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Genetic and environmental factors influencing neonatal resting-state functional connectivity.
Blanchett, Reid; Chen, Yuanyuan; Aguate, Fernando; Xia, Kai; Cornea, Emil; Burt, S Alexandra; de Los Campos, Gustavo; Gao, Wei; Gilmore, John H; Knickmeyer, Rebecca C.
Afiliação
  • Blanchett R; Genetics and Genome Sciences, Michigan State University, East Lansing, MI 48824, USA.
  • Chen Y; Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
  • Aguate F; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
  • Xia K; Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA.
  • Cornea E; Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA.
  • Burt SA; Department of Psychology, Michigan State University, East Lansing, MI 48824, USA.
  • de Los Campos G; Departments of Epidemiology and Biostatistics and Statistics and Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
  • Gao W; Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
  • Gilmore JH; Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA.
  • Knickmeyer RC; Department of Pediatrics and Human Development, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI 48824, USA.
Cereb Cortex ; 33(8): 4829-4843, 2023 04 04.
Article em En | MEDLINE | ID: mdl-36190430
ABSTRACT
Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Mapeamento Encefálico Tipo de estudo: Prognostic_studies Limite: Female / Humans / Pregnancy Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Encéfalo / Mapeamento Encefálico Tipo de estudo: Prognostic_studies Limite: Female / Humans / Pregnancy Idioma: En Ano de publicação: 2023 Tipo de documento: Article