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Current Epidemiology Reports

, Volume 6, Issue 4, pp 466–475 | Cite as

Social Experience and the Developing Brain: Opportunities for Social Epidemiologists in the Era of Population-Based Neuroimaging

  • Kaja Z. LeWinnEmail author
  • Emily W. Shih
Social Epidemiology (J Dowd, Section Editor)
  • 8 Downloads
Part of the following topical collections:
  1. Topical Collection on Social Epidemiology

Abstract

Purpose of Review

There are an increasing number of studies using neuroimaging to understand associations between social conditions, including socioeconomic status, and the development of brain structure and function. From a population health perspective, this review summarizes the challenges of this work and highlights opportunities for collaboration with social epidemiologists as neuroimaging studies become more population-based.

Recent Findings

While suggestive of broad associations with brain regions involved in cognitive and emotional processing, much of the work linking socioeconomic status to child brain structure or function is correlational and limited by the constraints common to most imaging work in this area: cross-sectional and observational study designs, inadequate adjustment for potential confounders, and non-representative samples.

Summary

As consortia implementing neuroimaging methods in large, population-based samples emerge, resulting datasets become more similar to epidemiological cohort studies where the outcomes of interest are measures of brain structure or function. Social epidemiologists, well versed in estimating causal associations from observational data, have the potential to contribute methods and theoretical models that this field may draw on improve causal inference. Synergy between the neurosciences and population health will result in more solution oriented, policy relevant work that will better inform efforts to reduce observed social disparities in child neurodevelopmental outcomes.

Keywords

Neuroimaging Socioeconomic factors Causal inference Neurodevelopment Population health Social epidemiology 

