The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: Findings from the ABCD study's baseline neurocognitive battery

Convergent and divergent brain-cognition relationships during development revealed by cross-sectional and longitudinal analyses in the ABCD Study

How brain networks and cognition co-evolve during development remains poorly understood. Using longitudinal data collected at baseline and Year 2 from 2,949 individuals (ages 8.9-13.5) in the Adolescent Brain Cognitive Development (ABCD) Study, we show that baseline resting-state functional connectivity (FC) more strongly predicts future cognitive ability than concurrent cognitive ability. Models trained on baseline FC to predict baseline cognition generalize better to Year 2 data, suggesting that brain-cognition relationships strengthen over time. Intriguingly, baseline FC outperforms longitudinal FC change in predicting future cognitive ability. Differences in measurement reliability do not fully explain this discrepancy: although FC change is less reliable (intraclass correlation, ICC = 0.24) than baseline FC (ICC = 0.56), matching baseline FC's reliability by shortening scan time only partially narrows the predictive gap. Furthermore, neither baseline FC nor FC change meaningfully predicts longitudinal change in cognitive ability. We also identify converging and diverging predictive network features across cross-sectional and longitudinal models of brain-cognition relationships, revealing a multivariate twist on Simpson's paradox. Together, these findings suggest that during early adolescence, stable individual differences in brain functional network organization exert a stronger influence on future cognitive outcomes than short-term changes.

An Integrative Approach for Subtyping Mental Disorders Using Multi-modality Data

Mental disorders exhibit significant heterogeneity, posing challenges for accurate subtyping and diagnosis. Traditional clustering methods do not integrate multi-modal data, limiting their clinical applicability. This study introduces the Mixed INtegrative Data Subtyping (MINDS) method, a Bayesian hierarchical joint model designed to identify subtypes of Attention-Deficit/Hyperactivity Disorder (ADHD) and Obsessive-Compulsive Disorder (OCD) in adolescents using multi-modality data from the Adolescent Brain Cognitive Development (ABCD) Study. MINDS integrates clinical assessments and neuro-cognitive measures while simultaneously performing clustering and dimension reduction. By leveraging Polya-Gamma augmentation, we propose an efficient Gibbs sampler to improve computational efficiency and provide subtype identification. Simulation studies demonstrate superior robustness of MINDS compared to traditional clustering techniques. Application to the ABCD study reveals more reliable and clinically meaningful subtypes of ADHD and OCD with distinct cognitive and behavioral profiles. These findings show the potential of multi-modal model-based clustering for advancing precision psychiatry in mental health.

Mediating Effects of Executive Function on the Relationship between Sleep Problems and Emotional and Behavioral Problems among Preschoolers: Physical Activity as a Protective Factor

This study investigated three waves of data from the Anhui Preschool Children Cohort study, with a total of 1,987 mother-child dyads participating. The questionnaires on sleep problems (Wave 1), executive function (EF) (Wave 2), physical activity (PA) (Wave 2), and emotional and behavioral problems (EBPs) (Wave 1 & Wave 3) were assessed by the mothers of the children. Results showed that sleep problems, GEC (total executive dysfunction scores), and PA are all positively related to EBPs. After controlling for covariates, GEC partially mediated the association between sleep problems and EBPs (β = 0.015, 95%CI: 0.002-0.028). PA moderated the relationship between sleep problems and preschoolers' GEC (β = -0.06, P<0.05). These findings reveal the mediating role of executive dysfunction in the association between sleep problems and EBPs. It also highlights that targeted interventions to reduce sleep problems and increase PA could help reduce the risk of EBPs in preschoolers.

Population-weighted Image-on-scalar Regression Analyses of Large Scale Neuroimaging Data

Recent advances in neuroimaging modeling highlight the importance of accounting for subgroup heterogeneity in population-based neuroscience research through various investigations in large scale neuroimaging data collection. To integrate survey methodology with neuroscience research, we present an imaging data analysis and yield population generalizability with screened subsets of data. The Adolescent Brain Cognitive Development (ABCD) Study has enrolled a large cohort of participants to reflect the individual variation of the U.S. population in adolescent development. To ensure population representation, the ABCD Study has released the base weights. We estimated the associations between brain activities and cognitive performance using the functional Magnetic Resonance Imaging (fMRI) data from the ABCD Study's N-Back working memory task. Notably, the imaging subsample exhibits differences from the baseline cohort in key child characteristics and such discrepancies cannot be addressed simply by applying the ABCD base weights. We developed new population weights specific to the subsample and included the adjusted weights in the image-on-scalar regression model. We validated the approach through synthetic simulations and applications to fMRI data from the ABCD Study. Our findings demonstrate that population weighting adjustments effectively capture active brain areas associated with cognition, enhancing the validity and generalizability of population neuroscience research.

Beyond Discrete Classifications: A Computational Approach to the Continuum of Cognition and Behavior in Children

Psychiatry is undergoing a shift toward precision medicine, demanding personalized approaches that capture the complexity of cognition and behavior. Here, we introduce a novel referential of four robust, replicable, and generalizable cognitive and behavioral profiles. These were derived from the most prominent pediatric cohort (n=10,843) and validated in two independent cohorts (n=195 and n=271). We demonstrate the profiles' longitudinal stability and consistency with clinical diagnoses while exposing critical discrepancies across parent-reported, youth-reported, and expert-derived diagnoses. Beyond validation, we showcase the real-world utility of our approach by linking profiles to environmental factors, revealing associations between parental influences and youths' cognition and behavior. Our fuzzy profiling framework moves beyond discrete classification, offering a powerful tool to refine psychiatric evaluation and intervention. We provide an open-source framework, enabling researchers and clinicians to fast-track implementation and foster a data-driven, domain-based approach to diagnosis. Our findings advocate for broadening the scope of psychiatric assessment.

Longitudinal analysis of the ABCD® study

The Adolescent Brain Cognitive Development® (ABCD) Study provides a unique opportunity to investigate developmental processes in a large, diverse cohort of youths, aged approximately 9-10 at baseline and assessed annually for 10 years. Given the size and complexity of the ABCD Study, researchers analyzing its data will encounter a myriad of methodological and analytical considerations. This review provides an examination of key concepts and techniques related to longitudinal analyses of the ABCD Study data, including: (1) characterization of the factors associated with variation in developmental trajectories; (2) assessment of how level and timing of exposures may impact subsequent development; (3) quantification of how variation in developmental domains may be associated with outcomes, including mediation models and reciprocal relationships. We emphasize the importance of selecting appropriate statistical models to address these research questions. By presenting the advantages and potential challenges of longitudinal analyses in the ABCD Study, this review seeks to equip researchers with foundational knowledge and tools to make informed decisions as they navigate and effectively analyze and interpret the multi-dimensional longitudinal data currently available.

Brain asymmetry and its association with inattention and heritability during neurodevelopment

The relationship between brain asymmetry and inattention, and their heritability is not well understood. Utilizing advanced neuroimaging, we examined brain asymmetry with data from the Adolescent Brain Cognitive Development (ABCD; n = 8943; 9-10 y) and the Human Connectome Project (HCP) cohorts (n = 1033; 5-100 y). Data-driven metrics from resting-state fMRI and morphometrics revealed reproducible and stable brain asymmetry patterns across the lifespan. In children, high levels of inattention were highly heritable (61%) and linked to reduced leftward asymmetry of functional connectivity in the dorsal posterior superior temporal sulcus (dpSTS), a region interconnected with a left-lateralized language network. However, reduced dpSTS asymmetry had low heritability (16%) and was associated with lower cognitive performance suggesting that non-genetic factors, such as those mediating cognitive performance, might underlie its association with dpSTS asymmetry. Interventions that enhance cognition might help optimize brain function and reduce inattention.

