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

The data release of Adolescent Brain Cognitive Development® (ABCD) Study represents an extensive resource for investigating factors relating to child development and mental wellbeing. The genotype data of ABCD has been used extensively in the context of genetic analysis, including genome-wide association studies and polygenic score predictions. However, there are unique opportunities provided by ABCD genetic data that have not yet been fully tapped. The diverse genomic variability, the enriched relatedness among ABCD subsets, and the longitudinal design of the ABCD challenge researchers to perform novel analyses to gain deeper insight into human brain development. Genetic instruments derived from the ABCD genetic data, such as genetic principal components, can help to better control confounds beyond the context of genetic analyses. To facilitate the use genomic information in the ABCD for inference, we here detail the processing procedures, quality controls, general characteristics, and the corresponding resources in the ABCD genotype data of release 4.0.

Understanding risk factors related to suicidal ideation (SI) and suicidal behaviors (SB) in youth is important for informing prevention and intervention efforts. While it appears that psychotic-like experiences (PLEs) are strongly associated with both SI and SB at different points across the lifespan, the longitudinal nature of this relationship in middle childhood and early adolescence is understudied. The study used the unique longitudinal Adolescent Brain Cognitive Development Study data. Mixed effects linear models examined associations between PLEs and SI and SB over time using three time points of data from ages 9-13. First, analyses indicated that endorsement of SI and SB increased as youth grew older for those with increased distressing PLEs. Analyses found evidence of bidirectional relationships between PLEs with SI and SB, with evidence that PLEs at baseline were associated with worsening SI and SB over time, including a transition from SI to SB ( = 0.032, FDR = 0.002). Exploratory analyses showed consistent evidence for strengthened associations over time for higher delusional ideation with both SI and SB ( s > 0.04, FDR < 0.001), and for perceptual distortions with SB ( s = 0.046, FDR < 0.001). When accounting for general psychopathology, for SB, the strengthened associations over time was significantly stronger for PLEs ( = 0.053, FDR < 0.001) compared to general psychopathology ( = 0.022, FDR = 0.01). The present study indicates both SI and SB show strengthened associations with PLEs over time, and that baseline PLEs may predict worsening of suicidality over time. The findings are important clarifications about the nature of the associations between youth-reported PLEs and suicidality over time.

•Using data from the Adolescent Brain Cognitive Development study, we identified four distinct and reproducible RSFC subtypes in children aged 9–11.•Our approach involved an innovative methodological pipeline with several phases: identification, Validation, Evaluation, Prediction, and Replication. Each phase played a critical role in confirming the reliability and importance of the RSFC subtypes.•In the identification phase, we used Leiden Community Detection to define the RSFC subtypes. The Validation phase confirmed their reproducibility through a robust split-sample technique.•The Evaluation phase revealed that each RSFC subtype is associated with unique cognitive and mental health profiles. Our Prediction phase showed that these subtypes can more accurately predict various cognitive and mental health characteristics than individual RSFC connections.•Lastly, the Replication stage used bootstrapping and down-sampling to further substantiate the reproducibility of these subtypes. The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9–11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.

Background Problematic screen use, defined as an inability to control use despite private, social, and professional life consequences, is increasingly common among adolescents and can have significant mental and physical health consequences. Adverse Childhood Experiences (ACEs) are important risk factors in the development of addictive behaviors and may play an important role in the development of problematic screen use. Methods Prospective data from the Adolescent Brain Cognitive Development Study (Baseline and Year 2; 2018–2020; N = 9,673, participants who did not use screens were excluded) were analyzed in 2023. Generalized logistic mixed effects models were used to determine associations with ACEs and the presence of problematic use among adolescents who used screens based on cutoff scores. Secondary analyses used generalized linear mixed effects models to determine associations between ACEs and adolescent-reported problematic use scores of video games (Video Game Addiction Questionnaire), social media (Social Media Addiction Questionnaire), and mobile phones (Mobile Phone Involvement Questionnaire). Analyses were adjusted for potential confounders including age, sex, race/ethnicity, highest parent education, household income, adolescent anxiety, depression, and attention-deficit symptoms, study site, and participants who were twins. Results The 9,673 screen-using adolescents ages 11–12 years old (mean age 12.0) were racially and ethnically diverse (52.9% White, 17.4% Latino/Hispanic, 19.4% Black, 5.8% Asian, 3.7% Native American, 0.9% Other). Problematic screen use rates among adolescents were identified to be 7.0% (video game), 3.5% (social media), and 21.8% (mobile phone). ACEs were associated with higher problematic video game and mobile phone use in both unadjusted and adjusted models, though problematic social media use was associated with mobile screen use in the unadjusted model only. Adolescents exposed to 4 or more ACEs experienced 3.1 times higher odds of reported problematic video game use and 1.6 times higher odds of problematic mobile phone use compared to peers with no ACEs. Conclusions Given the significant associations between adolescent ACE exposure and rates of problematic video and mobile phone screen use among adolescents who use screens, public health programming for trauma-exposed youth should explore video game, social media, and mobile phone use among this population and implement interventions focused on supporting healthy digital habits.