Notes

Compliance with Ethical Standards

Conflict of Interest

Kaja Z. LeWinn and Emily W. Shih each declare no potential conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Ben-Shlomo Y, Kuh D. A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Int J Epidemiol. 2002;31:285–93.  https://doi.org/10.1093/ije/31.2.285.CrossRefPubMedGoogle Scholar
  2. 2.
    Fox SE, Levitt P, Iii CAN. How the timing and quality of early experiences influence the development of brain architecture. Child Dev. 2010;81:28–40.  https://doi.org/10.1111/j.1467-8624.2009.01380.x.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    McEwen BS. Effects of adverse experiences for brain structure and function. Biol Psychiatry. 2000;48:721–31.  https://doi.org/10.1016/S0006-3223(00)00964-1.CrossRefPubMedGoogle Scholar
  4. 4.
    Rauh VA, Margolis AE. Research review: environmental exposures, neurodevelopment, and child mental health – new paradigms for the study of brain and behavioral effects. J Child Psychol Psychiatry. 2016;57:775–93.  https://doi.org/10.1111/jcpp.12537.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Alamy M, Bengelloun WA. Malnutrition and brain development: an analysis of the effects of inadequate diet during different stages of life in rat. Neurosci Biobehav Rev. 2012;36:1463–80.  https://doi.org/10.1016/j.neubiorev.2012.03.009.CrossRefPubMedGoogle Scholar
  6. 6.
    McEwen BS. The neurobiology of stress: from serendipity to clinical relevance. Brain Res. 2000;886:172–89.  https://doi.org/10.1016/S0006-8993(00)02950-4.CrossRefPubMedGoogle Scholar
  7. 7.
    Hensch TK. Critical period plasticity in local cortical circuits. Nat Rev Neurosci. 2005;6:877–88.  https://doi.org/10.1038/nrn1787.CrossRefPubMedGoogle Scholar
  8. 8.
    Knudsen EI. Sensitive periods in the development of the brain and behavior. J Cogn Neurosci. 2004;16:1412–25.  https://doi.org/10.1162/0898929042304796.CrossRefPubMedGoogle Scholar
  9. 9.
    Gabrieli JDE, Ghosh SS, Whitfield-Gabrieli S. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron. 2015;85:11–26.  https://doi.org/10.1016/j.neuron.2014.10.047.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    •• Farah MJ. Socioeconomic status and the brain: prospects for neuroscience-informed policy. Nat rev Neurosci. 2018;19:428–38.  https://doi.org/10.1038/s41583-018-0023-2Critical analysis of the extent to which neuroscientific approaches to understand socioeconomic disparities in outcomes can and should be used to inform policy. CrossRefPubMedGoogle Scholar
  11. 11.
    Symms M, Jäger HR, Schmierer K, Yousry TA. A review of structural magnetic resonance neuroimaging. J Neurol Neurosurg Psychiatry. 2004;75:1235–44.  https://doi.org/10.1136/jnnp.2003.032714.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Dibbets P, Bakker K, Jolles J. Functional MRI of task switching in children with specific language impairment (SLI). Neurocase. 2006;12:71–9.  https://doi.org/10.1080/13554790500507032.CrossRefPubMedGoogle Scholar
  13. 13.
    • Poldrack RA, Baker CI, Durnez J, Gorgolewski KJ, Matthews PM, Munafò MR, et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci. 2017;18:115–26.  https://doi.org/10.1038/nrn.2016.167Important work demonstrating multiple challenges (low statistical power, flexibility in data analysis, software errors and a lack of direct replication) in fMRI studies. CrossRefPubMedGoogle Scholar
  14. 14.
    Smith AB, Taylor E, Brammer M, Rubia K. Neural correlates of switching set as measured in fast, event-related functional magnetic resonance imaging. Hum Brain Mapp. 2004;21:247–56.  https://doi.org/10.1002/hbm.20007.CrossRefPubMedGoogle Scholar
  15. 15.
    Buckner RL, Krienen FM, Yeo BTT. Opportunities and limitations of intrinsic functional connectivity MRI. Nat Neurosci. 2013;16:832–7.  https://doi.org/10.1038/nn.3423.CrossRefPubMedGoogle Scholar
  16. 16.
