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Cited by 104

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The neuroscience of socioeconomic inequality

Noble, Kimberly G, Giebler, Melissa A·2020·Current Opinion in Behavioral Sciences
DOI 10.1016/j.cobeha.2020.05.007
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Research works printed but not linked: 47

  1. Farah, 2017, The neuroscience of socioeconomic status: correlates, causes, and consequences, Neuron, 96, 56, 10.1016/j.neuron.2017.08.034 · DOI · Paper page
  2. Noble, 2015, Socioeconomic disparities in neurocognitive development in the first two years of life, Dev Psychobiol, 57, 535, 10.1002/dev.21303 · DOI
  3. Moorman, 2018, Childhood socioeconomic status and genetic risk for poorer cognition in later life, Soc Sci Med, 212, 219, 10.1016/j.socscimed.2018.07.025 · DOI
  4. Merz, 2019, Socioeconomic disparities in chronic physiologic stress are associated with brain structure in children, Biol Psychiatry, 86, 921, 10.1016/j.biopsych.2019.05.024 · DOI
  5. Merz, 2019, Socioeconomic disparities in language input are associated with children’s language-related brain btructure and reading skills, Child Dev
  6. McDermott, 2019, Longitudinally mapping childhood socioeconomic status associations with cortical and subcortical morphology, J Neurosci, 39, 1365, 10.1523/JNEUROSCI.1808-18.2018 · DOI · Paper page
  7. Hair, 2015, Association of child poverty, brain development, and academic achievement, JAMA Pediatr, 169, 822, 10.1001/jamapediatrics.2015.1475 · DOI
  8. Mackey, 2015, Neuroanatomical correlates of the income-achievement gap, Psychol Sci, 26, 925, 10.1177/0956797615572233 · DOI · Paper page
  9. Wrigglesworth, 2019, Brain-derived neurotrophic factor DNA methylation mediates the association between neighborhood disadvantage and adolescent brain structure, Psychiatry Res-Neuroimaging, 285, 51, 10.1016/j.pscychresns.2018.12.012 · DOI · Paper page
  10. Whittle, 2017, Role of positive parenting in the association between neighborhood social disadvantage and brain development across adolescence, JAMA Psychiatry, 74, 824, 10.1001/jamapsychiatry.2017.1558 · DOI
  11. Marshall, 2018, Socioeconomic disadvantage and altered corticostriatal circuitry in urban youth, Hum Brain Mapp, 39, 1982, 10.1002/hbm.23978 · DOI
  12. Tooley, 2019, Associations between neighborhood ses and functional brain network development, Cereb Cortex
  13. Tomlinson, 2020, Neighborhood poverty predicts altered neural and behavioral response inhibition, Neuroimage, 10.1016/j.neuroimage.2020.116536 · DOI
  14. Piccolo, 2016, Pediatric Imaging NGS: Age-related differences in cortical thickness vary by socioeconomic status, PLoS One, 11, 10.1371/journal.pone.0162511 · DOI
  15. Brito, 2017, Associations between cortical thickness and neurocognitive skills during childhood vary by family socioeconomic factors, Brain Cogn, 116, 54, 10.1016/j.bandc.2017.03.007 · DOI
  16. Leonard, 2019, Associations between cortical thickness and reasoning differ by socioeconomic status in development, Dev Cogn Neurosci, 36, 10.1016/j.dcn.2019.100641 · DOI
  17. Gur, 2019, Burden of environmental adversity associated with psychopathology, maturation, and brain behavior parameters in youths, JAMA Psychiatry, 76, 966, 10.1001/jamapsychiatry.2019.0943 · DOI · Paper page
  18. Betancourt, 2016, Effect of socioeconomic status (ses) disparity on neural development in female African‐American infants at age 1 month, Dev Sci, 19, 947, 10.1111/desc.12344 · DOI · Paper page
  19. Holz, 2015, The long-term impact of early life poverty on orbitofrontal cortex volume in adulthood: results from a prospective study over 25 years, Neuropsychopharmacology, 40, 996, 10.1038/npp.2014.277 · DOI
  20. Raffington, 2019, Stable longitudinal associations of family income with children’s hippocampal volume and memory persist after controlling for polygenic scores of educational attainment, Dev Cogn Neurosci, 10.1016/j.dcn.2019.100720 · DOI
  21. Dufford, 2018, Socioeconomic disadvantage, brain morphometry, and attentional bias to threat in middle childhood, Cogn Affect Behav Neurosci, 1
  22. Ellwood-Lowe, 2018, Time-varying effects of income on hippocampal volume trajectories in adolescent girls, Dev Cogn Neurosci, 30, 41, 10.1016/j.dcn.2017.12.005 · DOI
  23. Merz, 2018, Socioeconomic status, amygdala volume, and internalizing symptoms in children and adolescents, J Clin Child Adolesc Psychol, 47, 312, 10.1080/15374416.2017.1326122 · DOI
  24. Jenkins, 2020, Subcortical structural variations associated with low socioeconomic status in adolescents, Hum Brain Mapp, 41, 162, 10.1002/hbm.24796 · DOI
