Research Publications

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Youth use different forms of screen time (e.g., streaming, gaming) that may be related to body mass index (BMI). Screen time is non-independent from other behaviors, including physical activity and sleep duration. Statistical approaches such as isotemporal substitution or compositional data analysis (CoDA) can model associations between these non-independent behaviors and health outcomes. Few studies have examined different types of screen time, physical activity, and sleep duration simultaneously in relation to BMI.

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

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There remains little consensus about the relationship between sex and brain structure, particularly in early adolescence. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest-many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years old (N = 7195). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. Additional sensitivity analyses found that male versus female differences in gyrification and white matter were largely accounted for by total brain volume, rather than sex per se. The model with sex, but not gender diversity, was the best-fitting model in 60.1% of gray matter regions and 61.9% of white matter regions after adjusting for brain volume. The proportion of variance accounted for by sex was negligible to small in all cases. While models including felt-gender explained a greater amount of variance in a few regions, the felt-gender score alone was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity.

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The COVID-19 pandemic has had profound but incompletely understood adverse effects on youth. To elucidate the role of brain circuits in how adolescents responded to the pandemic’s stressors, we investigated their prepandemic organization as a predictor of mental/emotional health in the first ~15 months of the pandemic. We analyzed resting-state networks from n = 2,641 adolescents [median age (interquartile range) = 144.0 (13.0) months, 47.7% females] in the Adolescent Brain Cognitive Development study, and longitudinal assessments of mental health, stress, sadness, and positive affect, collected every 2 to 3 months from May 2020 to May 2021. Topological resilience and/or network strength predicted overall mental health, stress and sadness (but not positive affect), at multiple time points, but primarily in December 2020 and May 2021. Higher resilience of the salience network predicted better mental health in December 2020 (β = 0.19, 95% CI = [0.06, 0.31], P = 0.01). Lower connectivity of left salience, reward, limbic, and prefrontal cortex and its thalamic, striatal, amygdala connections, predicted higher stress (β = -0.46 to -0.20, CI = [-0.72, -0.07], P < 0.03). Lower bilateral robustness (higher fragility) and/or connectivity of these networks predicted higher sadness in December 2020 and May 2021 (β = -0.514 to -0.19, CI = [-0.81, -0.05], P < 0.04). These findings suggest that the organization of brain circuits may have played a critical role in adolescent stress and mental/emotional health during the pandemic.

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One fundamental challenge in diffusion magnetic resonance imaging (dMRI) harmonization is to disentangle the contributions of scanner-related effects from the variable brain anatomy for the observed imaging signals. Conventional harmonization methods rely on establishing an atlas space to resolve anatomical variability and generate a unified inter-site mapping function. However, this approach is limited in accounting for the misalignment of neuroanatomy that still widely persists even after registration, especially in regions close to cortical boundaries. To overcome this challenge, we propose a personalized framework in this paper to more effectively address the confounding from the misalignment of neuroanatomy in dMRI harmonization. Instead of using a common template representing site-effects for all subjects, the main novelty of our method is the adaptive computation of personalized templates for both source and target scanning sites to estimate the inter-site mapping function. We integrate our method with the rotation invariant spherical harmonics (RISH) features to achieve the harmonization of dMRI signals between sites. In our experiments, the proposed approach is applied to harmonize the dMRI data acquired from two scanning platforms: Siemens Prisma and GE MR750 from the Adolescent Brain Cognitive Development dataset and compared with a state-of-the-art method based on RISH features. Our results indicate that the proposed harmonization framework achieves superior performance not only in reducing inter-site variations due to scanner differences but also in preserving sex-related biological variability in original cohorts. Moreover, we assess the impact of harmonization on the estimation of fiber orientation distributions and show the robustness of the personalized harmonization procedure in preserving the fiber orientation of original dMRI signals.

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Prior research has shown smaller cortical and subcortical gray matter volumes among individuals with attention-deficit/hyperactivity disorder (ADHD). However, neuroimaging studies often do not differentiate between inattention and hyperactivity/impulsivity, which are distinct core features of ADHD. The present study uses an approach to disentangle overlapping variance to examine the neurostructural heterogeneity of inattention and hyperactivity/impulsivity dimensions.

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Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI from = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

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Employing a developmental psychopathology framework, we tested the utility of the hormesis model in examining the strengthening of children and youth through limited levels of adversity in relation to internalizing and externalizing outcomes within a brain-by-development context.

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Puberty depicts a period of profound and multifactorial changes ranging from social to biological factors. While brain development in youths has been studied mostly from an age perspective, recent evidence suggests that pubertal measures may be more sensitive to study adolescent neurodevelopment, however, studies on pubertal timing in relation to brain development are still scarce.

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

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Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.

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The growing availability of large-population human biomedical datasets provides researchers with unique opportunities to conduct rigorous and impactful studies on brain and behavioral development, allowing for a more comprehensive understanding of neurodevelopment in diverse populations. However, the patterns observed in these datasets are more likely to be influenced by upstream structural inequities (that is, structural racism), which can lead to health disparities based on race, ethnicity and social class. This paper addresses the need for guidance and self-reflection in biomedical research on conceptualizing, contextualizing and communicating issues related to race and ethnicity. We provide recommendations as a starting point for researchers to rethink race and ethnicity choices in study design, model specification, statistical analysis and communication of results, implement practices to avoid the further stigmatization of historically minoritized groups, and engage in research practices that counteract existing harmful biases.

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Parents play a notable role in the development of child psychopathology. In this study we investigate the role of parent psychopathology and behaviors on child brain-symptom networks to understand the role of intergenerational transmission of psychopathology. Few studies have documented the interaction of child psychopathology, parent psychopathology, and child neuroimaging.

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Black youth are disproportionately exposed to school exclusionary discipline. We examined the impact of race on age at the onset of school disciplinary actions and police contact, and the rate of receiving increasingly severe disciplinary actions.

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To investigate child-parent concordance in reporting social victimization experiences and whether parent concordance with child report of victimization was associated with child behavioral symptoms.

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Although posttraumatic stress disorder (PTSD) has been well characterized in adults, its epidemiology in children is unclear. The current study provides the first population-based examination of the prevalence of PTSD, sociodemographic and psychiatric correlates, clinical sequelae, and associations with psychiatric treatment in preadolescents 9-10 years old in the United States. Data from the Adolescent Brain and Cognitive Development (ABCD) Study (release 5.0) was analyzed. Participants (unweighted n = 11,875) were recruited from 21 sites across the United States. Current and lifetime PTSD prevalence were estimated, as was treatment use among children with PTSD. Sociodemographic, psychiatric correlates and sequelae of PTSD were analyzed using logistic regression, as was the association between PTSD and psychiatric treatment. After the application of propensity weights, lifetime prevalence of PTSD was 2.17%. Sexual minority status, being multiracial, having unmarried parents, and family economic insecurity were associated with greater odds of PTSD. Among psychiatric disorders, separation anxiety was most strongly associated with PTSD, although general comorbid psychopathology was associated with greater odds of PTSD. Prior history of PTSD predicted a new onset of other psychiatric disorders after PTSD remission. Nearly one in three children with lifetime PTSD did not receive psychiatric treatment, despite negative long-term outcomes of PTSD and significant psychiatric comorbidity. Even among preadolescents who experience full remission of PTSD, a significant risk for future psychiatric illness remains. Further, the current findings underscore the need for improved efforts to reduce unmet treatment needs among those with PTSD at this age.