    Smith SM, Beckmann CF, Andersson J, Auerbach EJ, Bijsterbosch J, Douaud G, et al. Resting-state fMRI in the human Connectome project. NeuroImage. 2013;80:144–68.  https://doi.org/10.1016/j.neuroimage.2013.05.039.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Tavor I, Jones OP, Mars RB, Smith SM, Behrens TE, Jbabdi S. Task-free MRI predicts individual differences in brain activity during task performance. Science. 2016;352:216–20.  https://doi.org/10.1126/science.aad8127.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Le Bihan D, Mangin J-F, Poupon C, Clark CA, Pappata S, Molko N, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging. 2001;13:534–46.  https://doi.org/10.1002/jmri.1076.CrossRefPubMedGoogle Scholar
  19. 19.
    Soares JM, Marques P, Alves V, Sousa N. A hitchhiker’s guide to diffusion tensor imaging. Front Neurosci. 2013;7:1–14.  https://doi.org/10.3389/fnins.2013.00031.CrossRefGoogle Scholar
  20. 20.
    Gong B, Naveed S, Hafeez DM, Afzal KI, Majeed S, Abele J, et al. Neuroimaging in psychiatric disorders: a bibliometric analysis of the 100 most highly cited articles. J Neuroimaging. 2019;29:14–33.  https://doi.org/10.1111/jon.12570.CrossRefPubMedGoogle Scholar
  21. 21.
    Zhu D, Zhang T, Jiang X, Hu X, Chen H, Yang N, et al. Fusing DTI and FMRI data: a survey of methods and applications. NeuroImage. 2014;102(Pt 1):184–91.  https://doi.org/10.1016/j.neuroimage.2013.09.071.CrossRefPubMedGoogle Scholar
  22. 22.
    Baldin E, Hauser WA, Buchhalter JR, Hesdorffer DC, Ottman R. Utility of EEG activation procedures in epilepsy: a population-based study. J Clin Neurophysiol Off Publ Am Electroencephalogr Soc. 2017;34:512–9.  https://doi.org/10.1097/WNP.0000000000000371.CrossRefGoogle Scholar
  23. 23.
    Jamasebi R, Redline S, Patel SR, Loparo KA. Entropy-based measures of EEG arousals as biomarkers for sleep dynamics: applications to hypertension. Sleep. 2008;31:935–43.  https://doi.org/10.5665/sleep/31.7.935.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Farah MJ. The neuroscience of socioeconomic status: correlates, causes, and consequences. Neuron. 2017;96:56–71.  https://doi.org/10.1016/j.neuron.2017.08.034.CrossRefPubMedGoogle Scholar
  25. 25.
    Jensen SKG, Berens AE, Nelson CA. Effects of poverty on interacting biological systems underlying child development. Lancet Child Adolesc Health. 2017;1:225–39.  https://doi.org/10.1016/S2352-4642(17)30024-X.CrossRefPubMedGoogle Scholar
  26. 26.
    • Johnson SB, Riis JL, Noble KG. State of the Art Review: Poverty and the Developing Brain. Pediatrics 2016:137:e20153075. Doi: https://doi.org/10.1542/peds.2015-3075. Comprehensive review of neuroimaging work across modalities examining associations between socioeconomic status and brain structure and function. It should be noted that since this review, several large scale studies in this area have been published. CrossRefGoogle Scholar
  27. 27.
    Leijser LM, Siddiqi A, Miller SP. Imaging evidence of the effect of socio-economic status on brain structure and development. Semin Pediatr Neurol. 2018;27:26–34.  https://doi.org/10.1016/j.spen.2018.03.004.CrossRefPubMedGoogle Scholar
  28. 28.
    Muscatell KA. Socioeconomic influences on brain function: implications for health. Ann N Y Acad Sci. 2018;1428:14–32.  https://doi.org/10.1111/nyas.13862.CrossRefPubMedGoogle Scholar
  29. 29.
