| Literature DB >> 35702547 |
Timm B Poeppl1, Emile Dimas2, Katrin Sakreida1, Julius M Kernbach3, Ross D Markello4, Oliver Schöffski5, Alain Dagher6, Philipp Koellinger7, Gideon Nave8, Martha J Farah9, Bratislav Mišić4, Danilo Bzdok2.
Abstract
Socioeconomic status (SES) anchors individuals in their social network layers. Our embedding in the societal fabric resonates with habitus, world view, opportunity, and health disparity. It remains obscure how distinct facets of SES are reflected in the architecture of the central nervous system. Here, we capitalized on multivariate multi-output learning algorithms to explore possible imprints of SES in gray and white matter structure in the wider population (n ≈ 10,000 UK Biobank participants). Individuals with higher SES, compared with those with lower SES, showed a pattern of increased region volumes in the left brain and decreased region volumes in the right brain. The analogous lateralization pattern emerged for the fiber structure of anatomical white matter tracts. Our multimodal findings suggest hemispheric asymmetry as an SES-related brain signature, which was consistent across six different indicators of SES: degree, education, income, job, neighborhood and vehicle count. Hence, hemispheric specialization may have evolved in human primates in a way that reveals crucial links to SES.Entities:
Keywords: brain lateralization; hemispheric asymmetry; machine learning; multi-output pattern learning; population neuroscience; socioeconomic status
Year: 2022 PMID: 35702547 PMCID: PMC9188625 DOI: 10.1093/texcom/tgac020
Source DB: PubMed Journal: Cereb Cortex Commun ISSN: 2632-7376