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

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.

From mother to child: How intergenerational transfer is reflected in similarity of corticolimbic brain structure and mental health

“… Takagi et al (2021) provided evidence for parent-child similarity measured by resting state activation or gray matter volume. Similarly, Fehlbaum et al (2022) demonstrate intergenerational transfer for regions of the human reading network in mother-child dyads. Furthermore, mother-child similarity varied in dependence on the structural correlate under investigation (e.g., gray matter volume, surface area, and cortical thickness).…”

“…Intergenerational transfer effects thereby describe genetic and non-genetic traits’ transmission from parents to their children ( Ho et al, 2016 , Minami et al, 2022 , Yamagata et al, 2016 ). While such transfer effects are behaviorally well documented (e.g., for cognitive functions, physical and psychological well-being ( Anger and Heineck, 2010 ; Fan et al, 2020 ; Hardie and Turney, 2017 )), studies investigating intergenerational transfer effects on brain structure or function are scarce ( Colich et al, 2017 , Fehlbaum et al, 2022 , Minami et al, 2022 , Takagi et al, 2020 ). Ultimately, intergenerational neuroimaging studies may help us understand complex skill transfer, but also highlight mechanisms linked to familial predisposition and individual’s susceptibility for risk and resilience.…”

“…Neuroimaging studies may inform about the resemblance of brain morphology and activation patterns within families ( Colich et al, 2017 , Fehlbaum et al, 2022 , Poissant et al, 2014 , Takagi et al, 2021 ). Past studies have aimed to inform about mechanisms of familial risk transmission and included clinical populations, such as social anxiety ( Bas-Hoogendam et al, 2018 ), depression ( Abraham et al, 2020 , Colich et al, 2017 , Foland-Ross et al, 2016 , Minami et al, 2022 ), but also developmental disorders such as dyslexia ( Vandermosten et al, 2020 ).…”

“…Furthermore, mother-child similarity varied in dependence on the structural correlate under investigation (e.g., gray matter volume, surface area, and cortical thickness). Overall, studies highlight that parent-child similarity in brain structure and function is present and specific to relatives, with parent-child similarity being higher than similarity in non-related adult-child dyads ( Fehlbaum et al, 2022 , Takagi et al, 2021 ).…”

Genetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders

“…Genetic effects on between-network connectivity are also minimal in neonates ( 33 ). Intergenerational transmission of imaging phenotypes has been reported and likely reflects a combination of genetic, epigenetic, and environmental effects ( 34 , 35 , 36 ). One such study examined the intergenerational transmission from mothers to their 5-year-old children and reported significant effects on sulcal phenotypes in the right frontal and parietal cortices ( 35 ).…”