White Matter Brain Structure Predicts Language Performance and Learning Success

Author:

Sánchez Stella M.ORCID,Schmidt Helmut,Gallardo Guillermo,Anwander AlfredORCID,Brauer Jens,Friederici Angela D.,Knösche Thomas R.

Abstract

AbstractIndividual differences in the ability to deal with language have long been discussed. The neural basis of these, however, is yet unknown. Here we investigated the relationship between long-range white matter connectivity of the brain, as revealed by diffusion tractography, and the ability to process syntactically complex sentences in the participants’ native language as well as the improvement thereof by multi-day training. We identified specific network motifs that indeed related white matter tractography to individual language processing performance. First, for two such motifs, one in the left and one in the right hemisphere, their individual prevalence significantly predicted the individual language performance suggesting a predisposition for the individual ability to process syntactically complex sentences, which manifests itself in the white matter brain structure. Both motifs comprise a number of cortical regions, but seem to be dominated by areas known for the involvement in working memory rather than the classical language network itself. Second, we identified another left hemispheric network motif, whose change of prevalence over the training period significantly correlated with the individual change in performance, thus reflecting training induced white matter plasticity. This motif comprises diverse cortical areas including regions known for their involvement in language processing, working memory and motor functions. The present findings suggest that individual differences in language processing and learning can be explained, in part, by individual differences in the brain’s white matter structure. Brain structure may be a crucial factor to be considered when discussing variations in human cognitive performance, more generally.

Publisher

Cold Spring Harbor Laboratory

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