Scientific Publications

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We evaluated whether relationships between area deprivation (ADI), body mass index (BMI) and brain structure (e.g., cortical thickness, subcortical volume) during preadolescence supported the immunologic model of self-regulation failure (NI) and/or neuronal stress (NS) theories of overeating. The NI theory proposes that ADI causes structural alteration in the brain due to the neuroinflammatory effects of overeating unhealthy foods. The NS theory proposes that ADI-related stress negatively impacts brain structure, which causes stress-related overeating and subsequent obesity.

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The aim of this study was to determine the extent to which socioeconomic characteristics of the home and neighborhood are associated with racial inequalities in brain outcomes.

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Autism with co-occurring exceptional cognitive ability is often accompanied by severe internalizing symptoms and feelings of inadequacy. Whether cognitive ability also translates into greater risk for suicidal ideation is unclear. To investigate this urgent question, we examined two samples of high-ability autistic individuals for factors that were predictive of suicidal ideation. In the first sample (N = 1,074 individuals seen at a clinic specializing in gifted/talented youth), we observed a striking excess of parent-reported suicidal ideation in autistic individuals with IQ ≥ 120 (Odds Ratio = 5.9, p=0.0007). In a separate sample of SPARK participants, we confirmed higher rates of suicidal thoughts compared to non-autistic children from the ABCD cohort (combined N = 16,049, Odds Ratio = 6.8, p<2.2e-16), and further that autistic children with suicidal thoughts had significantly higher cognitive ability (p<2.2e-16) than those without. Elevated polygenic scores (PGS) for cognitive performance were associated with increased suicidal thoughts (N = 1,983, Z=2.16,p=0.03), with PGS for educational attainment trending in the same direction (Z=1.4,p=0.17). Notably, similar results were found in parents of these autistic youth, where higher PGS for educational attainment was associated with increasing thoughts of suicide (N = 736, Z=2.28,p=0.02). Taken together, these results suggest that on a phenotypic and genetic level, increasing cognitive ability is an unexpected risk factor for suicidal ideation in individuals diagnosed with, or at risk for autism.

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Causal explanations for the association of young motherhood with increased risk for child attention-deficit hyperactivity disorder (ADHD) remain unclear.

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Evidence suggests that caffeine use disproportionately impacts sleep functioning among youth with attention-deficit/hyperactivity disorder (ADHD). The present study aimed to examine the association of caffeine use with disrupted sleep, and to test moderating effects of ADHD, by leveraging differences within twin pairs to explore potential quasi-causal (i.e., within-pair) effects.

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With the increasing use of social media and online platforms among adolescents, the relationship between traumatic life events and cyberbullying remains unclear. This study aimed to determine the associations between adverse childhood experiences (ACEs) and cyberbullying victimization among a racially/ethnically and socioeconomically diverse sample of early adolescents.

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Women are more vulnerable to internalizing disorders (e.g., depression and anxiety). This study took an integrative developmental approach to investigate multidimensional factors associated with the emergence of sex differences in internalizing symptoms, using data from the Adolescent Brain Cognitive Development (ABCD) study. Indices of sex hormone levels (dehydroepiandrosterone, testosterone, and estradiol), physical pubertal development, task-based functional brain activity, family conflict, and internalizing symptoms were drawn from the ABCD study’s baseline sample (9- to 10-year-old; N = 11,844). Principal component analysis served as a data-driven dimensionality reduction technique on the internalizing subscales to yield a single robust measure of internalizing symptoms. Moderated mediation analyses assessed whether associations between known risk factors and internalizing symptoms vary by sex. Results revealed direct and indirect effects of physical pubertal development on internalizing symptoms through family conflict across sexes. No effects were found of sex hormone levels or amygdala response to fearful faces on internalizing symptoms. Females did not report overall greater internalizing symptoms relative to males, suggesting that internalizing symptoms have not yet begun to increase in females at this age. Findings provide an essential baseline for future longitudinal research on the endocrine, neurocognitive, and psychosocial factors associated with sex differences in internalizing symptoms.

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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.

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Intraindividual variability (IIV) during cognitive task performance is a key behavioral index of attention and a consistent marker of attention-deficit/hyperactivity disorder. In adults, lower IIV has been associated with anticorrelation between the default mode network (DMN) and dorsal attention network (DAN)-thought to underlie effective allocation of attention. However, whether these behavioral and neural markers of attention are 1) associated with each other and 2) can predict future attention-related deficits has not been examined in a developmental, population-based cohort.

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Suicidal ideation and attempts in youth are a growing health concern, and more data are needed regarding their biological underpinnings. Asthma is a common chronic inflammatory disorder in youth and has been associated with suicidal ideation and attempts in adolescent and adult populations, but data in younger children and early adolescents are lacking. We wished to study associations of asthma with childhood suicidality considering asthma’s potential as a clinically relevant model for childhood chronic immune dysregulation.

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Tobacco use during early adolescence can harm brain development and cause adverse health outcomes. Identifying susceptibility in early adolescence before initiation presents an opportunity for tobacco use prevention.

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Conduct problems are increasingly prevalent in girls and they uniquely predict negative outcomes. Yet, few reliable risk factors for aggression and violence in girls and women have been identified. Although preliminary evidence suggests peer relationships may be central to the development of youth conduct problems, especially in girls, rigorous interactive models of peer risk and protective factors for conduct problems are lacking. Based on 3104 10-13-year-old girls in the Adolescent Brain and Cognitive Development study, we tested the independent associations of separate peer risk factors (i.e., relational aggression victimization, physical aggression victimization, and deviant peer affiliation) with multidimensional conduct problems, including their moderation by peer support. Being the victim of relational aggression, being the victim of physical aggression, and deviant peer affiliation were each positively associated with conduct problems and perpetration of aggression whereas peer support was negatively associated with youth report conduct problems and perpetration of physical aggression. Further, elevated peer support significantly attenuated the association of being the victim of relational aggression with teacher-rated conduct problems. These results highlight the sensitivity of conduct problems to peer risk factors and suggest that peer support designates important configurations of risk that differentially relate to conduct problems in girls.

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Significant structural and functional brain development occurs during early adolescence. These changes underlie developments in central neurocognitive processes such as working memory (WM) and emotion regulation (ER). The preponderance of studies modeling trajectories of adolescent brain development use variable-centered approaches, omitting attention to individual differences that may undergird neurobiological embedding of early life stress and attendant psychopathology. This preregistered, data-driven study used latent transition analysis (LTA) to identify (1) latent profiles of neural function during a WM and implicit ER task, (2) transitions in profiles across 24 months, and 3) associations between transitions, parental support, and subsequent psychopathology. Using two waves of data from the ABCD Study (Mage T1 = 10; Mage T2 = 12), we found three unique profiles of neural function at both T1 and T2. The Typical, Emotion Hypo-response, and Emotion-Hyper response profiles were characterized by, respectively: moderate amygdala activation and fusiform deactivation; high ACC, fusiform, and insula deactivation; and high amygdala, ACC, and insula response to ER. While 69.5 % remained in the Typical profile from T1 to T2, 27.8 % of the sample moved from one profile at T1 to another at T2. However, neither latent profiles nor transitions exhibited associations between parental support or psychopathology symptoms.

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Children who endorse psychotic-like experiences (PLEs) appear to be at a greater risk for suicidal ideation and behavior (SI/SB) compared to their peers who do not endorse PLEs. Despite evidence of differential relations among subtypes of PLEs and SI/SB, the research on which PLE subtypes produce the strongest associations remains mixed. Further, though there is evidence that general psychological distress may help explain the relation between PLEs and SI/SB, no research has investigated the role of distress specific to PLEs in this association.

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American Indian and Alaska Native (AIAN) populations have suffered a history of exploitation and abuse within the context of mental health research and related fields. This history is rooted in assimilation policies, historical trauma, and cultural loss, and is promulgated through discrimination and disregard for traditional culture and community knowledge. In recognition of this history, it is imperative for researchers to utilize culturally sensitive approaches that consider the context of tribal communities to better address mental health issues for AIAN individuals. The public availability of data from large-scale studies creates both opportunities and challenges when studying mental health within AIAN populations. This manuscript has two goals; first, showcase an example of problematic use of Adolescent Brain Cognitive Development (ABCD) Study data to promulgate stereotypes about AIAN individuals and, second, in partnership with collaborators from Cherokee Nation, we provide five recommendations for utilizing data from publicly available datasets to advance health research in AIAN populations. Specifically, we argue for the consideration of (1) the heterogeneity of the communities represented, (2) the importance of focusing on AIAN health and well-being, (3) engagement of relevant communities and AIAN community leaders, (4) consideration of historical and ongoing injustices, and (5) engagement with AIAN regulatory agencies or review boards. These recommendations are founded on principles from broader indigenous research efforts emphasizing community-engaged research and principles of Indigenous Data Sovereignty and Governance.

