Adolescent Brain Cognitive DevelopmentSM Study Research Articles

Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples.

Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~ 100 subjects for structural and resting state MRI. Even with 100 random re-samplings of 100 subjects in discovery, prediction can be adequately powered with 66 subjects in replication for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many research programs and grants.

Prenatal and childhood air pollution exposure, cellular immune biomarkers, and brain connectivity in early adolescents

IntroductionAmbient air pollution is a neurotoxicant with hypothesized immune-related mechanisms. Adolescent brain structural and functional connectivity may be especially vulnerable to ambient pollution due to the refinement of large-scale brain networks during this period, which vary by sex and have important implications for cognitive, behavioral, and emotional functioning. In the current study we explored associations between air pollutants, immune markers, and structural and functional connectivity in early adolescence by leveraging cross-sectional sex-stratified data from the Adolescent Brain Cognitive Development℠ Study®. MethodsPollutant concentrations of fine particulate matter, nitrogen dioxide, and ozone were assigned to each child's primary residential address during the prenatal period and childhood (9-10 years-old) using an ensemble-based modeling approach. Data collected at 11-13 years-old included resting-state functional connectivity of the default mode, frontoparietal, and salience networks and limbic regions of interest, intracellular directional and isotropic diffusion of available white matter tracts, and markers of cellular immune activation. Using partial least squares correlation, a multivariate data-driven method that identifies important variables within latent dimensions, we investigated associations between 1) pollutants and structural and functional connectivity, 2) pollutants and immune markers, and 3) immune markers and structural and functional connectivity, in each sex separately. ResultsAir pollution exposure was related to white matter intracellular directional and isotropic diffusion at ages 11–13 years, but the direction of associations varied by sex. There were no associations between pollutants and resting-state functional connectivity at ages 11–13 years. Childhood exposure to nitrogen dioxide was negatively correlated with white blood cell count in males. Immune biomarkers were positively correlated with white matter intracellular directional diffusion in females and both white matter intracellular directional and isotropic diffusion in males. Lastly, there was a reliable negative correlation between lymphocyte-to-monocyte ratio and default mode network resting-state functional connectivity in females, as well as a compromised immune marker profile associated with lower resting-state functional connectivity between the salience network and the left hippocampus in males. In post-hoc exploratory analyses, we found that the PLSC-identified white matter tracts and resting-state networks related to processing speed and cognitive control performance from the NIH Toolbox. ConclusionsWe identified novel links between childhood nitrogen dioxide and cellular immune activation in males, and brain network connectivity and immune markers in both sexes. Future research should explore the potentially mediating role of immune activity in how pollutants affect neurological outcomes as well as the potential consequences of immune-related patterns of brain connectivity in service of improved brain health for all.

Interactive effects of participant and stimulus race on cognitive performance in youth: Insights from the ABCD study

An extensive literature shows that race information can impact cognitive performance. Two key findings include an attentional bias to Black racial cues in U.S. samples and diminished recognition of other-race faces compared to same-race faces in predominantly White adult samples. Yet face stimuli are increasingly used in psychological research often unrelated to race (Conley et al., 2018) or without consideration for how race information may influence cognitive performance, especially among developmental participants from different racial groups. In the current study we used open-access data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study® 4.0.1 release to test for developmentally similar other- and same-race effects of Black and White face stimuli on attention, working memory, and recognition memory in 9- and 10-year-old Black and White children (n=5,659) living in the U.S. Black and White children showed better performance when attending to Black versus White faces. We also show an advantage in recognition memory of same-race compared to other-race faces in White children that did not generalize to Black children. Together the findings highlight how race information, even when irrelevant to an experiment, may indirectly lead to misinterpretation of group differences in cognitive performance in children of different racial backgrounds.

