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Abstract There is a dearth of statistical models that adequately capture the total signal attributed to whole-brain imaging features. The total signal is often widely distributed across the brain, with individual imaging features exhibiting small effect sizes for predicting neurobehavioral phenotypes. The challenge of capturing the total signal is compounded by the distribution of neurobehavioral data, particularly responses to psychological questionnaires, which often feature zero-inflated, highly skewed outcomes. To close this gap, we have developed a novel Variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes. Our zero-inflated variance (ZIV) estimator estimates the fraction of variance explained (FVE) and the proportion of non-null effects (PNN) from large-scale imaging data. In simulations, ZIV demonstrates superior performance over other linear models. When applied to data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, we found that whole-brain imaging features contribute to a larger FVE for externalizing behaviors compared to internalizing behaviors. Moreover, focusing on features contributing to the PNN, ZIV estimator localized key neurocircuitry associated with neurobehavioral traits. To the best of our knowledge, the ZIV estimator is the first specialized method for analyzing zero-inflated neuroimaging data, enhancing future studies on brain-behavior relationships and improving the understanding of neurobehavioral disorders.

Early adolescent drinking onset is linked to myriad negative consequences. Using the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) baseline to year 8 data, this study (1) leveraged best subsets selection and Cox Proportional Hazards regressions to identify the most robust predictors of adolescent first and regular drinking onset, and (2) examined the clinical utility of drinking onset in forecasting later binge drinking and withdrawal effects. Baseline predictors included youth psychodevelopmental characteristics, cognition, brain structure, family, peer, and neighborhood domains. Participants (N=538) were alcohol-naïve at baseline. The strongest predictors of first and regular drinking onset were positive alcohol expectancies (Hazard Ratios [HRs]=1.67–1.87), easy home alcohol access (HRs=1.62–1.67), more parental solicitation (e.g., inquiring about activities; HRs=1.72–1.76), and less parental control and knowledge (HRs=.72–.73). Robust linear regressions showed earlier first and regular drinking onset predicted earlier transition into binge and regular binge drinking (βs=0.57–0.95). Zero-inflated Poisson regressions revealed that delayed first and regular drinking increased the likelihood (Incidence Rate Ratios [IRR]=1.62 and IRR=1.29, respectively) of never experiencing withdrawal. Findings identified behavioral and environmental factors predicting temporal paths to youthful drinking, dissociated first from regular drinking initiation, and revealed adverse sequelae of younger drinking initiation, supporting efforts to delay drinking onset.

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 HBCD Study aims to reflect the sociodemographic diversity of pregnant individuals in the U.S. The study will also oversample individuals who use substances during pregnancy and enroll similar individuals who do not use to allow for generalizable inferences of the impact of prenatal substance use on trajectories of child development. Without probability sampling or a randomization-based design, the study requires innovation during enrollment, close monitoring of group differences, and rigorous evaluation of external and internal validity across the enrollment period. In this article, we discuss the HBCD Study recruitment and enrollment data collection processes and potential analytic strategies to account for sources of heterogeneity and potential bias. First, we introduce the adaptive design and enrollment monitoring indices to assess and enhance external and internal validity. Second, we describe the visit schedule for in-person and remote data collection where dyads are randomly assigned to visit windows based on a jittered design to optimize longitudinal trajectory estimation. Lastly, we provide an overview of analytic procedures planned for estimating trajectories.

Dopaminergic projections from the ventral tegmental area (VTA) to limbic regions play a key role in the initiation and maintenance of substance use; however, the relationship between mesolimbic resting-state functional connectivity (RSFC) and alcohol use during development remains unclear. We examined the associations between alcohol use and VTA RSFC to subcortical structures in 796 participants (12–21 years old at baseline, 51 % female) across 9 waves of longitudinal data from the National Consortium on Alcohol and Neurodevelopment in Adolescence. Linear mixed effects models included interactions between age, sex, and alcohol use, and best fitting models were selected using log-likelihood ratio tests. Results demonstrated a positive association between alcohol use and VTA RSFC to the nucleus accumbens. Age was associated with VTA RSFC to the amygdala and hippocampus, and an age-by-alcohol use interaction on VTA-globus pallidus connectivity was driven by a positive association between alcohol and VTA-globus pallidus RSFC in adolescence, but not adulthood. On average, male participants exhibited greater VTA RSFC to the amygdala, nucleus accumbens, caudate, hippocampus, globus pallidus, and thalamus. Differences in VTA RSFC related to age, sex, and alcohol, may inform our understanding of neurobiological risk and resilience for alcohol use and other psychiatric disorders

Heavy alcohol drinking is a major, preventable problem that adversely impacts the physical and mental health of US young adults. Studies seeking drinking risk factors typically focus on young adults who enrolled in 4-year residential college programs (4YCP) even though most high school graduates join the workforce, military, or community colleges. We examined 106 of these understudied young adults (USYA) and 453 4YCPs from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) by longitudinally following their drinking patterns for 8 years from adolescence to young adulthood. All participants were no-to-low drinkers during high school. Whereas 4YCP individuals were more likely to initiate heavy drinking during college years, USYA participants did so later. Using mental health metrics recorded during high school, machine learning forecasted individual-level risk for initiating heavy drinking after leaving high school. The risk factors differed between demographically matched USYA and 4YCP individuals and between sexes. Predictors for USYA drinkers were sexual abuse, physical abuse for girls, and extraversion for boys, whereas 4YCP drinkers were predicted by the ability to recognize facial emotion and, for boys, greater openness. Thus, alcohol prevention programs need to give special consideration to those joining the workforce, military, or community colleges, who make up the majority of this age group.

