Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Family income, parental education and brain structure in children and adolescents

Nature Neuroscience volume 18, pages 773–778 (2015)Cite this article

Subjects

Abstract

Socioeconomic disparities are associated with differences in cognitive development. The extent to which this translates to disparities in brain structure is unclear. We investigated relationships between socioeconomic factors and brain morphometry, independently of genetic ancestry, among a cohort of 1,099 typically developing individuals between 3 and 20 years of age. Income was logarithmically associated with brain surface area. Among children from lower income families, small differences in income were associated with relatively large differences in surface area, whereas, among children from higher income families, similar income increments were associated with smaller differences in surface area. These relationships were most prominent in regions supporting language, reading, executive functions and spatial skills; surface area mediated socioeconomic differences in certain neurocognitive abilities. These data imply that income relates most strongly to brain structure among the most disadvantaged children.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Parent education is linearly associated with cortical surface area (N = 1,099).
Figure 2: Family income is logarithmically related to cortical surface area (N = 1,099).
Figure 3: Parental education is quadratically associated with left hippocampal volume (N = 1,099).

References

  1. Rosenzweig, M.R. Effects of differential experience on the brain and behavior. Dev. Neuropsychol. 24, 523–540 (2003).

    Article  PubMed  Google Scholar 

  2. Sowell, E.R. et al. Mapping cortical change across the human life span. Nat. Neurosci. 6, 309–315 (2003).

    Article  CAS  PubMed  Google Scholar 

  3. McLoyd, V.C. Socioeconomic disadvantage and child development. Am. Psychol. 53, 185–204 (1998).

    Article  CAS  PubMed  Google Scholar 

  4. Brooks-Gunn, J. & Duncan, G.J. The effects of poverty on children. Future Child. 7, 55–71 (1997).

    Article  CAS  PubMed  Google Scholar 

  5. Hackman, D.A. & Farah, M.J. Socioeconomic status and the developing brain. Trends Cogn. Sci. 13, 65–73 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Noble, K.G., Wolmetz, M.E., Ochs, L.G., Farah, M.J. & McCandliss, B.D. Brain-behavior relationships in reading acquisition are modulated by socioeconomic factors. Dev. Sci. 9, 642–654 (2006).

    Article  PubMed  Google Scholar 

  7. Raizada, R.D., Richards, T.L., Meltzoff, A. & Kuhl, P.K. Socioeconomic status predicts hemispheric specialisation of the left inferior frontal gyrus in young children. Neuroimage 40, 1392–1401 (2008).

    Article  PubMed  Google Scholar 

  8. Stevens, C., Lauinger, B. & Neville, H. Differences in the neural mechanisms of selective attention in children from different socioeconomic backgrounds: an event-related brain potential study. Dev. Sci. 12, 634–646 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Sheridan, M.A., Sarsour, K., Jutte, D., D'Esposito, M. & Boyce, W.T. The impact of social disparity on prefontal function in childhood. PLoS ONE 7, e35744 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tomalski, P. et al. Socioeconomic status and functional brain development – associations in early infancy. Dev. Sci. 16, 676–687 (2013).

    Article  PubMed  Google Scholar 

  11. Kim, P. et al. Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proc. Natl. Acad. Sci. USA 110, 18442–18447 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sowell, E.R. et al. Longitudinal mapping of cortical thickness and brain growth in normal children. J. Neurosci. 24, 8223–8231 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Noble, K.G. et al. Hippocampal volume varies with educational attainment across the life-span. Front. Hum. Neurosci. 6, 1–12 (2012).

    Article  Google Scholar 

  14. Noble, K.G., Houston, S.M., Kan, E. & Sowell, E.R. Neural correlates of socioeconomic status in the developing human brain. Dev. Sci. 15, 516–527 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Noble, K.G., Korgaonkar, M.S., Grieve, S.M. & Brickman, A.M. Higher education is an age-independent predictor of white matter integrity and cognitive control in late adolescence. Dev. Sci. 16, 653–664 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Chiang, M.-C. et al. Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29. NeuroImage 54, 2308–2317 (2010).

    Article  PubMed  Google Scholar 

  17. Hanson, J.L., Chandra, A., Wolfe, B.L. & Pollak, S.D. Association between income and the hippocampus. PLoS ONE 6, e18712 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hanson, J.L. et al. Family poverty affects the rate of human infant brain growth. PLoS ONE 8, e80954 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Jednoróg, K. et al. The influence of socioeconomic status on children's brain structure. PLoS ONE 7, e42486 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Lawson, G.M., Duda, J.T., Avants, B.B., Wu, J. & Farah, M.J. Associations between children's socioeconomic status and prefrontal cortical thickness. Dev. Sci. 16, 641–652 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Luby, J. et al. The effects of poverty on childhood brain development: the mediating effect of caregiving and stressful life events. JAMA Pediatr. 167, 1135–1142 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Staff, R.T. et al. Childhood socioeconomic status and adult brain size: childhood socioeconomic status influences adult hippocampal size. Ann. Neurol. 71, 653–660 (2012).

