Research Publications

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Adolescent neuroimaging studies of sex differences in the human brain predominantly examine average differences between males and females. This focus on mean differences without probing relative distributions and similarities may contribute to both conflation and overestimation of sex differences and sexual dimorphism in the developing human brain. We aimed to characterize the variance in brain macro- and micro-structure in early adolescence as it pertains to sex at birth using a large sample of 9-11-year-olds from the Adolescent Brain Cognitive Development (ABCD) Study (N = 7,723). For global and regional estimates of gray and white matter volume, cortical thickness, and white matter microstructure (i.e., fractional anisotropy and mean diffusivity), we examined: within- and between-sex variance, overlap between male and female distributions, inhomogeneity of variance, effect size, and CLES. We examined these sex differences using both unadjusted (raw) brain estimates and residualized brain estimates from mixed-effects modeling (adjusted) to account for variance better attributed to age, pubertal development, socioeconomic status, race, ethnicity, MRI scanner manufacturer, and total brain volume, where applicable. Contrary to the popular view of the brain as sexually dimorphic, we found high similarity and low difference between sexes in all regional measurements of brain structure examined after accounting for other sources of variance. However, the sex difference for adjusted total brain volume (TBV) had a medium effect size and a 71.9% probability that a randomly chosen male adolescent would have a larger brain than a randomly chosen female adolescent. All cortical and subcortical volumes showed significant inhomogeneity of variance between sexes, whereas a minority of brain regions showed significant sex differences in variance for cortical thickness, white matter volume, fractional anisotropy, and mean diffusivity. Previously reported sex differences in early adolescent regional human brain volume may, therefore, be driven by disparities in variance, rather than binary, sex-based phenotypes. This study builds upon previous findings illustrating the importance of considering variance when examining sex differences in brain structure.

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Sleep problems are common in adolescence and are associated with poorer mental and physical health. Parental factors may be associated with adolescent sleep, providing potential targets for sleep health interventions. Whether these associations are mediated through emotional regulation and screen use and whether they vary by adolescent sex remain unclear.

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Adverse childhood experiences (ACEs) are common and account for more than 25% of psychiatric disorders in youths, but the underlying neurobiological mechanisms associated with risk and resilience among children exposed to ACEs are poorly understood.

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Neighborhood adversity links to mental health and cognitive outcomes, but little is known about structural factors that may buffer these links. The current study addresses this gap by assessing the role of protective factors in the association of neighborhood deprivation, threat, and segregation with psychopathology symptoms and cognitive outcomes. Linear mixed models were run in ABCD sample participants (n = 5812) to test associations of neighborhood Area Deprivation Index (ADI; deprivation), crime (threat), and dissimilarity and interaction index (segregation) with attention difficulties, internalizing and externalizing symptoms, psychotic-like experiences (PLEs), and fluid and crystallized cognitive performance. School environment, neighborhood education child opportunity index, neighborhood cohesion, and green space were tested as moderators of the association of neighborhood adversity with outcomes. Higher neighborhood educational resources buffered the association of neighborhood deprivation with crystallized cognitive performance. The association of higher neighborhood crime with externalizing symptoms was weaker for youth in less supportive school environments. Further, higher neighborhood segregation was associated with internalizing symptoms more strongly for youth with more neighborhood educational resources. Taken together, results suggest adverse neighborhood environments are associated with higher psychopathology symptoms and lower cognitive performance. Access of neighborhood educational resources could buffer links of neighborhood deprivation and cognitive performance. While evidence of protective links was not widespread, studying these patterns is necessary for understanding possible environmental contributors to mental health and cognitive function. A video abstract of this article can be viewed at https://www.youtube.com/watch?v=dpGkdPkPiSw. SUMMARY: Adverse neighborhood environments were associated with higher psychopathology symptoms and lower cognitive performance, independent of individual socioeconomic status. Higher neighborhood educational resources buffered links of neighborhood deprivation with crystallized cognitive performance; deprivation was more strongly associated with lower crystallized cognition for youth with lower neighborhood educational resources. While evidence of protective links was not widespread, understanding these patterns is necessary for informing structural prevention and intervention targets for mental health and cognition.

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Sports participation benefits children but increases the risk of mild traumatic brain injury (mTBI) and orthopedic injury (OI). This study examines risks of mTBI vs. OI associated with specific sports and benefits of sports participation.

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Socioeconomic determinants of health impact childhood development and adult health outcomes. One key aspect is the physical environment and neighborhood where children live and grow. Emerging evidence suggests that neighborhood deprivation, often measured by the Area Deprivation Index (ADI), may influence neurodevelopment, but longitudinal and multimodal neuroimaging analyses remain limited.

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Prodromal symptoms of mania in children are predictive of the later development of bipolar disorder; yet, the neurostructural correlates of these early symptoms remain poorly understood. This study aimed to investigate the association between prodromal mania symptoms and brain structure in a large cohort of children.

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This study tested whether measurement of the Familism scale of the Mexican American Cultural Values Scale (comprising Support, Obligations, and Referent subscales) was invariant across parent and youth reporters in early adolescence and examined whether reporter discrepancies predicted youth functioning across substance use, problem behavior, academic, peer, and family domains 1 year later. The sample comprised 2410 multi-ethnic Hispanic/Latino/a youth (M = 12.87 years; 48% female) and their parents from the Adolescent Brain Cognitive Development (ABCD) Study. At least partial metric invariance was established for Support and Obligations subscales, suggesting associations between mean levels of these subscales with other measures can be meaningfully compared by parent and youth reporters. However, the Referent subscale and Total Familism scale demonstrated only configural invariance, meaning their structure was similar across reporters, but item loadings, latent means, and associations with other measures were not comparable. Reporter discrepancies in Support and Obligations did not account for unique variance in any indicator of youth functioning beyond the main effects of parent and youth Support and Obligations, both of which were associated with adaptive youth outcomes. Both parent and youth reports on the Familism scale of the Mexican American Cultural Values Scale have demonstrated reliability and validity in previous work, but our tests of measurement invariance suggest only the Support and Obligations subscales, but not the Referent or Familism scales, can be meaningfully compared across parent and youth reporters. This work has important implications for the assessment of familism in early adolescence and its role for youth well-being.

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Disadvantaged socioeconomic status (SES) is associated with elevated internalizing symptoms in adolescents and is potentially mediated by accelerated pubertal development. Neighborhood SES may have distinct effects beyond family influences, interacting with family SES to shape adolescents’ development. The present study examines the combined effects of family and neighborhood SES on pubertal development trajectory and internalizing symptoms and explores the mediating role of pubertal trajectory. This study included 5560 early adolescents (46.51% female; aged 9-10 years at baseline; M = 9.48; SD = 0.51) from the Adolescent Brain Cognitive Development (ABCD) study across four annual assessments. Three pubertal development trajectories were identified: “early-onset with slow progression”, “late-onset with rapid catch-up”, and “late-onset with slow catch-up”. The results revealed that accelerated pubertal trajectories mediated the association between multilevel SES disparities and internalizing symptoms. For adolescents from high-SES families, high neighborhood SES reduced the likelihood of early-onset and rapid catch-up trajectories, which were linked to fewer internalizing symptoms. However, for adolescents from low-SES families, higher neighborhood SES increased the likelihood of early-onset and rapid catch-up trajectories, which were associated with more internalizing symptoms. Sex differences were observed, with neighborhood SES predicting pubertal trajectories in males but not in females, and the rapid catch-up trajectory was associated with fewer anxious/depressed symptoms in males but more internalizing symptoms in females. This study emphasizes the crucial role of family and neighborhood SES disparities in shaping adolescent pubertal development, which in turn affects internalizing symptoms.

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Internalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the biological markers of internalizing problems are not well understood. Here, we used deep learning, a powerful tool for identifying complex and multi-dimensional brain-behaviour relationships, to predict cross-sectional and worsening longitudinal trajectories of internalizing problems. Data were extracted from four large-scale datasets: the Adolescent Brain Cognitive Development study, the Healthy Brain Network, the Human Connectome Project Development study, and the Province of Ontario Neurodevelopmental network. We developed deep learning models that used measures of brain structure (thickness, surface area, and volume) to (a) predict clinically significant internalizing problems cross-sectionally (N = 14,523); and (b) predict subsequent worsening trajectories (using the reliable change index) of internalizing problems (N = 10,540) longitudinally. A stratified cross-validation scheme was used to tune, train, and test the models, which were evaluated using the area under the receiving operating characteristic curve (AUC). The cross-sectional model performed well across the sample, reaching an AUC of 0.80 [95% CI: 0.71, 0.88]. For the longitudinal model, while performance was sub-optimal for predicting worsening trajectories in a sample of the general population (AUC = 0.66 [0.65, 0.67]), good performance was achieved in a small, external test set of primarily ND conditions (AUC = 0.80 [0.78, 0.81]), as well as across all ND conditions (AUC = 0.73 [0.70, 0.76]). Deep learning with features of brain structure is a promising avenue for biomarkers of internalizing problems, particularly for individuals who have a higher likelihood of experiencing difficulties.

