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

Journal name:
Nature Neuroscience
Volume:
18,
Pages:
773–778
Year published:
DOI:
doi:10.1038/nn.3983
Received
Accepted
Published online

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.

At a glance

Figures

  1. Parent education is linearly associated with cortical surface area (N = 1,099).
    Figure 1: Parent education is linearly associated with cortical surface area (N = 1,099).

    (a) Multiple regression showed that, when adjusting for age, age2, scanner, sex and genetic ancestry, parental education was significantly associated (P < 0.05, FDR corrected) with children's total cortical surface area in a number of regions. (b) The association between parent education and cortical surface area was mapped to visualize regional specificity. Left hemisphere regions where this association was significant included the left superior, middle, and inferior temporal gyri, inferior frontal gyrus, orbito-frontal gyrus, and the precuneus. Right hemisphere regions included the middle temporal gyrus, inferior temporal gyrus, supramarginal gryus, middle frontal gyrus and superior frontal gyrus. Bilateral regions included the fusiform gyrus, temporal pole, insula, superior frontal gyrus, medial frontal gyrus, the cingulate cortex, inferior parietal cortex, lateral occipital cortex and postcentral gyrus.

  2. Family income is logarithmically related to cortical surface area (N = 1,099).
    Figure 2: Family income is logarithmically related to cortical surface area (N = 1,099).

    (a) Multiple regression showed that, when adjusting for age, age2, scanner, sex and genetic ancestry, family income was significantly logarithmically associated with children's total cortical surface area, such that the steepest gradient was present at the lower end of the income spectrum (β = −0.19, P = 0.004). Income data are presented on the untransformed scale, fitted with a logarithmic curve, to enable visualization of this asymptotic relationship. This differential rate of change is visualized with the brain maps, where the steepest change in cortical surface area per unit income is visualized with warm colors and the shallowest change in cortical surface area per unit income is visualized with cool colors. (b) 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 lobes. Relationships were strongest in bilateral inferior temporal, insula and inferior frontal gyrus, and in the right occipital and medial prefrontal cortex. (c) When adjusting for age, age2, scanner, sex, genetic ancestry and parent education, ln (family income) was significantly associated with surface area in a smaller number of regions including bilateral inferior frontal, cingulate, insula and inferior temporal regions, and in the right superior frontal and precuneus cortex. Maps are thresholded at P < 0.05 (FDR correction). More stringent FDR correction thresholds of 0.01 and 0.001 are shown in Supplementary Figure 1a–c.

  3. Parental education is quadratically associated with left hippocampal volume (N = 1,099).
    Figure 3: Parental education is quadratically associated with left hippocampal volume (N = 1,099).

    Multiple regression revealed that, when adjusting for age, age2, scanner, sex, genetic ancestry and whole brain volume, parental education was significantly quadratically associated with children's left hippocampal volume, such that the steepest gradient was present at the lower end of the education spectrum (β = −0.494, P = 0.016).

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

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

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

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Author information

  1. These authors contributed equally to this work.

    • Kimberly G Noble &
    • Suzanne M Houston

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

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

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Author details

Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: Family Income is Related to Cortical Surface Area at Stringent FDR Thresholds (N=1099). (113 KB)

    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.

  2. Supplementary Figure 2: Surface Area Partially Mediates the Association between Family Income and Cognitive Control (N=1074). (30 KB)

    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).

  3. Supplementary Figure 3: Surface Area Partially Mediates the Association between Family Income and Working Memory (N=1084). (30 KB)

    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).

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    Supplementary Figures 1–3 and Supplementary Tables 1–4

  2. Supplementary Methods Checklist (408 KB)

Additional data