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Mother-child similarity in brain morphology : a comparison of structural characteristics of the brain's reading network

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Abstract
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 similar to 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.
Keywords
Intergenerational neuroimaging, Reading, Brain structure, Development, MRI, Brain similarity, GRAY-MATTER VOLUME, CORTICAL THICKNESS, SURFACE-AREA, CEREBRAL-CORTEX, INTERGENERATIONAL TRANSMISSION, ENVIRONMENTAL-INFLUENCES, DEVELOPMENTAL DYSLEXIA, LONGITUDINAL CHANGES, PRESCHOOL-CHILDREN, FAMILIAL RISK

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MLA
Fehlbaum, Lynn, et al. “Mother-Child Similarity in Brain Morphology : A Comparison of Structural Characteristics of the Brain’s Reading Network.” DEVELOPMENTAL COGNITIVE NEUROSCIENCE, vol. 53, 2022, doi:10.1016/j.dcn.2022.101058.
APA
Fehlbaum, L., Peters, L., Dimanova, P., Roell, M., Borbas, R., Ansari, D., & Raschle, N. M. (2022). Mother-child similarity in brain morphology : a comparison of structural characteristics of the brain’s reading network. DEVELOPMENTAL COGNITIVE NEUROSCIENCE, 53. https://doi.org/10.1016/j.dcn.2022.101058
Chicago author-date
Fehlbaum, Lynn, Lien Peters, Plamina Dimanova, Margot Roell, Reka Borbas, Daniel Ansari, and Nora M. Raschle. 2022. “Mother-Child Similarity in Brain Morphology : A Comparison of Structural Characteristics of the Brain’s Reading Network.” DEVELOPMENTAL COGNITIVE NEUROSCIENCE 53. https://doi.org/10.1016/j.dcn.2022.101058.
Chicago author-date (all authors)
Fehlbaum, Lynn, Lien Peters, Plamina Dimanova, Margot Roell, Reka Borbas, Daniel Ansari, and Nora M. Raschle. 2022. “Mother-Child Similarity in Brain Morphology : A Comparison of Structural Characteristics of the Brain’s Reading Network.” DEVELOPMENTAL COGNITIVE NEUROSCIENCE 53. doi:10.1016/j.dcn.2022.101058.
Vancouver
1.
Fehlbaum L, Peters L, Dimanova P, Roell M, Borbas R, Ansari D, et al. Mother-child similarity in brain morphology : a comparison of structural characteristics of the brain’s reading network. DEVELOPMENTAL COGNITIVE NEUROSCIENCE. 2022;53.
IEEE
[1]
L. Fehlbaum et al., “Mother-child similarity in brain morphology : a comparison of structural characteristics of the brain’s reading network,” DEVELOPMENTAL COGNITIVE NEUROSCIENCE, vol. 53, 2022.
@article{01HS1627W9B6BY9R4G1QXMXRFA,
  abstract     = {{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 similar to 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.}},
  articleno    = {{101058}},
  author       = {{Fehlbaum, Lynn and Peters, Lien and  Dimanova, Plamina and  Roell, Margot and  Borbas, Reka and  Ansari, Daniel and  Raschle, Nora M.}},
  issn         = {{1878-9293}},
  journal      = {{DEVELOPMENTAL COGNITIVE NEUROSCIENCE}},
  keywords     = {{Intergenerational neuroimaging,Reading,Brain structure,Development,MRI,Brain similarity,GRAY-MATTER VOLUME,CORTICAL THICKNESS,SURFACE-AREA,CEREBRAL-CORTEX,INTERGENERATIONAL TRANSMISSION,ENVIRONMENTAL-INFLUENCES,DEVELOPMENTAL DYSLEXIA,LONGITUDINAL CHANGES,PRESCHOOL-CHILDREN,FAMILIAL RISK}},
  language     = {{eng}},
  pages        = {{10}},
  title        = {{Mother-child similarity in brain morphology : a comparison of structural characteristics of the brain's reading network}},
  url          = {{http://doi.org/10.1016/j.dcn.2022.101058}},
  volume       = {{53}},
  year         = {{2022}},
}

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