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Morphological features of the lateral prefrontal cortex (PFC) in late childhood and early adolescence may provide important clues as to the developmental etiology of clinical conditions such as obesity. Body composition measurements and structural brain imaging were performed on 11226 youth at baseline (age 9 or 10 years) and follow-up (age 11 or 12 years). Baseline morphological features of the lateral PFC were examined as predictors of body composition. Findings revealed reliable associations between middle frontal gyrus volume, thickness and surface area and multiple indices of body composition. These findings were consistent across both time points and remained significant after covariate adjustment. Cortical thicknesses of the inferior frontal gyrus and lateral orbitofrontal cortex were also reliable predictors. Morphology effects on body composition were mediated by performance on a non-verbal reasoning task. Modest but reliable moderation effects were observed with respect to environmental self-regulatory demand after controlling for sex, race/ethnicity, income and methodological variables. Overall findings suggest that PFC morphology is a reliable predictor of body composition in early adolescence, as mediated through select cognitive functions and partially moderated by environmental characteristics.

Structural models of psychopathology consistently identify internalizing (INT) and externalizing (EXT) specific factors as well as a superordinate factor that captures their shared variance, the factor. Questions remain, however, about the meaning of these data-driven dimensions and the interpretability and distinguishability of the larger nomological networks in which they are embedded. The sample consisted of 10 645 youth aged 9-10 years participating in the multisite Adolescent Brain and Cognitive Development (ABCD) Study. , INT, and EXT were modeled using the parent-rated Child Behavior Checklist (CBCL). Patterns of associations were examined with variables drawn from diverse domains including demographics, psychopathology, temperament, family history of substance use and psychopathology, school and family environment, and cognitive ability, using instruments based on youth-, parent-, and teacher-report, and behavioral task performance. exhibited a broad pattern of statistically significant associations with risk variables across all domains assessed, including temperament, neurocognition, and social adversity. The specific factors exhibited more domain-specific patterns of associations, with INT exhibiting greater fear/distress and EXT exhibiting greater impulsivity. In this largest study of hierarchical models of psychopathology to date, we found that , INT, and EXT exhibit well-differentiated nomological networks that are interpretable in terms of neurocognition, impulsivity, fear/distress, and social adversity. These networks were, in contrast, obscured when relying on the a priori Internalizing and Externalizing dimensions of the CBCL scales. Our findings add to the evidence for the validity of , INT, and EXT as theoretically and empirically meaningful broad psychopathology liabilities.

Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR , that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR .

Unpredictability is a core but understudied dimension of adversities and has been receiving increasing attention recently. The effects of unpredictability on psychopathology and the underlying neural mechanisms, however, remain unclear. It is also unknown how unpredictability interacts with other dimensions of adversities in predicting brain development and psychopathology of youth. We applied cluster robust standard errors to examine how unpredictability was associated with the developmental changes in resting-state functional connectivity (rsFC) of large-scale brain networks implicated in psychopathology, as well as the moderating role of deprivation, using data from the Adolescent Brain Cognitive Development (ABCD) study, which included four measurements from baseline (mean ± s.d. age, 119.13 ± 7.51 months; 2815 females) to 3-year follow-up ( = 5885). After controlling for threat, unpredictability was associated with a smaller increase in rsFC within default mode network (DMN) and a smaller decrease in rsFC between cingulo-opercular network (CON) and DMN. Neighborhood educational deprivation moderated the associations between unpredictability and changes in rsFC within DMN and fronto-parietal network (FPN), as well as between CON and DMN. A smaller decrease in rsFC between CON and DMN mediated the association between unpredictability and externalizing problems. Neighborhood educational deprivation moderated the indirect pathway from unpredictability to externalizing problems via a smaller decrease in CON-DMN rsFC. Our findings shed light on the neural mechanisms underlying the associations between unpredictability and adolescents' psychopathology and the moderating role of deprivation, highlighting the significance of providing stable environment and abundant educational opportunities to facilitate optimal development.

This study examined how resting-state functional magnetic resonance imaging (rs-fMRI) data quality and availability relate to clinical and sociodemographic variables within the Adolescent Brain Cognitive Development Study. A sample of participants with an adequate sample of quality baseline rs-fMRI data containing low average motion (framewise displacement ≤ 0.15; low-noise; n = 4,356) was compared to a sample of participants without an adequate sample of quality data and/or containing high average motion (higher-noise; n = 7,437) using Chi-squared analyses and t-tests. A linear mixed model examined relationships between clinical and sociodemographic characteristics and average head motion in the sample with low-noise data. Relative to the sample with higher-noise data, the low-noise sample included more females, youth identified by parents as non-Hispanic white, and youth with married parents, higher parent education, and greater household incomes (ORs = 1.32–1.42). Youth in the low-noise sample were also older and had higher neurocognitive skills, lower BMIs, and fewer externalizing and neurodevelopmental problems (ds = 0.12–0.30). Within the low-noise sample, several clinical and demographic characteristics related to motion. Thus, participants with low-noise rs-fMRI data may be less representative of the general population and motion may remain a confound in this sample. Future rs-fMRI studies of youth should consider these limitations in the design and analysis stages in order to optimize the representativeness and clinical relevance of analyses and results.

