Elsevier

Biological Psychiatry

Volume 89, Issue 5, 1 March 2021, Pages 510-520
Biological Psychiatry

Archival Report
Multivariate Patterns of Brain-Behavior-Environment Associations in the Adolescent Brain and Cognitive Development Study

https://doi.org/10.1016/j.biopsych.2020.08.014Get rights and content

Abstract

Background

Adolescence is a critical developmental stage. A key challenge is to characterize how variation in adolescent brain organization relates to psychosocial and environmental influences.

Methods

We used canonical correlation analysis to discover distinct patterns of covariation between measures of brain organization (brain morphometry, intracortical myelination, white matter integrity, and resting-state functional connectivity) and individual, psychosocial, and environmental factors in a nationally representative U.S. sample of 9623 individuals (aged 9–10 years, 49% female) participating in the Adolescent Brain and Cognitive Development (ABCD) study.

Results

These analyses identified 14 reliable modes of brain-behavior-environment covariation (canonical rdiscovery = .21 to .49, canonical rtest = .10 to .39, pfalse discovery rate corrected < .0001). Across modes, neighborhood environment, parental characteristics, quality of family life, perinatal history, cardiometabolic health, cognition, and psychopathology had the most consistent and replicable associations with multiple measures of brain organization; positive and negative exposures converged to form patterns of psychosocial advantage or adversity. These showed modality-general, respectively positive or negative, associations with brain structure and function with little evidence of regional specificity. Nested within these cross-modal patterns were more specific associations between prefrontal measures of morphometry, intracortical myelination, and functional connectivity with affective psychopathology, cognition, and family environment.

Conclusions

We identified clusters of exposures that showed consistent modality-general associations with global measures of brain organization. These findings underscore the importance of understanding the complex and intertwined influences on brain organization and mental function during development and have the potential to inform public health policies aiming toward interventions to improve mental well-being.

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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