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Adverse childhood experiences (ACEs) are common and linked to negative health outcomes. Previous studies have found associations between ACEs and binge-eating disorder (BED), though they have mainly focused on adults and use cross-sectional data. The objective of this study was to examine the associations between ACEs and BED in a large, national cohort of 9-14-year-old early adolescents in the US.

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Although it is well-established that both genetics and the environment influence brain development, they are typically examined separately. Here, we aimed to prospectively investigate the interactive effects of genetic variants-from a genome-wide approach-and early life stress (ELS) on child subcortical brain structures, and their association with subsequent mental health problems.

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A large longitudinal study on the impact of the COVID-19 pandemic on mental health in children is limited. This large-scale longitudinal observational study examines the pandemic’s effects on children’s mental health while considering the effects of parental care styles. The Adolescent Brain Cognitive Development Study is a large-scale, longitudinal multicenter study in the United States. Of the 11,875 children aged 9-12 years in its database, 4702 subjects were selected for this study. The child behavior checklist and parental monitoring questionnaire (PMQ) were used to assess children’s mental health and parental support styles, respectively. Data collected before and during the pandemic were compared. Withdrawn/depressed and attention problems significantly worsened during compared to before the COVID-19 pandemic (p < 0.001, withdrawn/depressed; 53.4 ± 5.7 to 53.7 ± 5.9, attention problems; 53.4 ± 5.4 to 53.6 ± 5.6). However, the T scores are in the normal range both before and during the crisis. Simple slope analysis found withdrawn/depressed problems and aggressive behavior worsened when the PMQ was 1 SD below the mean, and rule-breaking behavior was improved when the PMQ was 1 SD above the mean. While the COVID-19 pandemic exacerbated children’s depressive symptoms and attention issues, the effects may be minor. Additionally, parental involvement serve as a protective factor for the child’s mental health even during the pandemic.

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The COVID-19 pandemic and associated responses have induced a host of crises worldwide, including an economic recession and a global mental health crisis. The specific effects of recession on youth mental health are understudied. We aimed to examine the mechanisms by which pandemic-related financial strain may affect mental health in a diverse sample of American adolescents.

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The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of psychiatric disorders. The pubertal timing of the expression of PC1-related genes implicates disrupted neurodevelopment in the pathogenesis of the spectrum of psychiatric diseases emerging during adolescence.

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Though largely substance-naïve at enrollment, a proportion of the youth in the Adolescent Brain Cognitive Development (ABCD) Study are expected to initiate substance use (SU) as they transition into later adolescence. With annual data from youth 9-13 years-old, this study aims to describe their SU patterns over time. Here, prevalence rates of use are reported, along with predicted odds of use while analyzing common risk-factors associated with youth SU.

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Deep learning algorithms for predicting neuroimaging data have shown considerable promise in various applications. Prior work has demonstrated that deep learning models that take advantage of the data’s 3D structure can outperform standard machine learning on several learning tasks. However, most prior research in this area has focused on neuroimaging data from adults. Within the Adolescent Brain and Cognitive Development (ABCD) dataset, a large longitudinal development study, we examine structural MRI data to predict gender and identify gender-related changes in brain structure. Results demonstrate that gender prediction accuracy is exceptionally high (>97%) with training epochs > 200 and that this accuracy increases with age. Brain regions identified as the most discriminative in the task under study include predominantly frontal areas and the temporal lobe. When evaluating gender predictive changes specific to a two-year increase in age, a broader set of visual, cingulate, and insular regions are revealed. Our findings show a robust gender-related structural brain change pattern, even over a small age range. This suggests that it might be possible to study how the brain changes during adolescence by looking at how these changes are related to different behavioral and environmental factors.

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There has been concern about the potential sequelae of mild traumatic brain injury (mTBI) in children. This study used data from the Adolescent Brain Cognitive Development (ABCD) study to investigate associations between mTBI and behavior and sleep in school-aged children. Generalized additive mixed models were run to examine the association between TBI and parent-reported Child Behavior Checklist and Sleep Disturbance Scale for Children scores. mTBI with or without loss of consciousness (LOC) in 9- and 10-year old children was associated with 1) higher internalizing, externalizing and total problems and 2) greater sleep disturbance scores on the CBCL. The study also demonstrated a higher incidence of mTBI with and without LOC in boys compared to girls. This study shows a statistically significant but modest association between mTBI and behavioral and sleep changes, suggesting that in a non-clinical, sociodemographically diverse community sample of school-aged children mTBI does not result in clinically significant behavioral or psychological sequelae.

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Individual differences in cognitive abilities emerge early during development, and children with poorer cognition are at increased risk for adverse outcomes as they enter adolescence. Caregiving plays an important role in supporting cognitive development, yet it remains unclear how specific types of caregiving behaviors may shape cognition, highlighting the need for large-scale studies. In the present study, we characterized replicable yet specific associations between caregiving behaviors and cognition in two large sub-samples of children ages 9-10 years old from the Adolescent Brain Cognitive Development Study (ABCD). Across both discovery and replication sub-samples, we found that child reports of caregiver monitoring (supervision or regular knowledge of the child’s whereabouts) were positively associated with general cognition abilities, after covarying for age, sex, household income, neighborhood deprivation, and parental education. This association was specific to the type of caregiving behavior (caregiver monitoring, but not caregiver warmth), and was most strongly associated with a broad domain of general cognition (but not executive function or learning/memory). Additionally, we found that caregiver monitoring partially mediated the association between household income and cognition, furthering our understanding of how socioeconomic disparities may contribute to disadvantages in cognitive development. Together, these findings underscore the influence of differences in caregiving behavior in shaping youth cognition. RESEARCH HIGHLIGHTS: Caregiver monitoring, but not caregiver warmth, is associated with cognitive performance in youth Caregiver monitoring partially mediates the association between household income and cognition Results replicated across two large matched samples from the Adolescent Brain Cognitive Development Study (ABCD).

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Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9-10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.

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Many studies have shown that parental knowledge/monitoring is correlated with adolescent substance use, but the association may be confounded by the many preexisting differences between families with low versus high monitoring. We attempted to produce more rigorous evidence for a causal relation using a longitudinal design that took advantage of within-family fluctuations in knowledge/monitoring during the COVID-19 pandemic.

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Associations between attention-deficit/hyperactivity disorder (ADHD) and brain morphology have been reported, although with several inconsistencies. These may partly stem from confounding bias, which could distort associations and limit generalizability. We examined how associations between brain morphology and ADHD symptoms change with adjustments for potential confounders typically overlooked in the literature (aim 1), and for the intelligence quotient (IQ) and head motion, which are generally corrected for but play ambiguous roles (aim 2).

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Gestational age (GA) is associated with later cognition and behavior. However, it is unclear how specific cognitive domains and brain structural development varies with the stepwise change of gestational duration.

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Prenatal caffeine exposure (PCE) has been positively associated with elevated body mass index (BMI) in children. Why this association occurs is unclear, but it is possible that PCE alters the in utero development of brain structures associated with food preference, leading to more total sugar intake (TSI, grams) later in childhood. To test this hypothesis, we investigated if PCE (daily/weekly/ 0.01) of excessive PCE (vs. no exposure) with elevated BMI (daily/weekly/daily limit; consistent in boys and girls), increased TSI (daily) and insular thickness (daily/weekly), as well as low middle frontal cortex (MFC) activation (daily). Our sub-analysis revealed an association of daily/weekly PCE (vs. no exposure) with increased gram sugar intake from soft drinks. We also identified a positive relationship of excessive PCE with elevated TSI and increased insular thickness (a key gustatory region), while in a Sobel test, reward sensitivity (reduced brain reactivity to reward anticipation in MFC; tracks reward outcomes) mediated (Test statistic = 2.23; p = 0.02) the PCE-linked BMI changes in adolescents. Our findings suggest that excessive PCE might be detrimental to frontal lobe development and altered reward sensitivity to food, thereby increasing risk for elevated TSI and obesity. Our results support recommendations to limit caffeine intake during pregnancy.

