Abstract
Background
The aim of this study was to determine the extent to which socioeconomic characteristics of the home and neighborhood are associated with racial inequalities in brain outcomes.
Methods
We performed a cross-sectional analysis of the baseline dataset (v.2.0.1) from the Adolescent Brain and Cognitive Development (ABCD) Study. Cognitive performance was assessed using the National Institutes of Health Toolbox (NIH-TB) cognitive battery. Standard socioeconomic indicators of the family and neighborhood were derived from census-related statistics. Cortical morphometric measures included MRI-derived thickness, area, and volume.
Results
9638 children were included. Each NIH-TB cognitive measure was negatively associated with household and neighborhood socioeconomic characteristics. Differences in cognitive scores between Black or Hispanic children and other racial groups were mitigated by higher household income. Most children from lowest-income families or residents in impoverished neighborhoods were BlackĀ orĀ Hispanic. These disparities were associated with racial differences in NIH-TB measures and mediated by smaller cortical brain volumes.
Conclusions
Neighborhood socioeconomic characteristics are associated with racial differences in preadolescent brain outcomes and mitigated by greater household income. Household income mediates racial differences more strongly than neighborhood-level socioeconomic indicators in brain outcomes. Highlighting these socioeconomic risks may direct focused policy-based interventions such as allocation of community resources to ensure equitable brain outcomes in children.
Impact
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Neighborhood socioeconomic characteristics are associated with racial differences in preadolescent brain outcomes and mitigated by greater household income.
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Household income mediates racial differences more strongly than neighborhood-level socioeconomic indicators in brain outcomes.
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Highlighting these disparities related to socioeconomic risks may direct focused policy-based interventions such as allocation of community resources to ensure equitable brain outcomes in children.
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References
Shonkoff, J. P., Boyce, W. T. & McEwen, B. S. Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. JAMA 301, 2252ā2259 (2009).
Nuru-Jeter, A. M., Sarsour, K., Jutte, D. P. & Boyce, W. T. Socioeconomic predictors of health and development in middle childhood: variations by socioeconomic status measure and race. Issues Compr. Pediatr. Nurs. 33, 59ā81 (2010).
Chetty, R., Hendren, N., Jones, M. R. & Porter, S. R. Race and economic opportunity in the United States: an intergenerational perspective. Q J. Econ. 135, 711ā783 (2020).
Noble, K. G. et al. Family income, parental education and brain structure in children and adolescents. Nat. Neurosci. 18, 773ā778 (2015).
Braveman, P. A. et al. Socioeconomic status in health research: one size does not fit all. JAMA 294, 2879ā2888 (2005).
Hackman, D. A. & Farah, M. J. Socioeconomic status and the developing brain. Trends Cogn. Sci. 13, 65ā73 (2009).
Hackman, D. A., Farah, M. J. & Meaney, M. J. Socioeconomic status and the brain: mechanistic insights from human and animal research. Nat. Rev. Neurosci. 11, 651ā659 (2010).
Volkow, N. D. et al. The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev. Cogn. Neurosci. 32, 4ā7 (2018).
Garavan, H. et al. Recruiting the ABCD sample: design considerations and procedures. Dev. Cogn. Neurosci. 32, 16ā22 (2018).
Weintraub, S. et al. Cognition assessment using the NIH Toolbox. Neurology 80, S54āS64 (2013).
Isaiah, A., Ernst, T., Cloak, C. C., Clark, D. B. & Chang, L. Association between habitual snoring and cognitive performance among a large sample of preadolescent children. JAMA Otolaryngol. Head Neck Surg. 147, 426ā433 (2021).
Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43ā54 (2018).
Fischl, B. FreeSurfer. Neuroimage 62, 774ā781 (2012).
Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1ā15 (2010).
Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14 (2014).
Kind, A. J. H. et al. Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Ann. Intern. Med. 161, 765ā774 (2014).
Zuber, V. & Strimmer, K. High-dimensional regression and variable selection using CAR scores. Stat. Appl. Genet. Mol. Biol. 10, 34 (2011).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289ā300 (1995).
Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, 1ā36 (2012).
Kline, R. B. Principles and Practice of Structural Equation Modeling 4th edn (Guilford Publications; 2015). 554 p.
Morales, D. X., Morales, S. A. & Beltran, T. F. Racial/ethnic disparities in household food insecurity during the COVID-19 pandemic: a nationally representative study. J. Racial Ethn. Health Disparities 8, 1300ā1314 (2021).
Morsy, L. & Rothstein, R. Mass incarceration and childrenās outcomes: criminal justice policy is education policy. Economic Policy Institute. https://www.epi.org/publication/mass-incarceration-and-childrens-outcomes/ (2016).
Hair, N. L., Hanson, J. L., Wolfe, B. L. & Pollak, S. D. Association of child poverty, brain development, and academic achievement. JAMA Pediatr. 169, 822ā829 (2015).
Ramphal, B. et al. Associations between amygdala-prefrontal functional connectivity and age depend on neighborhood socioeconomic status. Cereb. Cortex Commun. 1, tgaa033 (2020).
Taylor, R. L., Cooper, S. R., Jackson, J. J. & Barch, D. M. Assessment of neighborhood poverty, cognitive function, and prefrontal and hippocampal volumes in children. JAMA Netw. Open 3, e2023774 (2020).
Mackes, N. K. et al. Early childhood deprivation is associated with alterations in adult brain structure despite subsequent environmental enrichment. Proc. Natl Acad. Sci. USA 117, 641ā649 (2020).
Vargas, T., Damme, K. S. F. & Mittal, V. A. Neighborhood deprivation, prefrontal morphology and neurocognition in late childhood to early adolescence. Neuroimage 220, 117086 (2020).