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Brain networks are continuously modified throughout development, yet this plasticity can also make functional networks vulnerable to early life stress. Little is currently known about the effect of early life stress on the functional organization of the brain. The current study investigated the association between environmental stressors and network topology using data from the Adolescent Brain Cognitive Development (ABCD®) Study. Hierarchical modeling identified a general factor of environmental stress, representing the common variance across multiple stressors, as well as four subfactors including familial dynamics, interpersonal support, neighborhood SES deprivation, and urbanicity. Functional network topology metrics were obtained using graph theory at rest and during tasks of reward processing, inhibition, and affective working memory. The general factor of environmental stress was associated with less specialization of networks, represented by lower modularity at rest. Local metrics indicated that general environmental stress was also associated with less efficiency in the subcortical-cerebellar and visual networks while showing greater efficiency in the default mode network at rest. Subfactors of environmental stress were associated with differences in specialization and efficiency in select networks. The current study illustrates that a wide range of stressors in a child’s environment are associated with differences in brain network topology.

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Magnetic resonance imaging (MRI) is a vital tool for the study of brain structure and function. It is increasingly being used in individual differences research to examine brain-behaviour associations. Prior work has demonstrated low test-retest stability of functional MRI measures, highlighting the need to examine the longitudinal stability (test-retest reliability across long timespans) of MRI measures across brain regions and imaging metrics, particularly in adolescence. In this study, we examined the longitudinal stability of grey matter measures (cortical thickness, surface area, and volume) across brain regions, and testing sites in the Adolescent Brain Cognitive Development (ABCD) study release v4.0. Longitudinal stability ICC estimates ranged from 0 to .98, depending on the measure, parcellation, and brain region. We used Intra-Class Effect Decomposition (ICED) to estimate between-subjects variance and error variance, and assess the relative contribution of each across brain regions and testing sites on longitudinal stability. In further exploratory analyses, we examined the influence of parcellation used (Desikan-Killiany-Tourville and Destrieux) on longitudinal stability. Our results highlight meaningful heterogeneity in longitudinal stability across brain regions, structural measures (cortical thickness in particular), parcellations, and ABCD testing sites. Differences in longitudinal stability across brain regions were largely driven by between-subjects variance, whereas differences in longitudinal stability across testing sites were largely driven by differences in error variance. We argue that investigations such as this are essential to capture patterns of longitudinal stability heterogeneity that would otherwise go undiagnosed. Such improved understanding allows the field to more accurately interpret results, compare effect sizes, and plan more powerful studies.

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Mild traumatic brain injury (mTBI) is common in children. Long-term cognitive and behavioral outcomes as well as underlying structural brain alterations following pediatric mTBI have yet to be determined. In addition, the effect of age-at-injury on long-term outcomes is largely unknown.

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Obsessive-compulsive symptoms (OCS) are relatively common during adolescence although most individuals do not meet diagnostic criteria for obsessive-compulsive disorder (OCD). Nonetheless, OCS during adolescence are associated with comorbid psychopathologies and behavioral problems. Heightened levels of environmental stress and greater functional connectivity between the somatomotor network and putamen have been previously associated with elevated OCS in OCD patients relative to healthy controls. However, the interaction of these factors within the same sample of individuals has been understudied. This study examined somatomotor-putamen resting state connectivity, stress, and their interaction on OCS in adolescents from 9-12 years of age. Participants (n = 6,386) were drawn from the ABCD Study 4.0 release. Multilevel modeling was used to account for nesting in the data and to assess changes in OCS in this age range. Stress moderated the association between somatomotor-putamen connectivity and OCS (β = 0.35, S.E. = 0.13, p = 0.006). Participants who reported more stress than their average and had greater somatomotor-left putamen connectivity reported more OCS, whereas participants who reported less stress than their average and had greater somatomotor-left putamen connectivity reported less OCS. These data suggest that stress differentially affects the direction of association between somatomotor-putamen connectivity and OCS. Individual differences in the experience or perception of stress may contribute to more OCS in adolescents with greater somatomotor-putamen connectivity.

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

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Sex differences in psychopathology are well-established, with females demonstrating higher rates of internalizing (INT) psychopathology and males demonstrating higher rates of externalizing (EXT) psychopathology. Using two waves of data from the Adolescent Brain Cognitive Development Study ( = 6,778 at each wave), the current study tested whether the relations between sex and psychopathology might be accounted for by structural brain differences. In general, we found robust, relatively consistent relations between sex and structural morphometry across waves. Relatively few morphometric brain variables were significantly related to INT or EXT across waves, however, with very small effect sizes when present. Next, we tested the extent to which each morphometric brain variable could account for the associations of sex with INT and EXT psychopathology. We found a total of 26 brain regions that accounted for significant portions of the associations between sex and psychopathology across both waves (almost all related to EXT), although the effects present were very small. The current evidence suggests that in children aged 9-12, multiple whole-brain and regional brain variables appear to statistically account for small portions of the sex-psychopathology links, especially for externalizing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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Alcohol expectancies predict subsequent alcohol use and related problems among adolescents, although predictors of alcohol expectancies remain unclear. This study examined the longitudinal association between family conflict, a sociocultural factor strongly implicated in adolescent alcohol use, and positive and negative alcohol expectancies of adolescents of diverse racial/ethnic backgrounds.

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The study aimed to investigate longitudinal, bidirectional associations between discrimination due to multiple reasons (race/ethnicity, sexual orientation, weight; termed multiple discrimination) and substance use (SU) intention in late childhood. These associations were compared across youth with no, single, and multiple (i.e., intersecting) marginalized identities based on race/ethnicity, sexual orientation, and overweight status.

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Attention and executive function (EF) dysregulation are common in a number of disorders including autism and attention-deficit/hyperactivity disorder (ADHD). Better understanding of the relationship between indirect and direct measures of attention and EF and common neurodevelopmental diagnoses may contribute to more efficient and effective diagnostic assessment in childhood. We obtained cognitive (NIH Toolbox, Little Man Task, Matrix Reasoning Task, and Rey Delayed Recall) and symptom (CBCL, and BPMT) assessment data from the Adolescent Brain and Cognitive Development (ABCD) database for three groups, autistic (N = 110), ADHD (N = 878), and control without autism or ADHD diagnoses (N = 9130) and used ridge regression to determine which attention and EF assessments were most strongly associated with autism or ADHD. More variance was accounted for in the model for the ADHD group (31%) compared to the autism group (2.7%). Finally, we ran odds ratios (using clinical cutoffs where available and 2 standard deviations below the mean when not) for each assessment measure, which generally demonstrated a greater significance within the indirect measures when compared to the direct measures. These results add to the growing literature of symptom variably across diagnostic groups allowing for better understanding of presentations in autism and ADHD and how best to assess diagnosis. It also highlights the increased difficulty in differentiating autism and controls when compared to ADHD and controls and the importance of indirect measures of attention and EF in this differentiation.