    Article  PubMed  Google Scholar 

  23. Raznahan, A. et al. How does your cortex grow? J. Neurosci. 31, 7174–7177 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sowell, E.R. et al. Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cereb. Cortex 17, 1550–1560 (2007).

    Article  PubMed  Google Scholar 

  25. Schnack, H.G. et al. Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cereb. Cortex published online, doi:10.1093/cercor/bht357 (9 January 2014).

  26. Shaw, P. et al. Intellectual ability and cortical development in children and adolescents. Nature 440, 676–679 (2006).

    Article  CAS  PubMed  Google Scholar 

  27. Grieve, S.M., Korgaonkar, M.S., Clark, C.R. & Williams, L.M. Regional heterogeneity in limbic maturational changes: Evidence from integrating cortical thickness, volumetric and diffusion tensor imaging measures. Neuroimage 55, 868–879 (2011).

    Article  PubMed  Google Scholar 

  28. Duncan, G.J. & Magnuson, K.A. Can family socioeconomic resources account for racial and ethnic test score gaps? Future Child. 15, 35–54 (2005).

    Article  PubMed  Google Scholar 

  29. Duncan, G.J. & Magnuson, K. Socioeconomic status and cognitive functioning: moving from correlation to causation. Wiley Interdiscip. Rev. Cogn. Sci. 3, 377–386 (2012).

    Article  PubMed  Google Scholar 

  30. Vannest, J., Karunanayaka, P.R., Schmithorst, V.J., Szaflarski, J.P. & Holland, S.K. Language networks in children: Evidence from functional MRI studies. AJR Am. J. Roentgenol. 192, 1190–1196 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  31. McCandliss, B.D. & Noble, K.G. The development of reading impairment: a cognitive neuroscience model. Ment. Retard. Dev. Disabil. Res. Rev. 9, 196–204 (2003).

    Article  PubMed  Google Scholar 

  32. Gianaros, P.J. et al. Perigenual anterior cingulate morphology covaries with perceived social standing. Soc. Cogn. Affect. Neurosci. 2, 161–173 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Casey, B.J. et al. A developmental functional MRI study of prefrontal activation during performance on a go-no-go task. J. Cogn. Neurosci. 9, 835–847 (1997).

    Article  CAS  PubMed  Google Scholar 

  34. Klingberg, T., Forssberg, H. & Westerberg, H. Increased brain activity in frontal and parietal cortex underlies the development of visuospatial working memory capacity during childhood. J. Cogn. Neurosci. 14, 1–10 (2002).

    Article  PubMed  Google Scholar 

  35. Noble, K.G., McCandliss, B.D. & Farah, M.J. Socioeconomic gradients predict individual differences in neurocognitive abilities. Dev. Sci. 10, 464–480 (2007).

    Article  PubMed  Google Scholar 

  36. Noble, K.G., Norman, M.F. & Farah, M.J. Neurocognitive correlates of socioeconomic status in kindergarten children. Dev. Sci. 8, 74–87 (2005).

    Article  PubMed  Google Scholar 

  37. Evans, G.W. The environment of childhood poverty. Am. Psychol. 59, 77–92 (2004).

    Article  PubMed  Google Scholar 

  38. Lange, N., Froimowitz, M.P., Bigler, E.D., Lainhart, J.E. & Brain Development Cooperative Group. Associations between IQ, total and regional brain volumes, and demography in a large normative sample of healthy children and adolescents. Dev. Neuropsychol. 35, 296–317 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Trzaskowski, M. et al. Genetic influence on family socioeconomic status and children's intelligence. Intelligence 42, 83–88 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Raver, C.C. et al. CSRP's impact on low-income preschoolers' preacademic skills: self-regulation as a mediating mechanism. Child Dev. 82, 362–378 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Kitzman, H.J. et al. Enduring effects of prenatal and infancy home visiting by nurses on children: follow-up of a randomized trial among children at age 12 years. Arch. Pediatr. Adolesc. Med. 164, 412–418 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ziol-Guest, K.M., Duncan, G.J., Kalil, A. & Boyce, W.T. Early childhood poverty, immune-mediated disease processes and adult productivity. Proc. Natl. Acad. Sci. USA 109, 17289–17293 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Akshoomoff, N. et al. The NIH Toolbox Cognition Battery: results from a large normative developmental sample (PING). Neuropsychology 28, 1–10 (2014).