Resting-State Sensory-Motor Connectivity between Hand and Mouth as a Neural Marker of Socioeconomic Disadvantage, Psychosocial Stress, Cognitive Difficulties, Impulsivity, Depression, and Substance Use in Children

Background

The sensory-motor network is essential for integrating sensory input with motor function and higher-order cognition. Resting-state functional connectivity (rsFC) within this network undergoes significant developmental changes, and disruptions in these connections have been linked to behavioral and psychiatric outcomes. However, the relationship between sensory-motor connectivity, early-life adversity, and later health behaviors remains understudied.

Objective

This study examines the associations between rsFC within the sensory-motor network (mouth and hand regions) and key social, psychological, and behavioral factors, including baseline and past socioeconomic status (SES), trauma exposure, family conflict, impulsivity, major depressive disorder (MDD), and future substance use.

Methods

Data were drawn from the Adolescent Brain Cognitive Development (ABCD) Study, a national sample of U.S. children. Resting-state fMRI data were used to assess functional connectivity within the sensory-motor network. Bivariate analyses examined associations between rsFC in the sensory-motor mouth and hand regions and baseline SES, past SES, childhood trauma exposure, family conflict, impulsivity, and MDD. Longitudinal analyses assessed whether baseline rsFC predicted future substance use.

Results

Greater rsFC between the sensory-motor mouth and hand regions was significantly associated with lower SES, higher trauma exposure, and greater family conflict. Increased connectivity was also correlated with older age and more advanced puberty status. Higher rsFC between the sensory-motor mouth and hand regions was linked to greater impulsivity, lower cognitive function, an increased likelihood of MDD, and future marijuana use.

Conclusion

These findings suggest that sensory-motor connectivity is sensitive to socioeconomic and psychosocial stressors, with potential long-term implications for mental health and substance use risk. The results highlight the importance of early-life environmental factors in shaping neurodevelopmental trajectories and emphasize the need for targeted interventions to mitigate the effects of adversity on brain function and behavior. Future research should further explore the role of sensory-motor network alterations in behavioral health outcomes as a function of environmental stressors.

Callous-unemotional traits, cognitive functioning, and externalizing problems in a propensity-matched sample from the ABCD study

BACKGROUND

Many studies show that both callous-unemotional (CU) traits (e.g., low empathy, lack of guilt) and cognitive difficulties increase risk for externalizing psychopathology across development. However, other work suggests that some aggression (e.g., relational, proactive) may rely on intact cognitive function, which could vary based on the presence of CU traits. Moreover, no prior research has adequately accounted for common risk factors shared by CU traits, cognitive difficulties, and externalizing problems, which confounds conclusions that can be drawn about their purported relationships. The current study addressed these knowledge gaps by leveraging rigorous propensity matching methods to isolate associations between CU traits and different dimensions of cognitive function and externalizing problems.

METHODS

Associations between CU traits, cognitive functioning, and externalizing outcomes were tested within dimensional (n = 11,868) and propensity-matched group-based (n = 1,224) models using data from the Adolescent Brain Cognitive Development Study®, with rigorous statistical control for shared sociodemographic risk factors. Cross-sectional outcomes were parent-reported symptoms of conduct disorder (CD), oppositional defiant disorder (ODD), and attention deficit hyperactivity disorder (ADHD). Longitudinal outcomes were child-reported overt and relational aggression.

RESULTS

CU traits were uniquely related to more parent-reported CD, ODD, ADHD symptoms, as well as more child-reported aggressive behaviors. Effects of cognitive difficulties were domain specific and were not consistent across dimensional and propensity matched models. There was minimal evidence for divergent associations between CU traits and externalizing outcomes as a function of cognition (i.e., no moderation).

CONCLUSIONS

Rigorous control for sociodemographic factors within propensity-matched models establish CU traits as a robust and unique risk factor for externalizing psychopathology, over and above difficulties with cognitive functioning.

Extreme Heat Exposure is Associated with Lower Learning, General Cognitive Ability, and Memory among US Children

Background

The increasing frequency and intensity of extreme heat exposure is a significant consequence of climate change, with broad public health implications. While many health risks associated with heat exposure are well-documented, less research has focused on its impact on children's cognitive function.

Objectives

This study examines the relationship between extreme heat exposure and various domains of cognitive function in children.

Methods

Data were drawn from the Adolescent Brain Cognitive Development (ABCD) study. Key variables included race/ethnicity, age, gender, family socioeconomic status (SES), heatwave exposure, and multiple cognitive domains: total composite score, fluid composite score, crystallized intelligence, reading ability, picture vocabulary, pattern recognition, card sorting, and list recall. Structural equation modeling (SEM) was used for data analysis.

Results

A total of 11,878 children were included in the analysis. Findings revealed significant associations between extreme heat exposure and lower cognitive performance across multiple domains. The strongest adjusted effects were observed in pattern recognition (B = -0.064, p < 0.001) and reading ability (B = -0.050, p < 0.001), both within the learning domain, as well as total composite cognitive ability (B = -0.067, p < 0.001), fluid composite (B = -0.053, p < 0.001), and crystallized intelligence (B = -0.061, p < 0.001), all within general cognitive ability. Weaker but still significant associations were found for list recall (B = -0.025, p = 0.006) and card sorting (B = -0.043, p < 0.001) within the memory domain, as well as picture vocabulary (B = -0.025, p = 0.008) within general cognitive ability. These associations remained significant after controlling for demographic factors, race/ethnicity, family SES, and neighborhood SES.

Conclusions

This study underscores the impact of climate change on cognitive function disparities, particularly in learning and general cognitive ability among children exposed to extreme heat. Findings highlight the need for targeted interventions to mitigate the cognitive risks associated with heat exposure in vulnerable populations.

Balancing Data Quality and Bias: Investigating Functional Connectivity Exclusions in the Adolescent Brain Cognitive Development℠ (ABCD Study) Across Quality Control Pathways

Analysis of resting state fMRI (rs-fMRI) typically excludes images substantially degraded by subject motion. However, data quality, including degree of motion, relates to a broad set of participant characteristics, particularly in pediatric neuroimaging. Consequently, when planning quality control (QC) procedures researchers must balance data quality concerns against the possibility of biasing results by eliminating data. In order to explore how researcher QC decisions might bias rs-fMRI findings and inform future research design, we investigated how a broad spectrum of participant characteristics in the Adolescent Brain and Cognitive Development (ABCD) study were related to participant inclusion/exclusion across versions of the dataset (the ABCD Community Collection and ABCD Release 4) and QC choices (specifically, motion scrubbing thresholds). Across all these conditions, we found that the odds of a participant's exclusion related to a broad spectrum of behavioral, demographic, and health-related variables, with the consequence that rs-fMRI analyses using these variables are likely to produce biased results. Consequently, we recommend that missing data be formally accounted for when analyzing rs-fMRI data and interpreting results. Our findings demonstrate the urgent need for better data acquisition and analysis techniques which minimize the impact of motion on data quality. Additionally, we strongly recommend including detailed information about quality control in open datasets such as ABCD.

Psychiatric Symptoms, Cognition, and Symptom Severity in Children

Importance

Mental illnesses are a leading cause of disability globally, and functional disability is often in part caused by cognitive impairments across psychiatric disorders. However, studies have consistently reported seemingly opposite findings regarding the association between cognition and psychiatric symptoms.

Objective

To determine if the association between general cognition and mental health symptoms diverges at different symptom severities in children.

