Neural Predictors of Developmental Dyslexia

doi:10.1017/9781108553377.012

12 - Neural Predictors of Developmental Dyslexia

from Part II - Cross-Linguistic Perspectives on Developmental Dyslexia

Published online by Cambridge University Press: 27 September 2019

Elizabeth S. Norton ,

John D. E. Gabrieli and

Edited by

Charles Perfetti and

Ludo Verhoeven: Affiliation:
Radboud Universiteit Nijmegen
Charles Perfetti: Affiliation:
University of Pittsburgh
Kenneth Pugh: Affiliation:
Yale University, Connecticut

Book contents

Get access

Summary

Dyslexia, or difficulty in learning to read that is not caused by a sensory deficit or lack of effort or education, affects readers of all languages (Caravolas,2005). Based on this definition, dyslexia cannot be diagnosed until children have demonstrated trouble with reading acquisition. However, it would be ideal to identify which children will go on develop reading problems before they struggle or fail to learn to read. Children who are identified early and who receive early intervention are likely to have better reading outcomes (Bowyer-Crane et al., 2008; Torgesen, 2004; Schatschneider & Torgesen, 2004; Vellutino, Scanlon, & Tanzman, 1998) and may suffer fewer of the negative consequences associated with poor reading. Further, an understanding of which children are at greatest risk for reading difficulties would allow educators and clinicians to allocate limited intervention resources to students who need them most. Extensive behavioral research has sought to answer this question and yet models predicting reading are rarely employed in practice.

Type: Chapter
Information: Developmental Dyslexia across Languages and Writing Systems , pp. 253 - 276

DOI: https://doi.org/10.1017/9781108553377.012 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Bach, S., Richardson, U., Brandeis, D., Martin, E., & Brem, S. (2013). Print-specific multimodal brain activation in kindergarten improves prediction of reading skills in second grade. Neuroimage, 82, 605–615.Google Scholar

Bishop, D. V. M. (2007). Using mismatch negativity to study central auditory processing in developmental language and literacy impairments: Where are we, and where should we be going? Psychological Bulletin, 133(4), 651–672.Google Scholar

Boada, R., & Pennington, B. F. (2006). Deficient implicit phonological representations in children with dyslexia. Journal of Experimental Child Psychology, 95(3), 153–193.Google Scholar

Boets, B., Op de Beeck, H. P., Vandermosten, M., et al. (2013). Intact but less accessible phonetic representations in adults with dyslexia. Science, 342, 1251–1254.Google Scholar

Bowyer‐Crane, C., Snowling, M. J., Duff, F. J. et al. (2008). Improving early language and literacy skills: Differential effects of an oral language versus a phonology with reading intervention. Journal of Child Psychology and Psychiatry, 49(4), 422–432.Google Scholar

Button, K. S., Ioannidis, J. P., Mokrysz, C. et al. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376.Google Scholar

Caravolas, M. (2005). The nature and causes of dyslexia in different languages. In Snowling, M. J. & Hulme, C. (Eds.), The science of reading: A handbook (pp. 336–355). Malden, MA: Blackwell Publishing.CrossRef Google Scholar

Caravolas, M., Lervåg, A., Defior, S., Málková, G. S., & Hulme, C. (2013). Different patterns, but equivalent predictors, of growth in reading in consistent and inconsistent orthographies. Psychological Science, 24(8), 1398–1407.Google Scholar

Catts, H. W., Fey, M. E., Zhang, X., & Tomblin, J. B. (2001). Estimating the risk of future reading difficulties in kindergarten children: a research-based model and its clinical implementation. Language, Speech, and Hearing Services in Schools, 32(1), 38–50.Google Scholar

Catts, H. W., Nielsen, D. C., Bridges, M. S., Liu, Y. S., & Bontempo, D. E. (2015). Early identification of reading disabilities within an RTI framework. Journal of Learning Disabilities, 48(3), 281–297.Google Scholar

Clark, K. A., Helland, T., Specht, K. et al. (2014). Neuroanatomical precursors of dyslexia identified from pre-reading through to age 11. Brain, 137(12), 3136–3141.Google Scholar

Elbro, C., Borstrom, I., & Petersen, D. K. (1998). Predicting dyslexia from kindergarten: The importance of distinctness of phonological representations of lexical items. Reading Research Quarterly, 33, 36–60.CrossRef Google Scholar

Gabrieli, J. D., Ghosh, S. S., & Whitfield-Gabrieli, S. (2015). Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron, 85(1), 11–26.Google Scholar