Sleep disturbances are common in adolescence and associated with a host of negative outcomes. Here, we assess associations between multifaceted sleep disturbances and a broad set of psychological, cognitive, and demographic variables using a data-driven approach, canonical correlation analysis (CCA). Baseline data from 9093 participants from the Adolescent Brain Cognitive Development (ABCD) Study were examined using CCA, a multivariate statistical approach that identifies many-to-many associations between two sets of variables by finding combinations for each set of variables that maximize their correlation. We combined CCA with leave-one-site-out cross-validation across ABCD sites to examine the robustness of results and generalizability to new participants. The statistical significance of canonical correlations was determined by non-parametric permutation tests that accounted for twin, family, and site structure. To assess the stability of the associations identified at baseline, CCA was repeated using 2-year follow-up data from 4247 ABCD Study participants. Two significant sets of associations were identified: (1) difficulty initiating and maintaining sleep and excessive daytime somnolence were strongly linked to nearly all domains of psychopathology (r2 = 0.36, p < .0001); (2) sleep breathing disorders were linked to BMI and African American/black race (r2 = 0.08, p < .0001). These associations generalized to unseen participants at all 22 ABCD sites and were replicated using 2-year follow-up data. These findings underscore interwoven links between sleep disturbances in early adolescence and psychological, social, and demographic factors.

Background The spatial layout of large-scale functional brain networks exhibits considerable inter-individual variability, especially in the association cortex. Research has demonstrated a link between early socioeconomic status (SES) and variations in both brain structure and function, which are further associated with cognitive and mental health outcomes. However, the extent to which SES is associated with individual differences in personalized functional network topography during childhood remains largely unexplored. Methods We used a machine learning approach—spatially regularized non-negative matrix factorization (NMF)—to delineate 17 personalized functional networks in children aged 9–10 years, utilizing high-quality functional MRI data from 6001 participants in the Adolescent Brain Cognitive Development study. Partial least square regression approach with repeated random twofold cross-validation was used to evaluate the association between the multivariate pattern of functional network topography and three SES factors, including family income-to-needs ratio, parental education, and neighborhood disadvantage. Results We found that individual variations in personalized functional network topography aligned with the hierarchical sensorimotor-association axis across the cortex. Furthermore, we observed that functional network topography significantly predicted the three SES factors from unseen individuals. The associations between functional topography and SES factors were also hierarchically organized along the sensorimotor-association cortical axis, exhibiting stronger positive associations in the higher-order association cortex. Additionally, we have made the personalized functional networks publicly accessible. Conclusions These results offer insights into how SES influences neurodevelopment through personalized functional neuroanatomy in childhood, highlighting the cortex-wide, hierarchically organized plasticity of the functional networks in response to diverse SES backgrounds.

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

Purpose Incidental cerebellar tonsillar ectopia (ICTE) that meets the radiographic criterion for Chiari malformation type I (CMI) is an increasingly common finding in the clinical setting, but its significance is unclear. The present study examined posterior cranial fossa (PCF) morphometrics and a broad range of health instruments of pediatric ICTE cases and matched controls extracted from the Adolescent Brain Cognitive Development (ABCD) dataset. Methods One-hundred-six subjects with ICTE and 106 matched controls without ICTE were identified from 11,411 anatomical MRI of healthy screened pediatric subjects from the ABCD project. Subjects were matched by sex, age, body mass index, race, and ethnicity. Twenty-two brain morphometrics and 22 health instruments were compared between the two groups to identify unrecognized CMI symptoms and assess the general health impact of ICTE. Results Twelve and 15 measures were significantly different between the ICTE and control groups for females and males, respectively. Notably, for females, the anterior CSF space was significantly smaller ( p  =  0.00005) for the ICTE group than controls. For males, the clivus bone length was significantly shorter ( p  =  0.0002) for the ICTE group compared to controls. No significant differences were found among the 22 health instruments between the two groups. Conclusion This study demonstrated that pediatric ICTE subjects have similar PCF morphometrics to adult CMI. ICTE alone did not appear to cause any unrecognized CMI symptoms and had no impact on the subjects’ current mental, physical, or behavioral health. Still, given their cranial and brain morphology, these cases may be at risk for adult-onset symptomatic CMI.

We identify novel mechanisms of general intelligence involving activation patterns of large-scale brain networks. During hard, cognitively demanding tasks, the fronto-parietal network differentially activates relative to the default mode network, creating greater separation between the networks, while during easy tasks, network separation is reduced. In 920 adults in the Human Connectome Project dataset, we demonstrate that these network separation patterns across hard and easy task conditions are strongly associated with general intelligence, accounting for 21% of the variance in intelligence scores across individuals. Moreover, we identify the presence of a crossover relationship in which FPN-DMN separation profiles that strongly predict higher intelligence in hard task conditions reverse direction and strongly predict lower intelligence in easy conditions, helping to resolve conflicting findings in the literature. We further clarify key properties of FPN-DMN separation: It is a mediator, and not just a marker, of general intelligence, and FPN-DMN separation profiles during the task state can be reliably predicted from connectivity patterns during rest. We demonstrate the robustness of our results by replicating them in a second task and in an independent large sample of youth. Overall, our results establish FPN-DMN separation as a major locus of individual differences in general intelligence, and raise intriguing new questions about how FPN-DMN separation is regulated in different cognitive tasks, across the lifespan, and in health and disease.