IntroductionAlcohol expectancies predict subsequent alcohol use and related problems among adolescents, although predictors of alcohol expectancies remain unclear. This study examined the longitudinal association between family conflict, a sociocultural factor strongly implicated in adolescent alcohol use, and positive and negative alcohol expectancies of adolescents of diverse racial/ethnic backgrounds.MethodsData were from the Adolescent Brain Cognitive Development Study 4.0 release, a multisite longitudinal study (N = 6,231, baseline age 9–10). Linear mixed-effects regression, with interactions between race/ethnicity and family conflict, tested the association between family conflict and alcohol expectancies, for each racial/ethnicity (e.g., Black vs. non-Black; White vs. non-White).ResultsInteractions of family conflict with race/ethnicity in predicting negative and positive alcohol expectancies were statistically significant for models testing Black and White adolescents, but not for Asian, Hispanic, and Other. Family conflict at baseline predicted lower negative alcohol expectancy for Black adolescents (B = −.166, p = 0.033) and positive alcohol expectancy for White adolescents (B = 0.71, p = 0.023) at the year 3 follow-up. All models controlled for sex, age, family socioeconomic status, alcohol expectancies at year 1, and family conflict at year 3.ConclusionThe results indicate that family conflict is a potential risk factor for problematic alcohol expectancies for Black and White adolescents. Although we did not directly compare Black and White adolescents, our findings indicate that family conflict may operate differently for Black and White adolescents. Prevention and intervention efforts targeting family conflict may be relevant for different aspects of alcohol expectancies in Black and White families.

Background: Previous investigations that have examined associations between family history (FH) of alcohol/substance use and adolescent brain development have been primarily cross-sectional. Here, leveraging a large population-based sample of youths, we characterized frontal cortical trajectories among 9- to 13-year-olds with (FH+) versus without (FH−) an FH and examined sex as a potential moderator. Methods: We used data from 9710 participants in the Adolescent Brain Cognitive Development (ABCD) Study (release 4.0). FH+ was defined as having ≥1 biological parents and/or ≥2 biological grandparents with a history of alcohol/substance use problems (n = 2433). Our primary outcome was frontal cortical structural measures obtained at baseline (ages 9–11) and year 2 follow-up (ages 11–13). We used linear mixed-effects models to examine the extent to which FH status qualified frontal cortical development over the age span studied. Finally, we ran additional interactions with sex to test whether observed associations between FH and cortical development differed significantly between sexes. Results: For FH+ (vs. FH−) youths, we observed increased cortical thinning from 9 to 13 years across the frontal cortex as a whole. When we probed for sex differences, we observed significant declines in frontal cortical thickness among boys but not girls from ages 9 to 13 years. No associations were observed between FH and frontal cortical surface area or volume. Conclusions: Having a FH+ is associated with more rapid thinning of the frontal cortex across ages 9 to 13, with this effect driven primarily by male participants. Future studies will need to test whether the observed pattern of accelerated thinning predicts future substance use outcomes.

IntroductionThere are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.MethodsWe estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.ResultsThe results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = −0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.DiscussionOur findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.

Abstract With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.

Abstract Background Increased expression of the complement component 4A (C4A) gene is associated with a greater lifetime risk of schizophrenia. In the brain, C4A is involved in synaptic pruning; yet, it remains unclear the extent to which upregulation of C4A alters brain development or is associated with the risk for psychotic symptoms in childhood. Here, we perform a multi-ancestry phenome-wide association study in 7789 children aged 9–12 years to examine the relationship between genetically regulated expression (GREx) of C4A, childhood brain structure, cognition, and psychiatric symptoms. Results While C4A GREx is not related to childhood psychotic experiences, cognition, or global measures of brain structure, it is associated with a localized reduction in regional surface area (SA) of the entorhinal cortex. Furthermore, we show that reduced entorhinal cortex SA at 9–10 years predicts a greater number and severity of psychosis-like events at 1-year and 2-year follow-up time points. We also demonstrate that the effects of C4A on the entorhinal cortex are independent of genome-wide polygenic risk for schizophrenia. Conclusions Our results suggest neurodevelopmental effects of C4A on childhood medial temporal lobe structure, which may serve as a biomarker for schizophrenia risk prior to symptom onset.