Screen use in transgender and gender-questioning adolescents: Findings from the Adolescent Brain Cognitive Development (ABCD) Study

ObjectiveTo assess the association between transgender or gender-questioning identity and screen use (recreational screen time and problematic screen use) in a demographically diverse national sample of early adolescents in the U.S. MethodsWe analyzed cross-sectional data from Year 3 of the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®, N = 9859, 2019–2021, mostly 12–13-years-old). Multiple linear regression analyses estimated the associations between transgender or questioning gender identity and screen time, as well as problematic use of video games, social media, and mobile phones, adjusting for confounders. ResultsIn a sample of 9859 adolescents (48.8% female, 47.6% racial/ethnic minority, 1.0% transgender, 1.1% gender-questioning), transgender adolescents reported 4.51 (95% CI 1.17–7.85) more hours of total daily recreational screen time including more time on television/movies, video games, texting, social media, and the internet, compared to cisgender adolescents. Gender-questioning adolescents reported 3.41 (95% CI 1.16–5.67) more hours of total daily recreational screen time compared to cisgender adolescents. Transgender identification and questioning one’s gender identity was associated with higher problematic social media, video game, and mobile phone use, compared to cisgender identification. ConclusionsTransgender and gender-questioning adolescents spend a disproportionate amount of time engaging in screen-based activities and have more problematic use across social media, video game, and mobile phone platforms.

Genetic variation in endocannabinoid signaling: Anxiety, depression, and threat- and reward-related brain functioning during the transition into adolescence

BackgroundThe endocannabinoid system modulates neural activity throughout the lifespan. In adults, neuroimaging studies link a common genetic variant in fatty acid amide hydrolase (FAAH C385A)—an enzyme that regulates endocannabinoid signaling—to reduced risk of anxiety and depression, and altered threat- and reward-related neural activity. However, limited research has investigated these associations during the transition into adolescence, a period of substantial neurodevelopment and increased psychopathology risk. MethodsThis study included FAAH genotype and longitudinal neuroimaging and neurobehavioral data from 4811 youth (46% female; 9–11 years at Baseline, 11–13 years at Year 2) from the Adolescent Brain Cognitive DevelopmentSM Study. Linear mixed models examined the effects of FAAH and the FAAH x time interaction on anxiety and depressive symptoms, amygdala reactivity to threatening faces, and nucleus accumbens (NAcc) response to happy faces during the emotional n-back task. ResultsA significant main effect of FAAH on depressive symptoms was observed, such that depressive symptoms were lower across both timepoints in those with the AA genotype compared to both AC and CC genotypes (p’s<0.05). There were no significant FAAH x time interactions for anxiety, depression, or neural responses (p’s>0.05). Additionally, there were no main effects of FAAH on anxiety or neural responses (p’s>0.05). ConclusionsOur findings add to emerging evidence linking the FAAH C385A variant to lower risk of psychopathology, and extend these findings to a developmental sample. In particular, we found lower depressive symptoms in FAAH AA genotypes compared to AC and CC genotypes. Future research is needed to characterize the role of the FAAH variant and the eCB system more broadly in neurodevelopment and psychiatric risk.

Bifactor models of psychopathology using multi-informant and multi-instrument dimensional measures in the ABCD study.

Due to limitations of categorical definitions of mental illness, there is a need for quantitative empirical investigations of the dimensional structure of psychopathology. Using exploratory bifactor methods, this study investigated a comprehensive and representative structure of psychopathology in children to better understand how psychotic-like experiences (PLEs), autism spectrum disorder (ASD) symptoms, impulsivity, and sensitivity to reward and punishment, may be integrated into extant general factor models of psychopathology. We used seven child-report and three parent-report instruments capturing diverse mental health symptoms in 11,185 children aged 9-10 from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study. We built on previous modeling frameworks by conducting both split sample and full sample factor analytic approaches that harnessed recent methodological advances in bifactor exploratory structural equation modeling (B-ESEM) to examine a wide range of psychopathology measures not previously integrated into a single analysis. Validity of psychopathology dimensions was examined by investigating associations with sex, age, cognition, imaging measures, and medical service usage. All four factor analytic models showed excellent fit and similar structure within informant. PLEs loaded most highly onto a general psychopathology factor, suggesting that they may reflect non-specific risk for mental illness. ASD symptoms loaded separately from attention/hyperactivity symptoms. Symptoms of impulsivity and sensitivity to reward and punishment loaded onto specific factors, distinct from externalizing and internalizing factors. All identified factors were associated with clinically relevant risk factors, providing preliminary evidence for their construct validity. By integrating diverse child-report and parent-report psychopathology measures for children in the ABCD sample, we deliver data on the quantitative structure of psychopathology for an exceptionally large set of measurements and discuss implications for the field.