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Low socio-economic status, male sex, and body mass index (BMI) are known risk factors for high sugar sweetened beverage (SSB) consumption in adolescents. The present analysis aimed to predict SSB intake based on known risk factors and resting-state functional magnetic resonance (rsfMRI) connectivity from the Adolescent Brain Cognitive Development study.

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Research has linked early intercourse and romantic relationships to increased depressive symptoms, especially for female adolescents. However, less is known about the ways in which early noncoital sexual behaviors are associated with mental health. Thus, this study examined whether early kissing, sexual touching, and romantic relationships were associated with depressive symptoms, and whether these associations differed for male, female, and nonbinary adolescents.

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Perceived racial discrimination during childhood and adolescence is a critical social determinant of health disparities. However, few scales measure perceived racial discrimination in these age groups, and even fewer are validated with robust psychometric properties or demonstrate measurement invariance across racial and ethnic or sex groups.

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Youth whose parents have depression histories are at elevated risk for psychopathology. Familial depression-related patterns of neurodevelopment and environmental stress (e.g., family conflict) likely contribute to heightened risk. However, knowledge remains limited due to few studies, small sample sizes, and cross-sectional designs. We sought to identify how neural circuitry, familial risk for depression, and family conflict interact during preadolescence to predict adolescent psychopathology.

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The Adolescent Brain Cognitive Development (ABCD) Study® has significant potential to reveal the nature, causes, context, and consequences of pubertal development in diverse American youth. Optimal use of the data requires thoughtful consideration of puberty: how it is likely to affect psychological and neural development, and its measurement. We examined how ABCD puberty data have been used, and the relative advantages of two measures derived from the Pubertal Development Scale: the categorical measure provided in data releases and a continuous measure widely used outside ABCD.

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Perinatal insults (e.g., obstetric complications, substance exposure) are increasing in prevalence and confer risk for psychotic-like experiences in offspring, contributing to a growing public health burden. Perinatal insults often co-occur, creating methodological challenges in understanding their impacts on psychosis-spectrum phenotypes. Data-driven approaches to organizing perinatal insults and testing their longitudinal effects on psychotic-like experiences in youth increases ecological validity and translational utility. Using data from 11,417 youth ages 9-14 across five years of the Adolescent Brain Cognitive Development (ABCD) Study, data-driven dimensions of perinatal insults were derived through exploratory factor analysis of thirty-one perinatal insults. Latent growth modeling tested the effect of perinatal insult dimensions on trajectories (baseline, rate-of-change, year-four severity) of distressing psychotic-like experiences. Six dimensions of perinatal insults were observed (substance exposure, obstetric complications, birth complications, postnatal challenges, parental age, medical needs). Substance exposure (β = 0.42, 95% CI [0.20, 0.63]), obstetric complications (β = 0.34, 95% CI [0.08, 0.61]), and parental age (β = 1.00, 95% CI [0.76, 1.22]) were associated with elevated baseline psychotic-like experiences. Perinatal insult dimensions were not associated with increasing rates-of-change in psychotic-like experiences. Medical needs (β = -0.12, 95% CI [-0.20, -0.05]) and parental age (β = -0.11, 95% CI [-0.18, -0.03]) were associated with steeper declines in psychotic-like experiences. Perinatal insult dimensions remained associated with elevated psychotic-like experiences at year-four. Data-driven dimensions of perinatal insults are associated with stably elevated psychotic-like experience trajectories across early adolescence. Given the role of psychotic-like experiences in later psychopathology and functioning, early identification of at-risk offspring is critical in reducing the public health burden of these exposures.

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Females who experience menarche early have elevated risk for dysmenorrhea; yet, other puberty features precede menarche. Using data from the Adolescent Brain Cognitive Development Study®, pubertal timing was estimated via random effects linear growth curves of pubertal status indicators excluding menarche for postmenarcheal females with no ( = 1083), mild ( = 1239), or severe ( = 266) dysmenorrhea. Early pubertal timing increased odds for dysmenorrhea by 22-31%, making it a novel marker for dysmenorrhea risk.

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Not everyone is equally likely to experience mental illness. What is the contribution of an individual’s genetic background and experiences of childhood adversity to that likelihood? And how do these risk factors interact at the level of the brain? This study explores these questions by investigating the relationship between genetic liability for mental illness, childhood adversity, and cortico-limbic connectivity in a large developmental sample drawn from the ABCD cohort (N = 6535). Canonical Correlation Analysis – a multivariate data-reduction technique – revealed two genetic dimensions of mental illness from the polygenic risk scores for ADHD, Anxiety, Depression, and Psychosis. The first dimension represented liability for broad psychopathology which was positively correlated with adversity. The second dimension represented neurodevelopmental-specific risk which negatively interacted with adversity, suggesting that neurodevelopmental symptoms may arise from unique combinations of genetic and environmental factors that differ from other symptom domains. Next, we investigated the cortico-limbic signature of adversity and genetic liability using Partial Least Squares. We found that the neural correlates of adversity broadly mirrored those of genetic liability, with adversity capturing most of the shared variance. These novel findings suggest that genetic and environmental risk overlap in the neural connections that underlie mental health symptomatology.

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In longitudinal research with adolescents and adults, one-third of individuals who report self-injurious thoughts and behaviors (SITBs) fail to report this history in the future. However, there is limited information regarding this phenomenon in children. This study examined the prevalence, correlates, and developmental shifts of inconsistent reporting of SITBs in children using data from the Adolescent Brain Cognitive Development study baseline (BL, ages 9-10), Year 1 (Y1), and Year 2 (Y2) assessments. Lifetime SITBs were assessed annually using a computerized clinical interview. Reporting consistency was calculated for 1-year intervals in two partially overlapping cohorts (BL-Y1, = 1,350; Y1-Y2, = 1,086). Logistic regressions modeled BL sociodemographic, clinical, and neurocognitive correlates with inconsistent reporting from BL to Y1. Developmental differences in inconsistency were assessed by comparing rates between BL-Y1 and Y1-Y2. At Y1, 67% of SITB reporters from BL did not endorse past SITBs (inconsistency range = 67%-80% across SITBs). Less severe clinical symptoms (e.g., parent-reported youth psychopathology, longitudinal SITB reports), younger age, and lower general neurocognitive performance were significantly associated with higher odds of inconsistent reporting of SITBs. Inconsistency in nonsuicidal self-injury reporting was significantly lower from Y1 to Y2 (73%) compared to BL-Y1 (80%). In late childhood, inconsistency in reported SITBs is the norm and may hinder accurate risk assessment for youth. These patterns may be associated with lower clinical severity and neurocognitive and developmental immaturity. Further examination is needed to better understand features associated with inconsistent reporting (e.g., forgetting, reconceptualizing, or nondisclosure) to inform suicide risk assessments. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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Adolescence marks a critical window wherein individual differences in brain structure may influence the emergence of alcohol use behaviors. The orbitofrontal cortex (OFC), a region involved in reward processing and behavioral regulation, may play a key role in shaping early responses to alcohol. This study examined whether smaller OFC volume at ages 9-10 predicted likelihood of experiencing subjective effects of alcohol by ages 13-14. Participants (N = 206; 57 % female) were drawn from the Adolescent Brain Cognitive Development Study. Baseline medial and lateral OFC volumes were used. Subjective response to alcohol was measured during follow-up using a binary outcome (1 = any effect, 0 = no effects). Mixed-effects logistic regression models tested the association between OFC and alcohol response, adjusting for sex, parental education, race/ethnicity, intracranial volume, and site. Smaller left medial OFC at Baseline was significantly associated with greater odds of reporting subjective effects (OR = 1.70, p = .026). Youth who reported subjective effects also consumed more alcohol in the past year (p < .001), but did not differ in their alcohol expectancies. Among those reporting subjective effects, OFC volume was not significantly associated with the amount or frequency of alcohol use. These findings suggest that smaller OFC volume may not reflect pharmacological sensitivity per se, but instead relate to early drinking behavior sufficient to elicit noticeable effects. This may reflect underlying impulsivity-related traits or altered neurodevelopmental trajectories that predispose youth to early and potentially riskier patterns of alcohol use. Results underscore the potential value of identifying structural brain markers that contribute to individual vulnerability for alcohol use during adolescence.

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Racial discrimination has been linked with pubertal development among Black and Latinx American youth. The direction of effects, however, is poorly understood. We examined bidirectional associations between exposure to racial discrimination and pubertal development among Black and Latinx boys and girls from age 9 to age 11.