  25. Kim, 2019, Childhood poverty and the organization of structural brain connectome, Neuroimage, 184, 409, 10.1016/j.neuroimage.2018.09.041 · DOI
  26. Rosen, 2018, Socioeconomic disparities in academic achievement: a multi-modal investigation of neural mechanisms in children and adolescents, Neuroimage, 173, 298, 10.1016/j.neuroimage.2018.02.043 · DOI
  27. Gullick, 2016, Reading skill-fractional anisotropy relationships in visuospatial tracts diverge depending on socioeconomic status, Dev Sci, 19, 673, 10.1111/desc.12428 · DOI
  28. Dufford, 2017, Family income, cumulative risk exposure, and white matter structure in middle childhood, Front Hum Neurosci, 11, 547, 10.3389/fnhum.2017.00547 · DOI
  29. Takeuchi, 2018, The effects of family socioeconomic status on psychological and neural mechanisms as well as their sex differences, Front Hum Neurosci, 12, 543, 10.3389/fnhum.2018.00543 · DOI
  30. Ozernov-Palchik, 2019, The relationship between socioeconomic status and white matter microstructure in pre‐reading children: a longitudinal investigation, Hum Brain Mapp, 10.1002/hbm.24407 · DOI
  31. Brito, 2016, Associations among family socioeconomic status, eeg power at birth, and cognitive skills during infancy, Dev Cogn Neurosci, 19, 144, 10.1016/j.dcn.2016.03.004 · DOI
  32. Cantiani, 2019, Oscillatory gamma activity mediates the pathway from socioeconomic status to language acquisition in infancy, Infant Behav Dev, 57, 10.1016/j.infbeh.2019.101384 · DOI · Paper page
  33. Gao, 2015, Functional network development during the first year: relative sequence and socioeconomic correlations, Cereb Cortex, 25, 2919, 10.1093/cercor/bhu088 · DOI · Paper page
  34. Barch, 2016, Effect of hippocampal and amygdala connectivity on the relationship between preschool poverty and school-age depression, Am J Psychiatry, 173, 625, 10.1176/appi.ajp.2015.15081014 · DOI
  35. Gellci, 2019, Community and household-level socioeconomic disadvantage and functional organization of the salience and emotion network in children and adolescents, Neuroimage, 184, 729, 10.1016/j.neuroimage.2018.09.077 · DOI
  36. Younger, 2019, Brain lateralization of phonological awareness varies by maternal education, Dev Sci, 22, 10.1111/desc.12807 · DOI
  37. Conant, 2017, The relationship between maternal education and the neural substrates of phoneme perception in children: interactions between socioeconomic status and proficiency level, Brain Lang, 171, 14, 10.1016/j.bandl.2017.03.010 · DOI
  38. Romeo, 2018, Beyond the 30-million-word gap: children’s conversational exposure is associated with language-related brain function, Psychol Sci, 29, 700, 10.1177/0956797617742725 · DOI
  39. Ralph, 2020, Using the n400 event-related potential to study word learning from context in children from low-and higher-socioeconomic status homes, J Exp Child Psychol, 191, 10.1016/j.jecp.2019.104758 · DOI
  40. Sheridan, 2017, Dimensions of childhood adversity have distinct associations with neural systems underlying executive functioning, Dev Psychopathol, 29, 1777, 10.1017/S0954579417001390 · DOI · Paper page
  41. Finn, 2017, Functional brain organization of working memory in adolescents varies in relation to family income and academic achievement, Dev Sci, 20, 10.1111/desc.12450 · DOI
  42. White, 2019, Exposure to violence and low family income are associated with heightened amygdala responsiveness to threat among adolescents, Dev Cogn Neurosci, 40, 10.1016/j.dcn.2019.100709 · DOI
  43. Kim, 2017, Socioeconomic disadvantage, neural responses to infant emotions, and emotional availability among first-time new mothers, Behav Brain Res, 325, 188, 10.1016/j.bbr.2017.02.001 · DOI
  44. Farah, 2018, Socioeconomic status and the brain: prospects for neuroscience-informed policy, Nat Rev Neurosci, 19, 428, 10.1038/s41583-018-0023-2 · DOI
  45. von Stumm, 2019, Predicting educational achievement from genomic measures and socioeconomic status, Dev Sci
  46. Merz, 2019, Socioeconomic inequality and the developing brain: spotlight on language and executive function, Child Dev Perspect, 13, 15, 10.1111/cdep.12305 · DOI
  47. Romeo, 2018, Language exposure relates to structural neural connectivity in childhood, J Neurosci, 38, 7870, 10.1523/JNEUROSCI.0484-18.2018 · DOI

Cited by

Metadata

Authors
Noble, K. G., & Giebler, M. A.
Venue
Current Opinion in Behavioral Sciences
Publisher
Elsevier BV
Published
2020-12
Volume
36
Pages
23-28
ISSN
2352-1546
DOI
10.1016/j.cobeha.2020.05.007
Cited by
104
References
2
Language
en
Licence
Funders
National Institutes of Health

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