IntroductionImpulsivity is a known risk factor for the development of substance use disorders and other psychiatric conditions that is influenced by both genetics and environment. Although research has linked parental mental health to children’s impulsivity, potential mediators of this relationship remain understudied. The current investigation leverages the large national Adolescent Brain Cognitive Development (ABCD) Study to assess the mediating role of family conflict – an important social context for youth development – in the relationship between parental mental health and youth impulsivity.MethodsData were from the first three annual waves of the ABCD study (Baseline N = 11,876 children, Mage = 9.9 years; 48% female; 52% White). Parental mental health conditions were self-reported internalizing, externalizing, and total problems. Youth completed the family conflict scale, and Urgency, Planning (lack of), Perseverance (lack of), Sensation Seeking, and Positive Urgency (UPPS-P) scale to measure impulsivity. To determine if within-family change in conflict from baseline to year 1 explained changes in the strength of relations between baseline parental mental health and year 2 youth impulsivity, longitudinal causal mediation analyses were conducted, controlling for demographic factors (i.e., age, sex, race, household income, parental education, marital status), as well as baseline levels of family conflict and outcomes. Separate mediation models were run for each mental health condition and each UPPS-P subscale.ResultsAbove and beyond bivariate relations, longitudinal mediation models, which included covariates, showed family conflict significantly (ps

Subcortical brain morphometry matures across adolescence and young adulthood, a time when many youth engage in escalating levels of alcohol use. Initial cross-sectional studies have shown alcohol use is associated with altered subcortical morphometry. However, longitudinal evidence of sex-specific neuromaturation and associations with alcohol use remains limited. This project used generalized additive mixed models to examine sex-specific development of subcortical volumes and associations with recent alcohol use, using 7 longitudinal waves (n = 804, 51% female, ages 12–21 at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). A second, independent, longitudinal dataset, with up to four waves of data (n = 467, 43% female, ages 10–18 at baseline), was used to assess replicability. Significant, replicable non-linear normative volumetric changes with age were evident in the caudate, putamen, thalamus, pallidum, amygdala and hippocampus. Significant, replicable negative associations between subcortical volume and alcohol use were found in the hippocampus in all youth, and the caudate and thalamus in female but not male youth, with significant interactions present in the caudate, thalamus and putamen. Findings suggest a structural vulnerability to alcohol use, or a predisposition to drink alcohol based on brain structure, with female youth potentially showing heightened risk, compared to male youth.

Abstract Autism spectrum disorder (ASD) refers to a group of neurodevelopmental disorders which include deficits in behavior, social interaction and communication. ASD has a complex genetic architecture, and it is also influenced by certain environmental exposures. Both types of predisposing factors may be related to immunological mechanisms, involving, for example, immune system genes and infections. Past studies have shown an association between infections occurring during the pregnancy in the mother and increased risk of ASD in the child, an observation which has received recent support from experimental animal studies of ASD-like behavior. The aim of this study was to study the genetic contribution to this effect. We employed genetic correlation analyses across potential ASD subtypes stratified on the basis of maternal pregnancy-related infections within the iPSYCH ASD case-cohort sample, as well as a case-case GWAS. We validated the trends of the genetic correlation analyses observed in our sample using GWAS summary statistics from the PGC ASD study (excluding iPSYCH). The genetic correlation between ASD with a history of maternal pregnancy-related infections and ASD without a history of maternal infections in iPSYCH was r g = 0.3811. We obtained a similar estimate between the former and the PGC ASD phenotype (r g = 0.3997). Both estimates are lower compared to the genetic correlation between ASD without a history of maternal infections and the PGC ASD phenotype (r g = 0.6735), and between ASD with a history of maternal infections occurring only more than 2 months following childbirth and the PGC ASD phenotype (r g = 0.6293). Additionally, we observed genetic variance between the two main ASD phenotypes using summary statistics from the case-case GWAS in iPSYCH (h 2 cc = 0.1059), indicating genome-wide differences between the phenotypes. Our results suggest potentially different etiologies of ASD based on a history of maternal pregnancy-related infections, which may, in part, be genetic. This highlights the relevance of maternal pregnancy-related infections to genetic studies of ASD and provides new insights into the molecular underpinnings of ASD.