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The Sensitivity to Threat and Affiliative Reward (STAR) model proposes low threat sensitivity and low affiliation as risk factors for callous-unemotional (CU) traits. Preliminary evidence for the STAR model comes from work in early childhood. However, studies are needed that explore the STAR dimensions in late childhood and adolescence when severe conduct problems (CP) emerge. Moreover, it is unclear how variability across the full spectrum of threat sensitivity and affiliation gives rise to different forms of psychopathology beyond CU traits.

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A large body of functional MRI research has examined a potential role for subcortico-cortical loops in the pathogenesis of attention deficit hyperactivity disorder (ADHD), but has produced inconsistent findings. The authors performed a mega-analysis of six neuroimaging data sets to examine associations between ADHD diagnosis and traits and subcortico-cortical connectivity.

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Coming from a disadvantaged background can have negative impact on an individual’s educational trajectory. Some people however seem unaffected and cope well with the demands and challenges posed by school education, despite growing up in adverse conditions, a phenomenon termed academic resilience. While it is uncertain which underlying factors make some people more likely to circumvent unfavorable odds than others, both socioeconomic status (SES) and cognitive ability have robustly been linked to school performance. The objective of the present work is to investigate if individual cognitive abilities and SES interact in their effect on grades. For this purpose, we analyzed SES, cognitive, and school performance data from 5001 participants from the Adolescent Brain Cognitive Development (ABCD) Study. Ordinal logistic regression models suggest similar patterns of associations between three SES measures (parental education, income-to-needs ratio, and neighborhood deprivation) and grades at two timepoints, with no evidence for interaction effects between SES and time. Parental education and income-to-needs ratio were associated with grades at both timepoints, irrespective of whether cognitive abilities were modeled or not. Neighborhood deprivation, in contrast, was only a statistically significant predictor of reported grades when cognitive abilities were not factored in. Cognitive abilities interacted with parental education level, meaning that they could be a safeguard against effects of SES on school performance.

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Children who experience persistent psychotic-like experiences (PLEs) are at a higher risk of developing psychotic disorder later in life. The developmental trajectories of PLEs are influenced by various factors. Therefore, it is important to identify early characteristics that can distinguish and predict between different developmental trajectories of PLEs.

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Few studies have considered the neural underpinnings of binge eating disorder (BED) in children despite clinical and subclinical symptom presentation occurring in this age group. Symptom presentation at this age is of clinical relevance, as early onset of binge eating is linked to negative health outcomes. Studies in adults have highlighted dysfunction in the frontostriatal reward system as a potential candidate for binge eating pathophysiology although the exact nature of such dysfunction is currently unclear.

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

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Prenatal and perinatal complications are established risk factors for psychotic disorder, but far less is known about these measures and psychotic experiences (PEs). We investigated the longitudinal effect of prenatal risk factors (maternal behavior, medication complications) and perinatal risk factors (birth weight, medical complications) on frequency of PEs. We also examined the cumulative risk of prenatal/perinatal risk factors, and differences between transient PE, persistent PE, and controls.

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Further research is needed to understand factors associated with well-being during the COVID-19 pandemic among adolescents who have experienced adverse childhood experiences (ACEs).

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Nearly 50 years of research has focused on faces as a special visual category, especially during development. Yet it remains unclear how spatial patterns of neural similarity of faces and places relate to how information processing supports subsequent recognition of items from these categories. The current study uses representational similarity analysis and functional imaging data from 9- and 10-year-old youth during an emotional n-back task from the Adolescent Brain and Cognitive Development Study 3.0 data release to relate spatial patterns of neural similarity during working memory to subsequent out-of-scanner performance on a recognition memory task. Specifically, we examine how similarities in representations within face categories (neutral, happy, and fearful faces) and representations between visual categories (faces and places) relate to subsequent recognition memory of these visual categories. Although working memory performance was higher for faces than places, subsequent recognition memory was greater for places than faces. Representational similarity analysis revealed category-specific patterns in face-and place-sensitive brain regions (fusiform gyrus, parahippocampal gyrus) compared with a nonsensitive visual region (pericalcarine cortex). Similarity within face categories and dissimilarity between face and place categories in the parahippocampus was related to better recognition of places from the n-back task. Conversely, in the fusiform, similarity within face categories and their relative dissimilarity from places was associated with better recognition of new faces, but not old faces. These findings highlight how the representational distinctiveness of visual categories influence what information is subsequently prioritized in recognition memory during development.

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The early prediction of adolescent depression recurrence poses a significant challenge in the field. This study aims to investigate and compare the abilities of the general psychopathology factor () and the specific internalizing factor, in predicting depression recurrence over a 2-year course, as well as identifying remitted depressed adolescents from healthy adolescents. Longitudinal changes of these two factors in different trajectory groups were also tracked to examine their sensitivity to sustained remission and relapse.

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Multivariate machine learning techniques are a promising set of tools for identifying complex brain-behavior associations. However, failure to replicate results from these methods across samples has hampered their clinical relevance. Here we aimed to delineate dimensions of brain functional connectivity that are associated with child psychiatric symptoms in two large and independent cohorts: the Adolescent Brain Cognitive Development (ABCD) Study and the Generation R Study (total n = 6935). Using sparse canonical correlations analysis, we identified two brain-behavior dimensions in ABCD: attention problems and aggression/rule-breaking behaviors. Importantly, out-of-sample generalizability of these dimensions was consistently observed in ABCD, suggesting robust multivariate brain-behavior associations. Despite this, out-of-study generalizability in Generation R was limited. These results highlight that the degrees of generalizability can vary depending on the external validation methods employed as well as the datasets used, emphasizing that biomarkers will remain elusive until models generalize better in true external settings.

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Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage-involving feature selection, covariate correction, and dependence between subjects-on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.

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The endocannabinoid system modulates neural activity throughout the lifespan. In adults, neuroimaging studies link a common genetic variant in fatty acid amide hydrolase (FAAH C385A)-an enzyme that regulates endocannabinoid signaling-to reduced risk of anxiety and depression, and altered threat- and reward-related neural activity. However, limited research has investigated these associations during the transition into adolescence, a period of substantial neurodevelopment and increased psychopathology risk.

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The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

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Preterm infants with low birthweight are at heightened risk of developmental sequelae, including neurological and cognitive dysfunction that can persist into adolescence or adulthood. In addition, preterm birth and low birthweight can provoke changes in endocrine and metabolic processes that likely impact brain health throughout development. However, few studies have examined associations among birthweight, pubertal endocrine process, long-term neurological and cognitive development.

