Archival ReportMultivariate Patterns of Brain-Behavior-Environment Associations in the Adolescent Brain and Cognitive Development Study
Section snippets
Sample
The ABCD study recruited a nationally representative cohort of 11,875 participants aged 9–10 years at 22 U.S. sites using multistage probability sampling (Supplemental Methods, Section 1). The analyses presented here used data preprocessed by the ABCD study and downloaded on July 2019 as part of the ABCD Study Curated Annual Release 2.0.1 (https://data-archive.nimh.nih.gov/abcd). We selected ABCD participants based on the availability of nonimaging measures (NIMs) and of high-quality
Brain Morphometry
We considered data from 9623 ABCD participants with high-quality global and regional measures of cortical thickness and surface area and subcortical volumes (Figure S2; Tables S2 and S7). Five modes, labeled morphometry modes (MMs) 1 to 5, were statistically significant at pFDR < .0001 and replicable in the discovery (disc) (n = 8144; rdisc = .26 to .46) and test (n = 1479; rtest = .11 to .39) subsamples (Figure 1; Figures S3–S5). In MM1, which explained the largest amount of covariance, the
Discussion
Multivariate data-driven analyses identified 14 patterns of brain-behavior-environment covariation in a large U.S. population–based cohort of young adolescents. The findings were robust to sample size and composition and independent of site. In line with previous studies (18,19,21,22,30), life factors typically considered positive were associated with brain measures that reflect advantageous brain development, while the reverse was the case for factors considered negative. We demonstrated that
Acknowledgments and Disclosures
This work was supported by the National Institute of Mental Health (Grant Nos. R01MH113619 and R01 MH116147 [to SF]), National Institute on Aging (Grant No. R03-AG064001 [to GED]), National Institute of General Medical Sciences (Grant No. P20GM130447 [to GED]), National Institute of Child and Human Development (Grant No. R01-HD098883 [to AR]), and National Institute of Environmental Health Sciences (Grant No. R01-ES026904 [to AR]).
This work was supported in part through the computational
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