Goswami, U., Thomson, J., Richardson, U. et al. (2002). Amplitude envelope onsets and developmental dyslexia: A new hypothesis. Proceedings of the National Academy of Sciences, 99(16), 10911–10916.Google Scholar

Guttorm, T. K., Leppänen, P. H., Hämäläinen, J. A., Eklund, K. M., & Lyytinen, H. J. (2010). Newborn event-related potentials predict poorer pre-reading skills in children at risk for dyslexia. Journal of Learning Disabilities, 43(5), 391–401.Google Scholar

Guttorm, T. K., Leppänen, P. H., Poikkeus, A. M. et al. (2005). Brain event-related potentials (ERPs) measured at birth predict later language development in children with and without familial risk for dyslexia. Cortex, 41(3), 291–303.Google Scholar

Guttorm, T. K., Leppänen, P. H., Richardson, U., & Lyytinen, H. (2001). Event-related potentials and consonant differentiation in newborns with familial risk for dyslexia. Journal of Learning Disabilities, 34(6), 534–544.Google Scholar

Guttorm, T. K., Leppänen, P. H. T., Tolvanen, A., & Lyytinen, H. (2003). Event-related potentials in newborns with and without familial risk for dyslexia: Principal component analysis reveals differences between the groups. Journal of Neural Transmission, 110, 1059–1074.Google Scholar

Hämäläinen, J. A., Guttorm, T. K., Richardson, U. et al. (2013). Auditory event-related potentials measured in kindergarten predict later reading problems at school age. Developmental Neuropsychology, 38(8), 550–566.Google Scholar

Hämäläinen, J. A., Lohvansuu, K., Ervast, L., & Leppänen, P. H. (2015). Event-related potentials to tones show differences between children with multiple risk factors for dyslexia and control children before the onset of formal reading instruction. International Journal of Psychophysiology, 95(2), 101–112.Google Scholar

Halliday, L. F., Barry, J. G., Hardiman, M. J., & Bishop, D. V. (2014). Late, not early mismatch responses to changes in frequency are reduced or deviant in children with dyslexia: an event-related potential study. Journal of Neurodevelopmental Disorders, 6(21).CrossRef Google Scholar PubMed

Hensler, B. S., Schatschneider, C., Taylor, J., & Wagner, R. K. (2010). Behavioral genetic approach to the study of dyslexia. Journal of Developmental and Behavioral Pediatrics, 31(7), 525–32.Google Scholar

Huettel, S. A., Song, A. W., & McCarthy, G. (2009). Functional magnetic resonance imaging. Sunderland, MA: Sinauer Associates.Google Scholar

Humphrey, N., & Mullins, P. M. (2002). Self-concept and self-esteem in developmental dyslexia. Journal of Research in Special Educational Needs, 2, 1–13.Google Scholar

Im, K., Raschle, N. M., Smith, S. A., Grant, P. E., & Gaab, N. (2015). Atypical sulcal pattern in children with developmental dyslexia and at-risk kindergarteners. Cerebral Cortex, 26(3), 1138–1148.CrossRef Google Scholar PubMed

Kraemer, H. C., Schultz, S. K., & Arndt, S. (2002). Biomarkers in psychiatry: methodological issues. The American Journal of Geriatric Psychiatry, 10(6), 653–659.Google Scholar

Kraft, I., Cafiero, R., Schaadt, G. et al. (2015). Cortical differences in preliterate children at familiar risk of dyslexia are similar to those observed in dyslexic readers. Brain, 138(9), e378-e378.Google Scholar

Kraft, I., Schreiber, J., Cafiero, R. et al. (2016). Predicting early signs of dyslexia at a preliterate age by combining behavioral assessment with structural MRI. NeuroImage, 143, 378–386.Google Scholar

Landerl, K., & Wimmer, H. (2008). Development of word reading fluency and spelling in a consistent orthography: An 8-year follow-up. Journal of Educational Psychology, 100(1), 150–161.Google Scholar

Langer, N., Peysakhovich, B., Zuk, J. et al. (2017). White matter alterations in infants at risk for developmental dyslexia. Cerebral Cortex, 27(2), 1027–1036.Google Scholar

Leppänen, P. H. T., Hämäläinen, J. A., & Guttorm, T. K. et al. (2012). Infant brain responses associated with reading-related skills before school and at school age. Clinical Neurophysiology, 42, 35–41.Google Scholar

Leppänen, P. H., Hämäläinen, J. A., & Salminen, H. K. et al. (2010). Newborn brain event-related potentials revealing atypical processing of sound frequency and the subsequent association with later literacy skills in children with familial dyslexia. Cortex, 46(10), 1362–1376.Google Scholar