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Children with ADHD show abnormal brain function and structure. Neuroimaging studies found that stimulant medications may improve brain structural abnormalities in children with ADHD. However, prior studies on this topic were conducted with relatively small sample sizes and wide age ranges and showed inconsistent results. In this cross-sectional study, we employed latent class analysis and linear mixed-effects models to estimate the impact of stimulant medications using demographic, clinical measures, and brain structure in a large and diverse sample of children aged 9-11 from the Adolescent Brain and Cognitive Development Study. We studied 273 children with low ADHD symptoms and received stimulant medication (Stim Low-ADHD), 1002 children with high ADHD symptoms and received no medications (No-Med ADHD), and 5378 typically developing controls (TDC). After controlling for the covariates, compared to Stim Low-ADHD and TDC, No-Med ADHD showed lower cortical thickness in the right insula (INS, d = 0.340, P = 0.003) and subcortical volume in the left nucleus accumbens (NAc, d = 0.371, P = 0.003), indicating that high ADHD symptoms were associated with structural abnormalities in these brain regions. In addition, there was no difference in brain structural measures between Stim Low-ADHD and TDC children, suggesting that the stimulant effects improved both ADHD symptoms and ADHD-associated brain structural abnormalities. These findings together suggested that children with ADHD appear to have structural abnormalities in brain regions associated with saliency and reward processing, and treatment with stimulant medications not only improve the ADHD symptoms but also normalized these brain structural abnormalities.

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Due to limitations of categorical definitions of mental illness, there is a need for quantitative empirical investigations of the dimensional structure of psychopathology. Using exploratory bifactor methods, this study investigated a comprehensive and representative structure of psychopathology in children to better understand how psychotic-like experiences (PLEs), autism spectrum disorder (ASD) symptoms, impulsivity, and sensitivity to reward and punishment, may be integrated into extant general factor models of psychopathology.

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Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either grey or white matter metrics in humans, leaving open the key question as to whether grey or white matter microstructure play distinct or complementary roles supporting cognitive performance.To compare the role of grey and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with grey and white matter measures. Specifically, we compared how grey matter (volume, cortical thickness and surface area) and white matter measures (volume, fractional anisotropy and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study, 5680 female; 6196 male) at 10 years old.We found that grey and white matter metrics bring partly non-overlapping information to predict cognitive performance. The models with only grey or white matter explained respectively 15.4% and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in we additionally found that different metrics within grey and white matter had different predictive power, and that the tracts/regions that were most predictive of cognitive performance differed across metric.These results show that studies focusing on a single metric in either grey or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation. This paper enriches the recent debates on the challenges of linking variation in brain structure to phenotypic differences (Marek et al., 2022). We demonstrate that using latent variables (to improve power), structural equation modelling (to allow greater flexibility in linking brain to behaviour), and by simultaneously incorporating multiple measures of grey and white matter in a large sample, we demonstrate relatively strong and robust brain-behaviour associations, which highlight the complementarity of grey and white matter metrics in predicting cognitive performance as well as the importance of incorporating the full complexity of these associations over 1-to-1 linkages. This finding should lead researchers to consider integrating both grey and white matter measures when demonstrating a more comprehensive picture of brain-cognition relationships.

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Public genomic datasets like the 1000 Genomes project (1KGP), Human Genome Diversity Project (HGDP), and the Adolescent Brain Cognitive Development (ABCD) study are valuable public resources that facilitate scientific advancements in biology and enhance the scientific and economic impact of federally funded research projects. Regrettably, these datasets have often been developed and studied in ways that propagate outdated racialized and typological thinking, leading to fallacious reasoning among some readers that social and health disparities among the so-called races are due in part to innate biological differences between them. We highlight how this framing has set the stage for the racist exploitation of these datasets in two ways: First, we discuss the use of public biomedical datasets in studies that claim support for innate genetic differences in intelligence and other social outcomes between the groups identified as races. We further highlight recent instances of this which involve unauthorized access, use, and dissemination of public datasets. Second, we discuss the use of simple figures meant for quick dissemination among lay audiences, of population genetic data to argue for a biological basis for purported human racial groups. We close with recommendations for scientists, to preempt the exploitation and misuse of their data, and for funding agencies, to better enforce violations of data use agreements.

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Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

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Early exposure to neighborhood social fragmentation has been shown to be associated with schizophrenia. The impact of social fragmentation and friendships on distressing psychotic-like experiences (PLE) remains unknown. We investigate the relationships between neighborhood social fragmentation, number of friends, and distressing PLE among early adolescents.

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

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Alcohol intent (the susceptibility to initiating alcohol use) and alcohol sips (the initiation of alcohol) in youth are a multifactorial puzzle with many components. This research aims to examine the connection between genetic and environmental factors across sex, race and ethnicity.

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Screen time has been reported to be associated with binge-eating disorder (BED) among adolescents in the US; however, potential mediators remain unclear. This study aimed to evaluate depression symptoms as a mediator of the prospective association between screen time and BED.

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The complexity of brain signals may hold clues to understand brain-based disorders. Sample entropy, an index that captures the predictability of a signal, is a promising tool to measure signal complexity. However, measurement of sample entropy from fMRI signals has its challenges, and numerous questions regarding preprocessing and parameter selection require research to advance the potential impact of this method. For one example, entropy may be highly sensitive to the effects of motion, yet standard approaches to addressing motion (e.g., scrubbing) may be unsuitable for entropy measurement. For another, the parameters used to calculate entropy need to be defined by the properties of data being analyzed, an issue that has frequently been ignored in fMRI research. The current work sought to rigorously address these issues and to create methods that could be used to advance this field.

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This study aimed to characterize the role of sex and pubertal markers in reward motivation behavior and neural processing in early adolescence. We used baseline and two-year follow-up data from the Adolescent Brain and Cognitive Development study (15844 observations; 52% from boys; age 9-13). Pubertal development was measured with parent-reported Pubertal Development Scale, and DHEA, testosterone, and estradiol levels. Reward motivation behavior and neural processing at anticipation and feedback stages were assessed with the Monetary Incentive Delay task. Boys had higher reward motivation than girls, demonstrating greater accuracy difference between reward and neutral trials and higher task earnings. Girls had lower neural activation during reward feedback than boys in the nucleus accumbens, caudate, rostral anterior cingulate, medial orbitofrontal cortex, superior frontal gyrus and posterior cingulate. Pubertal stage and testosterone levels were positively associated with reward motivation behavior, although these associations changed when controlling for age. There were no significant associations between pubertal development and neural activation during reward anticipation and feedback. Sex differences in reward-related processing exist in early adolescence, signaling the need to understand their impact on typical and atypical functioning as it unfolds into adulthood.

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Attention deficit hyperactivity disorder (ADHD) has been characterized by impairments among distributed functional brain networks, e.g., the frontoparietal network (FPN), default mode network (DMN), reward and motivation-related circuits (RMN), and salience network (SAL). In the current study, we evaluated the complexity and functional connectivity (FC) of resting state fMRI (rsfMRI) in pre-adolescents with the behavioral symptoms of ADHD, for pathology-relevant networks. We leveraged data from the Adolescent Brain and Cognitive Development (ABCD) Study. The final study sample included 63 children demonstrating the behavioral features of ADHD and 92 healthy control children matched on age, sex, and pubertal development status. For selected regions in the relevant networks, ANCOVA compared multiscale entropy (MSE) and FC between the groups. Finally, differences in the association between MSE and FC were evaluated. We found significantly reduced MSE along with increased FC within the FPN of pre-adolescents demonstrating the behavior symptoms of ADHD compared to matched healthy controls. Significant partial correlations between MSE and FC emerged in the FPN and RMN in the healthy controls however the association was absent in the participants demonstrating the behavior symptoms of ADHD. The current findings of complexity and FC in ADHD pathology support hypotheses of altered function of inhibitory control networks in ADHD.

