Adolescence is a period of dramatic expansion of the knowledge and skills critical for transitioning into adulthood. Yet, there is much to learn about how adolescent experiences affect brain, social, and emotional development. Over the last decade, evidence has revealed associations between early life adversity (e.g., poverty) and later changes in brain structure and function [1]. More recently, research has shown that positive factors (e.g., perceived social supports; increased access to community resources) are associated with healthier development, even for children living in deep poverty, suggesting that protective factors may mitigate the possible negative influences of adverse experiences on health and development [2].
Looking into the next decade, important forces (e.g., digital media, racial inequities, climate change, long-term impacts of COVID-19) will affect adolescent health and wellbeing globally. Our imperative is to harness advances in science and technology to develop strategies that will enhance health and promote equity.
The Adolescent Brain Cognitive Development (ABCD) Study
The Adolescent Brain Cognitive Development (ABCD) study seeks to shed light on developmental trajectories by enrolling nearly 12,000 youth ages 9 and 10 from across the U.S. and collecting a vast array of data repeatedly for 10 years (Table 1).
Table 1 -.
ABCD Measures
| Domain | Examples of Measures |
|---|---|
| Demographics | parental education, employment, household income, race/ethnicity, sex at birth |
| Physiological Factors | anthropometrics, pubertal development and hormones, medical and developmental history, nutrition, sleep |
| Psychological Factors | family and youth history of mental health problems, externalizing and internalizing behaviors, peers and friendships |
| Substance Use Factors | current use by parents and youth, expectancies and attitudes, community risk and protective factors |
| Sociocultural Factors | economic hardship, ethnic identity, parental and peer behaviors, religiosity, extracurricular activities |
| Neurocognitive Factors | structural and functional MRI, DTI, tasks assessing executive control, fluid and crystalized intelligence, and emotional processing |
| Genetic Factors | Smokescreen array genotyping |
| Environmental Factors | neighborhood safety, school environment, pollution, food insecurity, area deprivation |
| Novel Technology and Passive Data | Fitbit, screen time monitoring, geocoding |
Participants were primarily recruited through a school-based strategy to mitigate selection bias and ensure diverse representation, as well as targeted recruitment from twin registries to include youth of multiple births. School selection was informed by gender, race and ethnicity, socioeconomic status (SES), and urbanicity. Real-time monitoring allowed for corrections for deviations from recruitment targets, resulting in a baseline sample that reflects the diversity of the U.S. population (Figure 1).
Figure 1.
ABCD Cohort Demographics
When the COVID-19 pandemic upended the world in late 2019, the ABCD study recognized its unique potential for understanding the impact of the pandemic on developmental trajectories. Questionnaires were sent repeatedly to participants between May 2020 and May 2021 to understand their experiences during the pandemic, such as family impacts (e.g., economic impact, home composition, parental support), school changes (e.g., quality, quantity, methods, supervision), changes in routine (e.g., sleep, physical activity, screen media use), as well as mental health, stress, and substance use. In addition, participants were asked about COVID-19 specific impacts such as exposure, diagnosis, attitudes and adherence to public health directives, and media/news exposure. Given the diversity of the ABCD cohort, the collection of data prior to, during, and after the acute pandemic will allow investigators to better understand the impact of the pandemic on adolescent development.
The ABCD dataset – An opportunity for the broader scientific community
From its inception, a major priority of the ABCD Study has been to adopt an open science model, making de-identified data rapidly available to the scientific community via the NIMH Data Archive (NDA). This includes fast-track neuroimaging data, curated data that are released annually, supplemental COVID Rapid Response Research data, as well as a Data Exploration and Analysis Portal (DEAP)—a powerful resource for performing multilevel statistical analyses including the nested study design and its more than 100,000 shared observations (for more information, please visit the ABCD Study website).
The ABCD Study’s open science policy and frequent data releases provide opportunities to researchers around the world to leverage ABCD’s complex dataset to explain behavior, create predictive models, and investigate mechanistic pathways to understand health and disease in adolescents. With over 230 publications using ABCD data to date, papers have spanned a wide array of topics including psychiatric conditions (e.g. ADHD, psychotic-like experiences, suicidality) [3–5], screen time usage [6, 7], neighborhood disadvantage [8,9], obesity and weight gain [10, 11], polygenic risk scores for substance use disorders among substance naïve youth [12, 13], and the interactions of these factors with brain structure and function. The range and depth of ABCD Study variables allow for highly actionable analyses that can have significant policy implications, as several publications have already shown [2, 14–15]. Combining ABCD Study data with other large datasets, like the UK Biobank, can provide enormous statistical power for genetic discovery [16, 17], and the longitudinal design of the study will provide an unprecedented opportunity for causal inferences and scientific rigor.