Impact of prenatal marijuana exposure on adolescent brain structural and functional connectivity and behavioural outcomes.

There has been an increase in the number of women using marijuana whilst pregnant. Previous studies have shown that children with prenatal marijuana exposure have developmental deficits in memory and decreased attentiveness. In this study, we assess whether prenatal marijuana exposure is associated with alterations in brain regional morphometry and functional and structural connectivity in adolescents. We downloaded behavioural scores and subject image files from the Adolescent Brain Cognitive DevelopmentSM Study. A total of 178 anatomical and diffusion magnetic resonance imaging files (88 prenatal marijuana exposure and 90 age- and gender-matched controls) and 152 resting-state functional magnetic resonance imaging files (76 prenatal marijuana exposure and 76 controls) were obtained. Behavioural metrics based on the parent-reported child behavioural checklist were also obtained for each subject. The associations of prenatal marijuana exposure with 17 subscales of the child behavioural checklist were calculated. We assessed differences in brain morphometry based on voxel-based and surface-based morphometry in adolescents with prenatal marijuana exposure versus controls. We also evaluated group differences in structural and functional connectivity in adolescents for region-to-region connectivity and graph theoretical metrics. Interactions of prenatal marijuana exposure and graph networks were assessed for impact on behavioural scores. Multiple comparison correction was performed as appropriate. Adolescents with prenatal marijuana exposure had greater abnormal or borderline child behavioural checklist scores in 9 out of 17 subscales. There were no significant differences in voxel- or surface-based morphometry, structural connectivity or functional connectivity between prenatal marijuana exposure and controls. However, there were significant differences in prenatal marijuana exposure-graph network interactions with respect to behavioural scores. There were three structural prenatal marijuana exposure-graph network interactions and seven functional prenatal marijuana exposure-graph network interactions that were significantly associated with behavioural scores. Whilst this study was not able to confirm anatomical or functional differences between prenatal marijuana exposure and unexposed pre-adolescent children, there were prenatal marijuana exposure-brain structural and functional graph network interactions that were significantly associated with behavioural scores. This suggests that altered brain networks may underlie behavioural outcomes in adolescents with prenatal marijuana exposure. More work needs to be conducted to better understand the prognostic value of brain structural and functional network measures in prenatal marijuana exposure.

Mapping potential pathways from polygenic liability through brain structure to psychological problems across the transition to adolescence.

We used a polygenic score for externalizing behavior (extPGS) and structural MRI to examine potential pathways from genetic liability to conduct problems via the brain across the adolescent transition. Three annual assessments of child conduct problems, attention-deficit/hyperactivity problems, and internalizing problems were conducted across across 9-13 years of age among 4,475 children of European ancestry in the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®). The extPGS predicted conduct problems in each wave (R2 = 2.0%-2.9%). Bifactor models revealed that the extPRS predicted variance specific to conduct problems (R2 = 1.7%-2.1%), but also variance that conduct problems shared with other measured problems (R2 = .8%-1.4%). Longitudinally, extPGS predicted levels of specific conduct problems (R2 = 2.0%), but not their slope of change across age. The extPGS was associated with total gray matter volume (TGMV; R2 = .4%) and lower TGMV predicted both specific conduct problems (R2 = 1.7%-2.1%) and the variance common to all problems in each wave (R2 = 1.6%-3.1%). A modest proportion of the polygenic liability specific to conduct problems in each wave was statistically mediated by TGMV. Across the adolescent transition, the extPGS predicted both variance specific to conduct problems and variance shared by all measured problems. The extPGS also was associated with TGMV, which robustly predicted conduct problems. Statistical mediation analyses suggested the hypothesis that polygenic variation influences individual differences in brain development that are related to the likelihood of conduct problems during the adolescent transition, justifying new research to test this causal hypothesis.

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.

Multivariate analytical approaches for investigating brain-behavior relationships.

Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection. Additionally, there are few studies illustrating how differences between methods manifest in examining brain-behavior relationships. The purpose of this study was to exemplify how the choice of multivariate approach can change brain-behavior interpretations. We used data from 9,027 9- to 10-year-old children from the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) to examine brain-behavior relationships with three commonly used multivariate approaches: canonical correlation analysis (CCA), partial least squares correlation (PLSC), and partial least squares regression (PLSR). We examined the associations between psychopathology dimensions including general psychopathology, attention-deficit/hyperactivity symptoms, conduct problems, and internalizing symptoms with regional brain volumes. The results of CCA, PLSC, and PLSR showed both consistencies and differences in the relationship between psychopathology symptoms and brain structure. The leading significant component yielded by each method demonstrated similar patterns of associations between regional brain volumes and psychopathology symptoms. However, the additional significant components yielded by each method demonstrated differential brain-behavior patterns that were not consistent across methods. Here we show that CCA, PLSC, and PLSR yield slightly different interpretations regarding the relationship between child psychopathology and brain volume. In demonstrating the divergence between these approaches, we exemplify the importance of carefully considering the method's underlying assumptions when choosing a multivariate approach to delineate brain-behavior relationships.

Childhood obesity is linked to putative neuroinflammation in brain white matter, hypothalamus, and striatum.

Neuroinflammation is both a consequence and driver of overfeeding and weight gain in rodent obesity models. Advances in magnetic resonance imaging (MRI) enable investigations of brain microstructure that suggests neuroinflammation in human obesity. To assess the convergent validity across MRI techniques and extend previous findings, we used diffusion basis spectrum imaging (DBSI) to characterize obesity-associated alterations in brain microstructure in 601 children (age 9-11years) from the Adolescent Brain Cognitive DevelopmentSM Study. Compared with children with normal-weight, greater DBSI restricted fraction (RF), reflecting neuroinflammation-related cellularity, was seen in widespread white matter in children with overweight and obesity. Greater DBSI-RF in hypothalamus, caudate nucleus, putamen, and, in particular, nucleus accumbens, correlated with higher baseline body mass index and related anthropometrics. Comparable findings were seen in the striatum with a previously reported restriction spectrum imaging (RSI) model. Gain in waist circumference over 1 and 2 years related, at nominal significance, to greater baseline RSI-assessed restricted diffusion in nucleus accumbens and caudate nucleus, and DBSI-RF in hypothalamus, respectively. Here we demonstrate that childhood obesity is associated with microstructural alterations in white matter, hypothalamus, and striatum. Our results also support the reproducibility, across MRI methods, of findings of obesity-related putative neuroinflammation in children.

Genetic and Environmental Contributions to Subcortical Gray Matter Microstructure and Volume in the Developing Brain

Using baseline (ages 9–10) and two-year follow-up (ages 11–12) data from monozygotic and dizygotic twins enrolled in the longitudinal Adolescent Brain Cognitive DevelopmentSM Study, we investigated the genetic and environmental contributions to microstructure and volume of nine subcortical gray matter regions. Microstructure was assessed using diffusion MRI data analyzed using restriction spectrum imaging (RSI) and diffusion tensor imaging (DTI) models. The highest heritability estimates (estimate [95% confidence interval]) for microstructure were found using the RSI model in the pallidum (baseline: 0.859 [0.818, 0.889], follow-up: 0.835 [0.787, 0.871]), putamen (baseline: 0.859 [0.819, 0.889], follow-up: 0.874 [0.838, 0.902]), and thalamus (baseline: 0.855 [0.814, 0.887], follow-up: 0.819 [0.769, 0.857]). For volumes the corresponding regions were the caudate (baseline: 0.831 [0.688, 0.992], follow-up: 0.848 [0.701, 1.011]) and putamen (baseline: 0.906 [0.875, 0.914], follow-up: 0.906 [0.885, 0.923]). The subcortical regions displayed high genetic stability (rA = 0.743–1.000) across time and exhibited unique environmental correlations (rE = 0.194–0.610). Individual differences in both gray matter microstructure and volumes can be largely explained by additive genetic effects in this sample.

A Phenome-Wide Association Study (PheWAS) of Late Onset Alzheimer Disease Genetic Risk in Children of European Ancestry at Middle Childhood: Results from the ABCD Study.