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Identifying neuroimaging risk markers for depression has been an elusive goal in psychopathology research. Despite this, smaller hippocampal volume has emerged as a potential risk marker for depression, with recent research suggesting this association is moderated by family income. The current pre-registered study aimed to replicate and extend these findings by examining the moderating role of family income and three dimensions of environmental experience on the link between hippocampus volume and later depression. Data were drawn from the Adolescent Brain Cognitive Development (ABCD) study and were comprised of 6693 youth aged 9-10 years at baseline. Results indicated that psychosocial threat moderated the association between right hippocampus volume and depression symptoms two years later, such that a negative association was evident in low-threat environments (std. beta=0.15, 95% CI [0.05, 0.24]). This interaction remained significant when baseline depression symptoms were included as a covariate, though only in youth endorsing 1 or more depression symptoms at baseline (β = 0.13, 95% CI = [0.03, 0.22]). These results suggest that hippocampus volume may not be a consistent correlate of depression symptoms in high risk environments and emphasize the importance of including measures of environmental heterogeneity when seeking risk markers for depression.

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Stimulant medication is the primary pharmacological treatment for attention dysregulation and is commonly prescribed for children with Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism. Neuroimaging studies of these groups commonly use a 24-48-hour washout period to mediate the effects of stimulant medication on functional connectivity (FC) metrics. However, the impact of washout on functional connectivity has received limited study.

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Social media can influence alcohol initiation behaviors such as sipping, which can lead to future adverse alcohol-related outcomes. Few studies have examined the role of problematic social media use, characterized by addiction, mood modification, tolerance, withdrawal, conflict, and relapse, especially in early adolescence.

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Behavioral features of binge eating disorder (BED) suggest abnormalities in reward and inhibitory control. Studies of adult populations suggest functional abnormalities in reward and inhibitory control networks. Despite behavioral markers often developing in children, the neurobiology of pediatric BED remains unstudied.

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

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

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Childhood adversity can lead to cognitive deficits or enhancements, depending on many factors. Though progress has been made, two challenges prevent us from integrating and better understanding these patterns. First, studies commonly use and interpret raw performance differences, such as response times, which conflate different stages of cognitive processing. Second, most studies either isolate or aggregate abilities, obscuring the degree to which individual differences reflect task-general (shared) or task-specific (unique) processes. We addressed these challenges using Drift Diffusion Modeling (DDM) and structural equation modeling (SEM). Leveraging a large, representative sample of 9-10 year-olds from the Adolescent Brain Cognitive Development (ABCD) study, we examined how two forms of adversity-material deprivation and household threat-were associated with performance on tasks measuring processing speed, inhibition, attention shifting, and mental rotation. Using DDM, we decomposed performance on each task into three distinct stages of processing: speed of information uptake, response caution, and stimulus encoding/response execution. Using SEM, we isolated task-general and task-specific variances in each processing stage and estimated their associations with the two forms of adversity. Youth with more exposure to household threat (but not material deprivation) showed slower task-general processing speed, but showed intact task-specific abilities. In addition, youth with more exposure to household threat tended to respond more cautiously in general. These findings suggest that traditional assessments might overestimate the extent to which childhood adversity reduces specific abilities. By combining DDM and SEM approaches, we can develop a more nuanced understanding of how adversity affects different aspects of youth’s cognitive performance. RESEARCH HIGHLIGHT: To understand how childhood adversity shapes cognitive abilities, the field needs analytical approaches that can jointly document and explain patterns of lowered and enhanced performance. Using Drift Diffusion Modeling and Structural Equation Modeling, we analyzed associations between adversity and processing speed, inhibition, attention shifting, and mental rotation. Household threat, but not material deprivation, was mostly associated with slower task-general processing speed and more response caution. In contrast, task-specific abilities were largely intact. Researchers might overestimate the impact of childhood adversity on specific abilities and underestimate the impact on general processing speed and response caution using traditional measures.

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To determine the association between sociodemographic characteristics and blood pressure among a demographically diverse population-based sample of 10-14-year-old US adolescents.

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A child’s socioeconomic environment can shape central aspects of their life, including vulnerability to mental disorders. Negative environmental influences in youth may interfere with the extensive and dynamic brain development occurring at this time. Indeed, there are numerous yet diverging reports of associations between parental socioeconomic status (SES) and child cortical brain morphometry. Most of these studies have used single metric- or unimodal analyses of standard cortical morphometry that downplay the probable scenario where numerous biological pathways account for SES-related cortical differences in youth.

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Peer victimization predicts the development of mental health symptoms in the transition to adolescence, but it is unclear whether and how parents and school environments can buffer this link.

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Maternal tobacco use during pregnancy (MTDP) persists across the globe. Longitudinal assessment of the association of MTDP with neurocognitive development of offspring at late childhood is limited.

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The use of tobacco products, including e-cigarettes and vaping, has rapidly increased among children. However, despite consistent associations found between smoking cigarettes and suicidal behaviors among adolescents and adults, there are limited data on associations between emerging tobacco products and suicidal behaviors, especially among preadolescent children.

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

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To develop an approach to evaluate multiple overlapping brain functional change patterns (FCPs) in functional network connectivity (FNC) and apply to study developmental changes in brain function.

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There is widespread recognition of the importance and complexity of measuring neighborhood contexts within research on child psychopathology. In this study, we assessed the cross-sectional associations between two measures of neighborhood quality and internalizing and externalizing behaviors in preadolescence.

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To determine the associations between screen time across several contemporary screen modalities (e.g., television, video games, text, video chat, social media) and adherence to the MIND (Mediterranean-DASH [Dietary Approaches to Stop Hypertension] Intervention for Neurodegenerative Delay) diet in early adolescents.

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Emerging research has provided valuable insights into the structural characteristics of the bilingual brain from studies of bilingual adults; however, there is a dearth of evidence examining brain structural alterations in childhood associated with the bilingual experience. This study examined the associations between bilingualism and white matter organization in bilingual children compared to monolingual peers leveraging the large-scale data from the Adolescent Brain Cognitive Development (ABCD) Study. Then, 446 bilingual children (ages 9-10) were identified from the participants in the ABCD data and rigorously matched to a group of 446 monolingual peers. Multiple regression models for selected language and cognitive control white matter pathways were used to compare white matter fractional anisotropy (FA) values between bilinguals and monolinguals, controlling for demographic and environmental factors as covariates in the models. Results revealed significantly lower FA values in bilinguals compared to monolinguals across established dorsal and ventral language network pathways bilaterally (i.e., the superior longitudinal fasciculus and inferior frontal-occipital fasciculus) and right-hemispheric pathways in areas related to cognitive control and short-term memory (i.e., cingulum and parahippocampal cingulum). In contrast to the enhanced FA values observed in adult bilinguals relative to monolinguals, our findings of lower FA in bilingual children relative to monolinguals may suggest a protracted development of white matter pathways associated with language and cognitive control resulting from dual language learning in childhood. Further, these findings underscore the need for large-scale longitudinal investigation of white matter development in bilingual children to understand neuroplasticity associated with the bilingual experience during this period of heightened language learning.

