The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Neural systems predicting long-term outcome in dyslexia
Open accessAbstract
Individuals with developmental dyslexia vary in their ability to improve reading skills, but the brain basis for improvement remains largely unknown. We performed a prospective, longitudinal study over 2.5 y in children with dyslexia ( n = 25) or without dyslexia ( n = 20) to discover whether initial behavioral or brain measures, including functional MRI (fMRI) and diffusion tensor imaging (DTI), can predict future long-term reading gains in dyslexia. No behavioral measure, including widely used and standardized reading and language tests, reliably predicted future reading gains in dyslexia. Greater right prefrontal activation during a reading task that demanded phonological awareness and right superior longitudinal fasciculus (including arcuate fasciculus) white-matter organization significantly predicted future reading gains in dyslexia. Multivariate pattern analysis (MVPA) of these two brain measures, using linear support vector machine (SVM) and cross-validation, predicted significantly above chance (72% accuracy) which particular child would or would not improve reading skills (behavioral measures were at chance). MVPA of whole-brain activation pattern during phonological processing predicted which children with dyslexia would improve reading skills 2.5 y later with >90% accuracy. These findings identify right prefrontal brain mechanisms that may be critical for reading improvement in dyslexia and that may differ from typical reading development. Brain measures that predict future behavioral outcomes (neuroprognosis) may be more accurate, in some cases, than available behavioral measures.
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Cites
Beyond mind-reading: multi-voxel pattern analysis of fMRI data
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.07.005 · Cited by 2,271
Perisylvian language networks of the human brain
Catani, M., Jones, D. K., & ffytche, D. H. (2005) · Annals of Neurology · doi:10.1002/ana.20319 · Cited by 1,832 · cited 3×
“This white-matter pathway includes fibers that connect ventrolateral prefrontal (Broca’s area in the left hemisphere) and parietal and temporal regions (Wernicke’s area in the left hemisphere) either through direct or indirect pathways (27) and, thus, may play important roles in language and reading.”
BackgroundMethodMachine learning classifiers and fMRI: A tutorial overview
Pereira, F., Mitchell, T., & Botvinick, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.11.007 · Cited by 1,624
Dyslexia: Cultural Diversity and Biological Unity
Paulesu, E., Démonet, J.-F., Fazio, F., McCrory, E., Chanoine, V., Brunswick, N., Cappa, S. F., Cossu, G., Habib, M., Frith, C. D., et al. (2001) · Science · doi:10.1126/science.1057179 · Cited by 1,173
Functional disruption in the organization of the brain for reading in dyslexia
Shaywitz, S. E., Shaywitz, B. A., Pugh, K. R., Fulbright, R. K., Constable, R. T., Mencl, W. E., Shankweiler, D. P., Liberman, A. M., Skudlarski, P., Fletcher, J. M., et al. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.5.2636 · Cited by 872
Nagy, Z., Westerberg, H., & Klingberg, T. (2004) · Journal of Cognitive Neuroscience · doi:10.1162/0898929041920441 · Cited by 794
Development of neural mechanisms for reading.
Turkeltaub, P. E., Gareau, L., Flowers, D. L., Zeffiro, T. A., & Eden, G. F. (2003) · Nature Neuroscience · doi:10.1038/nn1065 · Cited by 707
Instructional treatment associated with changes in brain activation in children with dyslexia
Aylward, E. H., Richards, T. L., Berninger, V. W., Nagy, W. E., Field, K. M., Grimme, A. C., Richards, A. L., Thomson, J. B., & Cramer, S. C. (2003) · Neurology · doi:10.1212/01.wnl.0000068363.05974.64 · Cited by 242
Amygdala reactivity to emotional faces predicts improvement in major depression
Canli, T., Cooney, R. E., Goldin, P., Shah, M., Sivers, H., Thomason, M. E., Whitfield-Gabrieli, S., Gabrieli, J. D. E., & Gotlib, I. H. (2005) · NeuroReport · doi:10.1097/01.wnr.0000174407.09515.cc · Cited by 174
Self-citationEvidence for a dysfunction of left posterior reading areas in German dyslexic readers.
Kronbichler, M., Hutzler, F., Staffen, W., Mair, A., Ladurner, G., & Wimmer, H. (2006) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2006.03.010 · Cited by 131
Cited by
Illiterate to literate: behavioural and cerebral changes induced by reading acquisition
Dehaene, S., Cohen, L., Morais, J., & Kolinsky, R. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3924 · Cited by 716
Livingston, L. A., & Happé, F. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.06.005 · Cited by 414
Decoding Patterns of Human Brain Activity
Tong, F., & Pratte, M. S. (2012) · Annual Review of Psychology · doi:10.1146/annurev-psych-120710-100412 · Cited by 375
Development of white matter and reading skills
Yeatman, J. D., Dougherty, R. F., Ben-Shachar, M., & Wandell, B. A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1206792109 · Cited by 368
A qualitative and quantitative review of diffusion tensor imaging studies in reading and dyslexia
Vandermosten, M., Boets, B., Wouters, J., & Ghesquière, P. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.04.002 · Cited by 358
Metadata
- Authors
- Hoeft, F., McCandliss, B. D., Black, J. M., Gantman, A., Zakerani, N., Hulme, C., Lyytinen, H., Whitfield-Gabrieli, S., Glover, G. H., Reiss, A. L., & Gabrieli, J. D. E.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-12-20
- Volume
- 108
- Issue
- 1
- Pages
- 361-366
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2088480485
- Cited by
- 475
- References
- 10
- FWCI
- 52.86
- Type
- journal article
- Language
- en
- Open access
- green
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- Hoeft, F., McCandliss, B. D., Black, J. M., Gantman, A., Zakerani, N., Hulme, C., Lyytinen, H., Whitfield-Gabrieli, S., Glover, G. H., Reiss, A. L., & Gabrieli, J. D. E. (2011). Neural systems predicting long-term outcome in dyslexia. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1008950108
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