Genetic risk for Late Onset Alzheimer Disease (AD) has been associated with lower cognition and smaller hippocampal volume in healthy young adults. However, whether these and other associations are present during childhood remains unclear. Using data from 5556 genomically-confirmed European ancestry youth who completed the baseline session of the ongoing the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®), our phenome-wide association study estimating associations between four indices of genetic risk for late-onset AD (i.e., AD polygenic risk scores (PRS), APOE rs429358 genotype, AD PRS with the APOE region removed (ADPRS-APOE), and an interaction between ADPRS-APOE and APOE genotype) and 1687 psychosocial, behavioral, and neural phenotypes revealed no significant associations after correction for multiple testing (all ps > 0.0002; all pfdr > 0.07). These data suggest that AD genetic risk may not phenotypically manifest during middle-childhood or that effects are smaller than this sample is powered to detect.

Differential Item Functioning in Reports of Delinquent Behavior Between Black and White Youth: Evidence of Measurement Bias in Self-Reports of Arrest in the Adolescent Brain Cognitive Development Study.

Youth self-reports are a mainstay of delinquency assessment; however, making valid inferences about delinquency using these assessments requires equivalent measurement across groups of theoretical interest. We examined whether a brief 10-item delinquency measure exhibited measurement invariance across non-Hispanic White (n = 6,064) and Black (n = 1,666) youth (ages 10-11 years old) in the Adolescent Brain Cognitive Developmentsm Study (ABCD Study®). We detected differential item functioning (DIF) in two items. Black youth were more likely to report being arrested or picked up by police than White youth with the same score on the latent delinquency trait. Although multiple covariates (income, urgency, and callous-unemotional traits) reduced mean-level difference in overall delinquency, they were generally unrelated to the DIF in the Arrest item. However, the DIF in the Arrest item was reduced in size and no longer significant after adjusting for neighborhood safety. Results illustrate the importance of considering measurement invariance when using self-reported delinquency scores to draw inferences about group differences, and the utility of measurement invariance analyses for helping to identify mechanisms that contribute to group differences generally.

Heritability Estimation of Cognitive Phenotypes in the ABCD Study® Using Mixed Models

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample.

Executive Network Activation Moderates the Association between Neighborhood Threats and Externalizing Behavior in Youth.

Neighborhood threats can increase risk for externalizing problems, including aggressive, oppositional, and delinquent behavior. Yet, there is substantial variability in how youth respond to neighborhood threats. Difficulty with cognitive functioning, particularly in the face of emotional information, may increase risk for externalizing in youth who live in neighborhoods with higher threats. However, little research has examined: 1) associations between neighborhood threats and executive networks involved in cognitive functioning or 2) whether executive networks may amplify risk for externalizing in the context of neighborhood threats. Further, most research on neighborhood threats does not account for youth's experiences in other social contexts. Utilizing the large, sociodemographically diverse cohort of youth (ages 9-10) included in the Adolescent Brain Cognitive DevelopmentSM Study, we identified four latent profiles of youth based on threats in their neighborhoods, families, and schools: low threat in all contexts, elevated family threat, elevated neighborhood threat, and elevated threat in all contexts. The elevated neighborhood threat and elevated all threat profiles showed lower behavioral performance on an emotional n-back task relative to low threat and elevated family threat profiles. Lower behavioral performance in the elevated neighborhood threat profile specifically was paralleled by lower executive network activity during a cognitive challenge. Moreover, among youth with lower executive network activity, higher probability of membership in the elevated neighborhood threat profile was associated with higher externalizing. Together, these results provide evidence that interactions between threats that are concentrated in youth's neighborhoods and attenuated executive network function may contribute to risk for externalizing problems.

Why weight? Analytic approaches for large-scale population neuroscience data

Population-based neuroimaging studies that feature complex sampling designs enable researchers to generalize their results more widely. However, several theoretical and analytical questions pose challenges to researchers interested in these data. The following is a resource for researchers interested in using population-based neuroimaging data. We provide an overview of sampling designs and describe the differences between traditional model-based analyses and survey-oriented design-based analyses. To elucidate key concepts, we leverage data from the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), a population-based sample of 11,878 9–10-year-olds in the United States. Analyses revealed modest sociodemographic discrepancies between the target population of 9–10-year-olds in the U.S. and both the recruited ABCD sample and the analytic sample with usable structural and functional imaging data. In evaluating the associations between socioeconomic resources (i.e., constructs that are tightly linked to recruitment biases) and several metrics of brain development, we show that model-based approaches over-estimated the associations of household income and under-estimated the associations of caregiver education with total cortical volume and surface area. Comparable results were found in models predicting neural function during two fMRI task paradigms. We conclude with recommendations for ABCD Study® users and users of population-based neuroimaging cohorts more broadly.