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The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive Development Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

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Image-on-scalar regression has been a popular approach to modeling the association between brain activities and scalar characteristics in neuroimaging research. The associations could be heterogeneous across individuals in the population, as indicated by recent large-scale neuroimaging studies, for example, the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD data can inform our understanding of heterogeneous associations and how to leverage the heterogeneity and tailor interventions to increase the number of youths who benefit. It is of great interest to identify subgroups of individuals from the population such that: (1) within each subgroup the brain activities have homogeneous associations with the clinical measures; (2) across subgroups the associations are heterogeneous, and (3) the group allocation depends on individual characteristics. Existing image-on-scalar regression methods and clustering methods cannot directly achieve this goal. We propose a latent subgroup image-on-scalar regression model (LASIR) to analyze large-scale, multisite neuroimaging data with diverse sociode-mographics. LASIR introduces the latent subgroup for each individual and group-specific, spatially varying effects, with an efficient stochastic expectation maximization algorithm for inferences. We demonstrate that LASIR outperforms existing alternatives for subgroup identification of brain activation patterns with functional magnetic resonance imaging data via comprehensive simulations and applications to the ABCD study. We have released our reproducible codes for public use with the software package available on Github.

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Prenatal substance exposure (PSE) has been linked to adverse health outcomes, but its interactions with environmental and genetic factors remain unclear. Using data from the adolescent brain cognitive development cohort ( = 9,838; baseline age: 9.92 ± 0.62 years), we tested for the robust associations of PSE-caffeine/alcohol/tobacco/marijuana with children’s health, cognition, and brain metrics after controlling for the environmental and genetic contexts. The environmental context involved birth, familial, and societal risk factors, while the genetic context included family histories and polygenic risk scores (PRSs) of mental disorders. In this sample, PSE-caffeine was observed in 59.8%, PSE-alcohol in 25.7%, PSE-tobacco in 13.2%, and PSE-marijuana in 5.6% of children. PSE-tobacco/marijuana was associated with higher environmental risks, PSE-alcohol was associated with lower familial risks, and all PSEs were associated with higher genetic risks. Controlling for these contexts reduced the number of significant health associations by 100, 91, 84, and 18% for PSE-tobacco/marijuana/caffeine/alcohol. Compared to the baseline, PSE-alcohol had the most health associations that were persistent over a 2-year period from preadolescence to adolescence, including associations with more sleep and mental health problems, improved cognitive functions, and larger brain volumes. These persistent associations with mental health problems and crystallized cognition were mediated by the surface areas of the frontal and the parietal cortices, respectively. Lower risk scores of the familial contexts attenuated associations between PSE-alcohol/marijuana and mental health problems. Higher PRS for substance use disorders enhanced late-onset associations of PSE-marijuana with externalizing problems. Results support the “health in context” concept, emphasizing modifiable factors mitigating adverse PSE effects.

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Identification of replicable neuroimaging correlates of ADHD has been hindered by small sample sizes, small effects, and heterogeneity of methods. Given considerable evidence that ADHD is associated with alterations in widely distributed brain networks, and the small effects of individual brain features, a whole-brain perspective focusing on cumulative effects is warranted. Use of large, multi-site samples is crucial for improving reproducibility and clinical utility of brain-wide MRI association studies. To address this, a polyneuro score (PNRS) representing cumulative, brain-wide, ADHD-associated resting-state functional connectivity was constructed and validated using data from the Adolescent Brain Cognitive Development (ABCD, N=5543 51.5% female) study. Association between the PNRS and ADHD symptoms was further tested in the independent Oregon-ADHD-1000 case-control cohort (N=553, 37.4% female). The ADHD PNRS was significantly associated with ADHD symptoms in both the ABCD and Oregon cohorts after accounting for relevant covariates (p-values <0.001). The most predictive PNRS involved all brain networks, though the strongest effects were concentrated among connections involving the default mode and cingulo-opercular networks. In the longitudinal Oregon-ADHD-1000, non-ADHD youth had significantly lower PNRS (Cohen’s =-0.318, robust p=5.5e-4) than children who met ADHD diagnostic criteria at >2 time points (age 7-19). The PNRS, however, did not mediate polygenic risk for ADHD. Brain-wide connectivity was robustly associated with ADHD symptoms in two independent cohorts, providing further evidence of widespread dysconnectivity in ADHD. Evaluation in enriched samples demonstrates the promise of the PNRS approach for improving reproducibility in neuroimaging studies and unraveling the complex relationships between brain connectivity and behavioral disorders. Neuroimaging studies of ADHD have been hindered by small sample sizes, small effects, and differences among study methods. We demonstrate that an ADHD polyneuro risk score (PNRS), representing brain-wide connectivity patterns, was robustly associated with ADHD symptoms in two independent cohorts. The study used data from the Adolescent Brain Cognitive Development Study and the Oregon-ADHD-1000 cohort, and provides further evidence of widespread dysconnectivity in ADHD. The findings highlight the promise of approaches examining cumulative, brain-wide effects, and the importance of using large samples for improving reproducibility of neuroimaging studies.

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Parents’ familism values predict a variety of Latinx American youth’s academic adjustment. However, it is unclear how cultural values such as familism interact with youth’s brain development, which is sensitive to sociocultural input, to shape their academic adjustment. Using a sample of 1916 Latinx American American youth (mean age = 9.90 years, SD = .63 years; 50% girls) and their primary caregivers (mean age = 38.43 years, SD = 6.81 years; 90% mothers) from the Adolescent Brain Cognitive Development Study, this study examined the longitudinal relation between parents’ familism values and youth’s school disengagement, as well as the moderating role of youth’s neural sensitivity to personal reward. Parents’ familism values predicted youth’s decreased school disengagement 1 year later, adjusting for their baseline school disengagement and demographic covariates. Notably, this association was more salient among youth who showed lower (vs. higher) neural activation in the ventral striatum and the lateral OFC during the anticipation of a personal reward. These findings underscore the protective role of familism for Latinx American youth, highlighting the necessity of developing culturally informed interventions that take into consideration of youth’s brain development.

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Exposure to maternal diabetes (DM) or hypertension (HTN) during pregnancy impacts offspring metabolic health in childhood and beyond. Animal models suggest that induction of hypothalamic inflammation and gliosis in the offspring’s hypothalamus is a possible mechanism mediating this effect. We tested, in children, whether in utero exposures to maternal DM or HTN were associated with mediobasal hypothalamic (MBH) gliosis as assessed by brain magnetic resonance imaging (MRI). The study included a subsample of 306 children aged 9-11 years enrolled in the ABCD Study®; 49 were DM-exposed, 53 were HTN-exposed, and 204 (2:1 ratio) were age- and sex-matched children unexposed to DM and/or HTN in utero. We found a significant overall effect of group for the primary outcome of MBH/amygdala (AMY) T2 signal ratio (F(2,300):3.51, p = 0.03). Compared to unexposed children, MBH/AMY T2 signal ratios were significantly higher in the DM-exposed (β:0.05, p = 0.02), but not the HTN-exposed children (β:0.03, p = 0.13), findings that were limited to the MBH and independent of adiposity. We concluded that children exposed to maternal DM in utero display evidence of hypothalamic gliosis, suggesting that gestational DM may have a distinct influence on offspring’s brain development and, by extension, children’s long-term metabolic health.