    Noble KG, Houston SM, Brito NH, Bartsch H, Kan E, Kuperman JM, et al. Family income, parental education and brain structure in children and adolescents. Nat Neurosci. 2015;18:773–8.  https://doi.org/10.1038/nn.3983.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Jednoróg K, Altarelli I, Monzalvo K, Fluss J, Dubois J, Billard C, et al. The influence of socioeconomic status on children’s brain structure. PLoS One. 2012;7:e42486.  https://doi.org/10.1371/journal.pone.0042486.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Raizada RDS, Richards TL, Meltzoff A, Kuhl PK. Socioeconomic status predicts hemispheric specialisation of the left inferior frontal gyrus in young children. NeuroImage. 2008;40:1392–401.  https://doi.org/10.1016/j.neuroimage.2008.01.021.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Noble KG, Houston SM, Kan E, Sowell ER. Neural correlates of socioeconomic status in the developing human brain. Dev Sci. 2012;15:516–27.  https://doi.org/10.1111/j.1467-7687.2012.01147.x.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Hair NL, Hanson JL, Wolfe BL, Pollak SD. Association of child poverty, brain development, and academic achievement. JAMA Pediatr. 2015;169:822–9.  https://doi.org/10.1001/jamapediatrics.2015.1475.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Hanson JL, Chandra A, Wolfe BL, Pollak SD. Association between income and the hippocampus. PLoS One. 2011;6:e18712.  https://doi.org/10.1371/journal.pone.0018712.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    • McDermott CL, Seidlitz J, Nadig A, Liu S, Clasen LS, Blumenthal JD, et al. Longitudinally mapping childhood socioeconomic status associations with cortical and subcortical morphology. J Neurosci. 2019;39:1365–73.  https://doi.org/10.1523/JNEUROSCI.1808-18.2018Recent study in a large, longitudinal sample of children examining associations between socioeconomic status and brain structure. This study also demonstrates that variation in brain structure mediates associations between SES and child IQ.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    • Gur RE, Moore TM, Rosen AF, Barzilay R, Roalf DR, Calkins ME, et al. Burden of environmental adversity associated with psychopathology, maturation, and brain behavior parameters in youths. JAMA psychiatry. 2019;76:966–75.  https://doi.org/10.1001/jamapsychiatry.2019.0943This is the largest study to date examining associations between SES and brain structure. CrossRefGoogle Scholar
  37. 37.
    Gianaros PJ, Kuan DC-H, Marsland AL, Sheu LK, Hackman DA, Miller KG, et al. Community socioeconomic disadvantage in midlife relates to cortical morphology via neuroendocrine and cardiometabolic pathways. Cereb Cortex. 2017;27:460–73.  https://doi.org/10.1093/cercor/bhv233.CrossRefPubMedGoogle Scholar
  38. 38.
    Luby J, Belden A, Botteron K, Marrus N, Harms MP, Babb C, et al. The effects of poverty on childhood brain development: the mediating effect of caregiving and stressful life events. JAMA Pediatr. 2013;167:1135–42.  https://doi.org/10.1001/jamapediatrics.2013.3139.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Mackey AP, Finn AS, Leonard JA, Jacoby-Senghor DS, West MR, Gabrieli CFO, et al. Neuroanatomical correlates of the income-achievement gap. Psychol Sci. 2015;26:925–33.  https://doi.org/10.1177/0956797615572233.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Joober R, Schmitz N, Annable L, Boksa P. Publication bias: what are the challenges and can they be overcome? J Psychiatry Neurosci JPN. 2012;37:149–52.  https://doi.org/10.1503/jpn.120065.CrossRefPubMedGoogle Scholar
  41. 41.
    Levine TR, Asada KJ, Carpenter C. Sample sizes and effect sizes are negatively correlated in meta-analyses: evidence and implications of a publication bias against nonsignificant findings. Commun Monogr. 2009;76:286–302.  https://doi.org/10.1080/03637750903074685.CrossRefGoogle Scholar
  42. 42.
    Christley RM. Power and error: increased risk of false positive results in underpowered studies. Open Epidemiol J. 2010;3:16–9.  https://doi.org/10.2174/1874297101003010016.CrossRefGoogle Scholar
  43. 43.
    Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013;14:365–76.  https://doi.org/10.1038/nrn3475.CrossRefPubMedGoogle Scholar
  44. 44.
    Ioannidis JPA. Excess significance bias in the literature on brain volume abnormalities. Arch Gen Psychiatry. 2011;68:773–80.  https://doi.org/10.1001/archgenpsychiatry.2011.28.CrossRefPubMedGoogle Scholar