Abstract Background Accurate measurement of trajectories in longitudinal studies, considered the gold standard method for tracking functional growth during adolescence, decline in aging, and change after head injury, is subject to confounding by testing experience. Methods We measured change in cognitive and motor abilities over four test sessions (baseline and three annual assessments) in 154 male and 165 female participants (baseline age 12–21 years) from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. At each of the four test sessions, these participants were given a test battery using computerized administration and traditional pencil and paper tests that yielded accuracy and speed measures for multiple component cognitive (Abstraction, Attention, Emotion, Episodic memory, Working memory, and General Ability) and motor (Ataxia and Speed) functions. The analysis aim was to dissociate neurodevelopment from testing experience by using an adaptation of the twice-minus-once tested method, which calculated the difference between longitudinal change (comprising developmental plus practice effects) and practice-free initial cross-sectional performance for each consecutive pairs of test sessions. Accordingly, the first set of analyses quantified the effects of learning (i.e., prior test experience) on accuracy and after speed domain scores. Then developmental effects were determined for each domain for accuracy and speed having removed the measured learning effects. Results The greatest gains in performance occurred between the first and second sessions, especially in younger participants, regardless of sex, but practice gains continued to accrue thereafter for several functions. For all 8 accuracy composite scores, the developmental effect after accounting for learning was significant across age and was adequately described by linear fits. The learning-adjusted developmental effects for speed were adequately described by linear fits for Abstraction, Emotion, Episodic Memory, General Ability, and Motor scores, although a nonlinear fit was better for Attention, Working Memory, and Average Speed scores. Conclusion Thus, what appeared as accelerated cognitive and motor development was, in most cases, attributable to learning. Recognition of the substantial influence of prior testing experience is critical for accurate characterization of normal development and for developing norms for clinical neuropsychological investigations of conditions affecting the brain.

Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework’s ability to reliably capture brain-behavior relationships across 3 cognitive scores – general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.

Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.

How the tissue composition of the human brain is determined remains unclear. Here, the authors apply advanced analyses to two large imaging genetic datasets to identify and validate genetic loci that simultaneously influence multiple brain regions.

Schizophrenia is a complex disorder where individuals experience different symptoms and outcomes that can be captured by patterns in other diagnoses. Here the authors use computational approaches to summarize these patterns and suggest they are associated with genetic and environmental exposure.

Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242–496, replication rate: 96–97%) in independent data when compared with the established min-P approach (# replicated loci: 26–55, replication rate: 91–93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

Background: Although a relatively large body of research has identified multiple factors associated with adolescent substance use, less is known about earlier substance-related factors during preadolescence, including curiosity to use substances. The present study examined individual-, peer-, and parent-level domains pertaining to substance use and how these domains vary by sociodemographic subgroups and substance type. Methods: Participants were 11,864 9- and 10-year-olds from the baseline sample of the Adolescent Brain Cognitive Development (ABCD) Study. Youth-reported measures were curiosity to use substances and perceived peer substance use. Parent-reported measures were availability of and rules about substances. Generalized logistic mixed models (GLMM) were used to compare these measures across alcohol, nicotine, and marijuana and across sociodemographic subgroupings (sex, race/ethnicity, household income, and family history of alcohol problems). GLMM was then used to examine predictors of curiosity to use by substance type. Results: The most striking descriptive differences were found between race/ethnicity and income categories (e.g., positive associations between greater income and greater availability of alcohol). In multivariable analyses, greater curiosity to use alcohol was associated with being male, higher household income, perceived peer alcohol use, and easy alcohol availability; greater curiosity to use nicotine was associated with being male, perceived peer cigarette use, easy availability of cigarettes, and no parental rules about cigarette use. Conclusions: This study identified substance use-related individual-, peer-, and parent-level factors among a diverse, national sample. Findings highlight the importance of considering sociodemographic and substance-specific variability and may help identify risk and protective factors preceding adolescent substance use.

Neuroticism as a personality trait represents a heritable risk for psychiatric disorders. The polygenic risk score for neuroticism (N-PRS) is used to study genetic vulnerability to neuroticism. The current data present the association of the genetic risk for neuroticism to neural reward-punishment processing using functional magnetic resonance imaging. N-PRS was computed based on the individual's genotype information and a genome-wide association study on the UK Biobank data. While individuals performed a monetary incentive delay task, their neural activations for upcoming incentives (reward: gain, punishment: loss) were measured in blood oxygen level dependent (BOLD) signals during the delay phase. Multivariate ANCOVAs were used to analyze BOLD signals for finding the association between N-PRS and reward-punishment processing by the incentive valence (Related research article: H. Park, K.L. Forthman, R. Kuplicki, T.A. Victor, Tulsa 1000 Investigators, H.W. Yeh, W.K. Thompson, M.P. Paulus, Polygenic risk for neuroticism modulates response to gains and losses in the amygdala and caudate: evidence from a clinical cohort. J. Affect. Disord. 293 (2021) 124–132. https://doi.org/10.1016/j.jad.2021.06.016). These data can be used as reference data for future studies examining the role of the genetic propensity for personality traits in the context of psychiatric disorders.