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Childhood poverty has been associated with increased internalizing and externalizing problems in adolescence, a period of peak onset for psychiatric problems. The underlying neural mechanisms remain unclear because longitudinal studies of poverty, brain structure, and changes in psychiatric symptoms are lacking.

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Multi-region pain during adolescence is associated with a higher symptom burden and lower quality of life. The purpose of this study was to describe and compare the biopsychosocial attributes of single-region and multi-region pain among healthy young adolescents.

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Early pubertal timing has been linked to increased risk for internalizing psychopathology in adolescents. Work in older adolescents and adults suggests that heightened reward sensitivity may buffer risk for internalizing symptoms. However, few studies have investigated these associations during the early transition to puberty, a window of vulnerability to mental health risk. In this preregistered study, we investigated the associations among pubertal timing, internalizing symptoms, and reward sensitivity in a large, population-based sample of 11,224 9-10 year-olds from the ABCD Study®. Using split-half analysis, we tested for within-sample replications of hypothesized effects across two age- and sex-matched subsets of the sample. Early pubertal timing was associated with higher internalizing symptoms in female and male participants across samples, with 9-10 year-olds in the mid-pubertal stage at the highest risk for internalizing symptoms. Additionally, early pubertal timing was robustly associated with greater self-reported reward sensitivity in both female and male participants. We observed inconsistent evidence for a moderating role of reward sensitivity across measurement domains (self-report, behavioral, and fMRI data), several of which differed by sex, but none of these interactions replicated across samples. Together, these findings provide unique insights into early indicators of risk for internalizing psychopathology during the transition to puberty in a large, population-based, demographically diverse sample of youth.

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Binge eating disorder (BED) is a pernicious psychiatric disorder which is linked with broad medical and psychiatric morbidity, and obesity. While BED may be characterized by altered cortical morphometry, no evidence to date examined possible sex-differences in regional gray matter characteristics among those with BED. This is especially important to consider in children, where BED symptoms often emerge coincident with rapid gray matter maturation.

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Different data sources can provide complementary information. Moving from a simple approach based on using one data source at a time to a systems approach that integrates multiple data sources provides an opportunity to understand complex brain disorders or cognitive processes. We propose a data fusion method, called Bayesian Multisource Data Integration, to model the interactions among data sources and behavioral variables. The proposed method generates representations from data sources and uses Bayesian network modeling to associate representations with behavioral variables. The generated Bayesian network is transparent and easy to understand. Bayesian inference is used to understand how the perturbation of representation is related to behavioral changes. The proposed method was assessed on the simulated data and data from the Adolescent Brain Cognitive Development study. For the Adolescent Brain Cognitive Development study, we found diffusion tensor imaging and resting-state functional magnetic resonance imaging were synergistic in understanding the fluid intelligence composite and the total score composite in healthy youth (9-11 years of age).

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As more states pass recreational cannabis laws (RCLs) for adults, there is concern that increasing (and state-sanctioned) cannabis acceptance will result in a reduced perception of risk of harm from cannabis among children. We aimed to discover whether children in states with RCLs had decreased perception of risk from cannabis compared with children in states with illicit cannabis. We analyzed data from the multisite multistate Adolescent Brain and Cognitive Development Study to determine how the perception of cannabis harm among children (age at baseline: 9-10; =10,395) changes over time in states with and without RCLs. Using multilevel modeling, we assessed survey responses from children longitudinally across 3 years, adjusting for state-, family-, and participant-level clustering and child-level factors, including demographics (sex, race, and socioeconomic status), religiosity, and trait impulsivity. There was no significant main effect of state RCLs on perceived risk of cannabis use, and no differences in change over time by state RCLs, even after controlling for demographic factors and other risk (e.g., impulsivity) and protective (e.g., religiosity) factors. This analysis indicates that state-level RCLs are not associated with differential perception of cannabis risk among children, even after controlling for demographics, trait impulsivity, and religiosity. Future studies could assess how perception of risk from cannabis changes as children and adolescents continue to mature in states with and without RCLs.

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The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.

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Understanding risk factors related to suicidal ideation (SI) and suicidal behaviors (SB) in youth is important for informing prevention and intervention efforts. While it appears that psychotic-like experiences (PLEs) are strongly associated with both SI and SB at different points across the lifespan, the longitudinal nature of this relationship in middle childhood and early adolescence is understudied.

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Impulsivity and reward sensitivity are personality traits associated with obesity and binge-eating disorder (BED), but little research has examined prospective associations between these traits and body mass index z-score (BMI-z) differentially for children with and without BED.

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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.

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Little is known about how exposure to limited socioeconomic resources (SER) in childhood gets “under the skin” to shape brain development, especially using rigorous whole-brain multivariate methods in large, adequately powered samples. The present study examined resting state functional connectivity patterns from 5821 youth in the Adolescent Brain Cognitive Development (ABCD) study, employing multivariate methods across three levels: whole-brain, network-wise, and connection-wise. Across all three levels, SER was associated with widespread alterations across the connectome. However, critically, we found that parental education was the primary driver of neural associations with SER. These parental education associations with the developing connectome exhibited notable concentrations in somatosensory and subcortical regions, and they were partially accounted for by home enrichment activities, child’s cognitive abilities, and child’s grades, indicating interwoven links between parental education, child stimulation, and child cognitive performance. These results add a new data-driven, multivariate perspective on links between household SER and the child’s developing functional connectome.

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Parental religious beliefs and practices (religiosity) may have profound effects on youth, especially in neurodevelopmentally complex periods such as adolescence. In n = 5566 children (median age = 120.0 months; 52.1% females; 71.2% with religious affiliation) from the Adolescent Brain Cognitive Development study, relationships between parental religiosity and non-religious beliefs on family values (data on youth beliefs were not available), topological properties of youth resting-state brain networks, and executive function, inhibitory control, and cognitive flexibility were investigated. Lower caregiver education and family income were associated with stronger parental beliefs (p < 0.01). Strength of both belief types was correlated with lower efficiency, community structure, and robustness of frontoparietal control, temporoparietal, and dorsal attention networks (p < 0.05), and lower Matrix Reasoning scores. Stronger religious beliefs were negatively associated (directly and indirectly) with multiscale properties of salience and default-mode networks, and lower Flanker and Dimensional Card Sort scores, but positively associated with properties of the precuneus. Overall, these effects were small (Cohen’s d ~ 0.2 to ~ 0.4). Overlapping neuromodulatory and cognitive effects of parental beliefs suggest that early adolescents may perceive religious beliefs partly as context-independent rules on expected behavior. However, religious beliefs may also differentially affect cognitive flexibility, attention, and inhibitory control and their neural substrates.

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Children with callous-unemotional (CU) traits are at risk for severe conduct problems. While CU traits are moderately heritable, parenting also predicts risk. However, few studies have investigated whether parenting factors (e.g., acceptance, conflict, parental psychopathology) moderate the etiology of CU traits, while accounting for gene-environment correlations. To address this knowledge gap, we used data from 772 twin pairs from the Adolescent Brain and Cognitive Development Study to test bivariate models that explored overlapping etiological influences on CU traits and child reports of their parenting environment. We also used gene-by-environment interaction models to test whether parenting moderated genetic versus environmental influences. There were no overlapping etiological influences on CU traits and parental acceptance, but modest genetic and non-shared environmental overlap between CU traits and family conflict. Parental acceptance and psychopathology moderated non-shared environmental influences, with stronger non-shared environmental influences on CU traits among children who experienced lower parental acceptance and greater parental psychopathology. Family conflict only moderated environmental influences when models did not covary for conduct problems. Parental acceptance and parental psychopathology may be specific environmental protective and risk factors for CU traits, whereas family conflict may represent a general environmental risk factor for both CU traits and conduct problems.