Design, Setting, and Participants

A total of 5175 children with complete data at 2 time points assessed 2 years apart (aged 9 to 11 years at the first assessment) from the ongoing Adolescent Brain and Cognitive Development (ABCD) study were evaluated for a general cognition factor and mental health symptoms from September 2016 to August 2020 at 21 sites across the US. Polynomial and generalized additive models afforded derivation of continuous associations between cognition and psychiatric symptoms across different ranges of symptom severity. Data were analyzed from December 2022 to April 2024.

Main Outcomes and Measures

Aggregate cognitive test scores (general cognition) were primarily evaluated in relation to total and subscale-specific symptoms reported from the Child Behavioral Checklist.

Results

The sample included 5175 children (2713 male [52.4%] and 2462 female [47.6%]; mean [SD] age, 10.9 [1.18] years). Previously reported mixed findings regarding the association between general cognition and symptoms may consist of several underlying, opposed associations that depend on the class and severity of symptoms. Linear models recovered differing associations between general cognition and mental health symptoms, depending on the range of symptom severities queried. Nonlinear models confirm that internalizing symptoms were significantly positively associated with cognition at low symptom burdens higher cognition = more symptoms) and significantly negatively associated with cognition at high symptom burdens.

Conclusions and Relevance

The association between mental health symptoms and general cognition in this study was nonlinear. Internalizing symptoms were both positively and negatively associated with general cognition at a significant level, depending on the range of symptom severities queried in the analysis sample. These results appear to reconcile mixed findings in prior studies, which implicitly assume that symptom severity tracks linearly with cognitive ability across the entire spectrum of mental health. As the association between cognition and symptoms may be opposite in low vs high symptom severity samples, these results reveal the necessity of clinical enrichment in studies of cognitive impairment.

Neurocognitive Latent Factors Associate With Early Tobacco and Alcohol Use Among Adolescent Brain Cognitive Development Study Youth

PURPOSE

Prospective associations between preadolescent neurocognitive structure and onset of substance use in adolescence have not been examined. This study investigated associations between cognitive structure among youth aged 9 - 10 years and the likelihood of experimentation with tobacco and alcohol by ages 13-14 years.

METHODS

A principal component (PC) analysis of nine neurocognitive assessments was used to identify the cognitive structure of unrelated adolescent brain cognitive development study participants (n = 9,655). We modeled associations between neurocognitive PCs and odds of tobacco or alcohol use by ages 13-14 years using generalized linear mixed models with a logit link and random intercept for recruitment sites. Demographics, family conflict, neighborhood safety, and externalizing and internalizing behavior were considered covariates.

RESULTS

Four neurocognitive PCs were identified and labeled general ability, executive function, learning and memory, and mental rotation. Mental rotation [odds ratio [OR] = 0.88, p-value = .013] was associated with lower odds of youth tobacco use; the association was stronger among female youth. General ability [OR = 1.20, p-value < .0001] among both males and females, and learning and memory [OR = 1.11, p-value = .024] among females, were associated with increased odds of youth alcohol use.

DISCUSSION

Among youth, higher neurocognitive performance was protective for tobacco use but increased the likelihood of alcohol use. Potential sex differences were identified. The role of cognition in processing the social contexts surrounding tobacco and alcohol use in the United States may contribute to the formation of disparate youth expectancies for tobacco and alcohol use.

Resting-State Functional Connectivity Predicts Attention Problems in Children: Evidence from the ABCD Study

Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder, and numerous functional and structural differences have been identified in the brains of individuals with ADHD compared to controls. This study uses data from the baseline sample of the large, epidemiologically informed Adolescent Brain Cognitive Development Study of children aged 9-10 years old (N = 7979). Cross-validated Poisson elastic net regression models were used to predict a dimensional measure of ADHD symptomatology from within- and between-network resting-state correlations and several known risk factors, such as biological sex, socioeconomic status, and parental history of problematic alcohol and drug use. We found parental history of drug use and biological sex to be the most important predictors of attention problems. The connection between the default mode network and the dorsal attention network was the only brain network identified as important for predicting attention problems. Specifically, we found that reduced magnitudes of the anticorrelation between the default mode and dorsal attention networks relate to increased attention problems in children. Our findings complement and extend recent studies that have connected individual differences in structural and task-based fMRI to ADHD symptomatology and individual differences in resting-state fMRI to ADHD diagnoses.

Inter-Modality Source Coupling: A Fully-Automated Whole-Brain Data-Driven Structure-Function Fingerprint Shows Replicable Links to Reading in a Large-Scale (N∼8K) Analysis

OBJECTIVE

Both structural and functional brain changes have been individually associated with developing cognitive processes such as reading. However, there is limited research about the combined influence of resting-state functional and structural magnetic resonance imaging (rs-fMRI and sMRI) features in reading development, which could provide insights into the interplay between brain structure and function in shaping cognitive growth. We propose a method called inter-modality source coupling (IMSC) to study the coupling between the rs-fMRI and sMRI and its relationship to reading ability in school-age children.

METHODS

This approach is applied to baseline data from four thousand participants (9-11 years) and replicated in a second group. Our analysis focused on the relationship of IMSC to overall reading score.

RESULTS

Our findings indicate that higher reading ability was linked with increased function-structure coupling among higher-level cortical regions, particularly those links between the inferior parietal lobule and inferior frontal areas, and conversely, lower reading ability was associated with enhanced function-structure coupling among the fusiform and lingual gyrus. Our study found evidence of spatial correspondence between the data indicating an interplay between brain structure and function in our participants.

CONCLUSION

Our approach revealed a linked pattern of whole brain structure to the corresponding functional connectivity pattern that correlated with reading ability. This novel IMSC analysis method provides a new approach to study the multimodal relationship between brain function and structure.

SIGNIFICANCE

These findings have interesting implications for understanding the multimodal complexity underlying the development of the neural basis for reading ability in school-aged children.

Measurement of emerging neurocognitive and language skills in the HEALthy Brain and Child Development (HBCD) study

The HEALthy Brain and Child Development (HBCD) study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The study plans enrolling over 7000 families across 27 sites. This manuscript presents the measures from the Neurocognition and Language Workgroup. Constructs were selected for their importance in normative development, evidence for altered trajectories associated with environmental influences, and predictive validity for child outcomes. Evaluation of measures considered psychometric properties, brevity, and developmental and cultural appropriateness. Both performance measures and caregiver report were used wherever possible. A balance of norm-referenced global measures of development (e.g., Bayley Scales of Infant Development-4) and more specific laboratory measures (e.g., deferred imitation) are included in the HBCD study battery. Domains of assessment include sensory processing, visual-spatial reasoning, expressive and receptive language, executive function, memory, numeracy, adaptive behavior, and neuromotor. Strategies for staff training and quality control procedures, as well as anticipated measures to be added as the cohort ages, are reviewed. The HBCD study presents a unique opportunity to examine early brain and neurodevelopment in young children through a lens that accounts for prenatal exposures, health and socio-economic disparities.

An investigation of multimodal predictors of adolescent alcohol initiation

BACKGROUND

Early alcohol initiation is associated with negative, alcohol-related outcomes. While previous work identifies numerous risk factors for early use, the relative contributions of known predictors remains understudied. The current project addresses this gap by 1) prospectively predicting early alcohol initiation using measures of inhibition control, reward sensitivity, and contextual risk factors and 2) interrogating the relative importance of each domain.

METHOD

This study leverages multimodal data from substance-naïve youth enrolled in the Adolescent Brain Cognitive Development (ABCD) Study® (n=11,694). Early initiation was defined as consuming a full standard drink containing alcohol prior to age 16. Propensity scores were used to match alcohol initiators (n=348) with demographically similar non-initiators at a 1:2 ratio (n=696). Independent logistic regressions were conducted for each domain followed by additive, hierarchical models.