Summary: Autism spectrum disorder (ASD) is diagnosed three to four times more frequently in males than in females. Genetic studies of rare variants support a female protective effect (FPE) against ASD. However, sex differences in common inherited genetic risk for ASD are less studied, particularly within families. Leveraging the Danish iPSYCH resource, we found siblings of female ASD cases (n = 1,707) had higher rates of ASD than siblings of male ASD cases (n = 6,270; p

Abstract Background Infections are a major disease burden worldwide. While they are caused by external pathogens, host genetics also plays a part in susceptibility to infections. Past studies have reported diverse associations between human leukocyte antigen (HLA) alleles and infections, but many were limited by small sample sizes and/or focused on only one infection. Methods We performed an immunogenetic association study examining 13 categories of severe infection (bacterial, viral, central nervous system, gastrointestinal, genital, hepatitis, otitis, pregnancy-related, respiratory, sepsis, skin infection, urological and other infections), as well as a phenotype for having any infection, and seven classical HLA loci (HLA-A, B, C, DPB1, DQA1, DQB1 and DRB1). Additionally, we examined associations between infections and specific alleles highlighted in our previous studies of psychiatric disorders and autoimmune disease, as these conditions are known to be linked to infections. Results Associations between HLA loci and infections were generally not strong. Highlighted associations included associations between DQB1*0302 and DQB1*0604 and viral infections (P = 0.002835 and P = 0.014332, respectively), DQB1*0503 and sepsis (P = 0.006053), and DQA1*0301 with “other” infections (a category which includes infections not included in our main categories e.g. protozoan infections) (P = 0.000369). Some HLA alleles implicated in autoimmune diseases showed association with susceptibility to infections, but the latter associations were generally weaker, or with opposite trends (in the case of HLA-C alleles, but not with alleles of HLA class II genes). HLA alleles associated with psychiatric disorders did not show association with susceptibility to infections. Conclusions Our results suggest that classical HLA alleles do not play a large role in the etiology of severe infections. The discordant association trends with autoimmune disease for some alleles could contribute to mechanistic theories of disease etiology.

A polygenic hazard score (PHS1) improves prostate cancer screening accuracy in European patients. Here, the authors test the performance of a version compatible with OncoArray genotypes (PHS2) in a multi-ethnic dataset and find that it risk-stratifies men for any, aggressive, and fatal prostate cancer.

Trait stability of measures is an essential requirement for individual differences research. Functional MRI has been increasingly used in studies that rely on the assumption of trait stability, such as attempts to relate task related brain activation to individual differences in behavior and psychopathology. However, recent research using adult samples has questioned the trait stability of task-fMRI measures, as assessed by test-retest correlations. To date, little is known about trait stability of task fMRI in children. Here, we examined within-session reliability and long-term stability of individual differences in task-fMRI measures using fMRI measures of brain activation provided by the adolescent brain cognitive development (ABCD) Study Release v4.0 as an individual's average regional activity, using its tasks focused on reward processing, response inhibition, and working memory. We also evaluated the effects of factors potentially affecting reliability and stability. Reliability and stability (quantified as the ratio of non-scanner related stable variance to all variances) was poor in virtually all brain regions, with an average value of 0.088 and 0.072 for short term (within-session) reliability and long-term (between-session) stability, respectively, in regions of interest (ROIs) historically-recruited by the tasks. Only one reliability or stability value in ROIs exceeded the ‘poor’ cut-off of 0.4, and in fact rarely exceeded 0.2 (only 4.9%). Motion had a pronounced effect on estimated reliability/stability, with the lowest motion quartile of participants having a mean reliability/stability 2.5 times higher (albeit still ‘poor’) than the highest motion quartile. Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations. This essential issue urgently needs to be addressed through optimization of task design, scanning parameters, data acquisition protocols, preprocessing pipelines, and data denoising methods.

The Adolescent Brain Cognitive Development (ABCD)SM study aims to retain a demographically diverse sample of youth and one parent across 21 sites throughout its 10-year protocol while minimizing selective (systematic) attrition. To evaluate the effectiveness of these efforts, the ABCD Retention Workgroup (RW) has employed a data-driven approach to examine, track, and intervene via three key metrics: (1) which youth completed visits late; (2) which youth missed visits; and (3) which youth withdrew from the study. The RW actively examines demographic (race, education level, family income) and site factors (visit satisfaction, distance from site, and enrollment in ancillary studies) to strategize efforts that will minimize disengagement and loss of participating youth and parents. Data showed that the most robust primary correlates of late visits were distance from study site, race, and parental education level. Race, lower parental education level, parental employment status, and lower family income were associated with higher odds of missed visits, while being enrolled in one of the ancillary studies was associated with lower odds of missed visits. Additionally, parents who were primary Spanish speakers withdrew at slightly higher rates. These findings provide insight into future targets for proactive retention efforts by the ABCD RW.