    Article  PubMed  Google Scholar 

  44. Fjell, A.M. et al. Multimodal imaging of the self-regulating developing brain. Proc. Natl. Acad. Sci. USA 109, 19620–19625 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Fischl, B. & Dale, A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. USA 97, 11050–11055 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bakken, T.E. et al. Association of common genetic variants in GPCPD1 with scaling of visual cortical surface area in humans. Proc. Natl. Acad. Sci. USA 109, 3985–3990 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Alexander, D.H. & Lange, K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinformatics 12, 246 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Rueda, M.R. et al. Development of attentional networks in childhood. Neuropsychologia 42, 1029–1040 (2004).

    Article  PubMed  Google Scholar 

  49. Tulsky, D.S. et al. V. NIH toolbox cognition battery (CB): measuring working memory. Monogr. Soc. Res. Child Dev. 78, 70–87 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was funded by the Annie E. Casey Foundation and the GH Sergievsky Center (K.G.N.) and by the following grants: RC2 DA029475 to T.L.J., L.C., T.M.E., A.M.D. and S.S.M. R01 HD053893 to E.R.S., R01 MH087563 to E.R.S. and R01 HD061414 to T.L.J.

Author information

Author notes

  1. Kimberly G Noble and Suzanne M Houston: These authors contributed equally to this work.

Authors and Affiliations

  1. College of Physicians and Surgeons, Columbia University, New York, New York, USA

    Kimberly G Noble

  2. Teachers College, Columbia University, New York, New York, USA

    Kimberly G Noble

  3. Department of Psychology, University of Southern California, Los Angeles, California, USA

    Suzanne M Houston

  4. The Saban Research Institute of Children's Hospital, Los Angeles, California, USA

    Suzanne M Houston, Eric Kan & Elizabeth R Sowell

  5. Department of Pediatrics of the Keck School of Medicine, University of Southern California, Los Angeles, California, USA

    Suzanne M Houston, Eric Kan & Elizabeth R Sowell

  6. Robert Wood Johnson Health and Society Scholar Program, Columbia University, New York, New York, USA

    Natalie H Brito

  7. Stein Institute for Research on Aging, University of California, San Diego, La Jolla, California, USA.,

    Hauke Bartsch

  8. Multimodal Imaging Laboratory, University of California, San Diego, La Jolla, California, USA.,

    Joshua M Kuperman & Anders M Dale

  9. Department of Radiology, University of California, San Diego, La Jolla, California, USA.,

    Joshua M Kuperman & Anders M Dale

  10. The Pediatric Imaging, Neurocognition, and Genetics Study, San Diego, California, USA

    Joshua M Kuperman, Natacha Akshoomoff, David G Amaral, Cinnamon S Bloss, Sarah S Murray, B J Casey, Linda Chang, Thomas M Ernst, Jean A Frazier, Jeffrey R Gruen, David N Kennedy, Peter Van Zijl, Stewart Mostofsky, Walter E Kaufmann, Tal Kenet, Anders M Dale, Terry L Jernigan & Elizabeth R Sowell

  11. Center for Human Development, University of California, San Diego, La Jolla, California, USA.,

    Natacha Akshoomoff & Terry L Jernigan

  12. Department of Psychiatry, University of California, San Diego, La Jolla, California, USA.,

    Natacha Akshoomoff & Terry L Jernigan

  13. The MIND Institute, University of California at Davis, Davis, California, USA

    David G Amaral

  14. The Qualcomm Institute, University of California, San Diego, La Jolla, California, USA.,

    Cinnamon S Bloss

  15. MD Revolution, Inc., La Jolla, California, USA

    Ondrej Libiger

  16. Human Biology, J. Craig Venter Institute, University of California, San Diego, La Jolla, California, USA.,

    Nicholas J Schork

  17. Department of Pathology, University of California, San Diego, La Jolla, California, USA.,

    Sarah S Murray

  18. Weill Medical College of Cornell University, New York, New York, USA

    B J Casey

  19. Department of Medicine, John A. Burns School of Medicine, University of Hawaii and the Queen's Medical Center, Honolulu, Hawaii, USA

