NRSN1associated grey matter volume of the visual word form area reveals dyslexia before school

Abstract

Literacy learning depends on the flexibility of the human brain to reconfigure itself in response to environmental influences. At the same time, literacy and disorders of literacy acquisition are heritable and thus to some degree genetically predetermined. Here we used a multivariate non-parametric genetic model to relate literacy-associated genetic variants to grey and white matter volumes derived by voxel-based morphometry in a cohort of 141 children. Subsequently, a sample of 34 children attending grades 4 to 8, and another sample of 20 children, longitudinally followed from kindergarten to first grade, were classified as dyslexics and controls using linear binary support vector machines. The NRSN1-associated grey matter volume of the 'visual word form area' achieved a classification accuracy of ~ 73% in literacy-experienced students and distinguished between later dyslexic individuals and controls with an accuracy of 75% at kindergarten age. These findings suggest that the cortical plasticity of a region vital for literacy might be genetically modulated, thereby potentially preconstraining literacy outcome. Accordingly, these results could pave the way for identifying and treating the most common learning disorder before it manifests itself in school.

Neuroimaging genetics studies of specific reading disability and developmental language disorder: A review

“…Imaging genetics studies of FOXP2 indicate that this gene affects brain regions associated with language production and speech motor planning (Liegeois et al, ; Pinel et al, ; Vargha‐Khadem, Gadian, Copp, & Mishkin, ), with preliminary links to activity in posterior language regions (Wilcke et al, ). CNTNAP2 has also been found to have effects on functional activation in language‐related regions, including inferior frontal and middle temporal gyri (Whalley et al, ) and on gray matter morphology in occipital, fusiform, and cerebellar regions (Tan et al, ; Uddén et al, ); in addition, this gene has effects on structural and functional connectivity among language‐associated regions and more domain general regions (Dennis et al, ; Skeide et al, ). The compelling neuroimaging genetic findings related to both of these genes may direct future research to further characterize their roles in DLD.…”

“…6 With respect to brain structure, two studies have identified regional reductions in gray matter volume associated with the risk allele on rs7794745 primarily affecting occipital, fusiform, and cerebellar regions (Tan, Doke, Ashburner, Wood, & Frackowiak, 2010;Uddén, Snijders, Fisher, & Hagoort, 2017). Additionally, altered white matter structure associated with CNTNAP2 has been reported in fronto-occipital and thalamic tracts (Tan et al, 2010) as well as midbrain tracts that facilitate communication among the cortex, cerebellum, and other central nervous system structures (Skeide et al, 2016). Using diffusion MRI, Dennis et al (2011) identified a pattern of white matter structure characterized by local rather than long-range connections in individuals homozygous for the risk allele on CNTNAP2 SNP rs2710102.…”

Neural signatures of co‐occurring reading and mathematical difficulties

“…This finding might also explain why LRLM children are vulnerable to difficulties in multiple cognitive domains. Whether such aberrations in the medial temporal lobe manifest early in development, and how this weakness in turn disrupts the communication between relevant cortical networks supporting reading and mathematical information processing remains to be investigated using appropriate longitudinal study designs in younger children (Kraft et al, 2016; Skeide et al, 2016). In addition, further research is also needed to examine whether early disruption of medial temporal lobe organization also contributes to learning difficulties in multiple other cognitive domains.…”

“…A recently proposed framework identifies three hypotheses that may explain the neural bases of LRLM (Ashkenazi et al, 2013). First, a domain-specific hypothesis states that additive problems in brain areas associated with both LR (i.e., left occipito-temporal and temporo-parietal cortices; (Hoeft et al, 2007; Skeide et al, 2016)) and LM (i.e., parietal and prefrontal cortices; (Price, Holloway, Räsänen, Vesterinen, & Ansari, 2007)) underlie LRLM. Second, a domain-general hypothesis posits that aberrations in brain structures serving attention or working memory, instantiated in ventro- and dorsolateral prefrontal cortices and medial temporal regions, underlie LRLM.…”

“…We then examined differences in intrinsic functional connectivity across these groups to identify functional brain circuitry that distinguishes the LRLM group. We predicted that a domain-specific basis for LRLM would be manifested in additive problems consistent with both LR and LM groups, including structural and functional aberrations in left occipito-temporal and temporo-parietal cortices, as well as bilateral parietal and prefrontal cortices (Price et al, 2007; Skeide et al, 2016). Alternatively, a domain-general basis for LRLM would manifest in aberrations to ventro- and dorso-lateral prefrontal regions subserving working memory and attention (LaBar, Gitelman, Parrish, & Mesulam, 1999), or medial temporal lobe regions involved in associative learning (Aminoff, Kveraga, & Bar, 2013).…”