RESULTS

The model of contextual factors (pseudo-R2=0.086, AUC=0.67) outperformed inhibition control (pseudo-R2=0.021, AUC=0.58) and reward sensitivity measures (pseudo-R2=0.020, AUC=0.59). The hierarchical model containing all measures (pseudo-R2=0.106, AUC=0.69) did not significantly improve the model of contextual factors alone (p>0.05). Examples of significant predictors (p<0.05) include externalizing behaviors, number of substances known, and non-religious alcohol sipping.

CONCLUSION

Contextual risk factors were the strongest predictors of early alcohol use; however, more work is needed to understand the causal nature of this relationship. Measures of inhibition control and reward sensitivity were not adequate in distinguishing initiators from non-initiators. These findings add to a body of evidence that contextual factors play a major role in alcohol initiation while highlighting specific predictor variables that could inform youth alcohol prevention.

Autism is associated with in vivo changes in gray matter neurite architecture

Postmortem investigations in autism have identified anomalies in neural cytoarchitecture across limbic, cerebellar, and neocortical networks. These anomalies include narrow cell mini-columns and variable neuron density. However, difficulty obtaining sufficient post-mortem samples has often prevented investigations from converging on reproducible measures. Recent advances in processing magnetic resonance diffusion weighted images (DWI) make in vivo characterization of neuronal cytoarchitecture a potential alternative to post-mortem studies. Using extensive DWI data from the Adolescent Brain Cognitive Developmentsm (ABCD®) study 142 individuals with an autism diagnosis were compared with 8971 controls using a restriction spectrum imaging (RSI) framework that characterized total neurite density (TND), its component restricted normalized directional diffusion (RND), and restricted normalized isotropic diffusion (RNI). A significant decrease in TND was observed in autism in the right cerebellar cortex (β = -0.005, SE =0.0015, p = 0.0267), with significant decreases in RNI and significant increases in RND found diffusely throughout posterior and anterior aspects of the brain, respectively. Furthermore, these regions remained significant in post-hoc analysis when the autism sample was compared against a subset of 1404 individuals with other psychiatric conditions (pulled from the original 8971). These findings highlight the importance of characterizing neuron cytoarchitecture in autism and the significance of their incorporation as physiological covariates in future studies.

Associations between Fine Particulate Matter Components, Their Sources, and Cognitive Outcomes in Children Ages 9-10 Years Old from the United States

BACKGROUND

Emerging literature suggests that fine particulate matter [with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 )] air pollution and its components are linked to various neurodevelopmental outcomes. However, few studies have evaluated how PM 2.5 component mixtures from distinct sources relate to cognitive outcomes in children.

OBJECTIVES

This cross-sectional study investigated how ambient concentrations of PM 2.5 component mixtures relate to neurocognitive performance in 9- to 10-year-old children, as well as explored potential source-specific effects of these associations, across the US.

METHODS

Using spatiotemporal hybrid models, annual concentrations of 15 chemical components of PM 2.5 were estimated based on the residential address of child participants from the Adolescent Brain Cognitive Development (ABCD) Study. General cognitive ability, executive function, and learning/memory scores were derived from the NIH Toolbox. We applied positive matrix factorization to identify six major PM 2.5 sources based on the 15 components, which included crustal, ammonium sulfate, biomass burning, traffic, ammonium nitrate, and industrial/residual fuel burning. We then utilized weighted quantile sum (WQS) and linear regression models to investigate associations between PM 2.5 components' mixture, their potential sources, and children's cognitive scores.

RESULTS

Mixture modeling revealed associations between cumulative exposure and worse cognitive performance across all three outcome domains, including shared overlap in detrimental effects driven by ammonium nitrates, silicon, and calcium. Using the identified six sources of exposure, source-specific negative associations were identified between ammonium nitrates and learning & memory, traffic and executive function, and crustal and industrial mixtures and general cognitive ability. Unexpected positive associations were also seen between traffic and general ability as well as biomass burning and executive function.

DISCUSSION

This work suggests nuanced associations between outdoor PM 2.5 exposure and childhood cognitive performance, including important differences in cognition related both to individual chemicals as well as to specific sources of these exposures. https://doi.org/10.1289/EHP14418.

Cross-Sectional and Longitudinal Relations Among Irritability, Attention-Deficit/Hyperactivity Disorder Symptoms, and Inhibitory Control

OBJECTIVE

Irritability and attention-deficit/hyperactivity disorder (ADHD) symptoms frequently co-occur in youth. Although ADHD has been associated with inhibitory control deficits, the literature on irritability and inhibitory control is mixed. Examining how irritability, ADHD symptoms, and inhibitory control interrelate both cross-sectionally and longitudinally across development could shed light on common and distinct mechanisms of youth psychopathology.

METHOD

We utilized a cross-lagged panel model with data from 2 time points (at ages 10 and 12 years) of the Adolescent Brain and Cognitive Development (ABCD) Study (N = 7,444, or ∼63% of the baseline sample with full data at each time point) to test cross-sectional and longitudinal associations among parent-reported irritability and ADHD symptoms and behaviorally assessed inhibitory control. This was performed separately across discovery and replication subsamples, each n = 3,722.

RESULTS

As expected, irritability and ADHD symptoms exhibited strong cross-sectional and reciprocal cross-lagged associations. Higher ADHD symptoms at age 10 years were associated concurrently with poorer inhibitory control and predicted poorer inhibitory control at age 12. Contrary to predictions, inhibitory control was not significantly associated with irritability cross-sectionally, nor was it predictive of later irritability or ADHD symptoms.

CONCLUSION

These findings highlight strong links between irritability and ADHD. Although inhibitory control deficits were linked to ADHD and predictive of its symptom course, inhibitory control had no significant associations with irritability. Future research should investigate other candidate mechanisms of the co-occurrence of irritability and ADHD symptoms and predictors of their developmental trajectories.

PLAIN LANGUAGE SUMMARY

This study investigated how irritability, ADHD symptoms, and inhibitory control interrelate both cross-sectionally and longitudinally in a sample of youth from the Adolescent Brain and Cognitive Development (ABCD) Study (N = 7,444). Results indicate that irritability and ADHD symptoms exhibit strong reciprocal predictive relationships; however, inhibitory control does not predict later irritability or ADHD, though ADHD symptoms predicted later inhibitory control deficits. These findings corroborate the predictive relations between irritability and ADHD over development and highlight the need for continued exploration of mechanisms underlying their co-occurrence.

Are factors that predict conversion to psychosis associated with initial transition to a high risk state? An adolescent brain cognitive development study analysis

OBJECTIVE

Previous work suggests that cognitive and environmental risk factors may predict conversion to psychosis in individuals at clinical high risk (CHRs) for the disorder. Less clear, however, is whether these same factors are also associated with the initial emergence of the high risk state in individuals who do not meet current threshold criteria for being considered high risk.

METHOD

Here, using data from the Adolescent Brain Cognitive Development (ABCD) study, we examined associations between factors previously demonstrated to predict conversion to psychosis in CHRs with transition to a "high risk" state, here defined as having a distress score between 2 and 5 on any unusual thought content question in the Prodromal Questionnaire-Brief Child version. Of a sample of 5237 children (ages 11-12) studied at baseline, 470 transitioned to the high-risk state the following year. A logistic regression model was evaluated using age, cognition, negative and traumatic experiences, decline in school performance, and family history of psychosis as predictors.

RESULTS

The overall model was significant (χ2 = 100.89, R2 = 0.042, p < .001). Significant predictors included number of negative life events, decline in school performance, number of trauma types, and verbal learning task performance.

CONCLUSIONS

These results suggest that factors that predict conversion in CHR teenagers are also associated with initial emergence of a "high-risk" state in preadolescents. Limitations regarding the degree to which model factors and outcome in this study parallel those used in previous work involving psychosis risk in older teenagers are discussed.

