Best-practice guides (peer-reviewed reading list)

Best-practice guides (peer-reviewed reading list)#

Last reviewed: October 2026. Have a paper to add? Open an issue or use our submission form.

An annotated reading list of practical, mostly peer-reviewed guides for working with ABCD. If you only read one, start with Saragosa-Harris et al. (2022). Preprints are labeled as such.

Start here: practical & responsible-use guides#

Paper	Why read it
Saragosa-Harris, N. M., et al. (2022). A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2022.101115	Written by trainees from the 2021 ABCD workshop. A checklist of analytic decisions, reproducibility, and equity considerations, useful for both authors and reviewers.
Simmons, C., et al. (2021). Responsible use of open-access developmental data: The Adolescent Brain Cognitive Development (ABCD) Study. Psychological Science. doi:10.1177/09567976211003564	Ethical and interpretive pitfalls of open developmental data, from consortium members.
Dick, A. S., et al. (2021). Meaningful associations in the Adolescent Brain Cognitive Development Study. NeuroImage. doi:10.1016/j.neuroimage.2021.118262	Effect sizes, covariate choice, weighting, and nesting in ABCD: how to interpret “small” effects.
Owens, M. M., et al. (2021). Recalibrating expectations about effect size: A multi-method survey of effect sizes in the ABCD study. PLOS ONE. doi:10.1371/journal.pone.0257535	Benchmarks for typical (small) effect sizes across ABCD measures.
Lopez, D. A., et al. (2024). Transparency and reproducibility in the Adolescent Brain Cognitive Development (ABCD) study. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2024.101408	Audit of 549 ABCD papers, with concrete recommendations for what to report.

Equity, race/ethnicity, and social context#

Paper	Why read it
Cardenas-Iniguez, C., & Gonzalez, M. R. (2024). Recommendations for the responsible use and communication of race and ethnicity in neuroimaging research. Nature Neuroscience. doi:10.1038/s41593-024-01608-4	How (and whether) to include race/ethnicity variables, and how to write about them.
Gonzalez, M. R., et al. (2025). Responsible research in health disparities using the ABCD study. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2024.101497	Social-determinants-of-health framework for disparities research in ABCD.

Further reading on race, racism, and social determinants of health:

Vyas, D. A., et al. (2020). Hidden in plain sight: Reconsidering the use of race correction in clinical algorithms. New England Journal of Medicine. doi:10.1056/NEJMms2004740
Alvidrez, J., et al. (2019). The National Institute on Minority Health and Health Disparities Research Framework. American Journal of Public Health. doi:10.2105/AJPH.2018.304883
Palmer, R. C., et al. (2019). Social determinants of health: Future directions for health disparities research. American Journal of Public Health. doi:10.2105/AJPH.2019.304964
APA Guidelines on Race and Ethnicity in Psychology
Racism and mental health: Examining the link between racism and depression from a social cognitive perspective (APA, 2015)
Racism: Science & Tools for the Public Health Professional (APHA Press)
The Cost of Racism for People of Color: Contextualizing Experiences of Discrimination (APA Books)

Study design, sampling, and weights#

Paper	Why read it
Volkow, N. D., et al. (2018). The conception of the ABCD study: From substance use to a broad NIH collaboration. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2017.10.002	Why the study exists and what it was designed to answer.
Karcher, N. R., & Barch, D. M. (2020). The ABCD study: Understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology. doi:10.1038/s41386-020-0736-6	Overview of the study and early findings on mental and physical health risk.
Garavan, H., et al. (2018). Recruiting the ABCD sample: Design considerations and procedures. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2018.04.004	School-based sampling, sites, and twins: the basis for modeling nesting and weights.
Barch, D. M., et al. (2018). Demographic, physical and mental health assessments in the ABCD study. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2017.10.010	Rationale for the core non-imaging measures.
Heeringa, S. G., & Berglund, P. A. (2020). A guide for population-based analysis of the ABCD Study baseline data. bioRxiv (preprint). doi:10.1101/2020.02.10.942011	How the ACS-raked propensity weights were built and when to use them.
Gard, A. M., et al. (2023). Why weight? Analytic approaches for large-scale population neuroscience data. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2023.101196	Empirical comparison of weighted vs. unweighted ABCD analyses.
Feldstein Ewing, S. W., et al. (2022). Measuring retention within the Adolescent Brain Cognitive Development (ABCD) study. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2022.101081	Who misses visits and why it matters for attrition bias.

