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Poster E16, Friday, November 10, 10:00 – 11:15 am, Harborview and Loch Raven Ballrooms

The dyslexic brain before and after literacy - unifying structural signs

Ulrike Kuhl1, Angela D. Friederici1, Michael A. Skeide1;1Max Planck Institute for Human Cognitive and Brain Sciences

Longitudinal studies following children from a preliterate age on are the gold standard for disentangling potential causes from consequences of dyslexia (Goswami, 2015). Here we overcome power limitations of recent pioneering work (Clark et al., 2014) by examining one of the largest longitudinal samples ever studied (N=16 children developing dyslexia, N=16 matched controls). Moreover, we extend the scope from cortical thickness to multimodal measures of cortical surface anatomy, including folding, gyrification and sulcus depth. Crucially, we unify these indices in a single multivariate model using an innovative random-forest classification method (Breiman, 2001). Our results reveal a co-occurrence of transient effects only present at a kindergarten age and continuous effects persisting into second grade. While transient differences (maximum accuracy: 85%) were observed in the left occipito-temporal cortex close to the “visual word form area” (Skeide et al., 2016), persisting differences were observed in phonological processing areas (superior temporal sulcus) (maximum accuracy: 82.5%) (van Attefeldt et al., 2004) and semantic processing areas (angular gyrus) (maximum accuracy: 90%) (Carreiras et al., 2009). This is in line with large-scale behavioral studies identifying phonological awareness (Ziegler et al., 2010) and rapid automatized naming (Moll et al., 2014) as most reliable predictors of literacy skills. These findings illuminate the developmental dynamics that ultimately lead to the most common learning disorder. References: Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. Carreiras, M., Seghier, M. L., Baquero, S., Estévez, A., Lozano, A., Devlin, J. T., & Price, C. J. (2009). An anatomical signature for literacy. Nature, 461(7266), 983-986. Clark, K. A., Helland, T., Specht, K., Narr, K. L., Manis, F. R., Toga, A. W., & Hugdahl, K. (2014). Neuroanatomical precursors of dyslexia identified from pre-reading through to age 11. Brain, 137(12), 3136-3141. Goswami, U. (2015). Sensory theories of developmental dyslexia: three challenges for research. Nature Reviews Neuroscience, 16(1), 43-54. Kraft, I., Cafiero, R., Schaadt, G., Brauer, J., Neef, N. E., Müller, B., ... & Skeide, M. A. (2015). Cortical differences in preliterate children at familiar risk of dyslexia are similar to those observed in dyslexic readers. Brain, 138(9), e378-e378. Moll, K., Ramus, F., Bartling, J., Bruder, J., Kunze, S., Neuhoff, N., ... & Tóth, D. (2014). Cognitive mechanisms underlying reading and spelling development in five European orthographies. Learning and Instruction, 29, 65-77. Skeide, M. A., Kraft, I., Müller, B., Schaadt, G., Neef, N. E., Brauer, J., ... & Friederici, A. D. (2016). NRSN1 associated grey matter volume of the visual word form area reveals dyslexia before school. Brain, 139(10), 2792-2803. Van Atteveldt, N., Formisano, E., Goebel, R., & Blomert, L. (2004). Integration of letters and speech sounds in the human brain. Neuron, 43(2), 271-282. Ziegler, J. C., Bertrand, D., Tóth, D., Csépe, V., Reis, A., Faísca, L., ... & Blomert, L. (2010). Orthographic depth and its impact on universal predictors of reading: A cross-language investigation. Psychological Science, 21(4), 551-559.

Topic Area: Language Disorders

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