  45. 45.
    Turner BO, Paul EJ, Miller MB, Barbey AK. Small sample sizes reduce the replicability of task-based fMRI studies. Commun Biol. 2018;1:1–10.  https://doi.org/10.1038/s42003-018-0073-z.CrossRefGoogle Scholar
  46. 46.
    Barch D, Pagliaccio D, Belden A, Harms MP, Gaffrey M, Sylvester CM, et al. Effect of hippocampal and amygdala connectivity on the relationship between preschool poverty and school-age depression. Am J Psychiatry. 2016;173:625–34.  https://doi.org/10.1176/appi.ajp.2015.15081014.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Ellwood-Lowe ME, Humphreys KL, Ordaz SJ, Camacho MC, Sacchet MD, Gotlib IH. Time-varying effects of income on hippocampal volume trajectories in adolescent girls. Dev Cogn Neurosci. 2018;30:41–50.  https://doi.org/10.1016/j.dcn.2017.12.005.CrossRefPubMedGoogle Scholar
  48. 48.
    Rosen ML, Sheridan MA, Sambrook KA, Meltzoff AN, McLaughlin KA. Socioeconomic disparities in academic achievement: a multi-modal investigation of neural mechanisms in children and adolescents. NeuroImage. 2018;173:298–310.  https://doi.org/10.1016/j.neuroimage.2018.02.043.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15:615.  https://doi.org/10.1097/01.ede.0000135174.63482.43.CrossRefPubMedGoogle Scholar
  50. 50.
    Stuart EA, Cole SR, Bradshaw CP, Leaf PJ. The use of propensity scores to assess the generalizability of results from randomized trials. J R Stat Soc Ser A Stat Soc. 2011;174:369–86.  https://doi.org/10.1111/j.1467-985X.2010.00673.x.CrossRefGoogle Scholar
  51. 51.
    • LeWinn KZ, Sheridan MA, Keyes KM, Hamilton A, McLaughlin KA. Sample composition alters associations between age and brain structure. Nat Commun. 2017;8:1–14.  https://doi.org/10.1038/s41467-017-00908-7This study demonstrates that current sampling practices (i.e. non-representative and biased towards high SES and Non-Hispanic White participants) in neuroimaging studies may produce systematic biases in our understanding of fundamental neural processes, such as the relationship between age and brain structure. CrossRefGoogle Scholar
  52. 52.
    Levin S, Sinclair S, Veniegas RC, Taylor PL. Perceived discrimination in the context of multiple group memberships. Psychol Sci. 2002;13:557–60.  https://doi.org/10.1111/1467-9280.00498.CrossRefPubMedGoogle Scholar
  53. 53.
    Ferraro KF, Farmer MM. Double jeopardy to health hypothesis for african americans: analysis and critique. J Health Soc Behav. 1996;37:27–43.  https://doi.org/10.2307/2137229.CrossRefPubMedGoogle Scholar
  54. 54.
    Bauman LJ, Silver EJ, Stein REK. Cumulative social disadvantage and child health. Pediatrics. 2006;117:1321–8.  https://doi.org/10.1542/peds.2005-1647.CrossRefPubMedGoogle Scholar
  55. 55.
    Waitzman NJ, Smith KR. The effects of occupational class transitions on hypertension: racial disparities among working-age men. Am J Public Health. 1994;84:945–50.  https://doi.org/10.2105/AJPH.84.6.945.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Farmer MM, Ferraro KF. Are racial disparities in health conditional on socioeconomic status? Soc Sci Med. 2005;60:191–204.  https://doi.org/10.1016/j.socscimed.2004.04.026.CrossRefPubMedGoogle Scholar
  57. 57.
    Braveman P. Health disparities and health equity: concepts and measurement. Annu Rev Public Health. 2006;27:167–94.  https://doi.org/10.1146/annurev.publhealth.27.021405.102103.CrossRefPubMedGoogle Scholar
  58. 58.
    Wehby GL, McCarthy AM. Economic gradients in early child neurodevelopment: a multi-country study. Soc Sci Med. 2013;78:86–95.  https://doi.org/10.1016/j.socscimed.2012.11.038.CrossRefPubMedGoogle Scholar
  59. 59.
    Ladson-Billings G. From the achievement gap to the education debt: understanding achievement in U.S. schools. Educ Res. 2006;35:3–12.  https://doi.org/10.3102/0013189X035007003.CrossRefGoogle Scholar