Longitudinal modeling and missing data#

Paper	Why read it
Hawes, S. W., et al. (2025). Longitudinal analysis of the ABCD® study. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2025.101518	ABCD-specific guide to longitudinal models.
McCormick, E. M., et al. (2023). The Hitchhiker’s guide to longitudinal models: A primer on model selection for repeated-measures methods. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2023.101281	General primer on choosing a longitudinal model, with a code companion.
King, K. M., et al. (2018). Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2017.11.009	Classic guide to the pitfalls of modeling change with brain data.
Flournoy, J. C., et al. (2020). Improving practices and inferences in developmental cognitive neuroscience. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2020.100807	Concrete recommendations for inference in developmental neuroscience.
Zacharek, S. J., et al. (2021). Leveraging big data to map neurodevelopmental trajectories in pediatric anxiety. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2021.100974	What large datasets like ABCD can and can’t tell us about developmental trajectories.
Li, et al. (2025). Missing data approaches for longitudinal neuroimaging research: Examples from the ABCD Study®. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2025.101563	Alternatives to listwise deletion, demonstrated on ABCD.
Thompson, W. K., et al. (2019). The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: Findings from the ABCD study’s baseline neurocognitive battery. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2018.12.004	Worked example of mixed models with site and family random effects.
Smith, D. M., et al. (2023). Heritability estimation of cognitive phenotypes in the ABCD Study® using mixed models. Behavior Genetics. doi:10.1007/s10519-023-10141-2	Modeling family relatedness (twins, siblings).

For cross-lagged panel (RI-CLPM) and latent change score tutorials and code, see the Methods & open-science toolkit.

Neuroimaging#

Paper	Why read it
Casey, B. J., et al. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2018.03.001	The MRI protocol and tasks.
Hagler, D. J., Jr., et al. (2019). Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage. doi:10.1016/j.neuroimage.2019.116091	The DAIRC pipeline behind the tabulated imaging measures.
Feczko, E., et al. (2021). Adolescent Brain Cognitive Development (ABCD) Community MRI Collection and Utilities. bioRxiv (preprint). doi:10.1101/2021.07.09.451638	ABCD-BIDS and the community collection (ABCC).
Parekh, P., et al. (2024). FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data. Human Brain Mapping. doi:10.1002/hbm.26579	The mixed-model engine behind DEAP analyses.
Marek, S., et al. (2022). Reproducible brain-wide association studies require thousands of individuals. Nature. doi:10.1038/s41586-022-04492-9	Sample size and replicability for brain–behavior associations.
Kennedy, J. T., et al. (2022). Reliability and stability challenges in ABCD task fMRI data. NeuroImage. doi:10.1016/j.neuroimage.2022.119046	Read before using task-fMRI measures as individual-difference variables.
Elliott, M. L., et al. (2020). What is the test-retest reliability of common task-functional MRI measures? New empirical evidence and a meta-analysis. Psychological Science. doi:10.1177/0956797620916786	The broader evidence on task-fMRI reliability.
Kragel, P. A., et al. (2020). fMRI can be highly reliable, but it depends on what you measure. PsyArXiv (preprint). doi:10.31234/osf.io/9eaxk	A counterpoint: which fMRI measures are reliable.
Chaarani, B., et al. (2021). Baseline brain function in the preadolescents of the ABCD Study. Nature Neuroscience. doi:10.1038/s41593-021-00867-9	Reference description of the baseline task-fMRI data.
Klapwijk, E. T., et al. (2021). Opportunities for increased reproducibility and replicability of developmental neuroimaging. Developmental Cognitive Neuroscience. doi:10.1016/j.dcn.2020.100902	Practical reproducibility steps for developmental imaging.

Puberty#

Paper	Why read it
Cheng, T. W., et al. (2021). A researcher’s guide to the measurement and modeling of puberty in the ABCD Study® at baseline. Frontiers in Endocrinology. doi:10.3389/fendo.2021.608575	Practical guide to the puberty measures and how to model them.
Herting, M. M., et al. (2021). Correspondence between perceived pubertal development and hormone levels in 9–10 year-olds from the ABCD Study. Frontiers in Endocrinology. doi:10.3389/fendo.2020.549928	How self/parent-report puberty relates to hormones.
Multi-method assessment of pubertal timing and associations with internalizing psychopathology in early adolescent girls (PsyArXiv preprint, 2020)	Comparing different ways to measure pubertal timing.