Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain's reading network. (February 2022)

• Record Type: Journal Article
• Authors: Fehlbaum, Lynn V.; Peters, Lien; Dimanova, Plamina; Roell, Margot; Borbás, Réka; Ansari, Daniel; Raschle, Nora M.
• Is Part Of: Developmental cognitive neuroscience. Volume 53(2022)
• Publication Date: 2022-02
• Subjects: CT cortical thickness -- GMV gray matter volume -- lG local gyrification -- NN neural network -- SA surface area; Intergenerational neuroimaging -- Reading -- Brain structure -- Development -- MRI -- Brain similarity; Cognitive neuroscience -- Periodicals; Developmental neurobiology -- Periodicals; Neuropsychology -- Periodicals; Neuropsychiatry -- Periodicals; 612.8233
• DOI: 10.1016/j.dcn.2022.101058
• Languages: English
• ISSNs: 1878-9293

Abstract

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~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. Graphical Abstract: ga1 Highlights: Intergenerational neuroimaging for familial transfer effects on brain structure. Familial specificity in structural brain similarity of the human reading network. Mother-child similarity for surface area, gyrification and gray matter volume. Higher similarity for surface area and cortical volume compared to cortical thickness. Intergenerational neuroimaging promises to strengthen knowledge on brain development.