  60. 60.
    Lugo-Gil J, Tamis-LeMonda CS. Family resources and parenting quality: links to children’s cognitive development across the first 3 years. Child Dev. 2008;79:1065–85.  https://doi.org/10.1111/j.1467-8624.2008.01176.x.CrossRefPubMedGoogle Scholar
  61. 61.
    Kaushal N, Nepomnyaschy L. Wealth, race/ethnicity, and children’s educational outcomes. Child Youth Serv Rev. 2009;31:963–71.  https://doi.org/10.1016/j.childyouth.2009.04.012.CrossRefGoogle Scholar
  62. 62.
    Chen E, Martin AD, Matthews KA. Understanding health disparities: the role of race and socioeconomic status in children’s health. Am J Public Health. 2006;96:702–8.  https://doi.org/10.2105/AJPH.2004.048124.CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Slopen N, Shonkoff JP, Albert MA, Yoshikawa H, Jacobs A, Stoltz R, et al. Racial disparities in child adversity in the U.S.: interactions with family immigration history and income. Am J Prev Med. 2016;50:47–56.  https://doi.org/10.1016/j.amepre.2015.06.013.CrossRefPubMedGoogle Scholar
  64. 64.
    Sarsour K, Sheridan M, Jutte D, Nuru-Jeter A, Hinshaw S, Boyce WT. Family socioeconomic status and child executive functions: the roles of language, home environment, and single parenthood. J Int Neuropsychol Soc. 2011;17:120–32.  https://doi.org/10.1017/S1355617710001335.CrossRefPubMedGoogle Scholar
  65. 65.
    Sirin SR. Socioeconomic status and academic achievement: a meta-analytic review of research. Rev Educ Res. 2005;75:417–53.  https://doi.org/10.3102/00346543075003417.CrossRefGoogle Scholar
  66. 66.
    Mezzacappa E. Alerting, orienting, and executive attention: developmental properties and sociodemographic correlates in an epidemiological sample of young, urban children. Child Dev. 2004;75:1373–86.  https://doi.org/10.1111/j.1467-8624.2004.00746.x.CrossRefPubMedGoogle Scholar
  67. 67.
    Brito NH, Noble KG. The independent and interacting effects of socioeconomic status and dual-language use on brain structure and cognition. Dev Sci. 2018;21:e12688.  https://doi.org/10.1111/desc.12688.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Brito NH, Piccolo LR, Noble KG. Associations between cortical thickness and neurocognitive skills during childhood vary by family socioeconomic factors. Brain Cogn. 2017;116:54–62.  https://doi.org/10.1016/j.bandc.2017.03.007.CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Zeka A, Melly SJ, Schwartz J. The effects of socioeconomic status and indices of physical environment on reduced birth weight and preterm births in eastern Massachusetts. Environ Health. 2008;7:60.  https://doi.org/10.1186/1476-069X-7-60.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Boardman JD, Powers DA, Padilla YC, Hummer RA. Low birth weight, social factors, and developmental outcomes among children in the United States. Demography. 2002;39:353–68.  https://doi.org/10.1353/dem.2002.0015.CrossRefPubMedGoogle Scholar
  71. 71.
    Anderson P, Doyle LW, Group and the VICS. Neurobehavioral outcomes of school-age children born extremely low birth weight or very preterm in the 1990s. JAMA 2003:289:3264–3272. Doi: https://doi.org/10.1001/jama.289.24.3264.CrossRefGoogle Scholar
  72. 72.
    Marks GN. Family size, family type and student achievement: cross-national differences and the role of socioeconomic and school factors. J Comp Fam Stud. 2006;37:1–24.  https://doi.org/10.3138/jcfs.37.1.1.CrossRefGoogle Scholar
  73. 73.
    Petterson SM, Albers AB. Effects of poverty and maternal depression on early child development. Child Dev. 2001;72:1794–813.  https://doi.org/10.1111/1467-8624.00379.CrossRefPubMedGoogle Scholar
  74. 74.
    Turkheimer E, Haley A, Waldron M, D’Onofrio B, Gottesman II. Socioeconomic status modifies heritability of IQ in young children. Psychol Sci. 2003;14:623–8.  https://doi.org/10.1046/j.0956-7976.2003.psci_1475.x.CrossRefPubMedGoogle Scholar