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Major Depression (MD) is a prevalent, disabling and life-limiting condition. The neurobiological associations of genetic risk for MD remain under-explored in large samples, with no comprehensive mega-analysis conducted to date. Our study analysed data from 11 separate studies, encompassing 50,975 participants from the ENIGMA Major Depressive Disorder Working Group. We developed highly consistent genetic and neuroimaging protocols and applied these throughout all participating studies, together with rigorous genetic methods to remove overlap between the polygenic risk scores (PRS) training and testing samples. Elevated PRS for MD correlated with lower intracranial volume and lower global measure of cortical surface area (β = -0.017, p = 1.97 × 10; β = -0.013, p = 4.5 × 10; pFDR < 3.62 × 10). The most significant cortical association was observed in the surface area of the frontal lobe (β = -0.011, p = 2.85 × 10, pFDR = 1.42 × 10), particularly in the left medial orbito-frontal gyrus (β = -0.021, p = 9.48 × 10, pFDR = 1.25 × 10). In subcortical regions, lower volumes of the thalamus, hippocampus, and pallidum correlated with higher PRS of MD (β ranged from -0.011 to -0.015, p ranged from 0.002-1.73 × 10, pFDR < 0.006). In a subsample of young individuals only (<25 years old, N = 5570), although there were no FDR-significant findings, directions of effects were highly consistent between the analyses of cortical surface areas in youth and the full sample (71.2% in the same direction, exact binomial test p-value = 7.56 × 10). Subsequent Mendelian randomisation analysis revealed potentially causal effects of smaller left hippocampal volume on higher liability for MD (Inverse variance weighted analysis β = -0.064, p = 8.04 × 10, pFDR = 0.04). Our findings represent an example of how extensive international collaborations can significantly advance our neurogenetic understanding of MD and give insights to avenues for early interventions in those at high risk for developing MD.

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Physical Activity (PA) is important for mental, physical, and brain health. Adolescence is marked by increased engagement in risky substance use (SU) behaviors, which can negatively affect brain development. This study aims to determine if PA influences SU experimentation and initiation among adolescents. We predicted higher levels of PA would be associated with less SU, with a larger effect in more vigorous compared to light PA. A sample of 2541 participants from the Adolescent Brain Cognitive Development (ABCD) Study provided three weeks of Fitbit-measured PA data at the 2-year follow-up, and SU outcomes at the 3- and 4-year follow-up. SU outcomes of experimentation (i.e., sip/puff/try of alcohol, nicotine, or cannabis) and initiation (i.e., full drink of alcohol, more than a puff/try of nicotine or cannabis, or anything else) were examined dichotomously (i.e., yes/no). Logistic regression analyses were conducted, controlling for demographics, externalizing, and depressive symptoms endorsed on Child Behavior Checklist (CBCL). Total PA was associated with 24 % decreased odds in SU initiation (OR 0.82, 95 % CI 0.69-0.99,  < .05). After examining PA intensities more closely, light PA predicted 26 % decreased odds of SU initiation (OR 0.73, 95 % CI 0.61-0.88, p = .001). No significant associations emerged between PA and experimentation, or moderate and vigorous PA and initiation. More engagement in total and light PA reduced the odds of SU initiation, suggesting that low-intensity activity, not moderate or vigorous PA, may provide protection against adolescent SU. Future research should examine underlying mechanisms and contextual factors that account for these results.

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Extensive neuroimaging research in temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) has identified brain atrophy as a disease phenotype. While it is also related to a complex genetic architecture, the transition from genetic risk factors to brain vulnerabilities remains unclear. Using a population-based approach, we examined the associations between epilepsy-related polygenic risk for HS (PRS-HS) and brain structure in healthy developing children, assessed their relation to brain network architecture, and evaluated its correspondence with case-control findings in TLE-HS diagnosed patients relative to healthy individuals We used genome-wide genotyping and structural T1-weighted magnetic resonance imaging (MRI) of 3,826 neurotypical children from the Adolescent Brain Cognitive Development (ABCD) study. Surface-based linear models related PRS-HS to cortical thickness measures, and subsequently contextualized findings with structural and functional network architecture based on epicentre mapping approaches. Imaging-genetic associations were then correlated to atrophy and disease epicentres in 785 patients with TLE-HS relative to 1,512 healthy controls aggregated across multiple sites. Higher PRS-HS was associated with decreases in cortical thickness across temporo-parietal as well as fronto-central regions of neurotypical children. These imaging-genetic effects were anchored to the connectivity profiles of distinct functional and structural epicentres. Compared with disease-related alterations from a separate epilepsy cohort, regional and network correlates of PRS-HS strongly mirrored cortical atrophy and disease epicentres observed in patients with TLE-HS, and highly replicable across different studies. Findings were consistent when using statistical models controlling for spatial autocorrelations and robust to variations in analytic methods. Capitalizing on recent imaging-genetic initiatives, our study provides novel insights into the genetic underpinnings of structural alterations in TLE-HS, revealing common morphological and network pathways between genetic vulnerability and disease mechanisms. These signatures offer a foundation for early risk stratification and personalized interventions targeting genetic profiles in epilepsy.

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The global prevalence of cannabis use during pregnancy is increasing, driven by perceived therapeutic benefits and greater societal acceptance. Concurrently, the psychoactive potency of cannabis products has risen significantly, due to increase in concentrations of Tetrahydrocannabinol (THC) from 5% to 30%. THC crosses the placenta, disrupts the endocannabinoid system critical for neurodevelopment, and accumulates in fetal tissues. THC is transferred into breast milk, with breastfed infants receiving ~2.5% of the maternal dose, raising concerns regarding neurodevelopmental consequences. An increasing number of studies and metanalysis have demonstrated association of prenatal cannabis exposure with low birth weight, preterm birth, neonatal intensive care unit admission, and reduced Apgar scores. Longitudinal studies show brain alterations in offspring, affecting memory, attention, and executive function. The inability to conduct randomized controlled trials due to ethical constraints necessitates reliance on observational studies, underscoring the need for rigorous longitudinal research to delineate causality.

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As an increasing realization, many behavioral relationships are interwoven with inherent variations in human populations. Presently, there is no clarity in the biomedical community on which sources of population variation are most dominant. The recent advent of population-scale cohorts like the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) are now offering unprecedented depth and width of phenotype profiling that potentially explains interfamily differences. Here, we leveraged a deep learning framework (conditional variational autoencoder) on the totality of the ABCD Study® phenome (8,902 candidate phenotypes in 11,875 participants) to identify and characterize major sources of population stratification. 80% of the top 5 sources of explanatory stratifications were driven by distinct combinations of 202 available socioeconomic status (SES) measures; each in conjunction with a unique set of non-overlapping social and environmental factors. Several sources of variation across this cohort flagged geographies marked by material poverty interlocked with mental health and behavioral correlates. Deprivation emerged in another top stratification in relation to urbanicity and its ties to immigrant and racial and ethnic minoritized groups. Conversely, two other major sources of population variation were both driven by indicators of privilege: one highlighted measures of access to educational opportunity and income tied to healthy home environments and good behavior, the other profiled individuals of European ancestry leading advantaged lifestyles in desirable neighborhoods in terms of location and air quality. Overall, the disclosed social stratifications underscore the importance of treating SES as a multidimensional construct and recognizing its ties into social determinants of health.

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Functional connectivity is frequently used to assess dynamic brain functioning and predict individual differences in behavioral outcomes, such as psychopathology. Inferences from functional connectivity analyses typically rely on group-averaged model statistics. However, heterogeneity between individuals may lead to group-level models that poorly reflect each individual. Poor individual-level precision may limit the ability to make individual-level predictions, which is necessary for key goals such as clinical translation. This registered report examined between-person heterogeneity in resting-state functional connectivity strength patterns by assessing similarity between group- and individual-level connectivity models in the Adolescent Brain Cognitive Development study. Using intraclass correlation coefficients, we found that a group-averaged region-of-interest-based connectivity model was a poor reflection of every individual. In contrast, a group-averaged model of between- and within-network connectivity was a good representation of most individuals. We then examined how individual-level distinctness from the group moderated predictive performance of several clinical and neurocognitive scales. Hypotheses that group-to-individual dissimilarity would worsen behavioral prediction were not supported with primary clinical outcomes. The little psychopathology reported in this sample was a notable limitation. In contrast, lower similarity to the group worsened prediction of performance on the pattern comparison test, providing minor support for hypotheses. Overall, results suggest that region-of-interest-based functional connectivity networks are highly heterogeneous and group-based models are inappropriate for individual-level inferences, but that network-based connectivity is largely similar across individuals. Additionally, we provide minor evidence of the impacts of heterogeneity on prediction that future studies should build on.

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Pain in youth is a growing public health concern. While research suggests pervasive differences in brain morphology with pain in adulthood, this relationship is less studied in adolescence. To address this gap, the present study explored pain-related associations with gray matter volume across 82 brain regions in a community sample of 7712 youth (mean age = 11.96) by comparing structural brain measures between those with (n = 2668; 34.6%) and without (n = 5044; 65.4%) past-month pain and examining associations with a continuous latent pain factor composed of average pain ratings, worst pain ratings, and pain limitations. Results showed no significant morphological differences between groups with and without past-month pain. Among participants reporting pain, average pain limitations were generally low, despite varied pain intensity, duration, and number of pain locations. Within this group, higher latent pain scores were associated with lower gray matter volume in six cortical regions (bilateral precentral, right postcentral, right inferior parietal, left supramarginal and left lateral occipital gyri; all p < 0.05). However, in secondary analyses adjusting for total intracranial volume (ICV), only the result in the right inferior parietal gyrus remained significant, suggesting this region may represent a more robust and regionally specific correlate of pain, independent of global brain effects. Together, these findings suggest that heightened pain experiences are related to lower gray matter volume in predominantly sensorimotor and parietal regions. Future work exploring the temporal dynamics of these morphological differences is needed to clarify their clinical implications. PERSPECTIVE: This article describes an association between lower gray matter volume in primarily sensorimotor and parietal areas and higher pain scores among youth reporting past-month pain (n = 2668) in a nonclinical community sample (n = 7712). The findings contribute to the understanding of neurobiological correlates of adolescent pain and pain-related neurodevelopmental patterns.