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Cannabis use among pregnant people has increased over the past decade. This is of concern as prenatal cannabis exposure (PCE) is associated with cognitive, motor, and social deficits among offspring. Here, we examined resting-state functional connectivity (rsFC) of the salience network (SN)-a core neurocognitive network that integrates emotional and sensory information-in children with (vs. without) PCE. Using neuroimaging and developmental history data collected from 10,719 children (M ± SD = 9.92 ± 0.62 years; 47.9% female) from the Adolescent Brain Cognitive Development study, we assessed the impact of parent-reported PCE (before or after knowledge of pregnancy) on rsFC within and between the SN and five other core neurocognitive networks. We also evaluated whether SN rsFC mediated the association between PCE and child psychopathology. Results showed that PCE before (but not after) knowledge of pregnancy was associated with lower SN-ventral attention network (VAN) rsFC. Furthermore, psychotic-like experiences mediated the association between PCE and SN-VAN rsFC, and reversal of the model was also significant, such that SN-VAN rsFC mediated the association between PCE and psychotic-like symptoms. However, these mediation effects were no longer significant after the inclusion of covariates. Taken together, these findings suggest that developmental alterations in SN-VAN interactions may explain the previously reported association between PCE and elevated risk of child psychopathology.

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Previous studies have found that offspring of depressed parents exhibit reduced striatal reward response to anticipating and receiving rewards, suggesting that this may constitute a neurobiological risk marker for depression. The present study aimed to assess whether maternal and paternal depression history have independent effects on offspring reward processing and whether greater family history density of depression is associated with increased blunting of striatal reward responses.

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Neurodevelopmental research has traditionally focused on development of individual structures, yet multiple lines of evidence indicate parallel development of large-scale systems, including canonical neural networks (i.e., default mode, frontoparietal). However, the relationship between region- vs. network-level development remains poorly understood. The current study tests the ability of a recently developed multi-task coactivation matrix approach to predict canonical resting state network engagement at baseline and at two-year follow-up in a large and cohort of young adolescents. Pre-processed tabulated neuroimaging data were obtained from the Adolescent Brain and Cognitive Development (ABCD) study, assessing youth at baseline (N = 6073, age = 10.0 ± 0.6 years, 3056 female) and at two-year follow-up (N = 3539, age = 11.9 ± 0.6 years, 1726 female). Individual multi-task co-activation matrices were constructed from the beta weights of task contrasts from the stop signal task, the monetary incentive delay task, and emotional N-back task. Activation-based predictive modeling, a cross-validated machine learning approach, was adopted to predict resting-state canonical network engagement from multi-task co-activation matrices at baseline. Note that the tabulated data used different parcellations of the task fMRI data (“ASEG” and Desikan) and the resting-state fMRI data (Gordon). Despite this, the model successfully predicted connectivity within the default mode network (DMN, rho = 0.179 ± 0.002, p < 0.001) across participants and identified a subset of co-activations within parietal and occipital macroscale brain regions as key contributors to model performance, suggesting an underlying common brain functional architecture across cognitive domains. Notably, predictive features for resting-state connectivity within the DMN identified at baseline also predicted DMN connectivity at two-year follow-up (rho = 0.258). These results indicate that multi-task co-activation matrices are functionally meaningful and can be used to predict resting-state connectivity. Interestingly, given that predictive features within the co-activation matrices identified at baseline can be extended to predictions at a future time point, our results suggest that task-based neural features and models are valid predictors of resting state network level connectivity across the course of development. Future work is encouraged to verify these findings with more consistent parcellations between task-based and resting-state fMRI, and with longer developmental trajectories.

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Although most research has linked video gaming to subsequent increases in aggressive behavior in children after accounting for prior aggression, findings have been divided with respect to video gaming’s association with cognitive skills.

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Suicide rates have been increasing among youth in the US. While the heritability of suicide risk is well established, there is limited understanding of how genetic risk is associated with suicidal thoughts and behaviors in young children.

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Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.

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Parental behavior problems have long-term effects on children’s limbic brain structures and functions. Parental behavior problems-related brain changes in children may lead to mental disorders and behavior dysfunction later in life. However, our understanding of the relationship between parental behavior and children’s brain structures is less obvious when children and adolescents are studied in a general population without mental disorders. The majority of studies on the relationship between parental behavior and adolescent brain structure have been focused on severe forms of the following parental behavior problems: (1) internalizing behavior associated with mood and anxiety disorders, and (2) externalizing behavior associated with substance use and violence. A few studies examined the effect of normative variations or subtle differences in parental behavior. Therefore, we utilized a large study-Adolescent Brain Cognitive Development (ABCD)-to determine relationships between normative variation in parental internalizing and externalizing behavior and limbic brain structures in children and adolescents without mental disorders. Quantile (median) regression models were used to compute associations between parental behavior and children’s limbic structures. We found that parental internalizing and externalizing behaviors are uniquely associated with children’s limbic structures after adjustment for biological confounders and parental socioeconomic status. Our findings indicate that normative parental behavior may have a significant early influence on limbic structures of normally developing children and adolescents. Accelerated or delayed limbic structure maturation may account for children’s and adolescents’ behavioral inadequacies and a risk of developing specific mood disorders or substance abuse problems later in life.

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Previous studies have found significant associations between estimated autozygosity – the proportion of an individual’s genome contained in homozygous segments due to distant inbreeding – and multiple traits, including educational attainment (EA) and cognitive ability. In one study, estimated autozygosity showed a stronger association with parental EA than the subject’s own EA. This was likely driven by parental EA’s association with mobility: more educated parents tended to migrate further from their hometown, and because of the strong correlation between ancestry and geography in the Netherlands, these individuals chose partners farther from their ancestry and therefore more different from them genetically. We examined the associations between estimated autozygosity, cognitive ability, and parental EA in a contemporary sub-sample of adolescents from the Adolescent Brain Cognitive Development Study℠ (ABCD Study®) (analytic N = 6,504). We found a negative association between autozygosity and child cognitive ability consistent with previous studies, while the associations between autozygosity and parental EA were in the expected direction of effect (with greater levels of autozygosity being associated with lower EA) but the effect sizes were significantly weaker than those estimated in previous work. We also found a lower mean level of autozygosity in the ABCD sample compared to previous autozygosity studies, which may reflect overall decreasing levels of autozygosity over generations. Variation in spousal similarities in ancestral background in the ABCD study compared to other studies may explain the pattern of associations between estimated autozygosity, EA, and cognitive ability in the current study.

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Peer victimization is a substantial early life stressor linked to psychiatric symptoms and poor academic performance. However, the sex-specific cognitive or behavioral outcomes of bullying have not been well-described in preadolescent children.

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While attention-deficit/hyperactivity disorder (ADHD) has been associated with differences in the structural connections formed by the brain’s white matter tracts, studies of such differences have yielded inconsistent findings, likely reflecting small sample sizes. Thus, we conducted a mega-analysis on in vivo measures of white matter microstructure obtained through diffusion tensor imaging of more than 6000 participants from 5 cohorts.

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Neuroimaging studies have a growing interest in learning the association between the individual brain connectivity networks and their clinical characteristics. It is also of great interest to identify the sub brain networks as biomarkers to predict the clinical symptoms, such as disease status, potentially providing insight on neuropathology. This motivates the need for developing a new type of regression model where the response variable is scalar, and predictors are networks that are typically represented as adjacent matrices or weighted adjacent matrices, to which we refer as scalar-on-network regression. In this work, we develop a new boosting method for model fitting with sub-network markers selection. Our approach, as opposed to group lasso or other existing regularization methods, is essentially a gradient descent algorithm leveraging known network structure. We demonstrate the utility of our methods via simulation studies and analysis of the resting-state fMRI data in a cognitive developmental cohort study.

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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 Development 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.

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The age at attaining infancy developmental milestones has been associated with later neurodevelopmental outcomes, but evidence from large and diverse samples is lacking. We investigated this by analyzing data of 5360 singleton children aged 9-10 from 17 states in the US enrolled in the Adolescent Brain Cognitive Development (ABCD) study during 2016-2020. Delays in four milestones (first roll over, unaided sitting, unaided walking, and speaking first words) were defined using the 90th percentile of age at attainment reported by children’s biological mothers. Childhood neurocognitive function was measured by research assistants using the NIH toolbox, and children reported their behavioral problems using the Brief Problem Monitor. Linear mixed-effects models were employed to investigate the association between delays in single or multiple milestones and childhood neurobehavioral outcomes. Delays in first roll over, unaided sitting, or walking were associated with poorer childhood neurocognitive function, while delay in speaking first words was associated with both poorer neurocognitive function and behavioral problems. Children who had delays in both motor and language milestones had the worst neurocognitive function and behavioral outcomes. Our results suggest that delays in motor and language milestone attainment during infancy are predictive of childhood neurobehavioral outcomes.

