The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Family income, parental education and brain structure in children and adolescents
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In older children and adolescents, lower family income and lower parental education have been associated with reduced volumes of cortical grey matter, the hippocampus, and the amygdala168 as well as reduced cortical surface area and cortical thickness169, 170.”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [170]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Measuring the thickness of the human cerebral cortex from magnetic resonance images
Fischl, B. R., & Dale, A. M. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200033797 · Cited by 5,995
Self-citationSocioeconomic disadvantage and child development
McLoyd, V. C. (1998) · American Psychologist · doi:10.1037/0003-066x.53.2.185 · Cited by 3,763
The effects of poverty on children.
Brooks-Gunn, J., & Duncan, G. J. (1997) · The Future of Children · doi:10.2307/1602387 · Cited by 2,837
The environment of childhood poverty
Evans, G. W. (2004) · American Psychologist · doi:10.1037/0003-066x.59.2.77 · Cited by 2,396
Mapping cortical change across the human life span
Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003) · Nature Neuroscience · doi:10.1038/nn1008 · Cited by 2,328
Self-citationSocioeconomic status and the developing brain
Hackman, D. A., & Farah, M. J. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.11.003 · Cited by 1,681
Enhancements to the ADMIXTURE algorithm for individual ancestry estimation
Alexander, D. H., & Lange, K. L. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-246 · Cited by 1,611
Intellectual ability and cortical development in children and adolescents
Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., Evans, A., Rapoport, J., & Giedd, J. (2006) · Nature · doi:10.1038/nature04513 · Cited by 1,543
Longitudinal Mapping of Cortical Thickness and Brain Growth in Normal Children
Sowell, E. R., Thompson, P. M., Leonard, C. M., Welcome, S. E., Kan, E., & Toga, A. W. (2004) · The Journal of Neuroscience · doi:10.1523/jneurosci.1798-04.2004 · Cited by 1,481
Self-citationDevelopment of attentional networks in childhood
Rueda, M. R., Fan, J., McCandliss, B. D., Halparin, J. D., Gruber, D. B., Lercari, L. P., & Posner, M. I. (2004) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2003.12.012 · Cited by 1,339
Flagged first, then by citations
Cited by
Sorted by citations
Items 1,001–1,001 of 1,001
Liu, H., Tang, J., Zhao, Z., Zhou, Y., Ding, H., Liu, N., Ji, Y., Wang, P., Wu, X., Liu, F., et al. (2026) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2026.101820
Metadata
- Authors
- Noble, K. G., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E., Kuperman, J. M., Akshoomoff, N., Amaral, D. G., Bloss, C. S., Libiger, O., Schork, N. J., Murray, S. S., Casey, B. J., Chang, L., Ernst, T. M., Frazier, J. A., Gruen, J. R., Kennedy, D. N., Van Zijl, P., Mostofsky, S., Kaufmann, W. E., Kenet, T., Dale, A. M., Jernigan, T. L., & Sowell, E. R.
- Affiliations
- Columbia University (US)
- University of Southern California (US)
- University of California San Diego (US)
- University of California, Davis (US)
- California Sea Grant (US)
- J. Craig Venter Institute (US)
- Cornell University (US)
- University of Hawaiʻi at Mānoa (US)
- University of Massachusetts Chan Medical School (US)
- Yale University (US)
- Kennedy Krieger Institute (US)
- Boston Children's Hospital (US)
- Harvard University (US)
- Corresponding author
- Noble, Kimberly G
- Sowell, Elizabeth R
- ORCID
- Noble, Kimberly G
- Brito, Natalie H
- Bartsch, Hauke
- Kan, Eric
- Kuperman, Joshua M
- Akshoomoff, Natacha
- Amaral, David G
- Bloss, Cinnamon S
- Schork, Nicholas J
- Murray, Sarah S
- Casey, B J
- Chang, Linda
- Frazier, Jean A
- Gruen, Jeffrey R
- Kennedy, David N
- Mostofsky, Stewart
- Kaufmann, Walter E
- Kenet, Tal
- Dale, Anders M
- Jernigan, Terry L
- Sowell, Elizabeth R
- Venue
- Nature Neuroscience
- Published
- 2015-03-30
- Volume
- 18
- Issue
- 5
- Pages
- 773-778
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC4414816
- OpenAlex
- W2098605272
- Cited by
- 1,423
- References
- 52
- Citations by year
- 2026: 99 · 2025: 130 · 2024: 116 · 2023: 133 · 2022: 159 · 2021: 139 · 2020: 154 · 2019: 120 · 2018: 123 · 2017: 113 · 2016: 99 · 2015: 36
- FWCI
- 186.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- The Journal of the American Medical Association (JAMA) Network (American Medical Association) (submittedVersion)
- ScholarSpace (University of Hawaii at Manoa) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH087563, R01MH083320)
- National Institute on Drug Abuse (RC2DA029475)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD079123, R01HD061414, L40 HD077610, R01HD053893)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Social Sciences › Health › Health disparities and outcomes
- Social Sciences › Sociology and Political Science › Intergenerational and Educational Inequality Studies
- Keywords
- household income, parental education, socioeconomic status, brain surface area, brain morphometry, cognitive development, executive function, socioeconomic disparities, children and adolescents, spatial ability
- MeSH terms
- Adolescent, Psychology, Adolescent, Age Factors, Anthropometry, Brain, Cerebral Cortex, Child, Psychology, Child, Child, Preschool, DNA, Educational Status, Genotype, Hippocampus, Humans, Income, Models, Neurological, Organ Size, Parents, Poverty, Psychological Tests, Regression Analysis, Socioeconomic Factors, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