  75. 75.
    Holmlund H, Lindahl M, Plug E. The causal effect of parents’ schooling on children’s schooling: a comparison of estimation methods. J Econ Lit. 2011;49:615–51.  https://doi.org/10.1257/jel.49.3.615.CrossRefGoogle Scholar
  76. 76.
    Singer JD, Willett JB. Applied longitudinal data analysis: modeling change and event occurrence: Oxford University Press; 2003.Google Scholar
  77. 77.
    Craig P, Katikireddi SV, Leyland A, Popham F. Natural experiments: an overview of methods, approaches, and contributions to public health intervention research. Annu Rev Public Health. 2017;38:39–56.  https://doi.org/10.1146/annurev-publhealth-031816-044327.CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Dunning T. Natural experiments in the social sciences: a design-based approach: Cambridge University Press; 2012.Google Scholar
  79. 79.
    Hamad R, Rehkopf DH. Poverty and child development: a longitudinal study of the impact of the earned income tax credit. Am J Epidemiol. 2016;183:775–84.  https://doi.org/10.1093/aje/kwv317.CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Lechner M. The estimation of causal effects by difference-in-difference methods. Found trends®. Econom. 2011;4:165–224.  https://doi.org/10.1561/0800000014.CrossRefGoogle Scholar
  81. 81.
    Hamad R, Batra A, Karasek D, LeWinn KZ, Bush NR, Davis RL, et al. The impact of the revised WIC food package on maternal nutrition during pregnancy and postpartum. Am J Epidemiol. 2019;188:1493–502.  https://doi.org/10.1093/aje/kwz098.CrossRefPubMedGoogle Scholar
  82. 82.
    Hamad R, Modrek S, White JS. Paid family leave effects on breastfeeding: a quasi-experimental study of US policies. Am J Public Health. 2018;109:164–6.  https://doi.org/10.2105/AJPH.2018.304693.CrossRefGoogle Scholar
  83. 83.
    Glymour MM. Natural experiments and instrumental variable analyses in social epidemiology. Methods Soc Epidemiol. 2006;1:429–60.Google Scholar
  84. 84.
    Nguyen TT, Tchetgen EJT, Kawachi I, Gilman SE, Walter S, Liu SY, et al. Instrumental variable approaches to identifying the causal effect of educational attainment on dementia risk. Ann Epidemiol 2016:26:71–76.e3. Doi: https://doi.org/10.1016/j.annepidem.2015.10.006.CrossRefGoogle Scholar
  85. 85.
    Gennetian LA, Magnuson K, Morris PA. From statistical associations to causation: what developmentalists can learn from instrumental variables techniques coupled with experimental data. Dev Psychol. 2008;44:381–94.  https://doi.org/10.1037/0012-1649.44.2.381.CrossRefPubMedPubMedCentralGoogle Scholar
  86. 86.
    Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res. 2007;16:309–30.  https://doi.org/10.1177/0962280206077743.CrossRefPubMedGoogle Scholar
  87. 87.
    Böckerman P, Viinikainen J, Pulkki-Råback L, Hakulinen C, Pitkänen N, Lehtimäki T, et al. Does higher education protect against obesity? Evidence using Mendelian randomization. Prev Med. 2017;101:195–8.  https://doi.org/10.1016/j.ypmed.2017.06.015.CrossRefPubMedGoogle Scholar
  88. 88.
    Oakes JM. Kaufman JS. Methods in Social Epidemiology: John Wiley & Sons; 2017.Google Scholar
  89. 89.
    Loucks EB, Buka SL, Rogers ML, Liu T, Kawachi I, Kubzansky LD, et al. Education and coronary heart disease risk associations may be affected by early-life common prior causes: a propensity matching analysis. Ann Epidemiol. 2012;22:221–32.  https://doi.org/10.1016/j.annepidem.2012.02.005.CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Glymour MM, Avendano M, Kawachi I. Socioeconomic status and health. Soc Epidemiol. 2014;2:17–63.Google Scholar
  91. 91.
    Nelson CA, Furtado EA, Fox NA, Zeanah CH. The deprived human brain: developmental deficits among institutionalized Romanian children—and later improvements—strengthen the case for individualized care. Am Sci. 2009;97:222–9.CrossRefGoogle Scholar