Leppänen, P. H., Pihko, E., Eklund, K. M., & Lyytinen, H. (1999). Cortical responses of infants with and without a genetic risk for dyslexia: II. Group effects. NeuroReport, 10(5), 969–973.Google Scholar

Leppänen, P. H. T., Richardson, U., & Pihko, E. (2002). Brain responses to changes in speech sound durations differ between infants with and without familial risk for dyslexia. Developmental Neuropsychology, 22(1), 407–422.Google Scholar

Lovio, B. R., Näätänen, R., & Kujala, T. (2010). Abnormal pattern of cortical speech feature discrimination in 6-year-old children at risk for dyslexia. Brain Research, 1335, 53–62.Google Scholar

Luck, S. J. (2014). An introduction to the event-related potential technique (2nd ed.). Cambridge, MA: MIT Press.Google Scholar

Lyon, G. R., Shaywitz, S. E., & Shaywitz, B. A. (2003). A definition of dyslexia. Annals of Dyslexia, 53(1), 1–14.Google Scholar

Maurer, U., Bucher, K., & Brem, S. et al. (2009). Neurophysiology in preschool improves behavioral prediction of reading ability throughout primary school. Biological Psychiatry, 66, 341–348.Google Scholar

Maurer, U., Bucher, K., Brem, S., & Brandeis, D. (2003). Altered responses to tone and phoneme mismatch in kindergartners at familial dyslexia risk. Neuroreport, 14(17), 2245–2250.Google Scholar

McBride-Chang, C., Lam, F., & Lam, C. et al. (2011). Early predictors of dyslexia in Chinese children: Familial history of dyslexia, language delay, and cognitive profiles. Journal of Child Psychology and Psychiatry, 52(2), 204–211.Google Scholar

Molfese, D. (2000). Predicting dyslexia at 8 years of age using neonatal brain responses. Brain and Language, 72, 238–245.CrossRef Google Scholar PubMed

Molfese, V. J., Molfese, D. L., & Modgline, A. A. (2001). Newborn and preschool predictors of second-grade reading scores: An evaluation of categorical and continuous scores. Journal of Learning Disabilities, 34(6), 545–554.Google Scholar

Moll, K., Ramus, F., & Bartling, J. et al. (2014). Cognitive mechanisms underlying reading and spelling development in five European orthographies. Learning and Instruction, 29, 65–77.CrossRef Google Scholar

Moore, J. K., & Linthicum, F. H. (2009). The human auditory system: A timeline of development. International Journal of Audiology, 46(9), 460–478.Google Scholar

Myers, C. A., Vandermosten, M., Farris, E. A. et al. (2014). Structural changes in white matter are uniquely related to children’s reading development. Psychological Science, 25(10), 1870–1883.Google Scholar

Näätänen, R. (2001). The perception of speech sounds by the human brain as reflected by the mismatch negativity (MMN) and its magnetic equivalent (MMNm). Psychophysiology, 38(1), 1–21.Google Scholar

Näätänen, R., Kujala, T., & Escera, C. et al. (2012). The mismatch negativity (MMN): A unique window to disturbed central auditory processing in ageing and different clinical conditions. Clinical Neurophysiology, 123(3), 424–458.Google Scholar

Noordenbos, M. W., Segers, E., Serniclaes, W., Mitterer, H., & Verhoeven, L. (2012). Neural evidence of allophonic perception in children at risk for dyslexia. Neuropsychologia, 50(8), 2010–2017.Google Scholar

Norton, E. S., Beach, S. D., & Gabrieli, J. D. E. (2015). Neurobiology of dyslexia. Current Opinion in Neurobiology, 30, 73–78.Google Scholar

Norton, E. S., Black, J. M., Stanley, L. M., Tanaka, H., Gabrieli, J. D. E., Sawyer, C., & Hoeft, F. (2014). Functional neuroanatomical evidence for the double-deficit hypothesis of developmental dyslexia. Neuropsychologia, 61, 235–246.Google Scholar

O’Connor, R. E., & Jenkins, J. R. (1999). Prediction of reading disabilities in kindergarten and first grade. Scientific Studies of Reading, 3, 159–197.Google Scholar

Odegard, T. N., Farris, E. A., Ring, J., McColl, R., & Black, J. (2009). Brain connectivity in non-reading impaired children and children diagnosed with developmental dyslexia. Neuropsychologia, 47(8), 1972–1977.Google Scholar