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Increasing evidence demonstrates that environmental factors meaningfully impact the development of the brain (Hyde et al., 2020; McEwen and Akil, 2020). Recent work from the Adolescent Brain Cognitive Development (ABCD) Study suggests that puberty may indirectly account for some association between the family environment and brain structure and function (Thijssen et al., 2020). However, a limited number of large studies have evaluated what, how, and why environmental factors impact neurodevelopment. When these topics are investigated, there is typically inconsistent operationalization of variables between studies which may be measuring different aspects of the environment and thus different associations in the analytic models. Multiverse analyses (Steegen et al., 2016) are an efficacious technique for investigating the effect of different operationalizations of the same construct on underlying interpretations. While one of the assets of Thijssen et al. (2020) was its large sample from the ABCD data, the authors used an early release that contained 38% of the full ABCD sample. Then, the analyses used several ‘researcher degrees of freedom’ (Gelman and Loken, 2014) to operationalize key independent, mediating and dependent variables, including but not limited to, the use of a latent factor of preadolescents’ environment comprised of different subfactors, such as parental monitoring and child-reported family conflict. While latent factors can improve reliability of constructs, the nuances of each subfactor and measure that comprise the environment may be lost, making the latent factors difficult to interpret in the context of individual differences. This study extends the work of Thijssen et al. (2020) by evaluating the extent to which the analytic choices in their study affected their conclusions. In Aim 1, using the same variables and models, we replicate findings from the original study using the full sample in Release 3.0. Then, in Aim 2, using a multiverse analysis we extend findings by considering nine alternative operationalizations of family environment, three of puberty, and five of brain measures (total of 135 models) to evaluate the impact on conclusions from Aim 1. In these results, 90% of the directions of effects and 60% of the -values (e.g. > .05 and < .05) across effects were comparable between the two studies. However, raters agreed that only 60% of the effects had replicated. Across the multiverse analyses, there was a degree of variability in beta estimates across the environmental variables, and lack of consensus between parent reported and child reported pubertal development for the indirect effects. This study demonstrates the challenge in defining which effects replicate, the nuance across environmental variables in the ABCD data, and the lack of consensus across parent and child reported puberty scales in youth.

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The association between restricted fetal growth and symptoms of attention deficit/hyperactivity disorder (ADHD) in childhood is well-replicated and robust. However, fetal growth is determined by many prenatal factors and associations with mental health may be confounded by familial and social context. In this study, we sought to quantify the relative contributions of prenatal factors and familial confounds to the association between fetal growth and ADHD symptoms. Two independent cohorts were analyzed, the Adolescent Brain Cognitive Development study (ABCD; United States) and the Growing Up in Ireland (GUI) study. ADHD symptoms were measured by the Child Behavior Checklist (ABCD) and the Strengths & Difficulties questionnaire (GUI) at age 9-10. Using sequential regression models, we assessed the change-in-association between fetal growth and ADHD symptoms after controlling for sex, familial factors (socioeconomic/demographic factors & family psychiatric history) and prenatal factors (pregnancy complications & maternal substance-use during pregnancy). Converging findings from cohorts suggested that over a quarter of the association between fetal growth and ADHD symptoms is attributable to familial confounds. The degree to which the association was explained by prenatal factors differed by cohort-pregnancy complications explained a larger proportion of the effect in ABCD (7.9%) than GUI (2.7%), and maternal substance-use explained a larger proportion of the effect in GUI (22.7%) compared to ABCD (4.8%). Different explanations of the fetal growth-ADHD association across cohorts suggests cohort-specific, and potentially nationally-specific, risk factors for fetal growth and related neurodevelopmental outcomes. The evidence suggests early prevention of ADHD in Ireland should focus on minimizing maternal smoking during pregnancy. In the US, prevention and treatment of pregnancy complications are highlighted as viable targets for intervention.

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A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.

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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.

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Adolescent suicide is a major health problem in the US marked by a recent increase in risk of suicidal behavior among Black/African American youth. While genetic factors partly account for familial transmission of suicidal behavior, it is not clear whether polygenic risk scores of suicide attempt can contribute to suicide risk classification.

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We sought to identify resting-state characteristics related to attention deficit/hyperactivity disorder, both as a categorical diagnosis and as a trait feature, using large-scale samples which were processed according to a standardized pipeline. In categorical analyses, we considered 1301 subjects with diagnosed ADHD, contrasted against 1301 unaffected controls (total N = 2602; 1710 males (65.72%); mean age = 10.86 years, sd = 2.05). Cases and controls were 1:1 nearest neighbor matched on in-scanner motion and key demographic variables and drawn from multiple large cohorts. Associations between ADHD-traits and resting-state connectivity were also assessed in a large multi-cohort sample (N = 10,113). ADHD diagnosis was associated with less anticorrelation between the default mode and salience/ventral attention (B = 0.009, t = 3.45, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.014), somatomotor (B = 0.008, t = 3.49, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.013), and dorsal attention networks (B = 0.01, t = 4.28, p-FDR < 0.001, d = 0.17, 95% CI = 0.006, 0.015). These results were robust to sensitivity analyses considering comorbid internalizing problems, externalizing problems and psychostimulant medication. Similar findings were observed when examining ADHD traits, with the largest effect size observed for connectivity between the default mode network and the dorsal attention network (B = 0.0006, t = 5.57, p-FDR < 0.001, partial-r = 0.06, 95% CI = 0.0004, 0.0008). We report significant ADHD-related differences in interactions between the default mode network and task-positive networks, in line with default mode interference models of ADHD. Effect sizes (Cohen's d and partial-r, estimated from the mega-analytic models) were small, indicating subtle group differences. The overlap between the affected brain networks in the clinical and general population samples supports the notion of brain phenotypes operating along an ADHD continuum.

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In a novel approach to model the demand for the children’s screen time as the result of a parent’s optimal labor-leisure choice, the study used a simple model of parental utility maximization subject to the money and time budget constraints to derive Marshallian parental demand functions for two types of child upbringing activities: time-intensive (violin lesson) and time-saving (video games). After the Slutsky decomposition, parental demand for children’s screen time was shown to be similar to a Giffen good. Using the National Longitudinal Survey of Youth 1979 and Adolescent Brain Cognitive Development data, the wage equation was first estimated based on Heckman’s two-step correction procedure. Then, the total effect of an increase in wage rate on the parental demand for screen time was empirically decomposed into the substitution effect and the income effect. The study findings indicate that the substitution effect is positive, the income effect is negative, and the negative income effect dominates the substitution effect. We add to the existing literature by showing that the empirical findings in the public health and psychology literature can be reconciled with the theoretical predictions of the standard economic labor-leisure trade-off paradigm.

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Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74-96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.

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Children that experience a mild traumatic brain injury (mTBI) are at an increased risk of neural alterations that can deteriorate mental health. We test the hypothesis that mTBI is associated with psychopathology and that structural brain metrics (e.g., volume, area) meaningfully mediate the relation in an adolescent population.

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Sensory over-responsivity (SOR) is recognized as a common feature of autism spectrum disorder. However, SOR is also common among typically developing children, in whom it is associated with elevated levels of psychiatric symptoms. The clinical significance and neurocognitive bases of SOR in these children remain poorly understood and actively debated.

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Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health.

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Psychiatric disorders are highly heritable and polygenic, and many have their peak onset in late childhood and adolescence, a period of tremendous changes. Although the neurodevelopmental antecedents of mental illness are widely acknowledged, research in youth population cohorts is still scarce, preventing our progress towards the early characterization of these disorders. We included 7,124 children (9-11 years old) from the Adolescent Brain and Cognitive Development Study to map the associations of structural and diffusion brain imaging with common genetic variants and polygenic scores for psychiatric disorders and educational attainment. We used principal component analysis to derive imaging components, and calculated their heritability. We then assessed the relationship of imaging components with genetic and clinical psychiatric risk with univariate models and Canonical correlation analysis (CCA). Most imaging components had moderate heritability. Univariate models showed limited evidence and small associations of polygenic scores with brain structure at this age. CCA revealed two significant modes of covariation. The first mode linked higher polygenic scores for educational attainment with less externalizing problems and larger surface area. The second mode related higher polygenic scores for schizophrenia, bipolar disorder, and autism spectrum disorder to higher global cortical thickness, smaller white matter volumes of the fornix and cingulum, larger medial occipital surface area and smaller surface area of lateral and medial temporal regions. While cross-validation suggested limited generalizability, our results highlight the potential of multivariate models to better understand the transdiagnostic and distributed relationships between mental health and brain structure in late childhood.

