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Putative protective neural mechanisms in prereaders with a family history of dyslexia who subsequently develop typical reading skills.

Xi YuJennifer ZukMeaghan V PerdueOla Ozernov-PalchikTalia RaneySara D BeachElizabeth S NortonYangming OuJohn D E GabrieliNadine Gaab
Published in: Human brain mapping (2020)
Developmental dyslexia affects 40-60% of children with a familial risk (FHD+) compared to a general prevalence of 5-10%. Despite the increased risk, about half of FHD+ children develop typical reading abilities (FHD+Typical). Yet the underlying neural characteristics of favorable reading outcomes in at-risk children remain unknown. Utilizing a retrospective, longitudinal approach, this study examined whether putative protective neural mechanisms can be observed in FHD+Typical at the prereading stage. Functional and structural brain characteristics were examined in 47 FHD+ prereaders who subsequently developed typical (n = 35) or impaired (n = 12) reading abilities and 34 controls (FHD-Typical). Searchlight-based multivariate pattern analyses identified distinct activation patterns during phonological processing between FHD+Typical and FHD-Typical in right inferior frontal gyrus (RIFG) and left temporo-parietal cortex (LTPC) regions. Follow-up analyses on group-specific classification patterns demonstrated LTPC hypoactivation in FHD+Typical compared to FHD-Typical, suggesting this neural characteristic as an FHD+ phenotype. In contrast, RIFG showed hyperactivation in FHD+Typical than FHD-Typical, and its activation pattern was positively correlated with subsequent reading abilities in FHD+ but not controls (FHD-Typical). RIFG hyperactivation in FHD+Typical was further associated with increased interhemispheric functional and structural connectivity. These results suggest that some protective neural mechanisms are already established in FHD+Typical prereaders supporting their typical reading development.
Keyphrases
  • working memory
  • young adults
  • magnetic resonance
  • functional connectivity
  • type diabetes
  • risk factors
  • resting state
  • computed tomography
  • deep learning
  • insulin resistance