Happy children! A network of psychological and environmental factors associated with the development of positive affect in 9-13 children

To deepen the development of positive affect during early adolescence and shed new light on its predictors, this study adopts an exploratory network approach to first identify the main domains that describe the variability of children's psychological, environmental, and behavioral characteristics, and then use these domains to longitudinally predict positive affect and its development within a latent growth framework. To this aim, we considered 10,904 US participants (9 years old at baseline; 13 years old 42 months later), six measurement occasions of positive affect, and 46 baseline indicators from the ABCD study. Our results not only confirm that positive affect declines between 9 and 13 years old, but also show that among the five domains identified (behavioral dysregulation, cognitive functioning, psychological problems, supportive social environment, and extracurricular activities), only a supportive social environment consistently predicts positive affect. This is crucial for practitioners and policymakers, as it can help them focus on the elements within our complex network of psychological, social, and environmental variability.

Associations between middle childhood executive control aspects and adolescent substance use and externalizing and internalizing problems

This study examines the degree to which two middle childhood executive control aspects, working memory and combined inhibitory control/flexible shifting, predict adolescent substance use and externalizing and internalizing problems. Participants were 301 children (ages 3-6 years; 48.2% male) recruited from a Midwestern city in the United States and followed into adolescence (ages 14-18 years). Working memory had a statistically significant unadjusted association with externalizing problems (r = -.30, p = .003) in a confirmatory factor analysis. Neither factor significantly predicted any of the adolescent outcomes in a structural equation model that adjusted for each EC aspect, sociodemographic covariates, and middle childhood externalizing and internalizing problems. Stronger prediction of EC aspects might not emerge until they become more fully differentiated later in development.

Enhancing Within-Person Estimation of Neurocognition and the Prediction of Externalizing Behaviors in Adolescents

Decades of research document an association between neurocognitive dysfunction and externalizing behaviors, including rule-breaking, aggression, and impulsivity. However, there has been very little work that examines how multiple neurocognitive functions co-occur within individuals and which combinations of neurocognitive functions are most relevant for externalizing behaviors. Moreover, Latent Profile Analysis (LPA), a widely used method for grouping individuals in person-centered analysis, often struggles to balance the tradeoff between good model fit (splitting participants into many latent profiles) and model interpretability (using only a few, highly distinct latent profiles). To address these problems, we implemented a non-parametric Bayesian form of LPA based on the Dirichlet process mixture model (DPM-LPA) and used it to study the relationship between neurocognitive functioning and externalizing behaviors in adolescents participating in the Adolescent Brain Cognitive Development Study. First, we found that DPM-LPA outperformed conventional LPA, revealing more distinct profiles and classifying participants with higher certainty. Second, latent profiles extracted from DPM-LPA were differentially related to externalizing behaviors: profiles with deficits in working memory, inhibition, and/or language abilities were robustly related to different expressions of externalizing. Together, these findings represent a step towards addressing the challenge of finding novel ways to use neurocognitive data to better describe the individual. By precisely identifying and specifying the variation in neurocognitive and behavioral patterns this work offers an innovative empirical foundation for the development of assessments and interventions that address these costly behaviors.

Childhood obesity's effect on cognition and brain connectivity worsens with low family income

Childhood obesity and its adverse health consequences have risen worldwide, with low socioeconomic status increasing the risk in high-income countries like the United States. Understanding the interplay between childhood obesity, cognition, socioeconomic factors, and the brain is crucial for prevention and treatment. Using data from the Adolescent Brain Cognitive Development (ABCD) study, we investigated how body mass index (BMI) relates to brain structural and functional connectivity metrics. Children with obesity or who are overweight (n = 2,356) were more likely to live in poverty and exhibited lower cognitive performance compared with children with a healthy weight (n = 4,754). Higher BMI was associated with multiple brain measures that were strongest for lower longitudinal diffusivity in corpus callosum; increased activity in cerebellum, insula, and somatomotor cortex; and decreased functional connectivity in multimodal brain areas, with effects more pronounced among children from low-income families. Notably, nearly 80% of the association of low income and 70% of the association of impaired cognition on BMI were mediated by higher brain activity in somatomotor areas. Increased resting activity in somatomotor areas and decreased structural and functional connectivity likely contribute to the higher risk of being overweight or having obesity among children from low-income families. Supporting low-income families and implementing educational interventions to improve cognition may promote healthy brain function and reduce the risk of obesity.

Inter-modality source coupling: a fully automated whole-brain data-driven structure-function fingerprint shows replicable links to reading in large-scale (N~8K) analysis

We propose a method called inter-modality source coupling (IMSC) to study the coupling between the function and structure of the brain and its relationship to reading ability in school-age children. We performed an analysis focused on the relationship of IMSC to overall reading score. Our findings indicate that key brain regions are linked to differences in function-structure coupling in the context of reading ability and language processing. Higher reading ability was linked with increased function-structure coupling among higher-level cortical regions and lower reading ability was associated with enhanced function-structure coupling among occipital networks and early language processes areas.

Brain community detection in the general children population

The fingerprint is known to be unique in every individual, and there is evidence that such individuality exists with the brain. Neuroimaging studies that research brain fingerprint patterns typically consider relationships between individuals and their brain patterns. However, there remains a question as to how such fingerprint patterns can be grouped among the general population. In this study, we implemented clustering-based methods to evaluate whether such subgrouping exists among individuals and evaluated the relationships between these clusters and individuals' developmental, cognitive, demographical, psychological status in the Adolescent Brain and Cognitive Development study cohort. Multiplex community detection and K-means clustering revealed the existence of clusters in our cohort, as well as significant group differences between these clusters in these datasets, indicative of heterogeneous subgrouping of brain fingerprint patterns in the general population.

Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.

TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography

Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.

Data-driven, generalizable prediction of adolescent sleep disturbances in the multisite Adolescent Brain Cognitive Development Study

STUDY OBJECTIVES

Sleep disturbances are common in adolescence and associated with a host of negative outcomes. Here, we assess associations between multifaceted sleep disturbances and a broad set of psychological, cognitive, and demographic variables using a data-driven approach, canonical correlation analysis (CCA).

METHODS

Baseline data from 9093 participants from the Adolescent Brain Cognitive Development (ABCD) Study were examined using CCA, a multivariate statistical approach that identifies many-to-many associations between two sets of variables by finding combinations for each set of variables that maximize their correlation. We combined CCA with leave-one-site-out cross-validation across ABCD sites to examine the robustness of results and generalizability to new participants. The statistical significance of canonical correlations was determined by non-parametric permutation tests that accounted for twin, family, and site structure. To assess the stability of the associations identified at baseline, CCA was repeated using 2-year follow-up data from 4247 ABCD Study participants.

RESULTS

Two significant sets of associations were identified: (1) difficulty initiating and maintaining sleep and excessive daytime somnolence were strongly linked to nearly all domains of psychopathology (r2 = 0.36, p < .0001); (2) sleep breathing disorders were linked to BMI and African American/black race (r2 = 0.08, p < .0001). These associations generalized to unseen participants at all 22 ABCD sites and were replicated using 2-year follow-up data.

CONCLUSIONS

These findings underscore interwoven links between sleep disturbances in early adolescence and psychological, social, and demographic factors.