Longitudinal assessment of brain structure and behaviour in youth with rapid weight gain: Potential contributing causes and consequences.

Independent of weight status, rapid weight gain has been associated with underlying brain structure variation in regions associated with food intake and impulsivity among pre-adolescents. Yet, we lack clarity on how developmental maturation coincides with rapid weight gain and weight stability. We identified brain predictors of 2-year rapid weight gain and its longitudinal effects on brain structure and impulsivity in the Adolescent Brain Cognitive DevelopmentSM Study®. Youth were categorized as Healthy Weight/Weight Stable (WSHW , n=527) or Weight Gainers (WG, n=221, >38lbs); 63% of the WG group were healthy weight at 9-to-10-years-old. A fivefold cross-validated logistic elastic-net regression revealed that rapid weight gain was associated with structural variation amongst 39 brain features at 9-to-10-years-old in regions involved with executive functioning, appetitive control and reward sensitivity. Two years later, WG youth showed differences in change over time in several of these regions and performed worse on measures of impulsivity. These findings suggest that brain structure in pre-adolescence may predispose some to rapid weight gain and that weight gain itself may alter maturational brain change in regions important for food intake and impulsivity. Behavioural interventions that target inhibitory control may improve trajectories of brain maturation and facilitate healthier behaviours.

Working memory and reaction time variability mediate the relationship between polygenic risk and ADHD traits in a general population sample

Endophenotypes are heritable and quantifiable traits indexing genetic liability for a disorder. Here, we examined three potential endophenotypes, working memory function, response inhibition, and reaction time variability, for attention-deficit hyperactivity disorder (ADHD) measured as a dimensional latent trait in a large general population sample derived from the Adolescent Brain Cognitive DevelopmentSM Study. The genetic risk for ADHD was estimated using polygenic risk scores (PRS) whereas ADHD traits were quantified as a dimensional continuum using Bartlett factor score estimates, derived from Attention Problems items from the Child Behaviour Checklist and Effortful Control items from the Early Adolescent Temperament Questionnaire-Revised. The three candidate cognitive endophenotypes were quantified using task-based performance measures. Higher ADHD PRSs were associated with higher ADHD traits, as well as poorer working memory performance and increased reaction time variability. Lower working memory performance, poorer response inhibition, and increased reaction time variability were associated with more pronounced ADHD traits. Working memory and reaction time variability partially statistically mediated the relationship between ADHD PRS and ADHD traits, explaining 14% and 16% of the association, respectively. The mediation effect was specific to the genetic risk for ADHD and did not generalise to genetic risk for four other major psychiatric disorders. Together, these findings provide robust evidence from a large general population sample that working memory and reaction time variability can be considered endophenotypes for ADHD that mediate the relationship between ADHD PRS and ADHD traits.

The ABCD stop signal data: Response to Bissett et al.

This paper responds to a recent critique by Bissett et al. of the fMRI Stop task used in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®). The critique focuses primarily on a task design feature related to race model assumptions (i.e., that the Go and Stop processes are fully independent). In response, we note that the race model is quite robust against violations of its assumptions. Most importantly, while Bissett raises conceptual concerns with the task we focus here on analyzes of the task data and conclude that the concerns appear to have minimal impact on the neuroimaging data (the validity of which do not rely on race model assumptions) and have far less of an impact on the performance data than the critique suggests. We note that Bissett did not apply any performance-based exclusions to the data they analyzed, a number of the trial coding errors they flagged were already identified and corrected in ABCD annual data releases, a number of their secondary concerns reflect sensible design decisions and, indeed, their own computational modeling of the ABCD Stop task suggests the problems they identify have just a modest impact on the rank ordering of individual differences in subject performance.