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Predicting early signs of dyslexia at a preliterate age by combining behavioral assessment with structural MRI

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Kraft,  Indra
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

Schreiber,  Jan
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Cafiero,  Riccardo
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Metere,  Riccardo
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Schaadt,  Gesa
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Psychology, Humboldt University Berlin, Germany;

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Brauer,  Jens
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Neef,  Nicole
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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

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Skeide,  Michael A.
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Kraft, I., Schreiber, J., Cafiero, R., Metere, R., Schaadt, G., Brauer, J., et al. (2016). Predicting early signs of dyslexia at a preliterate age by combining behavioral assessment with structural MRI. NeuroImage, 143, 378-386. doi:10.1016/j.neuroimage.2016.09.004.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-46F3-3
Abstract
Background Recent studies suggest that neurobiological anomalies are already detectable in pre-school children with a family history of developmental dyslexia (DD). However, there is a lack of longitudinal studies showing a direct link between those differences at a preliterate age and the subsequent literacy difficulties seen in school. It is also not clear whether the prediction of DD in pre-school children can be significantly improved when considering neurobiological predictors, compared to models based on behavioral literacy precursors only. Methods We recruited 53 pre-reading children either with (N = 25) or without a family risk of DD (N = 28). Quantitative T1 MNI data and literacy precursor abilities were assessed at kindergarten age. A subsample of 35 children was tested for literacy skills either one or two years later, that is, either in first or second grade. Results The group comparison of quantitative T1 measures revealed significantly higher T1 intensities in the left anterior arcuate fascicle (AF), suggesting reduced myelin concentration in preliterate children at risk of DD. A logistic regression showed that DD can be predicted significantly better (p = .024) when neuroanatomical differences between groups are used as predictors (80%) compared to a model based on behavioral predictors only (63%). The Wald statistic confirmed that the T1 intensity of the left AF is a statistically significant predictor of DD (p < .05). Conclusions Our longitudinal results provide evidence for the hypothesis that neuroanatomical anomalies in children with a family risk of DD are related to subsequent problems in acquiring literacy. Particularly, solid white matter organization in the left anterior arcuate fasciculus seems to play a pivotal role.