Ozernov-Palchik, O., & Gaab, N. (2016). Tackling the ‘dyslexia paradox’: reading brain and behavior for early markers of developmental dyslexia. WIREs Cognitive Science, 7: 156–176.Google Scholar

Ozernov-Palchik, O., Norton, E. S., Sideridis, G., Beach, S. D., Gabrieli, J. D. E., & Gaab, N. (2017). Early-reading profiles of children at kindergarten and longitudinally: Implications for early screening and theories of reading. Developmental Science, 20(5).Google Scholar

Pennington, B. F., & Lefly, D. L. (2001). Early reading development in children at family risk for dyslexia. Child Development, 72(3), 816–833.Google Scholar

Pennington, B. F., Santerre-Lemmon, L., Rosenberg, J. et al. (2012). Individual prediction of dyslexia by single vs. multiple deficit models. Journal of Abnormal Psychology, 121(1), 212–224.Google Scholar

Perlman, S. B. (2012). Neuroimaging in child clinical populations: Considerations for a successful research program. Journal of the American Academy of Child & Adolescent Psychiatry, 51(12), 1232–1235.CrossRef Google Scholar PubMed

Peterson, R. L., & Pennington, B. F. (2015). Developmental dyslexia. Annual Review of Clinical Psychology, 11, 283–307.Google Scholar

Pihko, E., Leppänen, P. H., Eklund, K. M. et al. (1999). Cortical responses of infants with and without a genetic risk for dyslexia: I. Age effects. Neuroreport, 10(5), 901–5.CrossRef Google Scholar PubMed

Plakas, A., van Zuijen, T., van Leeuwen, T., Thomson, J. M., & van der Leij, A. (2013). Impaired non-speech auditory processing at a pre-reading age is a risk-factor for dyslexia but not a predictor: an ERP study. Cortex, 49(4), 1034–1045.Google Scholar

Poldrack, R. A., Baker, C. I., Durnez, J. et al. (2017). Scanning the horizon: towards transparent and reproducible neuroimaging research. Nature Reviews Neuroscience, 18(2), 115–126.Google Scholar

Puolakanaho, A., Ahonen, T., Aro, M. et al. (2007). Very early phonological and language skills: Estimating individual risk of reading disability. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 48(9), 923–931.Google Scholar

Ramus, F., & Szenkovits, G. (2008). What phonological deficit? Quarterly Journal of Experimental Psychology, 61(1), 129–141.Google Scholar

Raschle, N. M., Chang, M., & Gaab, N. (2011). Structural brain alterations associated with dyslexia predate reading onset. NeuroImage, 57, 742–749.Google Scholar

Raschle, N., Lee, M., Buechler, R. et al. (2009). Making MR imaging child’s play: Pediatric neuroimaging protocol, guidelines, and procedure. Journal of Visualized Experiments, 29. www.jove.com/index/Details.stp?ID=1309 Google Scholar

Raschle, N. M., Zuk, J.,& Gaab, N. (2012). Functional characteristics of developmental dyslexia in left-hemispheric posterior brain regions predate reading onset. Proceedings of the National Academy of Sciences of the United States of America, 109, 2156–2161.Google Scholar

Raschle, N., Zuk, J., Ortiz-Manilla, S. et al. (2012). Pediatric neuroimaging in early childhood and infancy: Challenges and practical guidelines. Annals of the New York Academy of Sciences, 1252, 43–50.Google Scholar

Reuter, M., Tisdall, D., Qureshi, A. et al. (2015). Head motion during MRI acquisition reduces gray matter volume and thickness estimates. NeuroImage, 107, 107–115.Google Scholar

Richlan, F., Kronbichler, M., & Wimmer, H. (2013). Structural abnormalities in the dyslexic brain: A meta-analysis of voxel-based morphometry studies. Human Brain Mapping, 34(11), 3055–3065.Google Scholar

Saygin, Z. M., Norton, E. S., Osher, D. et al. (2013). Tracking the roots of reading ability: White matter volume and integrity correlate with phonological awareness in pre- and early-reading kindergarten children. The Journal of Neuroscience, 33(33), 13251–13258.Google Scholar

Schatschneider, C., Fletcher, J. M., Francis, D. J., Carlson, C. D., & Foorman, B. R. (2004). Kindergarten prediction of reading skills: A longitudinal comparative analysis. Journal of Educational Psychology, 96(2), 265–282.Google Scholar

Schatschneider, C., & Torgesen, J. (2004). Using our current understanding of dyslexia to support early identification and intervention. Journal of Child Neurology, 19(10), 759–765.Google Scholar