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Psychotic-like experiences (PLEs) may arise from genetic and environmental risk leading to worsening cognitive and morphometry metrics over time, which in turn lead to worsening PLEs. Analyses used three waves of unique longitudinal Adolescent Brain Cognitive Development Study data (ages 9-13) to test whether changes in cognition and global morphometry metrics attenuate associations between genetic and environmental risk with persistent distressing PLEs. Multigroup univariate latent growth models examined three waves of cognitive metrics and global morphometry separately for three PLE groups: persistent distressing PLEs (n=356), transient distressing PLEs (n=408), and low-level PLEs (n=7901). Persistent distressing PLEs showed greater decreases (i.e., more negative slopes) of cognition and morphometry metrics over time compared to those in low-level PLE groups. Analyses also provided novel evidence for extant theories that worsening cognition and global morphometry metrics may partially account for associations between environmental risk with persistent distressing PLEs.

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Humans and nonhuman primate studies suggest that timing and tempo of cortical development varies neuroanatomically along a sensorimotor-to-association (S-A) axis. Prior human studies have reported a principal S-A axis across various modalities but largely rely on cross-sectional samples with wide age-ranges. Here, we investigate developmental changes and individual variability in cortical organization along the S-A axis between the ages of 9-13 years using a large, longitudinal sample (N = 2487-3747, 46-50% female) from the Adolescent Brain Cognitive Development Study (ABCD Study®). This work assesses multiple aspects of neurodevelopment indexed by changes in cortical thickness, cortical microarchitecture, and resting-state low-frequency oscillations. First, we evaluated S-A organization in age-related changes and then, computed individual-level S-A alignment in brain changes and assessing differences therein due to age, sex, and puberty. Varying degrees of linear and quadratic age-related brain changes were identified along the S-A axis. Yet, these patterns of cortical development were overshadowed by considerable individual variability in S-A alignment. Even within individuals, there was little correspondence between S-A patterning across the different aspects of neurodevelopment investigated (i.e., cortical morphology, microarchitecture, function). Some of the individual variation in developmental patterning of cortical morphology and microarchitecture was explained by age, sex, and pubertal development. Altogether, this work contextualizes prior findings that regional age differences do progress along an S-A axis at a group level, while highlighting broad variation in developmental change between individuals and between aspects of cortical development, in part due to sex and puberty. Understanding normative patterns of adolescent brain change, and individual variability therein, is crucial for disentangling healthy and abnormal development. We used longitudinal human neuroimaging data to study several aspects of neurodevelopment during early adolescence and assessed their organization along a sensorimotor-to-association (S-A) axis across the cerebral cortex. Age differences in brain changes were linear and curvilinear along this S-A axis. However, individual-level sensorimotor-association alignment varied considerably, driven in part by differences in sex and pubertal development.

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Machine learning (ML) has significantly transformed biomedical research, leading to a growing interest in model development to advance classification accuracy in various clinical applications. However, this progress raises essential questions regarding how to rigorously compare the accuracy of different ML models. In this study, we highlight the practical challenges in quantifying the statistical significance of accuracy differences between two neuroimaging-based classification models when cross-validation (CV) is performed. Specifically, we propose an unbiased framework to assess the impact of CV setups (e.g., the number of folds) on the statistical significance. We apply this framework to three publicly available neuroimaging datasets to re-emphasize known flaws in current computation of p-values for comparing model accuracies. We further demonstrate that the likelihood of detecting significant differences among models varies substantially with the intrinsic properties of the data, testing procedures, and CV configurations of choice. Given that many of the above factors do not typically fall into the evaluation criteria of ML-based biomedical studies, we argue that such variability can potentially lead to p-hacking and inconsistent conclusions on model improvement. The obtained results from this study underscore that more rigorous practices in model comparison are urgently needed in order to mitigate the reproducibility crisis in biomedical ML research.

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Non-suicidal self-injury (NSSI) is common in adolescence and is strongly associated with depressive disorders (DDs), necessitating targeted screening and interventions for NSSI among depressed adolescents. This study aimed to build longitudinal predictive models to identify psychosocial factors distinguishing depressed adolescents who begin engaging in NSSI from those who do not and to compare these predictors with those in youth without DDs.

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To investigate the neurodevelopmental associations of moderate to late preterm (MLP) birth during late childhood.

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Impulsivity, often operationalized as delay discounting (DD) and as impulsive personality traits via the UPPS-P scales, is a key transdiagnostic construct across psychiatric disorders. Recent genome-wide association studies (GWAS) have studied the genetic basis of impulsivity in adults, but it remains unclear how similar the genetic architecture of DD is in children. The present study conducted GWAS of DD and impulsivity traits in 5548 children (ages 9-10 years old) of genetically inferred European ancestry from the Adolescent Brain Cognitive Development (ABCD) Study. Heritability estimates for DD (h = 0.20, S.E. = 0.10) and UPPS-P subscales (h = 0.08-0.11 S.E. = 0.05) were comparable to adult estimates. Genetic correlations between adult and child impulsivity were modest (r = 0.28-0.46), with positive urgency showing the strongest correlation (r = 0.83). While no genome-wide significant associations were identified, the top associated variants were mapped to genes previously linked to smoking initiation (rs3820908; p = 6.5 × 10) and UPPS-P Lack of Premeditation (rs17292179; p = 4.2 × 10). Polygenic score (PGS) associations were used to compare the genetic signals in children with those reported in adults. Adult PGSs for DD and positive and negative urgency indicators explained small but significant variance in the respective child impulsivity phenotypes (0.36%-0.44%, p < 7.5 × 10). Additionally, UPPS-P indices were broadly associated with PGSs derived from adult externalizing (0.42%-1.02%) and ADHD (0.23%-0.79%). This first GWAS of impulsivity in children offers a developmentally informed comparison of genetic influences, revealing both similarities and differences by developmental stage.

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Many communities in the United States are delaying school start times to improve youth sleep. Racial disparities exist in sleep. The extent to which school start times are associated with racial disparities in sleep is unclear, especially in early adolescent populations that are not the focus of research on school start times. This study examined the associations between school start times and actigraphy-assessed sleep, including duration, onset, and offset times among a national sample of racially diverse early adolescents.

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The present study examined the longitudinal, bidirectional associations between early adolescents’ electronic media use and family conflict across 3 years using data from the ABCD study (T1; = 11,787). Findings indicated that more overall electronic media use was related to later increases in family conflict, whereas the reverse association was not supported. Further analyses examined associations by unique types of electronic media. Social electronic media use at T1 in girls was related to less family conflict at T2, yet more family conflict at later timepoints. These findings suggest that more overall electronic media use promotes later conflict throughout early adolescence and that girls’ use of social types of media may be especially disruptive in families as they progress through early adolescence. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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Persistent and/or distressing psychotic-like experiences (PLEs) during adolescence are associated with poorer subsequent psychiatric outcomes. Modifiable lifestyle factors (such as sleep quality or regular exercise) may improve mental health outcomes; however, it is unknown how lifestyle factors are linked to trajectories of PLEs.

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Adolescence is a pivotal stage for brain development and a critical window for the emergence and transition of self-injury thoughts and behaviours (SITBs). However, the genetic and neurobiological mechanisms underlying SITBs transition during this developmental period are poorly understood.

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Although moderate-to-vigorous physical activity (MVPA) is a widely used construct in physical activity (PA) research, the lack of standardized assessment methods- particularly with the growing use of consumer-grade wearable activity trackers- poses challenges for comparability. Consumer-grade devices tend to rely on heart rate (HR)-based estimation methods to classify PA intensity, which contrasts with traditional research-grade accelerometers that use count- or raw-acceleration metrics. Comparability issues are particularly salient across individuals with varying weight status. In this commentary, we discuss systematic discrepancies between HR-based (relative intensity) and acceleration-based (absolute intensity) classifications of MVPA among individuals with differing weight statuses. Using Fitbit data from the Adolescent Brain Cognitive Development Study, we illustrate how HR-based PA intensity classification may indicate higher MVPA in youth with greater adiposity despite lower step counts and light PA levels. We highlight implications for research design, public health surveillance, messaging, policy, and interventions. We also call for greater transparency, standardized methodologies, and integrative measurement approaches to ensure more accurate assessment of PA behavior.

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The long-term impact of prenatal substance exposure (PSE) on obesity remains inconclusive. Few studies have explored the trajectories of Tri-Ponderal Mass Index (TMI), despite its greater accuracy and reliability in assessing adolescent adiposity. The aim of this study was to examine adiposity trajectories assessed by TMI from pre- to early adolescence and the influence of PSE on these patterns.