  92. 92.
    Nelson CA, Zeanah CH, Fox NA, Marshall PJ, Smyke AT, Guthrie D. Cognitive recovery in socially deprived young children: the Bucharest early intervention project. Science. 2007;318:1937–40.  https://doi.org/10.1126/science.1143921.CrossRefPubMedGoogle Scholar
  93. 93.
    McLaughlin KA, Zeanah CH, Fox NA, Nelson CA. Attachment security as a mechanism linking foster care placement to improved mental health outcomes in previously institutionalized children. J Child Psychol Psychiatry. 2012;53:46–55.  https://doi.org/10.1111/j.1469-7610.2011.02437.x.CrossRefPubMedGoogle Scholar
  94. 94.
    Sheridan MA, Fox NA, Zeanah CH, McLaughlin KA, Nelson CA. Variation in neural development as a result of exposure to institutionalization early in childhood. Proc Natl Acad Sci. 2012;109:12927–32.  https://doi.org/10.1073/pnas.1200041109.CrossRefPubMedGoogle Scholar
  95. 95.
    McLaughlin KA, Sheridan MA, Winter W, Fox NA, Zeanah CH, Nelson CA. Widespread reductions in cortical thickness following severe early-life deprivation: a neurodevelopmental pathway to attention-deficit/hyperactivity disorder. Biol Psychiatry. 2014;76:629–38.  https://doi.org/10.1016/j.biopsych.2013.08.016.CrossRefPubMedGoogle Scholar
  96. 96.
    Bick J, Zhu T, Stamoulis C, Fox NA, Zeanah C, Nelson CA. A randomized clinical trial of foster care as an intervention for early institutionalization: long term improvements in white matter microstructure. JAMA Pediatr. 2015;169:211–9.  https://doi.org/10.1001/jamapediatrics.2014.3212.CrossRefPubMedPubMedCentralGoogle Scholar
  97. 97.
    Petrowski N, Cappa C, Gross P. Estimating the number of children in formal alternative care: challenges and results. Child Abuse Negl. 2017;70:388–98.  https://doi.org/10.1016/j.chiabu.2016.11.026.CrossRefPubMedGoogle Scholar
  98. 98.
    Brody GH, Gray JC, Yu T, Barton AW, Beach SRH, Galván A, et al. Protective prevention effects on the association of poverty with brain development. JAMA Pediatr. 2017;171:46–52.  https://doi.org/10.1001/jamapediatrics.2016.2988.CrossRefPubMedPubMedCentralGoogle Scholar
  99. 99.
    Auchter AM, Hernandez Mejia M, Heyser CJ, Shilling PD, Jernigan TL, Brown SA, et al. A description of the ABCD organizational structure and communication framework. Dev Cogn Neurosci. 2018;32:8–15.  https://doi.org/10.1016/j.dcn.2018.04.003.CrossRefPubMedPubMedCentralGoogle Scholar
  100. 100.
    Garavan H, Bartsch H, Conway K, Decastro A, Goldstein RZ, Heeringa S, et al. Recruiting the ABCD sample: design considerations and procedures. Dev Cogn Neurosci. 2018;32:16–22.  https://doi.org/10.1016/j.dcn.2018.04.004.CrossRefPubMedPubMedCentralGoogle Scholar
  101. 101.
    Jernigan TL, Brown TT, Hagler DJ Jr, Akshoomoff N, Bartsch H, et al. The pediatric imaging, Neurocognition, and genetics (PING) data repository. NeuroImage. 2016;124:1149.  https://doi.org/10.1016/j.neuroimage.2015.04.057.CrossRefPubMedGoogle Scholar
  102. 102.
    Satterthwaite TD, Connolly JJ, Ruparel K, Calkins ME, Jackson C, Elliott MA, et al. The Philadelphia neurodevelopmental cohort: a publicly available resource for the study of normal and abnormal brain development in youth. NeuroImage. 2016;124:1115–9.  https://doi.org/10.1016/j.neuroimage.2015.03.056.CrossRefPubMedGoogle Scholar
  103. 103.
    Schumann G, Loth E, Banaschewski T, Barbot A, Barker G, Büchel C, et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol Psychiatry. 2010;15:1128–39.  https://doi.org/10.1038/mp.2010.4.CrossRefPubMedGoogle Scholar
  104. 104.
    Evans AC, Group BDC, others. The NIH MRI study of normal brain development. Neuroimage 2006:30:184–202. Doi: https://doi.org/10.1016/j.neuroimage.2005.09.068.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Psychiatry and Weil Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoUSA

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