Influential investigations of postmortem human brain tissue showed regional differences in tissue properties at early phases of development, such as between prefrontal and primary sensory cortical regions. Large-scale neuroimaging studies enable characterization of age-related trajectories with much denser sampling of cortical regions, assessment ages, and demographic variables than postmortem tissue analyses, but no single imaging measure perfectly captures what is measured with histology. Using publicly available data from the Pediatric Imaging, Neurocognition, and Genetics (PING) study, including 951 participants with ages ranging from 3 to 21 years, we characterized cortical regional variability in developmental trajectories of multimodal brain imaging measures. Multivariate analyses integrated morphometric and microstructural cortical surface measures. To replicate foundational histological work showing delayed synapse elimination in middle frontal gyrus relative to primary sensory areas, we tested whether developmental trajectories differ between prefrontal and visual or auditory cortex. We extended this to a whole-cortex analysis of interregional differences, producing cortical parcellations with maximally different developmental trajectories. Consistent with the general conclusions of postmortem analyses, our imaging results suggest that prefrontal regions show a protracted period of greater developmental change; however, they also illustrate the challenges of drawing conclusions about the relative maturational phases of different brain regions.

Regional brain morphology has a complex genetic architecture. Here the authors present MOSTest, a multivariate statistical framework, apply it to UK Biobank data, and discover hundreds of loci associated with regional brain morphology.

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9–14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

Our brain is constantly shaped by our immediate environments, and while some effects are transient, some have long-term consequences. Therefore, it is critical to identify which environmental risks have evident and long-term impact on brain development. To expand our understanding of the environmental context of each child, the Adolescent Brain Cognitive Development (ABCD) Study® incorporates the use of geospatial location data to capture a range of individual, neighborhood, and state level data based on the child’s residential location in order to elucidate the physical environmental contexts in which today’s youth are growing up. We review the major considerations and types of geocoded information incorporated by the Linked External Data Environmental (LED) workgroup to expand on the built and natural environmental constructs in the existing and future ABCD Study data releases. Understanding the environmental context of each youth furthers the consortium’s mission to understand factors that may influence individual differences in brain development, providing the opportunity to inform public policy and health organization guidelines for child and adolescent health.

The Adolescent Brain Cognitive Development (ABCD) Study of 11,880 youth incorporates a comprehensive range of measures assessing predictors and outcomes related to mental health across childhood and adolescence in participating youth, as well as information about family mental health history. We have previously described the logic and content of the mental health assessment battery at Baseline and 1-year follow-up. Here, we describe changes to that battery and issues and clarifications that have emerged, as well as additions to the mental health battery at the 2-, 3-, 4-, and 5-year follow-ups. We capitalize on the recent release of longitudinal data for caregiver and youth report of mental health data to evaluate trajectories of dimensions of psychopathology as a function of demographic factors. For both caregiver and self-reported mental health symptoms, males showed age-related decreases in internalizing and externalizing symptoms, while females showed an increase in internalizing symptoms with age. Multiple indicators of socioeconomic status (caregiver education, family income, financial adversity, neighborhood poverty) accounted for unique variance in both caregiver and youth-reported externalizing and internalizing symptoms. These data highlight the importance of examining developmental trajectories of mental health as a function of key factors such as sex and socioeconomic environment.

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children's health in the United States. A cohort of n = 11,880 children aged 9–10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.

Physical health in childhood is crucial for neurobiological as well as overall development, and can shape long-term outcomes into adulthood. The landmark, longitudinal Adolescent Brain Cognitive Development StudySM (ABCD study®), was designed to investigate brain development and health in almost 12,000 youth who were recruited when they were 9–10 years old and will be followed through adolescence and early adulthood. The overall goal of this paper is to provide descriptive analyses of physical health measures in the ABCD study at baseline, including but not limited to sleep, physical activity and sports involvement, and body mass index. Further this summary will describe how physical health measures collected from the ABCD cohort compare with current normative data and clinical guidelines. We propose this data set has the potential to facilitate clinical recommendations and inform national standards of physical health in this age group. This manuscript will also provide important information for ABCD users and help guide analyses investigating physical health including new avenues for health disparity research as it pertains to adolescent and young adult development.

Background: Self-reported physical activity is often inaccurate. Wearable devices utilizing multiple sensors are now widespread. The aim of this study was to determine acceptability of Fitbit Charge HR for children and their families, and to determine best practices for processing its objective data. Methods: Data were collected via Fitbit Charge HR continuously over the course of 3 weeks. Questionnaires were given to each child and their parent/guardian to determine the perceived usability of the device. Patterns of data were evaluated and best practice inclusion criteria recommended. Results: Best practices were established to extract, filter, and process data to evaluate device wear, r and establish minimum wear time to evaluate behavioral patterns. This resulted in usable data available from 137 (89%) of the sample. Conclusions: Activity trackers are highly acceptable in the target population and can provide objective data over longer periods of wear. Best practice inclusion protocols that reflect physical activity in youth are provided.

Post-traumatic stress disorder (PTSD) is a common mental health problem. Here, the authors report a GWAS from the Psychiatric Genomics Consortium in which they identify two risk loci in European ancestry and one locus in African ancestry individuals and find that PTSD is genetically correlated with several other psychiatric traits.

Gestational duration depends on both maternal and fetal genetic influences. Here, the authors perform a fetal genome-wide association meta-analysis and find that a locus on 2q13 is associated with pregnancy duration and further show that the lead SNP rs7594852 changes the binding properties of transcriptional repressor HIC1.