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Substance use has been associated with differences in adult brain morphology; however, it is unclear whether these differences precede or are a result of substance use substance use. We investigated the impact of polygenic risk scores (PRSs) for cannabis use disorder (CUD) and general substance use and substance use disorder liability (SU/SUD) on brain morphology in drug-naïve adolescents. Baseline data were used from 1874 European-descent participants (ages 9-11) comprising 222, 328 and 387 pairs of MZ twins, DZ twins, and Non-Twin Siblings, respectively, in the Adolescent Brain Cognitive Development Study. We fitted multivariate twin models to estimate the putative effects of CUD, SU/SUD, and brain region-specific PRSs. These models assessed their influence on six subcortical and two cortical phenotypes. PRS for CUD and SU/SUD were created based on GWAS conducted by Johnson et al. (Lancet Psychiatry 7:1032, 2020) and Hatoum et al. (Nat Ment Health 1:210-223, 2023), respectively. When decomposing variance in each brain region of interest (ROI), we used the corresponding ROI-specific PRS. Brain morphometry in drug-naive subjects was unrelated to CUD PRS. The variance explained in each ROI by its corresponding PRS ranged from 0.8 to 4.4%. The SU/SUD PRS showed marginally significant effects (0.2-0.4%) on cortical surface area and nucleus accumbens volume, but overall effect sizes were small. This study failed to reject the null hypothesis of no association between genetic risk for substance use and brain morphometry among baseline drug-naive adolescents. Genetic risk for CUD was not associated with brain morphometry among drug-naive adolescents, but a weak association with general addiction and substance use risk (SU/SUD) particularly in nucleus accumbens volume and total cortical surface area, was observed.

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The neighborhood context is increasingly recognized as a significant determinant of health. Advances in geospatial analysis and neuroimaging have facilitated an emerging field of research investigating how neighborhood conditions influence brain development. We conducted a systematic review, identifying 37 studies that examined associations between neighborhood conditions and brain structure in children and adolescents. We highlight key findings and research gaps across multiple domains of neighborhood conditions – socioeconomic status, demographic composition, social environment, built environment, physical environment, and health resources. Our review suggests that adverse neighborhood socioeconomic conditions are linked to structural brain differences, including reduced brain volume and white matter, and smaller surface areas. Additionally, observed race-related disparities in brain structures may be partially explained by residence in low-resourced neighborhoods, underscoring the role of structural inequities in shaping neurodevelopment. The majority of studies relied on the Adolescent Brain Cognitive Development Study dataset, limiting the generalizability of findings. Critically, neighborhood conditions beyond socioeconomic status remain understudied, offering opportunities for future research to examine how positive conditions (e.g., social cohesion, greenspace, health resources) may foster neurodevelopment. This review emphasizes the urgent need for policies to reduce structural inequities while leveraging protective neighborhood conditions to promote equity and youth neurodevelopment.

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The human cortex exhibits remarkable morphometric similarity between regions; however, the form and extent of lifespan network remodeling remain unknown. Here, we show the spatiotemporal maturation of morphometric brain networks, using multimodal neuroimaging data from 33,937 healthy participants aged 0-80 years. Global architecture matures from birth to early adulthood through enhanced modularity and small worldness. Early development features cytoarchitecturally distinct remodeling: sensory cortices exhibit increased morphometric differentiation, paralimbic cortices show increased morphometric similarity, and association cortices retain stable hub roles. Morphology-function coupling peaks in early adolescence and then decreases, supporting protracted functional maturation. These growth patterns of morphometric networks are correlated with gene expression related to synaptic signaling, neurodevelopment, and metabolism. Normative models based on morphometric networks identify person-specific, connectivity-phenotypic deviations in 1,202 patients with brain disorders. These data provide a blueprint for elucidating the principle of cortical network reconfiguration and a benchmark for quantifying interindividual network variations.

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Childhood behavioral problems are associated with significant long-term consequences, yet the underlying cognitive mechanisms remain poorly understood. In this study, we employed computational modeling alongside traditional reaction time (RT) measures to investigate cognitive control during a flanker task. We evaluated the predictive utility of these methods in explaining variance across eight transdiagnostic symptom domains in late childhood (mean age = 10.0 years; n = 10,343) from the Adolescent Brain Cognitive Development study. We compared simultaneous regression models across congruent and incongruent conditions using an RT-only model and a drift-diffusion model (DDM) that incorporated boundary separation, bias, drift rate, and non-decision time parameters. Results from the RT-only models indicated that slower reaction times across both task conditions were associated with higher scores on most symptom subscales, suggesting more behavioral problems. For both task conditions, DDM regressions accounted for more total variance across symptom domains compared to RT models. Additionally, DDM regressions demonstrated that impoverished evidence accumulation emerged as a shared feature of both internalizing and externalizing behaviors, while reductions in non-decision time, indicative of increased impulsiveness, were unique to rule-breaking and aggressive behaviors. These findings suggest that different aspects of cognitive control are associated with specific behavioral problems in children, rather than just overall response speed. Present results provide new insights into cognitive control dynamics and suggest that targeting ineffective cognitive control could be crucial for the prevention and intervention of childhood psychopathology.

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The purpose of this study was to: 1) examine the co-occurrence of household and community adverse childhood experiences (ACEs) among preadolescent youth using latent class analysis (LCA), and 2) examine the association of ACE latent clusters to clinical-range scores on the Child Behavior Checklist (CBCL). Data came from the baseline and year 1 survey of the Adolescent Brain Cognitive Development (ABCD) Study with 10,915 youth recruited from school-based catchment areas in the United States. We used LCA to examine 6 types of household adversity and 7 types of community adversity, including 4 types of discrimination. We identified 5 latent classes of household/community ACEs. The class with high levels of household and community ACEs together was most strongly associated with clinical-range CBCL scores in adjusted models. Assessing adversity comprehensively may improve identification of youth with elevated risk for behavioral symptoms, who are greatest in need of intervention.

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Early life stress (ELS) is associated with an increased risk of substance use in adolescence. The interaction of puberty with neurodevelopment during adolescence increases the sensitivity of the brain to both sex and stress hormones. This sensitivity may result in sex-specific pathways from ELS to adolescent alcohol use initiation. The current study examines the effect of ELS on alcohol initiation by age 13 via pubertal mediators, separated by sex and adjusted for the independent effects of age.

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Our genetic makeup, together with environmental and social influences, shape our brain’s development. Yet, the imaging-genetics field has struggled to integrate all these modalities to investigate the interplay between genetic blueprint, brain architecture, environment, human health and daily living skills. Here we interrogate the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and their corresponding implications on cognitive, behavioural, psychosocial and socioeconomic traits. We design a holistic pattern-learning framework that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 938 behavioural variables spanning 20 categories in 7,338 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks and specific parameters of the family wellbeing, including increased parental and child stress, anxiety and depression, or neighbourhood dynamics such as decreased safety. We thus find effects extending beyond the impairment of cognitive ability or language capacity which have been previously reported. Our investigation spotlights the interplay between genetic variation and subjective life quality in adolescents and their families.

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Fluid and crystallized intelligence are acknowledged as distinct facets of cognitive ability during brain development, but the specific neural substrates and molecular mechanisms underlying them remain unclear. This study used a sample comprising 7471 young adolescents (mean age 9.87 ± 0.62 years) from the ABCD cohort to elucidate the differential neural correlates of fluid and crystallized intelligence. Our findings indicated that micro-level brain MRI phenotypes such as water diffusivity were closely associated with fluid intelligence, whereas macro-level brain MRI phenotypes such as gray matter cortical thickness were indicative of crystallized intelligence. We further investigated the molecular mechanisms underlying fluid and crystallized intelligence by correlating the characteristic MRI markers with spatial transcriptome profiles and PET imaging. Results showed that fluid intelligence had significant associations with serotonin and glutamate system, while crystallized intelligence was related to serotonin, dopamine and acetylcholine system. Furthermore, we examined the impacts of lifestyle factors on these two forms of intelligence and how the molecular pathways mediated these impacts. Our investigation suggested that physical activities, screen use and sleep duration influenced fluid intelligence mainly through mGlu5 receptors and crystallized intelligence through 5HT1a and D2 receptors. In conclusion, these findings illustrated a distinct neural basis between fluid and crystallized intelligence from the perspectives of neuroimaging, neurotransmitters, and lifestyles in young adolescents.

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Maternal tobacco use during pregnancy (MTDP) remains a global and domestic public health issue. This study seeks to investigate the long-term impact of MTDP on brain morphology during late childhood and early adolescence using the Adolescent Brain Cognitive Development (ABCD) dataset. Children aged 9-10 were enrolled using the ABCD school selection probability sample method for national representation. Participants and their parents or guardians underwent interviews and surveys, and children underwent Magnetic Resonance Imaging (MRI) at baseline and 2-year follow-up. Morphometric brain measures of cortical thickness and sulcal depth across 34 regions of interest on T1-weighted MRI images were analyzed. Of 11,448 at baseline, 1,607 children fell into the MTDP group. Intracranial volume (p<0.001), total cortical surface area, and volume (p<0.0001) were significantly lower among MTDP children (vs. control) at both waves 1 and 2. A sustained difference was found in mean cortical thickness at the parahippocampal gyrus as well as sulcal depth at the isthmus cingulate, parahippocampal, lateral occipital, and lingual gyri. Several regions of interest demonstrated differences in the cortical thickness and sulcal depth at single time points. An association between MTDP and long-term outcomes of regional morphometric differences in cortical thickness and sulcal depth on MRI was found at both baseline among 9-10 years old and at 2-year follow-ups. Taken together with NIH cognitive testing from the same population comparison, the results suggest longstanding cognitive deficits corresponding to specific brain regions.

