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White matter brain structure predicts language performance and learning success

MPG-Autoren

Sánchez,  S. M.
National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina;
Methods and Development Group Brain Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Laureate Institute for Brain Research, Tulsa, OK, USA;

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Schmidt,  Helmut       
Methods and Development Group Brain Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic;

Gallardo,  G.
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Anwander,  Alfred       
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Brauer,  Jens
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Friedrich Schiller University Jena, Germany;

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Friederici,  Angela D.       
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Knösche,  Thomas R.       
Methods and Development Group Brain Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Institute for Biomedical Engineering and Informatics, TU Ilmenau. Germany;

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Zitation

Sánchez, S. M., Schmidt, H., Gallardo, G., Anwander, A., Brauer, J., Friederici, A. D., et al. (2023). White matter brain structure predicts language performance and learning success. Human Brain Mapping, 44(4), 1445-1455. doi:10.1002/hbm.26132.


Zitierlink: https://hdl.handle.net/21.11116/0000-000B-40EF-0
Zusammenfassung
Individual differences in the ability to process language have long been discussed. Much of 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 multiday training. We identified specific network motifs by singular value decomposition that indeed related white matter structural connectivity 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 an anatomical predisposition for the individual ability to process syntactically complex sentences. 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.