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Social victimization (SV) and altered neural connectivity have been associated with each other and psychotic-like experiences (PLE). However, research has not directly examined the associations between these variables, which may speak to mechanisms of psychosis-risk. Here, we utilized two-year follow-up data from the Adolescent Brain Cognitive Development study to test whether SV increases PLE through two neural networks mediating socio-affective processes: the default mode (DMN) and salience networks (SAN). We find that a latent SV factor was significantly associated with PLE outcomes. Simultaneous mediation analyses indicated that the DMN partially mediated the SV-PLE association while the SAN did not. Further, multigroup testing found that while Black and Hispanic adolescents experienced SV differently than their White peers, the DMN similarly partially mediated the effect of SV on PLE for these racial groups. These cross-sectional results highlight the importance of SV and its potential impact on social cognitive neural networks for psychosis risk.

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

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Adolescent traumatic brain injury (TBI) has long-term effects on brain functioning and behavior, impacting neural activity under cognitive load, especially in the reward network. Adolescent TBI is also linked to risk-taking behaviors including alcohol misuse. It remains unclear how TBI and neural functioning interact to predict alcohol experimentation during adolescence. Using Adolescent Brain Cognitive Development (ABCD) study data, this project examined if TBI at ages 9-10 predicts increased odds of alcohol sipping at ages 11-13 and if this association is moderated by neural activity during the Emotional EN-Back working memory task at ages 11-13. Logistic regression analyses showed that neural activity in regions of the fronto-basal ganglia network predicted increased odds of sipping alcohol by ages 11-13 (p < .05). TBI and left frontal pole activity interacted to predict alcohol sipping (OR = 0.507, 95% CI [0.303 – 0.846], p = .009) – increased activity predicted decreased odds of alcohol sipping for those with a TBI (OR = 0.516, 95% CI [0.314 – 0.850], p = .009), but not for those without (OR = 0.971, 95% CI [0.931 -1.012], p = .159). These findings suggest that for youth with a TBI, increased BOLD activity in the frontal pole, underlying working memory, may be uniquely protective against the early initiation of alcohol experimentation. Future work will examine TBI and alcohol misuse in the ABCD cohort across more time points and the impact of personality traits such as impulsivity on these associations.

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Some children, particularly females, who experience concussions develop long-lasting emotional and behavioral problems. Establishing the potential contribution of pre-existing behavioral problems and disrupted white matter maturation has been challenging due to a lack of pre-injury data.

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The purpose of this study was to test two non-exclusive mechanisms by which parental monitoring might reduce teen substance use. The first mechanism (M1) is that monitoring increases punishment for substance use since parents who monitor more are more likely to find out when substance use occurs. The second mechanism (M2) is that monitoring directly prevents/averts teens from using substances in the first place for fear that parents would find out.

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Past research suggests that parents’ familism values play a positive role in Latinx American youth’s prosocial tendencies. However, little is known about how individual differences in youth’s neural development may contribute to this developmental process. Therefore, using two-wave longitudinal data of 1916 early adolescents (mean age = 9.90 years; 50% girls) and their parents (mean age = 38.43 years; 90% mothers) from the Adolescent Brain Cognitive Development study, this pre-registered study took a biopsychosocial approach to examine the moderating role of youth’s neural reward sensitivity in the link between parents’ familism values and youth’s prosocial behaviors. Results showed that parents’ familism values were associated with increased prosocial behaviors among youth two years later, controlling for baseline prosocial behaviors and demographic covariates. Notably, parents’ familism values played a larger role in promoting youth’s prosocial behaviors among youth who showed lower ventral striatum activation during reward anticipation. Moreover, such association between parents’ familism values and youth’s later prosocial behaviors was stronger among youth who showed lower levels of prosocial behaviors initially. Taken together, the findings highlight individual differences in neurobiological development and baseline prosocial behaviors as markers of sensitivity to cultural environments with regard to Latinx American youth’s prosocial development.

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

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The contributions of religion to reduced suicide risk have been studied in adults and adolescents, though to our knowledge no comprehensive investigation has been conducted in early adolescents, at a time coinciding with emergence of suicide risk trajectories. In this largest study to date on this topic, we aimed to characterise the contributions of various measures of “private” and “public” religiosity to early adolescent suicide ideation (SI) and suicide attempt (SA) histories using information from a large, epidemiologically informed U.S. sample of adolescents (N = 7068; mean age = 12.89 years, 47% female) and their parents. In all youth, parent-reported adolescent religious importance was associated with reduced odds of SA (OR = 0.75, CI = 0.61-0.92, P = .005). Muslim youth were more likely (OR = 1.52, CI = 1.02-2.22, P = .033), and Catholic youth were less likely (OR = 0.80, CI = 0.67-0.95, P = .014), to report SI. A variety of sex differences were noted, with significant protective associations of adolescent self-reported religiosity on SI and SA, religious service attendance on SI, and religious importance on SI, in female-but not male-youth; and significant protective associations of religious importance on SA in male-but not female-youth. Against expectations, there was no evidence that parent religiosity moderated the link between youth religiosity and SI or SA. These results shed light on the roles of cultural and familial context in youth suicide risk, which may ultimately be targeted in screening and interventional approaches.

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

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Although pediatric growth curves provide clinical utility, using these metrics for within-person change over time can be misleading. As research is focused on understanding cardiometabolic consequences of weight gain, it is important to use precise metrics to analyze these longitudinal research questions. Despite several foundational recommendations to limit the use of reference pediatric growth curves (e.g., BMI z scores) for within-person longitudinal research, it has evolved into the “gold standard” for using growth curves for pediatric weight gain analyses. Therefore, the objective of this paper is to discuss (A) the methodology used to create reference growth curves; (B) the appropriate use of reference pediatric BMI growth curves within the context of cross-sectional and longitudinal analyses in research; and (C) how to select metrics based on desired evaluations. Careful consideration using standardized references scores is essential when assessing obesity-related questions and comorbid risk over time in pediatric populations.

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We used a polygenic score for externalizing behavior (extPGS) and structural MRI to examine potential pathways from genetic liability to conduct problems via the brain across the adolescent transition.