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Exposure to socioeconomic disadvantages (SED) can have negative impacts on mental health, yet SED are a multifaceted construct and the precise processes by which SED confer deleterious effects are less clear. Using a large and diverse sample of preadolescents (ages 9-10 years at baseline, n = 4038, 49% female) from the Adolescent Brain Cognitive Development Study, we examined associations among SED at both household (i.e., income-needs and material hardship) and neighborhood (i.e., area deprivation and neighborhood unsafety) levels, frontoamygdala resting-state functional connectivity, and internalizing symptoms at baseline and 1-year follow-up. SED were positively associated with internalizing symptoms at baseline and indirectly predicted symptoms 1 year later through elevated symptoms at baseline. At the household level, youth in households characterized by higher disadvantage (i.e., lower income-to-needs ratio) exhibited more strongly negative frontoamygdala coupling, particularly between the bilateral amygdala and medial OFC (mOFC) regions within the frontoparietal network. Although more strongly positive amygdala-mOFC coupling was associated with higher levels of internalizing symptoms at baseline and 1-year follow-up, it did not mediate the association between income-to-needs ratio and internalizing symptoms. However, at the neighborhood level, amygdala-mOFC functional coupling moderated the effect of neighborhood deprivation on internalizing symptoms. Specifically, higher neighborhood deprivation was associated with higher internalizing symptoms for youth with more strongly positive connectivity, but not for youth with more strongly negative connectivity, suggesting a potential buffering effect. Findings highlight the importance of capturing multilevel socioecological contexts in which youth develop to identify youth who are most likely to benefit from early interventions.

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School climates are important for children’s socioemotional development and may also serve as protective factors in the context of adversity. Nevertheless, little is known about the potential neural mechanisms of such associations, as there has been limited research concerning the relation between school climate and brain structure, particularly for brain regions relevant for mental health and socioemotional functioning. Moreover, it remains unclear whether the role of school climate differs depending on children’s socioeconomic status. We addressed these questions in baseline data for 9- to 10-year-olds from the Adolescent Brain and Cognitive Development study (analytic sample for socioemotional outcomes, n = 8887), conducted at 21 sites across the United States. Cortical thickness, cortical surface area, and subcortical volume were derived from T1-weighted brain magnetic resonance imaging. School climate was measured by youth report, and socioemotional functioning was measured by both youth and parent report. A positive school climate and higher family income were associated with lower internalizing and externalizing symptoms, with no evidence of moderation. There were no associations between school climate and cortical thickness or subcortical volume, although family income was positively associated with hippocampal volume. For cortical surface area, however, there was both a positive association with family income and moderation: There was an interaction between school climate and income for total cortical surface area and locally in the lateral orbitofrontal cortex. In all cases, there was an unexpected negative association between school climate and cortical surface area in the lower-income group. Consequently, although the school climate appears to be related to better socioemotional function for all youth, findings suggest that the association between a positive school environment and brain structure only emerges in the context of socioeconomic stress and adversity. Longitudinal data are needed to understand the role of these neural differences in socioemotional functioning over time.

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Adolescent screen usage is ubiquitous and influences development and behavior. Longitudinal screen usage data coupled with psychometrically valid constructs of problematic behaviors can provide insights into these relationships. We describe methods by which the screen usage questionnaire was developed in the Adolescent Brain Cognitive Development (ABCD) Study, demonstrate longitudinal changes in screen usage via child report and describe data harmonization baseline-year 2. We further include psychometric analyses of adapted social media and video game addiction scales completed by youth. Nearly 12,000 children ages 9-10 years at baseline and their parents were included in the analyses. The social media addiction questionnaire (SMAQ) showed similar factor structure and item loadings across sex and race/ethnicities, but that item intercepts varied across both sex and race/ethnicity. The videogame addiction questionnaire (VGAQ) demonstrated the same configural, metric and scalar invariance across racial and ethnic groups, however differed across sex. Video gaming and online social activity increased over ages 9/10-11/12 (p’s < 0.001). Compared with boys, girls engaged in greater social media use (p < .001) and demonstrated higher ratings on the SMAQ (p < .001). Compared with girls, boys played more video games (p < .001) and demonstrated higher ratings on the VGAQ (p < .001). Time spent playing video games increased more steeply for boys than girls from age 9/10-11/12 years (p < .001). Black youth demonstrated significantly higher SMAQ and VGAQ scores compared to all other racial/ethnic groups. These data show the importance of considering different screen modalities beyond total screen use and point towards clear demographic differences in use patterns. With these comprehensive data, ABCD is poised to address critical questions about screen usage changes across adolescence.

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Reaction time variability (RTV) has been estimated using Gaussian, ex-Gaussian, and diffusion model (DM) indices. Rarely have studies examined interrelationships among these performance indices in childhood, and the use of reaction time (RT) computational models has been slow to take hold in the developmental psychopathology literature. Here, we extend prior work in adults by examining the interrelationships among different model parameters in the ABCD sample and demonstrate how computational models of RT can clarify mechanisms of time-on-task effects and sex differences in RTs.

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Although there are pronounced sex differences for psychiatric disorders, relatively little has been published on the heterogeneity of sex-specific genetic effects for these traits until very recently for adults. Much less is known about children because most psychiatric disorders will not manifest until later in life and existing studies for children on psychiatric traits such as cognitive functions are underpowered. We used results from publicly available genome-wide association studies for six psychiatric disorders and individual-level data from the Adolescent Brain Cognitive Development (ABCD) study and the UK Biobank (UKB) study to evaluate the associations between the predicted polygenic risk scores (PRS) of these six disorders and observed cognitive functions, behavioral and brain imaging traits. We further investigated the mediation effects of the brain structure and function, which showed heterogeneity between males and females on the correlation between genetic risk of schizophrenia and fluid intelligence. There was significant heterogeneity in genetic associations between the cognitive traits and psychiatric disorders between sexes. Specifically, the PRSs of schizophrenia of boys showed stronger correlation with eight of the ten cognitive functions in the ABCD data set; whereas the PRSs of autism of females showed a stronger correlation with fluid intelligence in the UKB data set. Besides cognitive traits, we also found significant sexual heterogeneity in genetic associations between psychiatric disorders and behavior and brain imaging. These results demonstrate the underlying early etiology of psychiatric disease and reveal a shared and unique genetic basis between the disorders and cognition traits involved in brain functions between the sexes.

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Explainability and fairness are two key factors for the effective and ethical clinical implementation of deep learning-based machine learning models in healthcare settings. However, there has been limited work on investigating how unfair performance manifests in explainable artificial intelligence (XAI) methods, and how XAI can be used to investigate potential reasons for unfairness. Thus, the aim of this work was to analyze the effects of previously established sociodemographic-related confounders on classifier performance and explainability methods. A convolutional neural network (CNN) was trained to predict biological sex from T1-weighted brain MRI datasets of 4547 9- to 10-year-old adolescents from the Adolescent Brain Cognitive Development study. Performance disparities of the trained CNN between White and Black subjects were analyzed and saliency maps were generated for each subgroup at the intersection of sex and race. The classification model demonstrated a significant difference in the percentage of correctly classified White male ( ) and Black male ( ) children. Conversely, slightly higher performance was found for Black female ( ) compared with White female ( ) children. Saliency maps showed subgroup-specific differences, corresponding to brain regions previously associated with pubertal development. In line with this finding, average pubertal development scores of subjects used in this study were significantly different between Black and White females ( ) and males ( ). We demonstrate that a CNN with significantly different sex classification performance between Black and White adolescents can identify different important brain regions when comparing subgroup saliency maps. Importance scores vary substantially between subgroups within brain structures associated with pubertal development, a race-associated confounder for predicting sex. We illustrate that unfair models can produce different XAI results between subgroups and that these results may explain potential reasons for biased performance.