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A pervasive dilemma in brain-wide association studies (BWAS) is whether to prioritize functional magnetic resonance imaging (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size × scan time per participant). The model explains empirical prediction accuracies well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R = 0.89), spanning diverse scanners, acquisitions, racial groups, disorders and ages. For scans of ≤20 min, accuracy increases linearly with the logarithm of the total scan duration, suggesting that sample size and scan time are initially interchangeable. However, sample size is ultimately more important. Nevertheless, when accounting for the overhead costs of each participant (such as recruitment), longer scans can be substantially cheaper than larger sample size for improving prediction performance. To achieve high prediction performance, 10 min scans are cost inefficient. In most scenarios, the optimal scan time is at least 20 min. On average, 30 min scans are the most cost-effective, yielding 22% savings over 10 min scans. Overshooting the optimal scan time is cheaper than undershooting it, so we recommend a scan time of at least 30 min. Compared with resting-state whole-brain BWAS, the most cost-effective scan time is shorter for task-fMRI and longer for subcortical-to-whole-brain BWAS. In contrast to standard power calculations, our results suggest that jointly optimizing sample size and scan time can boost prediction accuracy while cutting costs. Our empirical reference is available online for future study design ( https://thomasyeolab.github.io/OptimalScanTimeCalculator/index.html ).

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Suicide is one of the leading causes of death among youth worldwide, yet existing studies that aimed to predict the first onset of suicidal thoughts or behaviors (STB) included a limited number of data modalities, and/or focused on adult populations. We aimed to prospectively predict first-onset STB across four-year follow-ups in adolescents using (1) an existing STB history classification model that was previously applied to baseline data, and (2) a new machine learning model with 195 biopsychosocial features.

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Various multilevel, multidomain factors at the individual-, family-, and environmental-level, and changes in neurobiology have been associated with the likelihood of developing alcohol use disorder (AUD) or binge drinking later in life. Prior studies have examined only limited subsets of these factors, typically focusing on cross-sectional associations with alcohol initiation, binge drinking, or AUD rather than exploring longitudinal alcohol use trajectories. Our study addresses these gaps by applying machine learning methods to a comprehensive set of multilevel, multidomain factors and multimodal brain imaging features (including brain structure and functional connectivity) to prospectively predict early alcohol sipping trajectories. Using data from the Adolescent Brain Cognitive Development Study, we identified functional connectivity features and multilevel factors that distinguish youth with an increasing alcohol sipping trajectory from those who initially experimented with alcohol but reduced their consumption over time. Moreover, structural and functional features predicted differences between youth who increasingly sipped over time and those who did not engage in alcohol experimentation. Interactions between age, socioeconomical status and positive attitudes towards drinking could predict a pattern of increasing alcohol sipping over time. These trends could inform how individual, family, environmental and neurobiological factors impact the development of different alcohol sipping trajectories over time.

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Sleep disturbances and mental health challenges often arise during adolescence, with a greater prevalence among females and individuals experiencing obesity. In this study, we examined the intricate relationships between sleep problems, obesity, and biological sex, and their combined relationship with well-being in a large, diverse cohort of US adolescents.

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Psychiatric conditions, known for their hereditary nature, exert significant impacts on various life domains. Leveraging this heritability, we examine the relations between genetic susceptibility to major psychiatric disorders and the multifaceted aspects of quality of life in two population-based cohorts, the Adolescent Brain Cognitive Development (ABCD) study (N = 3909 preadolescent children) and the UK Biobank (N = 269,293 adults). Genetic susceptibility to seven major psychiatric disorders was quantified by polygenic scores derived from extensive genome-wide association studies (N = 21,000-413,000). Pervasive associations were found between genetic risk for all seven major psychiatric disorders investigated and age-relevant real-life quality of life indices, with varied patterns of associations for different life domains. We especially highlight the role of genetic risks for ADHD and major depressive disorders. Our findings emphasize the continuous nature of psychiatric traits, extending their influence on daily life experiences and societal functioning beyond symptomatology and diagnostic classifications.

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Alcohol use during adolescence poses a significant public health problem due to its potential long-term consequences on both physical and mental health and increased risk for developing substance use disorders later in life. Both individual (e.g., genetic liability, neural functioning, personality features) and environmental (e.g., parenting, school environment) features play an important role in accelerating or buffering the progression of early alcohol consumption. This study used data from the Adolescent Brain Cognitive Development (ABCD) study (Release 5.1; N = 11,868) to provide a comprehensive examination of how genetic, neural, trait, and environmental factors are associated with risk for first sip of alcohol, first full drink, and the progression from first sip to full drink, both independently and uniquely. Cox proportional hazard models were used to examine the univariable associations between theoretically relevant genetic, neural, trait, and environmental variables and early alcohol use. Then, stepwise model-fitting was used to determine which indicators were uniquely associated with alcohol outcomes. Risk for early alcohol use was distributed across multiple domains highlighting the unique information provided by genetic, trait, and environmental variables. Results also indicated the importance of both environmental and genetic factors on time to first sip of alcohol, but that time to first full drink and the progression from sip to drink was most associated with genetic and trait factors rather than broad environmental influences. These findings highlight both potential etiological pathways driving early alcohol use as well as phenotypic and environmental process that can be targeted for early intervention efforts.

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Resilience to developing emotional disorders is critical for adolescent mental health, especially following childhood trauma. Yet, brain markers of resilience remain poorly understood. By analyzing brain responses to angry faces in a large-scale longitudinal adolescent cohort (IMAGEN), we identified two functional networks located in the orbitofrontal and occipital regions. In girls with high genetic risks for depression, higher orbitofrontal-related network activation was associated with a reduced impact of childhood trauma on emotional symptoms at age 19, whereas in those with low genetic risks, lower occipital-related network activation had a similar association. These findings reveal genetic risk-dependent brain markers of resilience (GRBMR). Longitudinally, the orbitofrontal-related GRBMR predicted subsequent emotional disorders in late adolescence, which were generalizable to an independent prospective cohort (ABCD). These findings demonstrate that high polygenic depression risk relates to activations in the orbitofrontal network and to resilience, with implications for biomarkers and treatment.

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Preterm birth can significantly impact cognitive development, particularly executive functions (EF). This study investigated hot (with emotional/motivational aspects) and cool (purely neutral/cognitive) EF trajectories in preterm and full-term children, examining brain-behavior relationships. It included 3508 participants aged 9-10 years (mean age 10.0 years) at baseline from the Adolescent Brain and Cognitive Development (ABCD®) study, evenly split between preterm and full-term births (54.36 % males; 1.05 % Asian American, 10.69 % Black, 15.68 % Hispanic, 61.57 % White, 11.09 % other). Participants were followed for 4 years, completing MRI scans and a cool EF task at baseline and at the 2-year follow-up, as well as hot/cool and hot EF tasks at the 1- and 3-year follow-ups. Linear mixed models showed varying effects of preterm birth across the different EF tasks. Specifically, preterm children showed persistent cool EF deficits and a catch-up pattern for hot EF, while performance on the hot/cool task showed no association with preterm birth. Brain-behavior bivariate latent change score analyses identified distinct bidirectional relationships in specific regions, suggesting altered cognitive-brain maturation interactions in preterm children. These findings highlight the complex nature of EF development following preterm birth: while cool EF deficits persist, hot EF shows catch-up growth in preterm children during early adolescence. This emphasizes the need for tailored interventions and long-term follow-up in this population.

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Adverse childhood experiences (ACEs) are key risk factors for adolescent mental health problems, including conduct problems (CP). While ACEs may impact CP through neurobiological pathways, it is unclear whether brain functional connectivity acts as the neurobiological link.

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Social functioning difficulties among youth with attention-deficit/hyperactivity disorder (ADHD) have been examined behaviorally; however, limited research has investigated brain networks associated with social difficulties among youth with ADHD. A growing body of literature supports the utility of the NIMH’s Research Domain Criteria (RDoC) framework, which emphasizes broad neurobiological based dimensions, allowing for the integration of models of both neural circuitry and behavior when examining externalizing behaviors in youth. We hypothesized that an ADHD classification system based on social functioning would better predict real-world psychosocial and academic outcomes compared to traditional Diagnostic and Statistical Manual of Mental Disorders (DSM-5) nosology of ADHD presentations. First, using data from the Adolescent Brain Cognitive Development (ABCD) Study, we identified four distinct profiles of youth with ADHD ranging from low social functioning to high social functioning. These social-data-derived profiles were linked to differential social challenges associated with caregiver income and mental health disorders. Next, our neuroimaging findings initially revealed differential patterns of functional connectivity across profiles involving attention-control, cingulo-opercular, sensorimotor networks. However, these connectivity differences were not consistently replicated, indicating that social functioning alone may not define neurobiologically distinct subgroups. Finally, in comparing our social functioning profiles to existing DSM-5 nosology with respect to real-world psychosocial outcomes, our social profiles demonstrated greater explanatory power for outcomes related to peer relationships, family conflict, and mental health. Overall, these findings emphasize the heterogeneity in social functioning among ADHD youth and suggest that while behavioral profiles are clinically meaningful, future work should integrate additional dimensions, such as executive functioning, to more precisely capture the neurobiological underpinnings of ADHD.

