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The Adolescent Brain Cognitive Development (ABCD) Study has substantially advanced developmental neuroscience through its large scale and open-science framework. This review synthesizes the study's significant statistical and methodological contributions over its first ten years, organized around the pillars of population neuroscience, longitudinal modeling, and causal inference. We first examine how ABCD's population-based design has prompted a reconsideration of how effect sizes are interpreted, helping to establish new benchmarks for distinguishing stable, biologically relevant signals from trivial associations in large-N contexts. We detail the computational innovations required to process high-dimensional data at scale, specifically highlighting new analytic tools like the Fast and Efficient Mixed Effects Algorithm (FEMA) framework for mass-univariate modeling and advanced strategies for managing selective attrition and missing data in large-scale longitudinal cohorts. In the domain of longitudinal and multilevel modeling, we discuss the transition from traditional cross-lagged designs to sophisticated frameworks - such as random-intercept cross-lagged panel models, latent growth curves, and parallel process models - that disentangle within-person developmental trajectories from stable between-person traits. We further highlight the study's role in advancing causal inference in observational research through "G-methods", marginal structural models, and quasi-experimental family-based designs. Finally, we explore how ABCD serves as a critical bridge for cross-cohort generalizability and lifespan validation using datasets like the UK Biobank. By contributing to new standards for reproducibility and methodological rigor, the ABCD Study has helped move neuroscience toward a "big data" era, providing a comprehensive statistical foundation for understanding the complex interplay between biology and environment during the transition to adulthood.

More information Original publication

DOI

10.1016/j.dcn.2026.101779

Type

Journal article

Publication Date

2026-07-09T00:00:00+00:00

Volume

81

Keywords

ABCD Study, Big data, Causal inference, Imaging genetics, Longitudinal modeling, Population neuroscience