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Rising and economically disproportionate rates of adverse mental health outcomes among children and youth warrant research investigating the complex pathways stemming from socioeconomic status. While adverse childhood experiences (ACEs) have been considered a possible mechanism linking socioeconomic status (SES) and child and youth psychopathology in previous studies, less is understood about how family environments might condition these pathways. Using data from a longitudinal, multiple-wave study, the present study addresses this gap by examining the direct relationships between family economic status and youth internalizing and externalizing symptoms, if ACEs mediate these relationships, and if conflictual family environments moderate these direct and indirect relationships. The data were obtained from 5510 youth participants [mean age at baseline = 9.52 (SD = 0.50), 47.7% female, 2.1% Asian, 10.3% Black, 17.6% Hispanic, 9.8% Multiracial/Multiethnic, 60.2% White] and their caretakers from the baseline, 1-year, and 2-year follow up waves. Conditional process analysis assessed the direct, indirect, and moderated relationships in separate, equivalent models based on youth- versus caregiver-raters of ACEs and youth psychopathology to capture potential differences based on the rater. The results of both the youth- and caregiver-rated models indicated that lower family economic status directly predicted higher levels of externalizing symptoms, and ACEs indirectly accounted for higher levels of internalizing and externalizing symptoms. Additionally, family conflict moderated some, but not all, of these relationships. The study’s findings highlight that lower family economic status and ACEs, directly and indirectly, contribute to early adolescent psychopathology, and conflictual family environments can further intensify these relationships. Implementing empirically supported policies and interventions that target ACEs and family environments may disrupt deleterious pathways between SES and youth psychopathology.

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The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, = 1.098 × 10) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits.

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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.

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Understanding complex influences on mental health problems in young people is needed to inform early prevention strategies. Both genetic and environmental factors are known to influence youth mental health, but a more comprehensive picture of their interplay, including wide-ranging environmental exposures – that is, the exposome – is needed. We perform an integrative analysis of genomic and exposomic data in relation to internalizing and externalizing symptoms in a cohort of 4,314 unrelated youth from the Adolescent Brain and Cognitive Development (ABCD) Study.

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The endocannabinoid (eCB) system plays a key role in modulating brain development, including myelination processes. Recent studies link a common variant (C385A, rs324420) in the fatty acid amide hydrolase (FAAH) gene to higher circulating eCB levels, lower anxiety, and altered frontolimbic development. Frontolimbic pathways, which demonstrate a protracted maturational course across childhood and adolescence, are associated with anxiety, and are vulnerable to environmental stressors such as trauma exposure. Here, we examined the impact of trauma exposure, FAAH genotype, and anxiety on frontolimbic white matter microstructure in children. We leveraged baseline data from the Adolescent Brain Cognitive Development (ABCD) study (=9969; mean±standard deviation age=9.92±0.62 years; 47.1% female). Saliva samples were used for genotyping, and caregivers reported on their child’s anxiety symptoms and trauma exposure. Fractional anisotropy (FA), a nonspecific measure of white matter integrity, was estimated for frontolimbic tracts. Thirty-six percent of youth experienced one or more potentially traumatic events according to Criterion A (64% controls), and 45% were FAAH A-allele carriers (55% noncarriers). Relative to controls, trauma-exposed youth demonstrated higher anxiety and higher FA of the left uncinate. The FAAH A-allele (vs. CC) was associated with lower FA in the left fornix and left parahippocampal cingulum, and there was an indirect effect of FAAH genotype on anxiety through FA of the left fornix. Moreover, genotype moderated the association between FA of the left cingulum and anxiety. Our findings demonstrate distinct effects of trauma exposure and the FAAH C385A variant on frontolimbic pathways and subsequent anxiety risk in preadolescent children. This line of work may provide important insights into neurodevelopmental mechanisms leading to anxiety risk, and potential targets for intervention.

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Our study characterized associations between three indicators of COVID-19’s community-level impact in 20 geographically diverse metropolitan regions and how worried youth and their caregivers in the Adolescent Brain Cognitive Development℠ Study have been about COVID-19. County-level COVID-19 case/death rates and monthly unemployment rates were geocoded to participants’ addresses. Caregivers’ (vs. youths’) COVID-19-related worry was more strongly associated with COVID-19’s community impact, independent of sociodemographics and pre-pandemic anxiety levels, with these associations varying by location. Public-health agencies and healthcare providers should avoid adopting uniform “one-size-fits-all” approaches to addressing COVID-19-related emotional distress and must consider specific communities’ needs, challenges, and strengths.

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Greater risk-taking behaviors, such as alcohol experimentation, are associated with different patterns of brain functioning in regions implicated in reward (nucleus accumbens, NA) and cognitive control (inferior frontal gyrus, IFG). These neural features have been observed in youth with greater risk-taking tendencies prior to substance use initiation, suggesting NA-IFG disruption may serve as an early marker for subsequent substance use disorders. Prospective studies are needed to determine if NA-IFG neural disruption predicts future substance use in school-age children, including those with minimal use of alcohol (e.g., sipping). The present large-sample prospective study sought to use machine learning to: (1) examine alcohol sipping at ages 9, 10 as a potential behavioral indicator of concurrent underlying altered neural responsivity to reward, and (2) determine if alcohol sipping and NA-IFG activation at ages 9, 10 can be used to predict which youth reported increased alcohol use at ages 11, 12. Additionally, low-level alcohol use and brain functioning at ages 9, 10 were examined as predictors of substance use and brain functioning at ages 11, 12.

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When brain networks deviate from typical development, this is thought to contribute to varying forms of psychopathology. However, research has been limited by the reliance on discrete diagnostic categories that overlook the potential for psychological comorbidity and the dimensional nature of symptoms.

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Children with ADHD face deficits in interference control due to abnormalities in brain structure. A low body mass index and high physical activity are factors promoting brain health and may have the potential to reduce ADHD-related cognitive deficits. We aimed to investigate the predictive values of ADHD, body mass index and physical activity for interference control and the potential mediation of these associations by brain structure.

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Ecological theories suggest that environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multilevel modeling, require manual specification of interacting factors, making them inept in their ability to search for interactions. This paper shows evidence that an explainable artificial intelligence approach, commonly employed in genomics research, can address this problem. The method entails using random intersection trees to decode interactions learned by random forest models. Here, this approach is used to extract interactions between features of a multi-level environment from random forest models of waist-to-height ratios using 11,112 participants from the Adolescent Brain Cognitive Development study. This study shows that methods used to discover interactions between genes can also discover interacting features of the environment that impact obesity. This new approach to modeling ecosystems may help shine a spotlight on combinations of environmental features that are important to obesity, as well as other health outcomes.

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The Adolescent Brain Cognitive Development (ABCD) Neurocognitive Prediction Challenge (ABCD-NP-Challenge) is a community-driven competition that challenges competitors to develop algorithms to predict fluid intelligence scores from T1-w MRI images. In this work, a two-step deep learning pipeline is proposed to improve the prediction accuracy of fluid intelligence scores. In terms of the first step, the main contributions of this study include the following: (1) the concepts of the residual network (ResNet) and the squeeze-and-excitation network (SENet) are utilized to improve the original 3D U-Net; (2) in the segmentation process, the pixels in symmetrical brain regions are assigned the same label; (3) to remove redundant background information from the segmented regions of interest (ROIs), a minimum bounding cube (MBC) is used to enclose the ROIs. This new segmentation structure can greatly improve the segmentation performance of the ROIs in the brain as compared with the classical convolutional neural network (CNN), which yields a Dice coefficient of 0.8920. In the second stage, MBCs are used to train neural network regression models for enhanced nonlinearity. The fluid intelligence score prediction results of the proposed method are found to be superior to those of current state-of-the-art approaches, and the proposed method achieves a mean square error (MSE) of 82.56 on a test data set, which reflects a very competitive performance.

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The landscape of tobacco use is changing. However, information about the association between early-age tobacco use and cognitive performances is limited, especially for emerging tobacco products such as electronic cigarettes (e-cigarettes).

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Although different aspects of socioeconomic status (SES) may represent distinct risk factors for poor mental health in children, knowledge of their differential and synergistic associations with the brain is limited.

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Although the American Academy of Sleep Medicine suggests at least 9 h of sleep per day for 6-12-year-olds, children in recent generations often report sleeping less than this amount. Because early adolescence is a crucial period for neurocognitive development, we aimed to investigate how insufficient sleep affects children’s mental health, cognition, brain function, and brain structure over 2 years.