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Adolescent digital media use is highly prevalent. However, potential harms are unclear, as prospective studies of outcomes of screen-naïve youth are sparse. This study assessed whether individual differences in 4 domains relevant to addiction, measured in late childhood, prospectively predicted average daily hours of screen time (ST) and problematic screen use (PSU), defined as screen use that is compulsive and distressing, of 3 screen activities in mid-adolescence.

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In adolescence, caffeinated beverage consumption is negatively associated with cognitive functioning. The default mode network and dorsal attention network are anticorrelated brain systems that are essentially implicated in attention. Despite the importance of the anticorrelation of default mode network – dorsal attention network on cognitive functioning, no studies have examined the association between this anticorrelation and recent caffeine consumption among youths. This study analyzed baseline data from the Adolescent Brain Cognitive Development℠ Study, the largest longitudinal study examining brain development and adolescent health in the United States, to explore the associations between caffeinated beverage consumption and the strength of anticorrelation between the default mode network – dorsal attention network.

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To identify associations of past-year witnessing violence with expectancies (anticipated effects) for alcohol and cannabis use in Black, Latinx, and White youth, including possible variations by level of neighborhood advantage and/or race/ethnicity.

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Large, population-based magnetic resonance imaging (MRI) studies of adolescents promise transformational insights into neurodevelopment and mental illness risk. However, youth MRI studies are especially susceptible to motion and other artifacts that introduce non-random noise. After visual quality control of 11,263 T1 MRI scans obtained at age 9-10 years through the Adolescent Brain Cognitive Development study, we uncovered bias in measurements of cortical thickness and surface area in 55.1% of the samples with suboptimal image quality. These biases impacted analyses relating structural MRI and clinical measures, resulting in both false-positive and false-negative associations. Surface hole number, an automated index of topological complexity, reproducibly identified lower-quality scans with good specificity, and its inclusion as a covariate partially mitigated quality-related bias. Closer examination of high-quality scans revealed additional topological errors introduced during image preprocessing. Correction with manual edits reproducibly altered thickness measurements and strengthened age-thickness associations. We demonstrate here that inadequate quality control undermines advantages of large sample size to detect meaningful associations. These biases can be mitigated through additional automated and manual interventions.

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Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. However each individual modality may overlook crucial aspects of brain analysis. By combining them, we can uncover hidden brain connections and gain a more comprehensive understanding. In previous work, we identified multivariate patterns of change in whole-brain function during adolescence. In this work, we focus on linking functional change patterns (FCPs) to brain structure. We introduced two approaches and applied them to data from the adolescent brain and cognitive development (ABCD) dataset. First, we evaluate voxel-wise sMRI- coupling to identify structural patterns linked to our previously identified FCPs. Our approach revealed multiple interesting patterns in functional network connectivity (FNC) and gray matter volume (GMV) data that were linked to subject-level variation. components 2 and 4 exhibit extensive associations between their loadings and voxel-wise GMV data. Secondly, we leveraged a symmetric multimodal fusion technique called multiset canonical correlation analysis (mCCA) + joint independent component analysis (jICA). Using this approach, we identified structured such as one showing increased connectivity between visual and sensorimotor domains and decreased connectivity between sensorimotor and cognitive control domains, linked to structural change patterns ( ) including alterations in the bilateral sensorimotor cortex. Interestingly, females show stronger connection between brain functional and structural changes than males, highlighting gender-related differences. The combined results from both asymmetric and symmetric multimodal fusion methods underscore the intricate gender-specific nuances in neural dynamics. By utilizing two complementary multimodal approaches, our study enhances our understanding of the evolving nature of whole brain connectivity and structure during the adolescent period, shedding light on the nuanced processes underlying adolescent brain development.

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The associations of family environment with adolescent behavioral health are well established, yet insights into how these associations are moderated by sex, particularly through changes in brain structure, are limited.

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Parents’ internalizing symptoms, such as anxiety and depression, may contribute to similar symptoms in their youth. However, these associations vary, as youth with better emotion regulation may be more protected from negative parental influence. Yet, it remains unclear how youth’s neural correlates of emotion regulation, particularly in prefrontal regions-such as dorsolateral prefrontal cortex (dlPFC), lateral orbitofrontal cortex (lOFC), and medial orbitofrontal cortex (mOFC)-play a role in these associations. To address this gap, this study used two-wave longitudinal data that spanned 2 yr from the nationwide Adolescent Brain Cognitive Development study including 7,932 youth (Mage = 9.96 yr, SD = 7.52; 49% females) and their parents. Results revealed significant longitudinal associations between parents’ and youth’s internalizing symptoms over 2 yr. Moreover, youth’s dlPFC, lOFC, and mOFC activity to negative emotions moderated these links. Youth with higher activity in these regions showed weaker parent-youth associations in internalizing symptoms over time. This study provides robust evidence that dlPFC, lOFC, and mOFC serve as neurobiological protective factors in the longitudinal links between parents and youth’s internalizing symptoms. Findings inform interventions targeting youth’s neural development in emotion regulation to promote emotional adjustment in families where parents face mental health challenges.

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Associations between childhood obesity and brain microstructural differences have been observed. It remains unknown whether these associations are driven by sex-specific excessive weight gain. Restriction spectrum imaging characterizes brain tissue microstructural health via water diffusion, where the restricted normalized isotropic (RNI) compartment assesses neuronal and glial cellularity, which may reflect neuroinflammation, synaptic pruning, or both.

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Although gaming disorder is recognized as a diagnosable behavioral addiction, uncertainty remains regarding its directional association with adolescent psychopathology. Clarifying this association is crucial for refining diagnostic frameworks and developing targeted interventions.

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Classical regression generally assumes that all subjects follow a common model with the same set of parameters. With ever advancing capabilities of modern technologies to collect more subjects and more covariates, it has become increasingly common that there exist subgroups of subjects, and each group follows a different regression model with a different set of parameters. In this article, we propose a new approach for subgroup analysis in regression modeling. Specifically, we model the relation between a response and a set of primary predictors, while we explicitly model the heterogenous association given another set of auxiliary predictors, through the interaction between the primary and auxiliary variables. We introduce penalties to induce the sparsity and group structures within the regression coefficients, and to achieve simultaneous feature selection for both primary predictors that are significantly associated with the response, as well as the auxiliary predictors that define the subgroups. We establish the asymptotic guarantees in terms of parameter estimation consistency and cluster estimation consistency. We illustrate our method with an analysis of the functional magnetic resonance imaging data from the Adolescent Brain Cognitive Development Study.

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Parental psychopathology is a known risk factor for child autistic-like traits. However, symptom-level associations and underlying mechanisms are poorly understood.

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Assessing dimensions of neighborhoods could aid identification of contextual features that influence psychopathology in children and contribute to uncovering mechanisms underlying these associations.

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Recent advancements in understanding the brain’s functional organization related to behavior have been pivotal, particularly in the development of predictive models based on brain connectivity. A major analytical strategy in this domain involves a two-step process by first constructing a connectivity matrix from predefined brain regions, and then linking these connections to behaviors or clinical outcomes. Although some advances considered subject-specific functionally homogeneous nodes without relying on predefined regions of interest (ROIs), all these approaches with unsupervised node partitions predict outcomes inefficiently with independently established connectivity. In this paper, we introduce the Supervised Brain Parcellation (SBP), a brain node parcellation scheme informed by the downstream predictive task. With voxel-level functional time courses generated under resting-state or cognitive tasks as input, our approach clusters voxels into nodes in a manner that maximizes the correlation between inter-node connections and the behavioral outcome, while also accommodating intra-node homogeneity. We rigorously evaluate the SBP approach using resting-state and task-based fMRI data from both the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP). Our analyses show that SBP significantly improves out-of-sample connectome-based predictive performance compared to conventional step-wise methods under various brain atlases. This advancement holds promise for enhancing our understanding of brain functional architectures with behavior and establishing more informative network neuromarkers for clinical applications.

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Functional brain networks are associated with both behavior and genetic factors. To uncover biological mechanisms of psychopathology, it is critical to define how the spatial organization of these networks relates to genetic risk during development.

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. Recently, US youth of color reported greater use of alcohol, tobacco, and cannabis than White youth. Increased levels of discrimination in recent years may have added to the chronic burden associated with increased use among youth of color. Little is known about this relationship, especially among youth who initiate substance use earlier in adolescence. This study assessed the prevalence of substance susceptibility (willingness and curiosity) and use (alcohol, tobacco and cannabis) among youth by race/ethnicity and ethnic discrimination’s role on this relationship.