Serniclaes, W., van Heghe, S., Mousty, P., Carre, R. & Sprenger-Charolles, L. (2004). Allophonic mode of speech perception in dyslexia. Journal of Experimental Child Psychology, 87, 336–361.Google Scholar

Skeide, M. A., Kraft, I., Müller, B. et al. (2016). NRSN1 associated grey matter volume of the visual word form area reveals dyslexia before school. Brain, 139(10), 2792–2803.Google Scholar

Snowling, M. J. (2013). Early identification and interventions for dyslexia: A contemporary view. Journal of Research in Special Educational Needs, 13(1), 7–14.Google Scholar

Snowling, M. J., & Melby-Lervåg, M. (2016). Oral language deficits in familial dyslexia: A meta-analysis and review. Psychological Bulletin, 142(5), 498–545.Google Scholar

Soares, J. M., Marques, P., Alves, V., & Sousa, N. (2013). A hitchhiker’s guide to diffusion tensor imaging. Frontiers in Neuroscience, 7, 31.Google Scholar

Tisdall, M. D., Hess, A. T., Reuter, M., et al. (2012). Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magnetic Resonance in Medicine, 68(2), 389–399.Google Scholar

Torgesen, J. K. (2004). Avoiding the devastating downward spiral: the evidence that early intervention prevents reading failure. American Educator, 28, 6–19.Google Scholar

Torkildsen, J.von K., Syversen, Simonsen, G., H. et al. (2007). Brain responses to lexical-semantic priming in children at-risk for dyslexia. Brain and Language, 102, 243–261.Google Scholar

Vanderauwera, J., Wouters, J., Vandermosten, M., & Ghesquière, P. (2017). Early dynamics of white matter deficits in children developing dyslexia. Developmental Cognitive Neuroscience, 27, 69–77.Google Scholar

Vandermosten, M., Hoeft, F., & Norton, E. S. (2016). Integrating MRI brain imaging studies of pre-reading children with current theories of developmental dyslexia: A review and quantitative meta-analysis. Current Opinion in Behavioral Science, 10, 155–161.Google Scholar

Vandermosten, M., Vanderauwera, J., Theys, C. et al. (2015). A DTI tractography study in pre-readers at risk for dyslexia. Developmental Cognitive Neuroscience, 14, 8–15.Google Scholar

van Herten, M., Pasman, J., van Leeuwen, T. H. et al. (2008). Differences in AERP responses and atypical hemispheric specialization in 17-month-old children at risk of dyslexia. Brain Research, 1201, 100–105.Google Scholar

van Leeuwen, T., Been, P., & Kuijpers, C. et al. (2006). Mismatch response is absent in 2-month-old infants at risk for dyslexia. Neuroreport, 17(4), 351–355.CrossRef Google Scholar PubMed

van Leeuwen, T., Been, P., van Herten, M. et al. (2007). Cortical categorization failure in 2-month-old infants at risk for dyslexia. Neuroreport, 18(9), 857–861.Google Scholar

van Leeuwen, T., Been, P., van Herten, M. et al. (2008). Two-month-old infants at risk for dyslexia do not discriminate/bAk/from/dAk: A brain-mapping study. Journal of Neurolinguistics, 21(4), 333–348.Google Scholar

van Zuijen, T. L., Plakas, A., Maassen, B. A. M., Maurits, N. M., & van der Leij, A. (2013). Infant ERPs separate children at risk of dyslexia who become good readers from those who become poor readers. Developmental Science, 16, 554–563.Google Scholar

Vellutino, F. R., Scanlon, D. M., & Tanzman, M. S. (1998). The case for early intervention in diagnosing specific reading disability. Journal of School Psychology, 36(4), 367–397.Google Scholar

Wang, Y., Mauer, M. V., & Raney, T. et al. (2017). Development of tract-specific white matter pathways during early reading development in at-risk children and typical controls. Cerebral Cortex., 27(4), 2469–2485.Google Scholar

Williams, V. J., Juranek, J., Cirino, P., & Fletcher, J. M. (2018). Cortical thickness and local gyrification in children with developmental dyslexia. Cerebral Cortex., 28(3), 963–973Google Scholar

Wolf, M., & Bowers, P. G. (1999). The double-deficit hypothesis for the developmental dyslexias. Journal of Educational Psychology, 91(3), 415.Google Scholar

Yamada, Y., Stevens, C., Dow, M. et al. (2011). Emergence of the neural network for reading in five-year-old beginning readers of different levels of pre-literacy abilities: an fMRI study. Neuroimage, 57(3), 704–713.Google Scholar