The impact of menarche on hippocampal mechanisms of severity of psychotic-like experiences in the ABCD study

Accumulating evidence suggests that estrogens play an important modulatory role in the pathogenesis of psychosis. Estrogens come online within a dynamic developmental context of emerging psychopathology and neurodevelopment. As a result, estradiol (the primary form of estrogen) may influence psychosis lability directly or indirectly through its neurodevelopmental influence on estrogens-sensitive areas like the hippocampus. Understanding this influence may provide novel insight into mechanisms of psychosis lability. This study included baseline and year 2 timepoints from 4422 female participants from the Adolescent Brain Cognitive Development (ABCD) study (age 8-13), who varied in estradiol availability (pre-menarche, post-menarche, pre- and post-menarche timepoints). Estradiol availability was related to psychotic-like experiences (PLE) severity both directly and as an interactive effect with hippocampal connectivity using menarche status (pre/post) in a multilevel model. PLE severity was highest in individuals with early menarche emphasizing the importance of the developmental timing. Although PLE severity decreased over time in the sample, it stayed clinically-relevant over 2 years. Lower hippocampal connectivity was related to elevated PLE severity. This effect was moderated by estradiol; before the availability of estradiol (pre-menarche), lower hippocampal connectivity significantly contributed to the PLE severity, but when estradiol was available (post-menarche) hippocampal dysconnectivity did not account for PLE severity. This moderation suggests that the estrodiol's influence on hippocampal plasticity also reduced the mechanistic role of the hippocampus on PLE severity. Further, the lack of a significant direct reduction of PLE severity post-menarche, may suggest an increased role for other interacting psychosis lability factors during this critical developmental period.

A precision functional atlas of personalized network topography and probabilities

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

Multi-level fMRI analysis applied to hemispheric specialization in the language network, functional areas, and their behavioral correlations in the ABCD sample

Prior research suggests that the organization of the language network in the brain is left-dominant and becomes more lateralized with age and increasing language skill. The age at which specific components of the language network become adult-like varies depending on the abilities they subserve. So far, a large, developmental study has not included a language task paradigm, so we introduce a method to study resting-state laterality in the Adolescent Brain Cognitive Development (ABCD) study. Our approach mixes source timeseries between left and right homotopes of the (1) inferior frontal and (2) middle temporal gyri and (3) a region we term "Wernicke's area" near the supramarginal gyrus. Our large subset sample size of ABCD (n = 6153) allows improved reliability and validity compared to previous, smaller studies of brain-behavior associations. We show that behavioral metrics from the NIH Youth Toolbox and other resources are differentially related to tasks with a larger linguistic component over ones with less (e.g., executive function-dominant tasks). These baseline characteristics of hemispheric specialization in youth are critical for future work determining the correspondence of lateralization with language onset in earlier stages of development.

Identification of a Composite Latent Dimension of Reward and Impulsivity Across Clinical, Behavioral, and Neurobiological Domains Among Youth

BACKGROUND

Individual differences in reward processing are central to heightened risk-taking behaviors during adolescence, but there is inconsistent evidence for the relationship between risk-taking phenotypes and the neural substrates of these behaviors.

METHODS

Here, we identify latent features of reward in an attempt to provide a unifying framework linking together aspects of the brain and behavior during early adolescence using a multivariate pattern learning approach. Data (N = 8295; n male = 4190; n female = 4105) were acquired as part of the Adolescent Brain Cognitive Development (ABCD) Study and included neuroimaging (regional neural activity responses during reward anticipation) and behavioral (e.g., impulsivity measures, delay discounting) variables.

RESULTS

We revealed a single latent dimension of reward driven by shared covariation between striatal, thalamic, and anterior cingulate responses during reward anticipation, negative urgency, and delay discounting behaviors. Expression of these latent features differed among adolescents with attention-deficit/hyperactivity disorder and disruptive behavior disorder, compared with those without, and higher expression of these latent features was negatively associated with multiple dimensions of executive function and cognition.

CONCLUSIONS

These results suggest that cross-domain patterns of anticipatory reward processing linked to negative features of impulsivity exist in both the brain and in behavior during early adolescence and that these are representative of 2 commonly diagnosed reward-related psychiatric disorders, attention-deficit/hyperactivity disorder and disruptive behavior disorder. Furthermore, they provide an explicit baseline from which multivariate developmental trajectories of reward processes may be tracked in later waves of the ABCD Study and other developmental cohorts.

A general exposome factor explains individual differences in functional brain network topography and cognition in youth

Childhood environments are critical in shaping cognitive neurodevelopment. With the increasing availability of large-scale neuroimaging datasets with deep phenotyping of childhood environments, we can now build upon prior studies that have considered relationships between one or a handful of environmental and neuroimaging features at a time. Here, we characterize the combined effects of hundreds of inter-connected and co-occurring features of a child's environment ("exposome") and investigate associations with each child's unique, multidimensional pattern of functional brain network organization ("functional topography") and cognition. We apply data-driven computational models to measure the exposome and define personalized functional brain networks in pre-registered analyses. Across matched discovery (n=5139, 48.5% female) and replication (n=5137, 47.1% female) samples from the Adolescent Brain Cognitive Development study, the exposome was associated with current (ages 9-10) and future (ages 11-12) cognition. Changes in the exposome were also associated with changes in cognition after accounting for baseline scores. Cross-validated ridge regressions revealed that the exposome is reflected in functional topography and can predict performance across cognitive domains. Importantly, a single measure capturing a child's exposome could more accurately and parsimoniously predict cognition than a wealth of personalized neuroimaging data, highlighting the importance of children's complex, multidimensional environments in cognitive neurodevelopment.

Associations between handedness and brain functional connectivity patterns in children

Handedness develops early in life, but the structural and functional brain connectivity patterns associated with it remains unknown. Here we investigate associations between handedness and the asymmetry of brain connectivity in 9- to 10-years old children from the Adolescent Brain Cognitive Development (ABCD) study. Compared to right-handers, left-handers had increased global functional connectivity density in the left-hand motor area and decreased it in the right-hand motor area. A connectivity-based index of handedness provided a sharper differentiation between right- and left-handers. The laterality of hand-motor connectivity varied as a function of handedness in unimodal sensorimotor cortices, heteromodal areas, and cerebellum (P < 0.001) and reproduced across all regions of interest in Discovery and Replication subsamples. Here we show a strong association between handedness and the laterality of the functional connectivity patterns in the absence of differences in structural connectivity, brain morphometrics, and cortical myelin between left, right, and mixed handed children.

Gene-environment pathways to cognitive intelligence and psychotic-like experiences in children

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning, influenced by genetic and environmental factors, is hypothesized to mediate the relationship between these factors and childhood PLEs. Using large-scale longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children. We leveraged large-scale multimodal data of 6,602 children from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling). Our findings indicate that lower cognitive intelligence and higher PLEs are significantly associated with lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, and less supportive environments. Specifically, cognitive intelligence mediates the effects of these factors on PLEs, with supportive parenting and positive school environments showing the strongest impact on reducing PLEs. This study underscores the influence of genetic and environmental factors on PLEs through their effects on cognitive intelligence. Our findings have policy implications in that improving school and family environments and promoting local economic development may enhance cognitive and mental health in children.

Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9-10 years; n = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.

Physical and mental health in adolescence: novel insights from a transdiagnostic examination of FitBit data in the ABCD study

Adolescence is among the most vulnerable period for the emergence of serious mental illnesses. Addressing this vulnerability has generated interest in identifying markers of risk for symptoms and opportunities for early intervention. Physical fitness has been linked to psychopathology and may be a useful risk marker and target for early intervention. New wearable technology has made assessing fitness behavior more practical while avoiding recall and self-report bias. Still, questions remain regarding the clinical utility of physical fitness metrics for mental health, both transdiagnostically and along specific symptom dimensions. The current study includes 5007 adolescents (ages 10-13) who participated in the Adolescent Brain Cognitive Development (ABCD) study and additional sub-study that collected fitness data from wearable technology and clinical symptom measures. Physical fitness metrics included resting heart rate (RHR- an index of cardiovascular health), time spent sedentary (associated with increased inflammation and cardiovascular disease), and time spent in moderate physical activity (associated with increased neurogenesis, neuroplasticity, and healthy neurodevelopment). Self-report clinical symptoms included measures of psychosis-like experiences (PLE), internalizing symptoms, and externalizing symptoms. Increased RHR- lower cardiovascular fitness- related only to greater internalizing symptoms (t = 3.63). More sedentary behavior related to elevated PLE severity (t = 5.49). More moderate activity related to lower PLE (t = -2.69) and internalizing (t = -6.29) symptom severity. Wearable technology fitness metrics linked physical health to specific mental health dimensions, which emphasizes the utility of detailed digital health data as a marker for risk and the need for precision in targeting physical health behaviors to benefit symptoms of psychopathology.