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As youth transition into adolescence, their desire for autonomy leads to changes in the family dynamic, resulting in increased family conflict and possible disruptions to children’s psychological health. Previous literature, however, has largely neglected to consider whether the association between family conflict and child behavioral difficulties is uni- or bi-directional. The current study used latent curve growth models with structured residuals (LCMs-SR) to investigate this question in the Adolescent Brain & Cognitive Development (ABCD) study. At four annual waves (baseline through 3-year follow-up), youth ( = 11,868; at Time 1 = 9.48 years; 48% female; 50% White) reported on family conflict while parents reported on youths’ internalizing and externalizing behaviors. Youth reported family conflict levels as increasing over four years. Furthermore, family conflict was bidirectionally associated with externalizing behavior, in that families with greater than expected conflict had children with more externalizing behaviors, and youth with more externalizing behaviors reported greater than expected conflict at home. Internalizing behavior, however, did not predict later family conflict, though family conflict predicted deviations in later internalizing behavior. These findings add to the literature by demonstrating bidirectional influences between children’s behavior and family functioning across emerging adolescence.

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We present the novel Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC) model. BRAINIAC allows for estimation of total variance explained by all features for a given cognitive phenotype, as well as a principled assessment of the impact of annotations on relative enrichment of predictive features compared to others in terms of variance explained, without relying on a potentially unrealistic assumption of sparsity of brain-behavior associations. We validate BRAINIAC in Monte Carlo simulation studies. In real data analyses, we train the BRAINIAC model on resting state functional magnetic resonance imaging (rsMRI) and neuropsychiatric data from the Adolescent Brain Cognitive Development (ABCD) Study and use the trained model in an out-of-study application to harmonized resting-state data from the Human Connectome Project Development (HCP-D), demonstrating a substantial improvement in out-of-study predictive power by incorporating relevant annotations into the BRAINIAC model.

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Subthreshold anxiety (STA) is a significant risk factor for developing anxiety disorder (AX), particularly in adolescence. Understanding the risk and protective factors of the development of STA in early life is essential for early prevention and intervention efforts. However, research on this topic is scarce. We examined the data of 11,876 early adolescents from the Adolescent Brain and Cognitive Development (ABCD) Study to explore the factors influencing the development of STA between ages 9 and 13. The outcomes included developing AX, persistent STA, and remission from STA. Using the Child Behavior Checklist (CBCL), we identified 786 participants with STA. To predict STA transitions, we analyzed 31 diathesis-stress-related variables covering demographics, mental and physical health, and environmental factors, employing logistic regression. Compared to baseline healthy controls (HCs), adolescents with STA showed an odds ratio (OR) of 6.9 for converting to AX. The pivotal risk factors for progression from STA to AX were lack of perseverance and area deprivation, with females being more likely to maintain STA. Protective factors for a favorable prognosis of STA included the absence of traumatic history, lack of premeditation, increased physical activity, and positive school environment. Healing traumatic experiences, increased physical activity, and enhancing school and family environments could help prevent adverse outcomes. By targeting these modifiable factors, adolescents at high risk can be identified and provided with interventions early in life.

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In order to transition between a resting state and carrying out cognitively-demanding processes the brain makes a host of subtle changes to its network organization. In adults, less reconfiguration relates to better task performance, suggesting a preconfigured brain organization at rest is beneficial, such that only minute changes are required to execute task demands. Here, we take a developmental lens to this phenomenon, examining reconfiguration in late childhood by leveraging a large sample of 9-11 year olds from the Adolescent Brain and Cognitive Development Study. We find more reconfiguration between the resting state and two executive function tasks is related to better task performance. These relationships hold even when accounting for network segregation, though segregation was negatively related to reconfiguration. Reconfiguration was also related to crystallized intelligence, with diverging effects across tasks. Overall, these findings demonstrate that in contrast to adulthood during late childhood, before functional brain networks are fully mature, greater reconfiguration promotes successful task performance.

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Sleep duration and physical activity have been associated with internalizing problems. However, genetic confounding and measurement error may introduce bias. We assessed genetic confounding in the associations of modifiable lifestyle with internalizing problems using device-based and questionnaire assessments to estimate shared genetic risk across different assessments in adolescents.

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With the widespread adoption of screen-based devices among adolescents, there is growing concern that more screen time could contribute to mental health problems such as depression. It is thus critical to identify potential mediating factors that could help explain this potential risk relationship. Recent evidence indicates that more screen time could impact sleep duration and brain structural connectivity (ie, white matter organization), which are critical for emotional health. Notably, sleep duration is a modifiable behavior that health care providers can easily target.

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Lower parental socioeconomic status (SES) is consistently linked to lower academic achievement among adolescents, with early disparities persisting into adulthood. However, the neurobiological mechanisms underlying these associations are not well understood. This preregistered study investigates the associations between household and neighborhood disadvantage-measured by income-to-needs ratio, parental educational attainment, and neighborhood SES-changes in within-network functional connectivity, and school grades, using longitudinal data from the Adolescent Brain Cognitive Development Study (N = 4745; age at baseline = 119.1 ± 7.5 months; age at 2-year follow up = 143.6 ± 7.8 months; age at 3-year follow up = 154.8 ± 7.7 months). Within-network connectivity changed significantly from baseline to follow-up, increasing in most networks (e.g., default mode, fronto-parietal) and decreasing in a few (e.g., salience, ventral attention). After controlling for other SES indicators, parental education and neighborhood disadvantage, but not income, were associated with changes in connectivity across several brain networks, including reduced increases in default mode, cingulo-opercular, and visual network connectivity and greater increases in within-sensorimotor network connectivity. Further, changes in sensorimotor connectivity mediated the relationship between parental educational attainment and academic achievement three years later. These findings highlight the importance of parental education and neighborhood environments in shaping neurodevelopmental trajectories that influence academic outcomes. Understanding the mechanisms that link socioeconomic disadvantage with academic outcomes could inform interventions aimed at reducing persistent achievement gaps.

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A key step toward understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organisation at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organisation of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex.

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Increasing child and adolescent use of social media, video games, and mobile phones has raised concerns about potential links to youth mental health problems. Prior research has largely focused on total screen time rather than longitudinal addictive use trajectories.

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Pregnancy- and birth-related factors affect offspring brain development, emphasizing the importance of early life exposures. While most previous studies have focused on a few variables in isolation, here we investigated associations between a broad range of pregnancy- and birth-related variables and multivariate cortical brain MRI features. Our sample consisted of 8,396 children aged 8.9 to 11.1 y from the Adolescent Brain Cognitive Development Study. Through multiple correspondence analysis and factor analysis of mixed data, we distilled numerous pregnancy and birth variables into four overarching dimensions; maternal pregnancy complications, maternal substance use, low birth weight and prematurity, and newborn birth complications. Vertex-wise measures of cortical thickness (CT), surface area (SA), and curvature were fused using linked independent component analysis. Linear mixed-effects models showed that maternal pregnancy complications and low birth weight and prematurity were associated with smaller global SA. Additionally, low birth weight and prematurity was associated with complex regional cortical patterns reflecting bidirectional variations in both SA and CT. Newborn birth complications showed multivariate patterns reflecting smaller occipital- and larger temporal area, bidirectional frontal area variations, and reduced CT across the cortex. Maternal substance use showed no associations with child cortical structure. By employing a multifactorial and multivariate morphometric fusion approach, we connected complications during pregnancy and fetal size and prematurity to global SA and specific regional signatures across child cortical MRI features.

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The increased use of screen media has raised unknown effects on mental health among adolescents. This study aimed to examine the correlational and causal association between screen media activity (SMA) and mental health problems, and the mediating role of brain functions and structures in this relationship. Data from 4557 adolescents (mean age = 9.955 ± 0.164 years) in the Adolescent Brain Cognitive Development (ABCD) study were analysed across four time points: baseline, 1-year, 2-year, and 3-year follow-ups. Linear mixed models assessed SMA’s association with mental health indices and the brain’s developmental pattern, respectively. Cross-lagged panel models examined the SMA-mental health problems’ longitudinal and causal relationship. Mediation analyses explored brain functions and structures as mediators on the SMA-mental health correlation. Baseline SMA positively correlated with internalizing, externalizing, and stress problems; and negatively correlated with brain volume, area and diverse sets of resting-state functional connectivity (RSFC) after three years. Higher baseline SMA associated with increased internalizing ( = 0.030, = 0.012, = 0.016), and stress problems ( = 0.026, = 0.012, = 0.037) three years later. The RSFC between the cingulo-opercular network (CON) and the retrosplenial temporal network (RTN) mediated the effects of SMA on externalizing ( = 0.002, = 0.042) and stress problems ( = -0.003, = 0.022). TV watching predicted higher externalizing problems (β = 0.054, pfdr < 0.001), while video watching predicted increased internalizing (β = 0.061, pfdr < 0.001), externalizing (β = 0.033, pfdr = 0.035), and stress problems (β = 0.060, pfdr < 0.001). The findings indicate the negative impact of SMA, particularly TV and video watching, on adolescent mental health, mediated by changes in CON and RTN functional connectivity. Future research can explore the specific risks associated with video streaming and consider the role of emerging technologies such as virtual reality in SMA on adolescent mental health.

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Late childhood is a crucial period for individuals with psychiatric disorders. While common single-nucleotide polymorphisms explain a large proportion of inherited risk, structural variations including copy number variants (CNVs) play a significant role in the genetic architecture of neurodevelopmental disorders. The relevance of CNVs to child psychopathology and cognitive function in the general population remains underexplored. The authors conducted a comprehensive exploration of the CNV architecture underlying dimensions of psychopathology and cognitive phenotypes within the Adolescent Brain Cognitive Development (ABCD) Study.

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