METHODS: This cross-sectional study investigated the relationship between prenatal marijuana exposure and neuroimaging markers of brain health. Data was obtained from the Adolescent Brain Cognitive Development study, a large, demographically diverse, multicenter cohort. The study included 1,085 children, 418 of whom were prenatally exposed to marijuana and 667 matched controls with no prenatal exposure, with a mean age of 9.9 (SD = 0.6) years in both groups. RESULTS: We found that prenatal exposure to marijuana is associated with brain alterations in white matter tracts and cortical regions essential for goal directed behaviors, including motivation, cognitive skills for achieving specific objectives, and emotional processing. Direct group comparisons revealed significantly reduced white matter integrity in prenatally exposed children, with an overall reduction in lower fractional anisotropy and neurite density, and higher mean diffusivity and radial diffusivity. Furthermore, mixed linear model regressions revealed that prenatal marijuana exposure was significantly associated with decreased white matter microstructure, predominantly in the superior corticostriate tract and corticostriate projections via the external capsule to the superior parietal and frontal cortices and with reduced cortical surface area on the left hemisphere parahippocampal and right hemisphere postcentral gyrus. CONCLUSIONS: Overall, our findings suggest that prenatal exposure to marijuana may have long lasting alterations in children brain neurodevelopment. These alterations may impair critical skills needed as children grow into adolescence. ABBREVIATIONS: WM = white matter; DTI = Diffusion Tensor Imaging; FA = Fractional Anisotropy; MD = Mean Diffusivity;RD = Radial Diffusivity; ND = Neurite Density; sMRI = structural MRI; ABCD = Adolescent Brain Cognitive Development

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by diverse presentations, which complicates the identification of consistent biological markers. This study examined whether integrating multimodal neuroimaging and physical-health measures from a population-based cohort can improve ASD classification and reveal interpretable markers that reflect both clinical and community variation. Methods: Data were drawn from the Adolescent Brain Cognitive Development (ABCD) Study, a large community-based cohort of adolescents recruited from the general population. Participants with and without ASD were selected from this cohort, allowing contrasts that reflect natural variability across individuals. Structural, diffusion, and resting-state functional magnetic resonance imaging (MRI) data were integrated with physical-health markers, including sleep, growth, and early development. Propensity-score matching created demographically balanced groups, and multimodal machine learning models were evaluated through stratified cross-validation. Results: The multimodal integration of brain and physical-health markers outperformed single-modality models (area under the receiver operating characteristic curve [AUC-ROC] = 0.68, 95% confidence interval [CI]: 0.62–0.73; area under the precision-recall curve [AUC-PR] = 0.66, 95% CI: 0.60–0.73). Among physical-health markers, sleep function contributed most strongly to ASD classification, while neuroimaging predictors included cortical thickness in the right superior temporal gyrus and connectivity between the cingulo-opercular and default mode networks. These findings indicate that integrating modalities capturing both neural and physiological systems provides complementary information for identifying ASD-related differences within a population-based framework. Conclusions: This study provides a proof of concept that combining multimodal MRI and physical-health data within a large, demographically representative cohort can enhance ASD classification and yield biologically interpretable features. The population-based design situates these findings within a community context and offers a preliminary framework for integrating neural and physiological measures in future large-scale studies of neurodevelopmental diversity.

The National Institutes of Health (NIH) Toolbox cognition battery and Trail Making Tests (TMT) are widely used to quantify cognitive aging and to detect early cognitive vulnerability in Alzheimer’s disease and related conditions. However, these tests are often treated as interchangeable markers of global cognition, despite likely differences in their dependence on specific brain systems, limiting interpretability across studies and clinical contexts. To address this gap, we examined associations between four commonly used cognitive measures—fluid cognition, crystallized cognition, TMT-A, and TMT-B—and multimodal MRI metrics in 725 healthy volunteers aged 36 to 100 years from the Human Connectome Project–Aging. Voxel-wise diffusion MRI and vertex-wise cortical thickness and volume analyses were adjusted for age, sex, and years of education. Higher crystallized and fluid cognition scores and faster TMT-A/B completion times were generally associated with greater white matter integrity. TMT-B showed the most extensive diffusion and cortical associations, involving major projection, commissural, and association pathways and frontoparietal and temporo-occipital cortices. TMT-A and crystallized cognition demonstrated intermediate, overlapping patterns, whereas fluid cognition showed only focal brainstem and limited cortical correlates. These findings demonstrate systematic differences in the neuroanatomical substrates underlying commonly used cognitive tests and provide normative structure–cognition reference maps that can improve test selection, mechanistic interpretation, and sensitivity to brain health in studies of aging, vascular risk, and preclinical neurodegenerative disease.