Improved estimation of general cognitive ability and its neural correlates with a large battery of cognitive tasks

Elucidating the neural mechanisms of general cognitive ability (GCA) is an important mission of cognitive neuroscience. Recent large-sample cohort studies measured GCA through multiple cognitive tasks and explored its neural basis, but they did not investigate how task number, factor models, and neural data type affect the estimation of GCA and its neural correlates. To address these issues, we tested 1,605 Chinese young adults with 19 cognitive tasks and Raven's Advanced Progressive Matrices (RAPM) and collected resting state and n-back task fMRI data from a subsample of 683 individuals. Results showed that GCA could be reliably estimated by multiple tasks. Increasing task number enhances both reliability and validity of GCA estimates and reliably strengthens their correlations with brain data. The Spearman model and hierarchical bifactor model yield similar GCA estimates. The bifactor model has better model fit and stronger correlation with RAPM but explains less variance and shows weaker correlations with brain data than does the Spearman model. Notably, the n-back task-based functional connectivity patterns outperform resting-state fMRI in predicting GCA. These results suggest that GCA derived from a multitude of cognitive tasks serves as a valid measure of general intelligence and that its neural correlates could be better characterized by task fMRI than resting-state fMRI data.

Personalized functional brain network topography is associated with individual differences in youth cognition

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain's functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex's sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

Growth Velocities Across Distinct Early Life Windows and Child Cognition: Insights from a Contemporary US Cohort

OBJECTIVE

To evaluate the relative importance of overall and period-specific postnatal growth and their interaction with fetal growth on cognition in a generally well-nourished population.

STUDY DESIGN

We included 1052 children from Project Viva, a prospective cohort in Boston, Massachusetts. Using linear spline mixed-effects models, we modeled length/height and body mass index (BMI) trajectories from birth to 7 years and estimated standardized overall (0-7 years) and period-specific growth velocities ie, early infancy (0-4 months), late infancy (4-15 months), toddlerhood (15-37 months), and early childhood (37-84 months). We investigated associations of growth velocities as well as their interactions with birthweight-for-gestational age on mid-childhood (mean age: 7.9 years) IQ, visual memory and learning, and visual motor ability.

RESULTS

Greater overall height velocity was associated with modestly higher design memory score, (adjusted β [95% CI] 0.19 [-0.01,0.38] P = .057])points per SD increase but lower verbal IQ (-0.88 [-1.76,0.00] P = .051). Greater early infancy height velocity was associated with higher visual motor score (1.92 [0.67,3.18]). Greater overall BMI velocity was associated with lower verbal IQ (-0.71 [-1.52,0.11] P = .090). Greater late infancy BMI velocity was associated with lower verbal IQ (-1.21 [-2.07,-0.34]), design memory score (-0.22 [-0.42,-0.03)], but higher picture memory score (0.22 [0.01,0.43]). Greater early infancy height velocity (-1.5 SD vs 1.5 SD) was associated with higher nonverbal IQ (margins [95% CI] 102.6 [98.9106.3] vs 108.2 [104.9111.6]) among small-for-gestational age infants (P-interaction = 0.04).

CONCLUSIONS

Among generally well-nourished children, there might not be clear cognitive gains with faster linear growth except for those with lower birthweight-for-gestational age, revealing the potential importance of early infancy compensatory growth.

Can we diagnose mental disorders in children? A large-scale assessment of machine learning on structural neuroimaging of 6916 children in the adolescent brain cognitive development study

Background

Prediction of mental disorders based on neuroimaging is an emerging area of research with promising first results in adults. However, research on the unique demographic of children is underrepresented and it is doubtful whether findings obtained on adults can be transferred to children.

Methods

Using data from 6916 children aged 9-10 in the multicenter Adolescent Brain Cognitive Development study, we extracted 136 regional volume and thickness measures from structural magnetic resonance images to rigorously evaluate the capabilities of machine learning to predict 10 different psychiatric disorders: major depressive disorder, bipolar disorder (BD), psychotic symptoms, attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, post-traumatic stress disorder, obsessive-compulsive disorder, generalized anxiety disorder, and social anxiety disorder. For each disorder, we performed cross-validation and assessed whether models discovered a true pattern in the data via permutation testing.

Results

Two of 10 disorders can be detected with statistical significance when using advanced models that (i) allow for non-linear relationships between neuroanatomy and disorder, (ii) model interdependencies between disorders, and (iii) avoid confounding due to sociodemographic factors: ADHD (AUROC = 0.567, p = 0.002) and BD (AUROC = 0.551, p = 0.002). In contrast, traditional models perform consistently worse and predict only ADHD with statistical significance (AUROC = 0.529, p = 0.002).

Conclusion

While the modest absolute classification performance does not warrant application in the clinic, our results provide empirical evidence that embracing and explicitly accounting for the complexities of mental disorders via advanced machine learning models can discover patterns that would remain hidden with traditional models.

COMT Val/Met, stressful life events and externalizing behaviors in youth: A longitudinal study from the ABCD sample

Early adolescence is a crucial time for understanding and detecting the risk factors that may influence youth externalizing/disruptive behaviors and disorders. Previous literature reported evidence that risk factors for disruptive behaviors include catechol-O-methyltransferase (COMT) Val158Met (rs4680) polymorphism and environmental influences. An unanswered question is whether there is a change in these risk factors over stages of youth development. This longitudinal study examines the interaction effect of Val158Met and stressful life events (SLE) on youth externalizing behaviors from ages 9-11. Participants were 2363 children of European ancestry recruited as part of the Adolescent Brain Cognitive Development study. Repeated measures linear mixed models were used to examine the effect of the interaction between Val158Met and SLE (G × E) on disruptive behaviors over development. Externalizing behaviors were analyzed at both baseline and two-year follow-up. Both Val158Met genotype and SLE scores demonstrated significant main effects on disruptive behaviors in youth, and those effects were consistent at both time points. G × E was not associated with externalizing behaviors. Youth who carried the Val allele and/or were exposed to higher SLE consistently had increased externalizing behavior scores. To our knowledge, this is the first study to longitudinally examine the interaction effects of Val158Met and SLE on externalizing behaviors in youth. The results highlight the importance of understanding the genetic and environmental factors underlying externalizing behaviors for better detection of at-risk youth, helping further with early prevention efforts. The findings propose that COMT Val158Met genotype may act as a biomarker for development of novel treatment strategies for disruptive behaviors.

