Genetic and environmental factors influencing neonatal resting-state functional connectivity

Author:

Blanchett Reid1,Chen Yuanyuan23,Aguate Fernando4,Xia Kai5ORCID,Cornea Emil5,Burt S Alexandra6,de Los Campos Gustavo78,Gao Wei23,Gilmore John H5,Knickmeyer Rebecca C910ORCID

Affiliation:

1. Genetics and Genome Sciences, Michigan State University , East Lansing, MI 48824 , USA

2. Biomedical Imaging Research Institute , Department of Biomedical Sciences and Imaging, , Los Angeles, CA 90048 , USA

3. Cedars-Sinai Medical Center , Department of Biomedical Sciences and Imaging, , Los Angeles, CA 90048 , USA

4. Institute for Quantitative Health Science and Engineering, Michigan State University , East Lansing, MI 48824 , USA

5. Department of Psychiatry, University of North Carolina , Chapel Hill, NC 27599 , USA

6. Department of Psychology, Michigan State University , East Lansing, MI 48824 , USA

7. Departments of Epidemiology and Biostatistics and Statistics and Probability , Institute for Quantitative Health Science and Engineering, , East Lansing, MI 48824 , USA

8. Michigan State University , Institute for Quantitative Health Science and Engineering, , East Lansing, MI 48824 , USA

9. Department of Pediatrics and Human Development , Institute for Quantitative Health Sciences and Engineering, , East Lansing, MI 48824 , USA

10. Michigan State University , Institute for Quantitative Health Sciences and Engineering, , East Lansing, MI 48824 , USA

Abstract

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.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Cellular and Molecular Neuroscience,Cognitive Neuroscience

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