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Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual’s educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

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Despite its crucial role in the regulation of vital metabolic and neurological functions, the genetic architecture of the hypothalamus remains unknown. Here we conducted multivariate genome-wide association studies (GWAS) using hypothalamic imaging data from 32,956 individuals to uncover the genetic underpinnings of the hypothalamus and its involvement in neuropsychiatric traits. There were 23 significant loci associated with the whole hypothalamus and its subunits, with functional enrichment for genes involved in intracellular trafficking systems and metabolic processes of steroid-related compounds. The hypothalamus exhibited substantial genetic associations with limbic system structures and neuropsychiatric traits including chronotype, risky behaviour, cognition, satiety and sympathetic-parasympathetic activity. The strongest signal in the primary GWAS, the ADAMTS8 locus, was replicated in three independent datasets (N = 1,685-4,321) and was strengthened after meta-analysis. Exome-wide association analyses added evidence to the association for ADAMTS8, and Mendelian randomization showed lower ADAMTS8 expression with larger hypothalamic volumes. The current study advances our understanding of complex structure-function relationships of the hypothalamus and provides insights into the molecular mechanisms that underlie hypothalamic formation.

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Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ an integrative approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, associations between cortical thickness similarity and traits were limited to anthropometrics such as height, weight, and birth weight, as well as a marker of neighborhood socioeconomic conditions. Common genetic variants explained significant proportions of variance across nearly all included outcomes, although estimates were somewhat lower than previous reports. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as neighborhood socioeconomic conditions and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample.

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Many recent studies have demonstrated that environmental contexts, both social and physical, have an important impact on child and adolescent neural and behavioral development. The adoption of geospatial methods, such as in the Adolescent Brain Cognitive Development (ABCD) Study, has facilitated the exploration of many environmental contexts surrounding participants’ residential locations without creating additional burdens for research participants (i.e., youth and families) in neuroscience studies. However, as the number of linked databases increases, developing a framework that considers the various domains related to child and adolescent environments external to their home becomes crucial. Such a framework needs to identify structural contextual factors that may yield inequalities in children’s built and natural environments; these differences may, in turn, result in downstream negative effects on children from historically minoritized groups. In this paper, we develop such a framework – which we describe as the “adolescent neural urbanome” – and use it to categorize newly geocoded information incorporated into the ABCD Study by the Linked External Data (LED) Environment & Policy Working Group. We also highlight important relationships between the linked measures and describe possible applications of the Adolescent Neural Urbanome. Finally, we provide a number of recommendations and considerations regarding the responsible use and communication of these data, highlighting the potential harm to historically minoritized groups through their misuse.

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Consistent evidence documents powerful effects of social inequality on health, well-being and academic achievement. Yet research on whether social inequality may also be linked to brain structure and function has, until recently, been rare. Here we describe three methodological approaches that can be used to study this question-single site, single study; multi-site, single study; and spatial meta-analysis. We review empirical work that, using these approaches, has observed associations between neural outcomes and structural measures of social inequality-including structural stigma, community-level prejudice, gender inequality, neighbourhood disadvantage and the generosity of the social safety net for low-income families. We evaluate the relative strengths and limitations of these approaches, discuss ethical considerations and outline directions for future research. In doing so, we advocate for a paradigm shift in cognitive neuroscience that explicitly incorporates upstream structural and contextual factors, which we argue holds promise for uncovering the neural correlates of social inequality.

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The rise of new media has greatly changed the lifestyles, leading to increased time on these platforms and less time spent reading. This shift has particularly profound impacts on early adolescents, who are in a critical stage of brain development. Previous studies have found associations between screen use and mental health, but it remains unclear whether screen use is the direct cause of the outcomes. Here, the Adolescent Brain Cognitive Development (ABCD) dataset is utlized to examine the causal relationships between screen use and brain development. The results revealed adverse causal effects of screen use on language ability and specific behaviors in early adolescents, while reading has positive causal effects on their language ability and brain volume in the frontal and temporal regions. Interestingly, increased screen use is identified as a result, rather than a cause, of certain behaviors such as rule-breaking and aggressive behaviors. Furthermore, the analysis uncovered an indirect influence of screen use, mediated by changes in reading habits, on brain development. These findings provide new evidence for the causal influences of screen use on brain development and highlight the importance of monitoring media use and related habit change in children.

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Interpreting the neural response elicited during task functional magnetic resonance imaging (fMRI) remains a challenge in neurodevelopmental research. The monetary incentive delay (MID) task is an fMRI reward processing task that is extensively used in the literature. However, modern psychometric tools have not been used to evaluate measurement properties of the MID task fMRI data. The current study uses data for a similar task design across three adolescent samples (N = 346 [Age 12.0; 44 % Female]; N = 97 [19.3; 58 %]; N = 112 [20.2; 38 %]) to evaluate multiple measurement properties of fMRI responses on the MID task. Confirmatory factor analysis (CFA) is used to evaluate an a priori theoretical model for the task and its measurement invariance across three samples. Exploratory factor analysis (EFA) is used to identify the data-driven measurement structure across the samples. CFA results suggest that the a priori model is a poor representation of these MID task fMRI data. Across the samples, the data-driven EFA models consistently identify a six-to-seven factor structure with run and bilateral brain region factors. This factor structure is moderately-to-highly congruent across the samples. Altogether, these findings demonstrate a need to evaluate theoretical frameworks for popular fMRI task designs to improve our understanding and interpretation of brain-behavior associations.

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Environmental exposures play a crucial role in shaping children’s behavioral development. However, the mechanisms by which these exposures interact with brain functional connectivity and influence behavior remain unexplored.

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Research suggests that bilingual children experience an extension or delay in the closing of the sensitive/critical period of language development due to multiple language exposure. Moreover, bilingual experience may impact the development of subcortical regions, although these conclusions are drawn from research with adults, as there is a scarcity of research during late childhood and early adolescence. The current study included 1215 bilingual and 5894 monolingual children from the ABCD Study to examine the relationship between subcortical volume and English vocabulary in heritage Spanish bilingual and English monolingual children, as well as volumetric differences between the language groups. We also examined the unique effects of language usage in bilingual children’s subcortical volumes. In general, bilingual children had less cerebellar volume and greater volume in the putamen, thalamus, and globus pallidus than monolingual children. English vocabulary was positively related to volume in the cerebellum, thalamus, caudate, putamen, nucleus accumbens, and right pallidum in all children. Moreover, the positive relationship between vocabulary and volume in the nucleus accumbens was stronger for monolingual adolescents than bilingual adolescents. The results are somewhat in line with existing literature on the dynamic volume adaptation of subcortical brain regions due to bilingual development and experience. Future research is needed to further explore these regions longitudinally across development to examine structural changes in bilingual brains.

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A longstanding cerebral lateralization hypothesis predicts that disrupted development of typical leftward structural asymmetry of auditory cortex explains why children have problems learning to read. Small sample sizes and small effects, potential sex-specific effects, and associations that are limited to specific dimensions of language are thought to have contributed inconsistent results. The large ABCD study dataset (baseline visit: N = 11,859) was used to test the hypothesis of significant associations between surface area asymmetry of auditory cortex and receptive vocabulary performance across boys and girls, as well as an oral word reading effect that was specific to boys. The results provide modest support (Cohen’s effect sizes ≤ 0.10) for the cerebral lateralization hypothesis.

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Previous investigations that have examined associations between family history (FH) of alcohol/substance use and adolescent brain development have been primarily cross-sectional. Here, leveraging a large population-based sample of youths, we characterized frontal cortical trajectories among 9- to 13-year-olds with (FH+) versus without (FH-) an FH and examined sex as a potential moderator.

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