Working Memory-Related Neurofunctional Correlates Associated with the Frontal Lobe in Children with Familial vs. Non-Familial Attention Deficit/Hyperactivity Disorder

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with high prevalence, heritability, and heterogeneity. Children with a positive family history of ADHD have a heightened risk of ADHD emergence, persistence, and executive function deficits, with the neural mechanisms having been under investigated. The objective of this study was to investigate working memory-related functional brain activation patterns in children with ADHD (with vs. without positive family histories (ADHD-F vs. ADHD-NF)) and matched typically developing children (TDC). Voxel-based and region of interest analyses were conducted on two-back task-based fMRI data of 362 subjects, including 186, 96, and 80 children in groups of TDC, ADHD-NF, and ADHD-F, respectively. Relative to TDC, both ADHD groups had significantly reduced activation in the left inferior frontal gyrus (IFG). And the ADHD-F group demonstrated a significant positive association of left IFG activation with task reaction time, a negative association of the right IFG with ADHD symptomatology, and a negative association of the IFG activation laterality index with the inattention symptom score. These results suggest that working memory-related functional alterations in bilateral IFGs may play distinct roles in ADHD-F, with the functional underdevelopment of the left IFG significantly informing the onset of ADHD symptoms. Our findings have the potential to assist in tailored diagnoses and targeted interventions in children with ADHD-F.

Physical and Mental Health in Adolescence: Novel Insights from a transdiagnostic examination of FitBit data in the ABCD Study

Adolescence is among the most vulnerable period for the emergence of serious mental illnesses. Addressing this vulnerability has generated interest in identifying markers of risk for symptoms and opportunities for early intervention. Physical fitness has been linked to psychopathology and may be a useful risk marker and target for early intervention. New wearable technology has made assessing fitness behavior more practical while avoiding recall and self-report bias. Still, questions remain regarding the clinical utility of physical fitness metrics for mental health, both transdiagnostically and along specific symptom dimensions. The current study includes 5007 adolescents (ages 10 to 13) who participated in the Adolescent Brain Cognitive Development (ABCD) study and additional sub-study that collected fitness data from wearable technology and clinical symptom measures. Physical fitness metrics included resting heart rate (RHR- an index of cardiovascular health), time spent sedentary (associated with increased inflammation and cardiovascular disease), and time spent in moderate physical activity (associated with increased neurogenesis, neuroplasticity, and healthy neurodevelopment). Self-report clinical symptoms included measures of internalizing symptoms, externalizing symptoms, and psychosis-like experiences - PLE). Increased RHR- lower cardiovascular fitness- related only to greater internalizing symptoms (t = 3.63). More sedentary behavior related to elevated PLE severity (t = 5.49). More moderate activity related to lower PLE (t=-2.69) and internalizing (t=-6.29) symptom severity. Wearable technology fitness metrics linked physical health to specific mental health dimensions, which emphasizes the utility of detailed digital health data as a marker for risk and the need for precision in targeting physical health behaviors to benefit symptoms of psychopathology.

Child Neuropsychological Functioning and Interpersonal Callousness as Predictors of Externalising Behaviour in Early Adolescence: A Prospective Population-based Study

Externalizing problems are a key predictor of individual functioning in adulthood. Therefore, identifying possible risk factors for externalising problems is valuable for optimising prevention and treatment programmes. Previous research has shown that (domains of) neuropsychological functioning predict externalising problems later in life. However, the influence of callous traits, and sex as potential moderators in this relation remains unclear. The aim of this study was to examine associations between neuropsychological functioning in children (at age 8 years) and later externalising behaviour in adolescence (at age 14 years), as well as to test the role of callous traits (at age 10 years) and sex as moderating factors. The analyses were conducted using data from 661 Dutch children from the population-based Generation R Study (47.2% female). We found no association between neuropsychological functioning and later externalising behaviour. However, callous traits predicted externalising problems at age 14 years. Further, callous traits moderated the association between neuropsychological functioning and externalising behaviour, though this association dropped below the statistical significance level when adjusted for confounders. Specifically, while higher neuropsychological functioning was associated with more externalising behaviour in children with high callous traits, lower neuropsychological functioning was not associated with externalising behaviour in children with low callous traits. Although boys showed significantly higher externalising behaviours compared to girls, no moderating effect of sex was found on the association between neuropsychological functioning and externalising behaviour. These results add to a growing body of evidence supporting a distinct neurocognitive profile in children with high vs low callousness.

Effects of family income on brain functional connectivity in US children: associations with cognition

Higher family income (FI) is associated with larger cortical gray matter volume and improved cognitive performance in children. However, little is known about the effects of FI on brain functional and structural connectivity. This cross-sectional study investigates the effects of FI on brain connectivity and cognitive performance in 9- to 11-years old children (n = 8739) from the Adolescent Brain Cognitive Development (ABCD) study. Lower FI was associated with decreased global functional connectivity density (gFCD) in the default-mode network (DMN), inferior and superior parietal cortices and in posterior cerebellum, and increased gFCD in motor, auditory, and extrastriate visual areas, and in subcortical regions both for girls and boys. Findings demonstrated high reproducibility in Discovery and Reproducibility samples. Cognitive performance partially mediated the association between FI and DMN connectivity, whereas DMN connectivity did not mediate the association between FI and cognitive performance. In contrast, there was no significant association between FI and structural connectivity. Findings suggest that poor cognitive performance, which likely reflects multiple factors (genetic, nutritional, the level and quality of parental interactions, and educational exposure [1]), contributes to reduced DMN functional connectivity in children from low-income families. Follow-up studies are needed to help clarify if this leads to reductions in structural connectivity as these children age.

The Relationship Between Cortical Thickness and Executive Function Measures in Children With and Without ADHD

OBJECTIVE

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by inattention, hyperactivity, and impulsivity; however, other executive function dysregulation is common, including inhibition and working memory. This study aims to identify CT differences based on executive function performance in individuals with and without ADHD.

METHODS

Data for this study was acquired from the Adolescent Brain and Cognitive Development (ABCD) database (61 ADHD, and 61 age and sex matched controls). General linear models were used to assess relationships between measures, CT, and diagnosis.

RESULTS

We found a significant relation between CT and working memory scores in the right precentral area. Additionally, we found significant interactions between CT, diagnosis, and measure outcome in the Flanker assessment (in the left fusiform area) and the attention score of the CBCL (in the right precentral region).

CONCLUSION

This suggests that there may be different relationships that exist between CT and executive function in children with ADHD.

Neighborhood air pollution is negatively associated with neurocognitive maturation in early adolescence

The ability to maintain focus and process task-relevant information continues developing during adolescence, but the specific physical environmental factors that influence this development remain poorly characterized. One candidate factor is air pollution. Evidence suggests that small particulate matter and NO2 concentrations in the air may negatively impact cognitive development in childhood. We assessed the relationship between neighborhood air pollution and the changes in performance on the n-back task, a test of attention and working memory, in the Adolescent Brain Cognitive Development (ABCD) Study's baseline (ages 9-10) and two-year-follow-up releases (Y2, ages 11-12; n = 5,256). In the behavioral domain, multiple linear regression showed that developmental change in n-back task performance was negatively associated with neighborhood air pollution (β = -.044, t = -3.11, p = .002), adjusted for covariates capturing baseline cognitive performance of the child, their parental income and education, family conflicts, and their neighborhood's population density, crime rate, perceived safety, and Area Deprivation Index (ADI). The strength of the adjusted association for air pollution was similar to parental income, family conflict, and neighborhood ADI. In the neuroimaging domain, we evaluated a previously published youth cognitive composite Connectome-based Predictive Model (ccCPM), and again found that decreased developmental change in the strength of the ccCPM from pre- to early adolescence was associated with neighborhood air pollution (β = -.110, t = -2.69, p = .007), adjusted for the covariates mentioned above and head motion. Finally, we found that the developmental change in ccCPM strength was predictive of the developmental change in n-back performance (r = .157, p < .001), and there was an indirect-only mediation where the effect of air pollution on change in n-back performance was mediated by the change in the ccCPM strength (βindirect effect = -.013, p = .029). In conclusion, neighborhood air pollution is associated with lags in the maturation of youth cognitive performance and decreased strengthening of the brain networks supporting cognitive abilities over time.