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Nature and nurture shape structural connectivity in the face processing brain network

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Face processing is a key ability facilitating social cognition. Only a few studies explored how nature and nurture shape face processing ontogeny at the behavioral and neural level. Also, very… Click to show full abstract

Face processing is a key ability facilitating social cognition. Only a few studies explored how nature and nurture shape face processing ontogeny at the behavioral and neural level. Also, very little is known about the contributions of nature and nurture to the establishment of white matter fibers supporting this specific human ability. The main purpose of this study was to assess genetic and environmental influences on white matter bundles connecting atlas-defined and functionally-defined face-responsive areas in the brain. Diffusion weighted images from 408 twins (monozygotic= 264, dizygotic = 144) were obtained from the WU-Minn Human Connectome Project. Fractional anisotropy - a widely used measure of fiber quality - of seven white matter tracts in the face network and ten global white matter tracts was analyzed by means of Structural Equation Modeling for twin data. Results revealed small and moderate genetic effects on face network fiber quality in addition to their shared variance with global brain white matter integrity. Furthermore, a theoretically expected common latent factor accounted for limited genetic and larger environmental variance in multiple face network fibers. The findings suggest that both genetic and environmental factors explain individual differences in fiber quality within the face network, as compared with much larger genetic effects on global brain white matter quality. In addition to heritability, individual-specific environmental influences on the face processing brain network are large, a finding that suggests to connect nature and nurture views on this remarkably specific human ability.

Keywords: network; face processing; nature nurture; face; brain; white matter

Journal Title: NeuroImage
Year Published: 2021

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