    Linda Chang & Thomas M Ernst

  20. University of Massachusetts Medical School, Worcester, Massachusetts, USA

    Jean A Frazier & David N Kennedy

  21. Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut, USA

    Jeffrey R Gruen

  22. Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA

    Jeffrey R Gruen

  23. Department of Investigative Medicine, Yale University School of Medicine, New Haven, Connecticut, USA

    Jeffrey R Gruen

  24. Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA

    Peter Van Zijl

  25. Kennedy Krieger Institute, Baltimore, Maryland, USA

    Peter Van Zijl & Stewart Mostofsky

  26. Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA

    Walter E Kaufmann

  27. Harvard Medical School, Boston, Massachusetts, USA

    Walter E Kaufmann & Tal Kenet

  28. Department of Neurology, Massachusetts General Hospital, Massachusetts, USA

    Tal Kenet

  29. Department of Cognitive Science, University of California, San Diego, La Jolla, California, USA.,

    Anders M Dale & Terry L Jernigan

  30. Department of Neurology, Department of Neurosciences, University of California, San Diego, La Jolla, California, USA.,

    Anders M Dale

  31. Center for Translational Imaging and Personalized Medicine, University of California San Diego, La Jolla, California, USA

    Anders M Dale

Authors
  1. Kimberly G Noble
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suzanne M Houston
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Natalie H Brito
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hauke Bartsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eric Kan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joshua M Kuperman
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Natacha Akshoomoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. David G Amaral
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Cinnamon S Bloss
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ondrej Libiger
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Nicholas J Schork
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sarah S Murray
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. B J Casey
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Linda Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Thomas M Ernst
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Jean A Frazier
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jeffrey R Gruen
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. David N Kennedy
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Peter Van Zijl
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Stewart Mostofsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Walter E Kaufmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Tal Kenet
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Anders M Dale
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Terry L Jernigan
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Elizabeth R Sowell
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.G.N. developed the theory, conducted analyses, wrote the introduction, results, discussion and methods. S.M.H. compiled/collected data, compiled methods, and created tables and figures. N.H.B. conducted analyses, wrote a portion of the results and edited the manuscript. A.M.D., H.B. and J.M.K. developed the portal in which most analyses were conducted and assisted with interpretation of results and images. E.K. compiled and lent expertise regarding the imaging data and created figures. O.L., N.J.S., C.S.B. and S.S.M. performed analysis of genetic data. D.G.A., P.V.Z., D.N.K., L.C., B.J.C., N.A., T.K., J.A.F., J.R.G., W.E.K. and S.M. oversaw participant accrual, behavioral assessment and imaging at data collection sites. T.M.E. developed and maintained MRI sequences and protocols, and ensured quality of MRI data across sites and time. E.R.S. and T.L.J. contributed to theory development and interpretation of results, oversaw participant accrual, behavioral assessment and imaging at one site, and edited the manuscript. L.C. was involved in designing the research protocol and supervised the data collection at one site.All of the authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Kimberly G Noble or Elizabeth R Sowell.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Family Income is Related to Cortical Surface Area at Stringent FDR Thresholds (N=1099).

When adjusting for age, age2, scanner, sex, and genetic ancestry, ln(family income) was significantly associated with surface area in widespread regions of children’s bilateral frontal, temporal, and parietal cortices as well as right occipital cortex. Relationships were strongest in bilateral inferior temporal, insula and inferior frontal gyrus, and in the right occipital and medial prefrontal cortex Results are shown thresholded at A. the traditional p < 0.05 threshold (FDR-corrected); B. at p <.01 (FDR-corrected); and C. at the stringent threshold of p < 0.001 (FDR-corrected), where results remained significant bilaterally in the insula, temporal pole, and anterior and posterior cingulate, and in the right dorsal frontal region extending onto the medial surface.

Source data

Supplementary Figure 2 Surface Area Partially Mediates the Association between Family Income and Cognitive Control (N=1074).

Multiple regression analyses revealed that, when adjusting for age, age2, scanner, sex, and GAF, the direct effect of income on flanker scores (β=0.050, t(1074)= 2.68, p =.007) was reduced when controlling for surface area (β=0.043, t(1074) = 2.27, p =.023). A Sobel test indicated that this reduction was significant, suggesting partial mediation (Sobel z = 2.4, p =. 02).

Source data

Supplementary Figure 3 Surface Area Partially Mediates the Association between Family Income and Working Memory (N=1084).

Multiple regression analyses revealed that, when adjusting for age, age2, scanner, sex, and GAF, the direct effect of income on working memory scores (β= 0.069, t(1084) =3.77, p = 0.0002) was reduced when controlling for surface area (β= 0.061, t(1084) = 3.31, p = 0.001). The Sobel test was significant, suggesting partial mediation (Sobel z = 2.6, p =. 009).

Source data

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3 and Supplementary Tables 1–4 (PDF 335 kb)

Supplementary Methods Checklist (PDF 398 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noble, K., Houston, S., Brito, N. et al. Family income, parental education and brain structure in children and adolescents. Nat Neurosci 18, 773–778 (2015). https://doi.org/10.1038/nn.3983

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3983

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing