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

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-46F3-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1DD7-B
Genre: Journal Article

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 Creators:
Kraft, Indra1, Author              
Schreiber, Jan1, Author
Cafiero, Riccardo1, Author              
Metere, Riccardo2, Author              
Schaadt, Gesa1, 3, Author              
Brauer, Jens1, Author              
Neef, Nicole1, Author              
Müller, Bent4, Author
Kirsten, Holger4, 5, Author
Wilcke, Arndt4, Author
Boltze, Johannes4, 6, Author
Friederici, Angela D.1, Author              
Skeide, Michael A.1, Author              
Affiliations:
1Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634551              
2Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634558              
3Department of Psychology, Humboldt University Berlin, Germany, ou_persistent22              
4Department of Cognitive Genetics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany, ou_persistent22              
5Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany, ou_persistent22              
6Fraunhofer Institute for Marine Biotechnology, Lübeck, Germany, ou_persistent22              

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Free keywords: Developmental dyslexia; Arcuate fascicle; Aortical thickness; Quantitative T1; Diffusion-weighted imaging; Reading
 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.

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Language(s): eng - English
 Dates: 2016-08-232016-04-052016-09-022016-09-052016-12
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2016.09.004
PMID: 27608602
Other: Epub 2016
 Degree: -

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 143 Sequence Number: - Start / End Page: 378 - 386 Identifier: ISSN: 1053-8119
CoNE: /journals/resource/954922650166