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Cognitive abilities are one of the major transdiagnostic domains in the National Institute of Mental Health’s Research Domain Criteria (RDoC). Following RDoC’s integrative approach, we aimed to develop brain-based predictive models for cognitive abilities that (a) are developmentally stable over years during adolescence and (b) account for the relationships between cognitive abilities and socio-demographic, psychological and genetic factors. For this, we leveraged the unique power of the large-scale, longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (n ~ 11 k) and combined MRI data across modalities (task-fMRI from three tasks: resting-state fMRI, structural MRI and DTI) using machine-learning. Our brain-based, predictive models for cognitive abilities were stable across 2 years during young adolescence and generalisable to different sites, partially predicting childhood cognition at around 20% of the variance. Moreover, our use of ‘opportunistic stacking’ allowed the model to handle missing values, reducing the exclusion from around 80% to around 5% of the data. We found fronto-parietal networks during a working-memory task to drive childhood-cognition prediction. The brain-based, predictive models significantly, albeit partially, accounted for variance in childhood cognition due to (1) key socio-demographic and psychological factors (proportion mediated = 18.65% [17.29%-20.12%]) and (2) genetic variation, as reflected by the polygenic score of cognition (proportion mediated = 15.6% [11%-20.7%]). Thus, our brain-based predictive models for cognitive abilities facilitate the development of a robust, transdiagnostic research tool for cognition at the neural level in keeping with the RDoC’s integrative framework.

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Major depressive disorder (MDD) is a polygenic disorder associated with brain alterations but until recently, there have been no brain-based metrics to quantify individual-level variation in brain morphology. Here, we evaluated and compared the performance of a new brain-based ‘Regional Vulnerability Index’ (RVI) with polygenic risk scores (PRS), in the context of MDD. We assessed associations with syndromal MDD in an adult sample ( = 702, age = 59 ± 10) and with subclinical depressive symptoms in a longitudinal adolescent sample (baseline  = 3,825, age = 10 ± 1; 2-year follow-up  = 2,081, age = 12 ± 1).

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Cross-sectional studies have demonstrated associations between screen time and disruptive behavior disorders (conduct disorder and oppositional defiant disorder); however, prospective associations remain unknown. This study’s objective was to determine the prospective associations of contemporary screen time modalities with conduct and oppositional defiant disorder in a national cohort of 9-11-year-old children.

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Little is known about the long-term effects of maternal diabetes during pregnancy (DP), either gestational diabetes or preexisting diabetes (type 1 or type 2), on offspring’s brain morphometry and neurocognitive functioning (NCF). This study examined the effect of prenatal exposure to maternal DP on the brain structure and NCF in children between 9 and 10 years of age. This study used cross-sectional neuroimaging and NCF data from the baseline wave of the Adolescent Brain and Cognitive Development® study. Exposure to maternal DP was assigned from the developmental history questionnaire. Differences in the brain cortical thickness (CTh) and five cognitive abilities (executive function, working and episodic memory, processing speed, and language abilities) were examined in diabetes-exposed and diabetes-unexposed children. Linear mixed effect models and generalized linear models were used to adjust for the effect of confounding variables. A total of 9,967 children (718 diabetes-exposed and 9249 unexposed) were included in the analysis. Diabetes-exposed children had lower whole-brain CTh [mean: exposed vs unexposed = 2.725 mm vs 2.732 mm; difference (95%CI): -0.007 mm (-0.013, -0.001)] compared to unexposed children after adjusting for confounding variables. Diabetes-exposed children had lower CTh in most part of the occipital lobe of both hemispheres, right postcentral gyrus, and left superior parietal cortex. Diabetes-exposed children also had lower scores in processing speed task [mean difference (95%CI): -1.7 (-2.8, -0.6)] and total cognition [mean difference (95%CI): -0.6 (-1.2, -0.02)]. Diabetes-exposed children have reduced CTh and NCF during preadolescence, which might have implications for psychomotor development during later life. Prospective studies are needed to confirm our findings.

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Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the ML approach in estimating brain-age in youth is important because age-related brain changes in this age-group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5-22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9-10 years and another comprising 594 individuals aged 5-21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, number of extreme outliers, and sample size. Tree-based models and algorithms with a nonlinear kernel performed comparably well, with the latter being especially computationally efficient. Extreme Gradient Boosting (MAE of 1.49 years), Random Forest Regression (MAE of 1.58 years), and Support Vector Regression (SVR) with Radial Basis Function (RBF) Kernel (MAE of 1.64 years) emerged as the three most accurate models. Linear algorithms, with the exception of Elastic Net Regression, performed poorly. Findings of the present study could be used as a guide for optimizing methodology when quantifying brain-age in youth.

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Children in the United States sleep less than the recommended amount and sleep deficiencies may be worse among disadvantaged children. Prior studies that compared sleep time in children of different race/ethnic groups mostly relied on questionnaires or were limited to small sample sizes. Our study takes advantage of the Adolescent Brain Cognitive Development study to compare total sleep time using a week of actigraphy data among American children (n = 4,207, 9 to 13 y old) of different racial/ethnic and income groups. We also assessed the effects of neighborhood deprivation, experience of discrimination, parent’s age at child’s birth, body mass index (BMI), and time the child fell asleep on sleep times. Daily total sleep time for the sample was 7.45 h and race/ethnicity, income, sex, age, BMI, were all significant predictors of total sleep time. Black children slept less than White children (∼34 min; Cohen’s d = 0.95), children from lower income families slept less than those from higher incomes (∼16 min; Cohen’s d = 0.44), boys slept less than girls (∼7 min; Cohen’s d = 0.18), and older children slept less than younger ones (∼32 min; Cohen’s d = 0.91); mostly due to later sleep times. Children with higher BMI also had shorter sleep times. Neither area deprivation index, experience of discrimination, or parent’s age at child’s birth significantly contributed to sleep time. Our findings indicate that children in the United States sleep significantly less than the recommended amount for healthy development and identifies significant racial and income disparities. Interventions to improve sleep hygiene in children will help improve health and ameliorate racial disparities in health outcomes.

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Socioeconomic disadvantage is associated with volumetric differences in stress-sensitive neural structures, including the hippocampus, and deficits in episodic memory. Rodent studies provide evidence that memory deficits arise via stress-related structural differences in hippocampal subdivisions; however, human studies have only provided limited evidence to support this notion. We used a sample of 10,695 9-13-year-old participants from two timepoints of the Adolescent Brain and Cognitive Development (ABCD) Study to assess whether socioeconomic disadvantage relates to episodic memory performance through hippocampal volumes. We explored associations among socioeconomic disadvantage, measured via the Area Deprivation Index (ADI), concurrent subregion (anterior, posterior) and subfield volumes (CA1, CA3, CA4/DG, subiculum), and episodic memory, assessed via the NIH Toolbox Picture Sequence Memory Test at baseline and 2-year follow-up (Time 2). Results showed that higher baseline ADI related to smaller concurrent anterior, CA1, CA4/DG, and subiculum volumes and poorer Time 2 memory performance controlling for baseline memory. Moreover, anterior, CA1, and subiculum volumes mediated the longitudinal association between the ADI and memory. Results suggest that greater socioeconomic disadvantage relates to smaller hippocampal subregion and subfield volumes and less age-related improvement in memory. These findings shed light on the neural mechanisms linking socioeconomic disadvantage and cognitive ability in childhood.

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Alcohol use confers risk for suicidal thoughts and behaviors (ideation, attempt) in early adolescents. The Research Domain Criteria provides a framework for examination of multidimensional and modifiable risk factors. We examined distinct latent profiles based on patterns of positive valence (reward responsivity) and cognitive systems (neurocognition) from the ABCD Study (age 9−10, N = 10,414) at baseline enrollment. Longitudinal associations were determined between baseline positive valence and cognitive profiles and group classification (alcohol use, suicidal thoughts and behaviors, or their co-occurrence) two-years after initial assessment (ages 11−12). Three unique profiles of positive valence, cognition, alcohol use, and suicidal thoughts and behaviors were identified. Two baseline profiles predicted alcohol use and suicidal thoughts and behaviors, two-years after initial assessment. Low positive valence with high cognition (but low impulsivity) predicted alcohol use (OR = 1.414, p< 0.001), while high positive valence with low cognition (but high impulsivity) predicted suicidal thoughts and behaviors (OR = 1.25, p = 0.038), compared to average positive valence and cognition. Unique profiles of positive valence and cognitive systems among 9−12-year-olds may be predictive of alcohol use and suicidal thoughts and behaviors over a two-year period. Findings underscore the potential for trajectory research on positive valence and cognitive profiles to enhance prevention for early-adolescents.

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To determine the associations between Adverse Childhood Experiences (ACEs), adolescent screen time, and physical activity during the early COVID-19 pandemic.

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To determine the prevalence and sociodemographic correlates of cyberbullying victimization and perpetration among a racially, ethnically and socioeconomically diverse population-based sample of 11-12-year-old early adolescents.

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