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Neural systems predicting long-term outcome in dyslexia

Fumiko Hoeft, Bruce D. McCandliss, Jessica M. Black, Alexander Gantman, Nahal Zakerani, Charles Hulme, Heikki Lyytinen, Susan Whitfield-Gabrieli, Gary H. Glover, Allan L. Reiss, and John D. E. Gabrieli
PNAS published ahead of print December 20, 2010 https://doi.org/10.1073/pnas.1008950108
Fumiko Hoeft
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Bruce D. McCandliss
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Jessica M. Black
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Charles Hulme
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Heikki Lyytinen
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Susan Whitfield-Gabrieli
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Gary H. Glover
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  1. Edited by Marcus E. Raichle, Washington University of St. Louis, St. Louis, MO, and approved November 2, 2010 (received for review June 24, 2010)

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Abstract

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.

  • inferior frontal gyrus
  • prediction
  • compensation
  • fractional anisotropy
  • rhyming

Footnotes

  • 1To whom correspondence should be addressed. E-mail: fumiko{at}stanford.edu.
  • Author contributions: F.H., B.D.M., and J.D.E.G. designed research; F.H., J.M.B., A.G., N.Z., S.W.-G., and J.D.E.G. performed research; F.H. contributed new reagents/analytic tools; F.H., B.D.M., J.M.B., A.G., N.Z., S.W.-G., G.H.G., A.L.R., and J.D.E.G. analyzed data; and F.H., C.H., H.L., S.W.-G., G.H.G., A.L.R., and J.D.E.G. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1008950108/-/DCSupplemental.

Freely available online through the PNAS open access option.

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Neural systems predicting long-term outcome in dyslexia
Fumiko Hoeft, Bruce D. McCandliss, Jessica M. Black, Alexander Gantman, Nahal Zakerani, Charles Hulme, Heikki Lyytinen, Susan Whitfield-Gabrieli, Gary H. Glover, Allan L. Reiss, John D. E. Gabrieli
Proceedings of the National Academy of Sciences Dec 2010, 201008950; DOI: 10.1073/pnas.1008950108

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Neural systems predicting long-term outcome in dyslexia
Fumiko Hoeft, Bruce D. McCandliss, Jessica M. Black, Alexander Gantman, Nahal Zakerani, Charles Hulme, Heikki Lyytinen, Susan Whitfield-Gabrieli, Gary H. Glover, Allan L. Reiss, John D. E. Gabrieli
Proceedings of the National Academy of Sciences Dec 2010, 201008950; DOI: 10.1073/pnas.1008950108
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