To better understand the phenotypic relationships of complex traits it is also important to understand their genetic overlap. Here, Frei et al. develop MiXeR which uses GWAS summary statistics to evaluate the polygenic overlap between two traits irrespective of their genetic correlation.

Abstract Alzheimer’s disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer’s dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer’s Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only.

Schizophrenia (SCZ) risk is influenced by genetic and environmental factors. Here, the authors develop a statistical method for analyzing gene-by-environment effects in SCZ risk across Denmark with fine spatial resolution.

Significant immunological, physical and neurological benefits of breastfeeding in infancy are well-established, but to what extent these gains persist into later childhood remain uncertain. This study examines the association between breastfeeding duration and subsequent domain-specific cognitive performance in a diverse sample of 9–10-year-olds enrolled in the Adolescent Brain Cognitive Development (ABCD) Study®. The analyses included 9,116 children that attended baseline with their biological mother and had complete neurocognitive and breastfeeding data. Principal component analysis was conducted on data from an extensive battery of neurocognitive tests using varimax-rotation to extract a three-component model encompassing General Ability, Executive Functioning, and Memory. Propensity score weighting using generalized boosted modeling was applied to balance the distribution of observed covariates for children breastfed for 0, 1–6, 7–12, and more than 12 months. Propensity score-adjusted linear regression models revealed significant association between breastfeeding duration and performance on neurocognitive tests representing General Ability, but no evidence of a strong association with Executive Function or Memory. Benefits on General Ability ranged from a 0.109 (1–6 months) to 0.301 (>12 months) standardized beta coefficient difference compared to those not breastfed. Results indicate clear cognitive benefits of breastfeeding but that these do not generalize to all measured domains, with implications for public health policy as it pertains to nutrition during infancy.

AimTo examine individual variability between perceived physical features and hormones of pubertal maturation in 9–10-year-old children as a function of sociodemographic characteristics.MethodsCross-sectional metrics of puberty were utilized from the baseline assessment of the Adolescent Brain Cognitive Development (ABCD) Study—a multi-site sample of 9–10 year-olds (n = 11,875)—and included perceived physical features via the pubertal development scale (PDS) and child salivary hormone levels (dehydroepiandrosterone and testosterone in all, and estradiol in females). Multi-level models examined the relationships among sociodemographic measures, physical features, and hormone levels. A group factor analysis (GFA) was implemented to extract latent variables of pubertal maturation that integrated both measures of perceived physical features and hormone levels.ResultsPDS summary scores indicated more males (70%) than females (31%) were prepubertal. Perceived physical features and hormone levels were significantly associated with child’s weight status and income, such that more mature scores were observed among children that were overweight/obese or from households with low-income. Results from the GFA identified two latent factors that described individual differences in pubertal maturation among both females and males, with factor 1 driven by higher hormone levels, and factor 2 driven by perceived physical maturation. The correspondence between latent factor 1 scores (hormones) and latent factor 2 scores (perceived physical maturation) revealed synchronous and asynchronous relationships between hormones and concomitant physical features in this large young adolescent sample.ConclusionsSociodemographic measures were associated with both objective hormone and self-report physical measures of pubertal maturation in a large, diverse sample of 9–10 year-olds. The latent variables of pubertal maturation described a complex interplay between perceived physical changes and hormone levels that hallmark sexual maturation, which future studies can examine in relation to trajectories of brain maturation, risk/resilience to substance use, and other mental health outcomes.

While low socioeconomic status (SES) introduces risk for developmental outcomes among children, there are an array of proximal processes that determine the ecologies and thus the lived experiences of children. This study examined interrelations between 22 proximal measures in the economic, psychosocial, physiological, and perinatal ecologies of children, in association with brain structure and cognitive performance in a diverse sample of 8,158 9–10-year-old children from the Adolescent Brain Cognitive Development (ABCD) study. SES was measured by the income-to-needs ratio (INR), a measure used by federal poverty guidelines. Within the ABCD study, in what is one of the largest and most diverse cohorts of children studied in the United States, we replicate associations of low SES with lower total cortical surface area and worse cognitive performance. Associations between low SES (