Responsible data use
Alongside expanding opportunities for accessing complex datasets is the responsibility for ethical data analysis and interpretation. Large, complex datasets, such as ABCD, leverage interdisciplinary expertise to understand and predict behavior, but there are important caveats to consider: a fundamental reality of population-based research is that observed associations may be statistically significant even when they explain a small proportion of the outcome variability. Such findings raise questions about how to interpret a small effect, and under what conditions it may be important [18].
Researchers must also be aware of the assumptions they make when choosing variables to measure social constructs such as race, ethnicity and SES, among others, and should understand the limitations of their choice of measurement and address them in their manuscripts. Social constructs are often broadly defined and should be avoided as independent variables in isolation especially in predictive models that attempt to explain variability. Research that misrepresents constructs (e.g., race as a proxy for racism), or that is not placed in context with other factors, may lead readers to draw unwarranted conclusions from observed differences that fail to consider the wider context for developmental change and environmental adaptation. Recommended practices for responsible data use include addressing factors such as study design, analytic approaches, interpretation, and communication. Above all, researchers should be mindful of the potential impact of their study on individuals, communities, and society, beyond the written manuscript (see [19] for broad discussion of these topics).
Conclusion
The diversity of the ABCD cohort, the breadth of data collected, and the longitudinal design of ABCD will provide opportunities for investigating the interplay of environments and experiences with long-term health outcomes. These data have the potential to facilitate the development of strategies for enhancing adolescent health and equity for generations to come.
Footnotes
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References
- [1].Blair C, Blair Raver CC. Poverty, Stress, and Brain Development: New Directions for Prevention and Intervention. Acad Pediatr. 2016; Apr;16(3 Suppl):S30–6. 10.1016/j.acap.2016.01.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2].Gonzalez MR, Palmer CE, Uban KA, Jernigan TL, Thompson WK and Sowell ER. Positive Economic, Psychosocial, and Physiological Ecologies Predict Brain Structure and Cognitive Performance in 9–10-Year-Old Children. Front. Hum. Neurosci. 2020; 14:578822. 10.3389/fnhum.2020.578822. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Harman G, Kliamovich D, Morales AM, Gilbert S, Barch DM, Mooney MA, Feldstein Ewing SW, Fair DA, Nagel BJ. Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features. PLoS One. 2021; 16(5): e0252114. 10.1371/journal.pone.0252114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Janiri D, Doucet GE, Pompili M, Sani G, Luna B, Brent DA, Frangou S. Risk and protective factors for childhood suicidality: a US population-based study. Lancet Psychiatry. 2020; 7(4): 317–326. 10.1016/S2215-0366(20)30049-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [5].Lees B, Squeglia LM, McTeague LM, Forbes MK, Krueger RF, Sunderland M, Baillie AJ, Koch F, Teesson M, Mewton L. Altered Neurocognitive Functional Connectivity and Activation Patterns Underlie Psychopathology in Preadolescence. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021; 6(4):387–398. 10.1016/j.bpsc.2020.09.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [6].Nagata JM, Iyer P, Chu J, Baker FC, Gabriel KP, Garber AK, Murray SB, Bibbins-Domingo K, Ganson KT. Contemporary screen time usage among children 9–10-years-old is associated with higher body mass index percentile at 1-year follow-up: A prospective cohort study. Pediatric Obesity. 2021; 16(12):e12827. 10.1111/ijpo.12827. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Kirlic N, Colaizzi JM, Cosgrove KT, Cohen ZP, Yeh H-W, Breslin F, Morris AS, Aupperle RL, Singh MK, Paulus MP. Extracurricular Activities, Screen Media Activity, and Sleep May Be Modifiable Factors Related to Children’s Cognitive Functioning: Evidence From the ABCD Study ® Child Dev. 2021; 92(5):2035–2052. 