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Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain’s reading network
BACKGROUND: Substantial evidence acknowledges the complex gene-environment interplay impacting brain development and learning. Intergenerational neuroimaging allows the assessment of familial transfer effects on brain structure, function and behavior by investigating neural similarity in caregiver-c...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749220/ https://www.ncbi.nlm.nih.gov/pubmed/34999505 http://dx.doi.org/10.1016/j.dcn.2022.101058 |
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author | Fehlbaum, Lynn V. Peters, Lien Dimanova, Plamina Roell, Margot Borbás, Réka Ansari, Daniel Raschle, Nora M. |
author_facet | Fehlbaum, Lynn V. Peters, Lien Dimanova, Plamina Roell, Margot Borbás, Réka Ansari, Daniel Raschle, Nora M. |
author_sort | Fehlbaum, Lynn V. |
collection | PubMed |
description | BACKGROUND: Substantial evidence acknowledges the complex gene-environment interplay impacting brain development and learning. Intergenerational neuroimaging allows the assessment of familial transfer effects on brain structure, function and behavior by investigating neural similarity in caregiver-child dyads. METHODS: Neural similarity in the human reading network was assessed through well-used measures of brain structure (i.e., surface area (SA), gyrification (lG), sulcal morphology, gray matter volume (GMV) and cortical thickness (CT)) in 69 mother-child dyads (children’s age~11 y). Regions of interest for the reading network included left-hemispheric inferior frontal gyrus, inferior parietal lobe and fusiform gyrus. Mother-child similarity was quantified by correlation coefficients and familial specificity was tested by comparison to random adult-child dyads. Sulcal morphology analyses focused on occipitotemporal sulcus interruptions and similarity was assessed by chi-square goodness of fit. RESULTS: Significant structural brain similarity was observed for mother-child dyads in the reading network for lG, SA and GMV (r = 0.349/0.534/0.542, respectively), but not CT. Sulcal morphology associations were non-significant. Structural brain similarity in lG, SA and GMV were specific to mother-child pairs. Furthermore, structural brain similarity for SA and GMV was higher compared to CT. CONCLUSION: Intergenerational neuroimaging techniques promise to enhance our knowledge of familial transfer effects on brain development and disorders. |
format | Online Article Text |
id | pubmed-8749220 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-87492202022-01-13 Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain’s reading network Fehlbaum, Lynn V. Peters, Lien Dimanova, Plamina Roell, Margot Borbás, Réka Ansari, Daniel Raschle, Nora M. Dev Cogn Neurosci Original Research BACKGROUND: Substantial evidence acknowledges the complex gene-environment interplay impacting brain development and learning. Intergenerational neuroimaging allows the assessment of familial transfer effects on brain structure, function and behavior by investigating neural similarity in caregiver-child dyads. METHODS: Neural similarity in the human reading network was assessed through well-used measures of brain structure (i.e., surface area (SA), gyrification (lG), sulcal morphology, gray matter volume (GMV) and cortical thickness (CT)) in 69 mother-child dyads (children’s age~11 y). Regions of interest for the reading network included left-hemispheric inferior frontal gyrus, inferior parietal lobe and fusiform gyrus. Mother-child similarity was quantified by correlation coefficients and familial specificity was tested by comparison to random adult-child dyads. Sulcal morphology analyses focused on occipitotemporal sulcus interruptions and similarity was assessed by chi-square goodness of fit. RESULTS: Significant structural brain similarity was observed for mother-child dyads in the reading network for lG, SA and GMV (r = 0.349/0.534/0.542, respectively), but not CT. Sulcal morphology associations were non-significant. Structural brain similarity in lG, SA and GMV were specific to mother-child pairs. Furthermore, structural brain similarity for SA and GMV was higher compared to CT. CONCLUSION: Intergenerational neuroimaging techniques promise to enhance our knowledge of familial transfer effects on brain development and disorders. Elsevier 2022-01-04 /pmc/articles/PMC8749220/ /pubmed/34999505 http://dx.doi.org/10.1016/j.dcn.2022.101058 Text en © 2022 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Research Fehlbaum, Lynn V. Peters, Lien Dimanova, Plamina Roell, Margot Borbás, Réka Ansari, Daniel Raschle, Nora M. Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain’s reading network |
title | Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain’s reading network |
title_full | Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain’s reading network |
title_fullStr | Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain’s reading network |
title_full_unstemmed | Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain’s reading network |
title_short | Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain’s reading network |
title_sort | mother-child similarity in brain morphology: a comparison of structural characteristics of the brain’s reading network |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749220/ https://www.ncbi.nlm.nih.gov/pubmed/34999505 http://dx.doi.org/10.1016/j.dcn.2022.101058 |
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