The ability to effectively suppress motor response tendencies is essential for focused and goal-directed behavior. Here, we tested the hypothesis that developmental improvement in the ability to cancel a motor response is reflected by maturational changes in the white matter underlying the right presupplementary motor area (preSMA) and posterior inferior frontal gyrus (IFG), two cortical key areas of the fronto-basal ganglia “stopping” network. Eighty-eight typically-developing children and adolescents, aged 7–19 years, were longitudinally assessed with the stop-signal task (SST) and diffusion tensor imaging (DTI) of the brain over a period of six years. Participants were examined from two to nine times with an average of 6.6 times, resulting in 576 SST-DTI datasets. We applied tract-based spatial statistics to extract mean fractional anisotropy (FA) from regions-of-interest in the white matter underlying the right IFG (IFGFA) and right preSMA (preSMAFA) at each time point. Motor response cancelation performance, estimated with the stop-signal reaction time (SSRT), improved with age. Initially well performing children plateaued around the age of 11 years, while initially poor performers caught up at the age of 13–14 years. White matter microstructure continued to mature across the investigated age range. Males generally displayed linear maturational trajectories, while females displayed more curvilinear trajectories that leveled off around 12–14 years of age. Maturational increases in right preSMAFA but not right IFGFA were associated with developmental improvements in SSRT. This association differed depending on the mean right preSMAFA across the individual maturational trajectory. Children with lower mean right preSMAFA exhibited poorer SSRT performance at younger ages but steeper developmental trajectories of SSRT improvement. Children with higher mean right preSMAFA exhibited flatter trajectories of SSRT improvement along with faster SSRT already at the first assessments. The results suggest that no further improvement in motor response cancellation is achieved once a certain level of maturity in the white matter underlying the right preSMA is reached. Similar dynamics may apply to other behavioral read-outs and brain structures and, thus, need to be considered in longitudinal MRI studies designed to map brain structural correlates of behavioral changes during development.

Biospecimen collection in the Adolescent Brain Cognitive Development (ABCD) study – of hair samples, shed deciduous (baby) teeth, and body fluids – will serve dual functions of screening for study eligibility, and providing measures of biological processes thought to predict or correlate with key study outcomes on brain and cognitive development. Biosamples are being collected annually to screen for recency of drug use prior to the neuroimaging or cognitive testing visit, and to store for the following future studies: (1) on the effects of exposure to illicit and recreational drugs (including alcohol and nicotine); (2) of pubertal hormones on brain and cognitive developmental trajectories; (3) on the contribution of genomics and epigenomics to child and adolescent development and behavioral outcomes; and (4) with pre- and post-natal exposure to environmental neurotoxicants and drugs of abuse measured from novel tooth analyses. The present manuscript describes the rationales for inclusion and selection of the specific biospecimens, methodological considerations for each measure, future plans for assessment of biospecimens during follow-up visits, and preliminary ABCD data to illustrate methodological considerations. Keywords: Biospecimens, ABCD study, Substance use, Gonadal hormones, Genetics, Environmental exposures

Abstract Background There is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr). Results We propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power. Conclusions With our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS.

Abstract Introduction We characterize long‐term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative. Methods We apply a latent time joint mixed‐effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference. Results We find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid‐β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis. Discussion The latent time joint mixed‐effects model can (1) uncover long‐term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject‐specific prognostic estimates of the time until onset of symptoms.

Longitudinal study provides a robust method for tracking developmental trajectories. Yet inherent problems of retesting pose challenges in distinguishing biological developmental change from prior testing experience. We examined factors potentially influencing change scores on 16 neuropsychological test composites over 1 year in 568 adolescents in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) project. The twice-minus-once-tested method revealed that performance gain was mainly attributable to testing experience (practice) with little contribution from predicted developmental effects. Group mean practice slopes for 13 composites indicated that 60% to ∼100% variance was attributable to test experience; General Ability accuracy showed the least practice effect (29%). Lower baseline performance, especially in younger participants, was a strong predictor of greater gain. Contributions from age, sex, ethnicity, examination site, socioeconomic status, or family history of alcohol/substance abuse were nil to small, even where statistically significant. Recognizing that a substantial proportion of change in longitudinal testing, even over 1-year, is attributable to testing experience indicates caution against assuming that performance gain observed during periods of maturation necessarily reflects development. Estimates of testing experience, a form of learning, may be a relevant metric for detecting interim influences, such as alcohol use or traumatic episodes, on behavior. Keywords: Cognitive development, Motor development, Longitudinal, Alcohol, Adolescence, Practice effects

The Adolescent Brain Cognitive Development (ABCD) study is poised to be the largest single-cohort long-term longitudinal study of neurodevelopment and child health in the United States. Baseline data on N= 4521 children aged 9–10 were released for public access on November 2, 2018. In this paper we performed principal component analyses of the neurocognitive assessments administered to the baseline sample. The neurocognitive battery included seven measures from the NIH Toolbox as well as five other tasks. We implemented a Bayesian Probabilistic Principal Components Analysis (BPPCA) model that incorporated nesting of subjects within families and within data collection sites. We extracted varimax-rotated component scores from a three-component model and associated these scores with parent-rated Child Behavior Checklist (CBCL) internalizing, externalizing, and stress reactivity. We found evidence for three broad components that encompass general cognitive ability, executive function, and learning/memory. These were significantly associated with CBCL scores in a differential manner but with small effect sizes. These findings set the stage for longitudinal analysis of neurocognitive and psychopathological data from the ABCD cohort as they age into the period of maximal adolescent risk-taking. Keywords: Adolescence, Neurocognition, NIH toolbox, Principal components analysis, Child behavior checklist, Externalizing, Internalizing, Stress reactivity