Far and wide: Associations between childhood socio-economic status and brain connectomics. (April 2021)

• Record Type: Journal Article
• Authors: Johnson, Amy; Bathelt, Joe; Akarca, Danyal; Crickmore, Gemma; Astle, Duncan E.
• Is Part Of: Developmental cognitive neuroscience. Volume 48(2021)
• Publication Date: 2021-04
• Subjects: Socio-economic status -- connectomics -- Diffusion tensor imaging (DTI) -- Cognitive ability -- educational attainment -- behaviour; Cognitive neuroscience -- Periodicals; Developmental neurobiology -- Periodicals; Neuropsychology -- Periodicals; Neuropsychiatry -- Periodicals; 612.8233
• DOI: 10.1016/j.dcn.2020.100888
• Languages: English
• ISSNs: 1878-9293

Abstract

Abstract: Previous studies have identified localized associations between childhood environment – namely their socio-economic status (SES) – and particular neural structures. The primary aim of the current study was to test whether associations between SES and brain structure are widespread or limited to specific neural pathways. We employed advances in whole-brain structural connectomics to address this. Diffusion tensor imaging was used to construct whole-brain connectomes in 113 6−12 year olds. We then applied an adapted multi-block partial-least squares (PLS) regression to explore how connectome organisation is associated with childhood SES (parental income, education levels, and neighbourhood deprivation). The Fractional Anisotropy (FA) connectome was significantly associated with childhood SES and this effect was widespread. We then pursued a secondary aim, and demonstrated that the connectome mediated the relationship between SES and cognitive ability (matrix reasoning and vocabulary). However, the connectome did not significantly mediate SES relationships with academic ability (maths and reading) or internalising and externalising behavior. This multivariate approach is important for advancing our theoretical understanding of how brain development may be shaped by childhood environment, and the role that it plays in predicting key outcomes. We also discuss the limitations with this new methodological approach.