10.1111/cdev.13578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Hackman DA, Cserbik D, Chen J-C, Berhane K, Minaravesh B, McConnell R, Herting MM. Association of Local Variation in Neighborhood Disadvantage in Metropolitan Areas With Youth Neurocognition and Brain Structure. JAMA Pediatr. 2021; 175(8):e210426. doi: 10.1001/jamapediatrics.2021.0426. doi: 10.1001/jamapediatrics.2021.0426. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Taylor RL, Cooper SR, Jackson JJ, Barch DM. Assessment of Neighborhood Poverty, Cognitive Function, and Prefrontal and Hippocampal Volumes in Children. JAMA Netw Open. 2020; 3(11):e2023774. doi: 10.1001/jamanetworkopen.2020.23774. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Adise S, Allgaier N, Laurent J, Hahn S, Chaarani B, Owens M, Yuan D, Nyugen P, Mackey S, Potter A, Garavan HP. Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD Study®. Dev Cog Neurosci. 2021. Jun;49:100948. 10.1016/j.dcn.2021.100948. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Rapuano KM, Laurent JS, Hagler DJ Jr., Hatton SN, Thompson WK, Jernigan TL, Dale AM, Casey BJ, Watts R. Nucleus accumbens cytoarchitecture predicts weight gain in children. PNAS 117 (43) 26977–2698. 10.1073/pnas.2007918117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Hatoum AS, Johnson EC, Baranger DAA2, Paul SE, Agrawal A, Bogdan R. Polygenic Risk Scores for Alcohol Involvement Relate to Brain Structure in Substance-Naïve Children: Results from the ABCD Study. Genes Brain Behav. 2021;20:e12756. 10.1111/gbb.12756. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Johnson EC, Demontis D, Thorgeirsson TE, Walters RK, Polimanti R, Hatoum AS, Sanchez-Roige S, Paul SE, Wendt FR, Clarke T-K, Lai D, Reginsson GW, Zhou H, He J, Baranger DA, Gudbjartsson DF, Wedow R, Adkins DE,…Agrawal A. A large-scale genome-wide association study meta-analysis of cannabis use disorder. The Lancet Psychiatry. 2020; 7 (12): 1032–1045. 10.1016/S2215-0366(20)30339-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Marshall AT, Betts S, Kan EC, McConnell R, Lanphear BP, Sowell ER. Association of lead-exposure risk and family income with childhood brain outcomes. Nature Medicine 2020; 26: 91–97. 10.1038/s41591-019-0713-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Fadus MC, Valadez EA, Bryant BE, Garcia AM, Neelon B, Tomko RL, Squeglia LM. Racial Disparities in Elementary School Disciplinary Actions: Findings From the ABCD Study. J Am Acad Child Adolesc Psychiatry 2021; 60(8): 998–1009. 10.1016/j.jaac.2020.11.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Naqvi S, Sleyp Y, Hoskens H, Indencleef K, Spence JP, Bruffaerts R, Radwan A, Eller RJ, Richmond S, Shriver MD, Shaffer JR, Weinberg SM, Walsh S,Thompson J, Pritchard JK, Sunaert S, Peeters H, Wysocka J, Claes P. Shared heritability of human face and brain shape. Nat Genet. 2021; 53: 830–839. 10.1038/s41588-021-00827-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Warrier V, Kwong ASF, Luo M, Dalvie S, Croft J, Sallis HM, Baldwin J, Munafò MR, Nievergelt CM, Grant AJ, Burgess S, Moore TM, Barzilay R, McIntosh A, van IJzendoorn MH, Cecil CAM. Gene-environment correlations and causal effects of childhood maltreatment on physical and mental health: a genetically informed approach. Lancet Psychiatry 2021; 8(5):373–386. 10.1016/S2215-0366(20)30569-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Dick AS, Lopez DA, Watts AL, Heeringa S, Reuter C, Bartsch H, Fan CC, Kennedy DN, Palmer C, Marshall A, Haist F, Hawes S, Nichols TE, Barch DM, Jernigan TL, Garavan H, Grant S, Pariyadath V, Hoffman E, Neale M, Stuart EA, Paulus MP, Sher KJ, Thompson WK. Meaningful Associations in the Adolescent Brain Cognitive Development Study. Neuroimage. 2021; 239:118262. 10.1016/j.neuroimage.2021.118262. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Simmons C, Conley MI, Gee DG, Baskin-Sommers A, Barch DM, Hoffman EA, Huber RS, Iacono WG, Nagel, Palmer, Sheth CS, Sowell ER, Thompson WK, Casey BJ. Responsible Use of Open-Access Developmental Data: The Adolescent Brain Cognitive Development (ABCD) Study. Psychol Sci. 2021; 32(6):866–870. 10.1177/09567976211003564. [DOI] [PMC free article] [PubMed] [Google Scholar]