Vanneman, A., Hamilton, L., Anderson, J. B. & Rahman, T. Achievement gaps: how Black and White students in public schools perform in mathematics and reading on the National Assessment of Educational Progress. Statistical Analysis Report. NCES 2009-455. National Center for Education Statistics. https://eric.ed.gov/?id=ED505903 (2009).
Ottolini, K. M., Andescavage, N., Keller, S. & Limperopoulos, C. Nutrition and the developing brain: the road to optimizing early neurodevelopment: a systematic review. Pediatr. Res. 87, 194ā201 (2020).
Rushton, J. P. & Jensen, A. R. Thirty years of research on race differences in cognitive ability. Psychol. Public Policy Law 11, 235ā294 (2005).
Nisbett, R. E. Heredity, environment, and race differences in IQ: a commentary on Rushton and Jensen (2005). Psychol. Public Policy Law 11, 302ā310 (2005).
Thompson, P. M. et al. Genetic influences on brain structure. Nat. Neurosci. 4, 1253ā1258 (2001 Dec).
Kweon, H. et al. Human brain anatomy reflects separable genetic and environmental components of socioeconomic status. Sci. Adv. 8, eabm2923 (2022).
Lu, Y. C. et al. Association between socioeconomic status and in utero fetal brain development. JAMA Netw. Open 4, e213526- (2021).
Jenkins, L. M. et al. Subcortical structural variations associated with low socioeconomic status in adolescents. Hum. Brain Mapp. 41, 162ā171 (2019).
Torres, V. A. et al. The impact of socioeconomic status (SES) on cognitive outcomes following radiotherapy for pediatric brain tumors: a prospective, longitudinal trial. Neuro Oncol. 23, 1173ā1182 (2021).
Assari, S. et al. Parental educational attainment, the superior temporal cortical surface area, and reading ability among American children: a test of marginalization-related diminished returns. Children 8, 412 (2021).
Assari, S., Boyce, S. & Bazargan, M. Subjective family socioeconomic status and adolescentsā attention: Blacksā diminished returns. Children 7, 80 (2020).
Letang, S. K., Lin, S. S., Parmelee, P. A. & McDonough, I. M. Ethnoracial disparities in cognition are associated with multiple socioeconomic status-stress pathways. Cogn. Res. Princ. Implic. 6, 1ā7 (2021).
Beauchamp, M. S., Lee, K. E., Argall, B. D. & Martin, A. Integration of auditory and visual information about objects in superior temporal sulcus. Neuron 41, 809ā823 (2004).
du Boisgueheneuc, F. et al. Functions of the left superior frontal gyrus in humans: a lesion study. Brain 129, 3315ā3328 (2006).
Vanni, S., Tanskanen, T., SeppƤ, M., Uutela, K. & Hari, R. Coinciding early activation of the human primary visual cortex and anteromedial cuneus. Proc. Natl Acad. Sci. USA 98, 2776ā2780 (2001).
Stoeckel, C., Gough, P. M., Watkins, K. E. & Devlin, J. T. Supramarginal gyrus involvement in visual word recognition. Cortex 45, 1091ā1096 (2009).
Nishida, Y. et al. Stereopsis-processing regions in the human parieto-occipital cortex. Neuroreport 12, 2259ā2263 (2001).
Marshall, A. T. et al. Association of lead-exposure risk and family income with childhood brain outcomes. Nat. Med. 26, 91ā97 (2020).
Mossakowski, K. N. in The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society 2154ā2160 (American Cancer Society, 2014).
Bale, T. L. Epigenetic and transgenerational reprogramming of brain development. Nat. Rev. Neurosci. 16, 332ā344 (2015).
Compton, W. M., Dowling, G. J. & Garavan, H. Ensuring the best use of data: the Adolescent Brain Cognitive Development Study. JAMA Pediatr. 173, 809ā810 (2019).
Troller-Renfree, S. V. et al. The impact of a poverty reduction intervention on infant brain activity. Proc. Natl Acad. Sci. USA 119, e2115649119 (2022).
Johnson, S. B., Riis, J. L. & Noble, K. G. State of the art review: poverty and the developing brain. Pediatrics 137, e20153075 (2016).
Shonkoff, J. P. et al. The lifelong effects of early childhood adversity and toxic stress. Pediatrics 129, e232āe246 (2012).
Milgrom, J. et al. Early sensitivity training for parents of preterm infants: impact on the developing brain. Pediatr. Res. 67, 330ā335 (2010).
Acknowledgements
Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9ā10 and follow them over 10 years into early adulthood. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.15154/1523037. Instructions on how to create an NDA study are available at https://nda.nih.gov/training/modules/study.html).
Funding
The ABCD StudyĀ® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html.
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Contributions
A.I. conceptualized and designed the study, performed statistical analysis, drafted the initial manuscript, and reviewed and revised the manuscript. L.C., and T.M.E. conceptualized and designed the study, the data collection instruments, supervised data collection, and reviewed and revised the manuscript. S.M.E., H.L., N.L., G.R., C.G., D.K., M.R., and P.J.R. performed data collection, and critically reviewed and revised the manuscript for important intellectual content.
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The authors declare no competing interests.
Ethics approval and consent to participate
The study protocol was approved by the local as well as the constituent member Institutional Review Boards of the ABCD Study. Parents of all children gave written consent to participate in the study.
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Isaiah, A., Ernst, T.M., Liang, H. et al. Associations between socioeconomic gradients and racial disparities in preadolescent brain outcomes. Pediatr Res 94, 356ā364 (2023). https://doi.org/10.1038/s41390-022-02399-9
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DOI: https://doi.org/10.1038/s41390-022-02399-9