·Brain regions mostly encode incentive valence or magnitude during reward processing.·This encoding specialization is highly consistent from pre to early adolescence.·Brain regions’ encoding specialization changes with reward processing phase.·Neural reactivity during success feedback increased from pre to early adolescence.·Success feedback reactivity increases were in prefrontal and subcortical regions. Neural activation during reward processing is thought to underlie critical behavioral changes that take place during the transition to adolescence (e.g., learning, risk-taking). Though literature on the neural basis of reward processing in adolescence is booming, important gaps remain. First, more information is needed regarding changes in functional neuroanatomy in early adolescence. Another gap is understanding whether sensitivity to different aspects of the incentive (e.g., magnitude and valence) changes during the transition into adolescence. We used fMRI from a large sample of preadolescent children to characterize neural responses to incentive valence vs. magnitude during anticipation and feedback, and their change over a period of two years. Data were taken from the Adolescent Cognitive and Brain DevelopmentSM (ABCD®) study release 3.0. Children completed the Monetary Incentive Delay task at baseline (ages 9–10) and year 2 follow-up (ages 11–12). Based on data from two sites (N = 491), we identified activation-based Regions of Interest (ROIs; e.g., striatum, prefrontal regions, etc.) that were sensitive to trial type (win $5, win $0.20, neutral, lose $0.20, lose $5) during anticipation and feedback phases. Then, in an independent subsample (N = 1470), we examined whether these ROIs were sensitive to valence and magnitude and whether that sensitivity changed over two years. Our results show that most ROIs involved in reward processing (including the striatum, prefrontal cortex, and insula) are specialized, i.e., mainly sensitive to either incentive valence or magnitude, and this sensitivity was consistent over a 2-year period. The effect sizes of time and its interactions were significantly smaller (0.002≤η2≤0.02) than the effect size of trial type (0.06≤η2≤0.30). Interestingly, specialization was moderated by reward processing phase but was stable across development. Biological sex and pubertal status differences were few and inconsistent. Developmental changes were mostly evident during success feedback, where neural reactivity increased over time. Our results suggest sub-specialization to valence vs. magnitude within many ROIs of the reward circuitry. Additionally, in line with theoretical models of adolescent development, our results suggest that the ability to benefit from success increases from pre- to early adolescence. These findings can inform educators and clinicians and facilitate empirical research of typical and atypical motivational behaviors during a critical time of development.

•Dynamic conditional correlations (DCC) method used to investigate dynamic functional connectivity (FC).•Large sample from ABCD Study showed dynamic FC relationships with age, sex assigned at birth, and pubertal development.•Variability of FC within frontolimbic network increased from ages 9 to 14, especially in those assigned female at birth.•Controlling for age, both those assigned female at birth and those with advanced pubertal development showed decreased variability in all networks studied.•Variability of graph theoretical measures show differential patterns to overall FC variability. Understanding the relative influences of age, pubertal development, and sex assigned at birth on brain development is a key priority of developmental neuroscience given the complex interplay of these factors in the onset of psychopathology. Previous research has investigated how these factors relate to static (time-averaged) functional connectivity (FC), but little is known about their relationship with dynamic (time-varying) FC. The present study aimed to investigate the unique and overlapping roles of these factors on dynamic FC in children aged approximately 9 to 14 in the ABCD Study using a sample of 5122 low-motion resting-state scans (from 4136 unique participants). Time-varying correlations in the frontolimbic, default mode, and dorsal and ventral corticostriatal networks, estimated using the Dynamic Conditional Correlations (DCC) method, were used to calculate variability of within- and between-network connectivity and of graph theoretical measures of segregation and integration. We found decreased variability in global efficiency across the age range, and increased variability within the frontolimbic network driven primarily by those assigned female at birth (AFAB). AFAB youth specifically also showed increased variability in several other networks. Controlling for age, both advanced pubertal development and being AFAB were associated with decreased variability in all within- and between-network correlations and increased variability in measures of network segregation. These results potentially suggest advanced brain maturation in AFAB youth, particularly in key networks related to psychopathology, and lay the foundation for future investigations of dynamic FC.

Although the prevalence rates of preadolescent eating disorders (EDs) are on the rise, considerably less is known about the correlates and treatment of EDs in this age group. Clarifying the epidemiology of EDs in preadolescent children is a necessary first step to understand the nature and scope of this problem in this age group. Analysis of data collected in the ABCD Study release 2.0.1. The ABCD cohort was a population-based sample that consisted of 11 721 children ages 9-10 years. Measures included reports of a lifetime and current mental disorders determined using a diagnostic interview for DSM-5 disorders, sociodemographic factors, and psychiatric treatment utilization. The lifetime prevalence of EDs was 0.95%. Being Black, multiracial, having unmarried parents, and family economic insecurity were significant predictors for developing an ED. Among psychiatric conditions, the major depressive disorder was most robustly associated with EDs in both cross-sectional and temporal analyses. Only 47.40% of children who had a lifetime ED received some type of psychiatric treatment. EDs were not a significant predictor of psychiatric treatment utilization after accounting for sex, sexual orientation, parent marital status, economic insecurity, and all other psychiatric diagnoses. Despite increasing prevalence rates of preadolescent EDs, the current findings suggest that the majority of children with these disorders remain untreated. Devoting increased attention and resources to reaching families of children with EDs with the least means for receiving care, and screening for EDs in children with depression, may be important steps for reducing this unmet need.

Background Youth use different forms of screen time (e.g., streaming, gaming) that may be related to body mass index (BMI). Screen time is non-independent from other behaviors, including physical activity and sleep duration. Statistical approaches such as isotemporal substitution or compositional data analysis (CoDA) can model associations between these non-independent behaviors and health outcomes. Few studies have examined different types of screen time, physical activity, and sleep duration simultaneously in relation to BMI. Methods Data were baseline (2017–2018) and one-year follow-up (2018–2019) from the Adolescent Brain Cognitive Development Study, a multi-site study of a nationally representative sample of U.S. youth ( N  = 10,544, mean [SE] baseline age = 9.9 [0.03] years, 48.9% female, 45.4% non-White). Participants reported daily minutes of screen time (streaming, gaming, socializing), physical activity, and sleep. Sex-stratified models estimated the association between baseline behaviors and follow-up BMI z -score, controlling for demographic characteristics, internalizing symptoms, and BMI z -score at baseline. Results In females, isotemporal substitution models estimated that replacing 30 min of socializing (β [95% CI] = -0.03 [-0.05, -0.002]), streaming (-0.03 [-0.05, -0.01]), or gaming (-0.03 [-0.06, -0.01]) with 30 min of physical activity was associated with a lower follow-up BMI z -score. In males, replacing 30 min of socializing (-0.03 [-0.05, -0.01]), streaming (-0.02 [-0.03, -0.01]), or gaming (-0.02 [-0.03, -0.01]) with 30 min of sleep was associated with a lower follow-up BMI z -score. In males, replacing 30 min of socializing with 30 min of gaming was associated with a lower follow-up BMI z -score (-0.01 [-0.03, -0.0001]). CoDA estimated that in males, a greater proportion of time spent in baseline socializing, relative to the remaining behaviors, was associated with a higher follow-up BMI z -score (0.05 [0.02, 0.08]). In females, no associations between screen time and BMI were observed using CoDA. Conclusions One-year longitudinal associations between screen time and BMI may depend on form of screen time, what behavior it replaces (physical activity or sleep), and participant sex. The alternative statistical approaches yielded somewhat different results. Experimental manipulation of screen time and investigation of biopsychosocial mechanisms underlying the observed sex differences will allow for causal inference and can inform interventions.

Background Low socioeconomic status is associated with lower cognitive performance and long‐term disparities in achievement and success. However, not all children from low‐income backgrounds exhibit lower cognitive performance. Characterizing the factors that promote such resilience in youth from low‐income households is of crucial importance. Methods We used baseline data from participants in the lowest tertile of income‐to‐needs in the Adolescent Brain Cognitive Development study and machine learning to identify the factors that predict fluid and crystallized cognitive resilience among youth from low‐income backgrounds. Predictors included 164 variables across child characteristics, family and developmental history, and environment. Results Our models were reliably able to predict resilience but were substantially more accurate for crystallized cognition (AUC = 0.75) than for fluid cognition (AUC = 0.67). Key predictors included developmental factors such as birthweight and duration of breastfeeding, neighborhood‐level factors (e.g., living in concentrated privilege, enrollment in advanced placement courses), children's own temperament and mental health, and other factors such as physical activity and involvement in extracurricular activities. Conclusion Our findings highlight the importance of a multifaceted approach to promoting cognitive resilience among children from low‐income households in future intervention work. Machine learning based prediction of cognitive resilience among low income youth.