Statistical Analysis of White Matter Integrity for the Clinical Study of Typical Specific Language Impairment in Children

  • Emmanuel Vallée
  • Olivier CommowickEmail author
  • Camille Maumet
  • Aymeric Stamm
  • Elisabeth Le Rumeur
  • Catherine Allaire
  • Jean-Christophe Ferré
  • Clément de Guibert
  • Christian Barillot
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


Children affected by Specific Language Impairment (SLI) fail to develop a normal language capability. To date, the etiology of SLI remains largely unknown. It induces difficulties with oral language which cannot be directly attributed to intellectual deficit or other developmental delay. Whereas previous studies on SLI focused on the psychological and genetic aspects of the pathology, few imaging studies investigated defaults in neuroanatomy or brain function. We propose to investigate the integrity of white matter in SLI thanks to diffusion Magnetic Resonance Imaging . An exploratory analysis was performed without a prior on the impaired regions. A region of interest statistical analysis was performed based, first, on regions defined from Catani’s atlas and, then, on tractography-based regions. Both the mean fractional anisotropy and mean apparent diffusion coefficient were compared across groups. To the best of our knowledge, this is the first study focusing on white matter integrity in specific language impairment. Twenty-two children with SLI and 19 typically developing children were involved in this study. Overall, the tractography-based approach to group comparison was more sensitive than the classical ROI-based approach. Group differences between controls and SLI patients included decreases in FA in both the perisylvian and ventral pathways of language, comforting findings from previous functional studies.


Apparent Diffusion Coefficient Fractional Anisotropy Specific Language Impairment Oral Language White Matter Integrity 
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  1. 1.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodological) 57(1), 289–300 (1995)Google Scholar
  2. 2.
    Bishop, D.V.M.: Uncommon Understanding: Development and Disorders of Language Comprehension in Children, vol. viii. Psychology Press/Erlbaum, Hove (1997)Google Scholar
  3. 3.
    Bishop, D.V.M.: Genetic and environmental risks for specific language impairment in children. Philos. Trans. R. Soc. Lond. Ser. B 356(1407), 369–380 (2001). doi:10.1098/rstb.2000.0770CrossRefGoogle Scholar
  4. 4.
    Bishop, D.V.M.: What causes specific language impairment in children? Curr. Dir. Psychol. Sci. 15(5), 217–221 (2006). doi:10.1111/j.1467-8721.2006.00439.xCrossRefGoogle Scholar
  5. 5.
    Catani, M., Mesulam, M.: The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state. Cortex 44(8), 953–961 (2008). doi:10.1016/j.cortex. 2008.04.002CrossRefGoogle Scholar
  6. 6.
    Catani, M., Thiebaut de Schotten, M.: A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 44(8), 1105–1132 (2008)Google Scholar
  7. 7.
    de Guibert, C., Maumet, C., Jannin, P., Ferré, J.C., Tréguier, C., Barillot, C., Le Rumeur, E., Allaire, C., Biraben, A.: Abnormal functional lateralization and activity of language brain areas in typical specific language impairment (developmental dysphasia). Brain 134(Pt 10), 3044–3058 (2011). doi:10.1093/brain/awr141CrossRefGoogle Scholar
  8. 8.
    Filippi, C.G., Lin, D.D.M., Tsiouris, A.J., et al.: Diffusion-tensor MR imaging in children with developmental delay: preliminary findings. Radiology 229(1), 44–50 (2003)CrossRefGoogle Scholar
  9. 9.
    Fillard, P., Pennec, X., Arsigny, V., Ayache, N.: Clinical DT-MRI estimation, smoothing, and fiber tracking with log-euclidean metrics. IEEE TMI 26(11), 1472–1482 (2007). doi:10.1109/TMI.2007.899173Google Scholar
  10. 10.
    Guimond, A., Meunier, J., Thirion, J.: Average brain models: a convergence study. CVIU 77(2), 192–210 (2000)Google Scholar
  11. 11.
    Kim, J., Kim, Y.W., Park, C.I., Park, E.S., Kim, H.H., Lee, S.K., Kim, D.I.: Diffusion-tensor magnetic resonance imaging in children with language impairment. NeuroReport 17(12), 1279–1282 (2006). doi:10.1097/01.wnr.0000230516.86090.67CrossRefGoogle Scholar
  12. 12.
    Löbel, U., Sedlacik, J., Güllmar, D., Kaiser, W., Reichenbach, J., Mentzel, H.J.: Diffusion tensor imaging: the normal evolution of ADC, RA, FA, and eigenvalues studied in multiple anatomical regions of the brain. Neuroradiology 51(4), 253–263 (2009)CrossRefGoogle Scholar
  13. 13.
    Mandonnet, E., Nouet, A., Gatignol, P., Capelle, L., Duffau, H.: Does the left inferior longitudinal fasciculus play a role in language? A brain stimulation study. Brain 130(3), 623–629 (2007)Google Scholar
  14. 14.
    Mao, H., Polensek, S.H., Goldstein, F.C., Holder, C.A., Ni, C.: Diffusion tensor and functional magnetic resonance imaging of diffuse axonal injury and resulting language impairment. J. Neuroimaging 17(4), 292–294 (2007). doi:10.1111/j.1552-6569.2007.00146.xCrossRefGoogle Scholar
  15. 15.
    Mori, S., Crain, B.J., Chacko, V.P., Van Zijl, P.C.M.: Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45(2), 265–269 (1999). doi:10.1002/1531-8249(199902)45:2 < 265::AID-ANA21 > 3.0.CO;2-3Google Scholar
  16. 16.
    Ourselin, S., Roche, A., Prima, S., Ayache, N.: Block matching: a general framework to improve robustness of rigid registration of medical images. In: MICCAI, Pittsburgh, 2000. LNCS, vol. 1935, pp. 557–566Google Scholar
  17. 17.
    Preis, S., Steinmetz, H., Knorr, U., Jäncke, L.: Corpus callosum size in children with developmental language disorder. Cogn. Brain Res. 10(1–2), 37–44 (2000). doi:10.1016/S0926-6410(00)00020-3CrossRefGoogle Scholar
  18. 18.
    Rapin, I.: Practitioner review: developmental language disorders: a clinical update. J. Child Psychol. Psychiatry 37(6), 643–655 (1996). doi:10.1111/j.1469-7610.1996.tb01456.xCrossRefGoogle Scholar
  19. 19.
    Suarez, R.O., Commowick, O., Prabhu, S.P., Warfield, S.K.: Automated delineation of white matter fiber tracts with a multiple region-of-interest approach. NeuroImage 59(4), 3690–3700 (2012). doi:10.1016/j.neuroimage.2011.11.043CrossRefGoogle Scholar
  20. 20.
    Wiest-Daesslé, N., Prima, S., Coupé, P., Morrissey, S.P., Barillot, C.: Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: applications to DT-MRI. In: MICCAI (2), New York, 2008. LNCS, vol. 5242, pp. 171–179Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Emmanuel Vallée
    • 1
  • Olivier Commowick
    • 1
    Email author
  • Camille Maumet
    • 1
  • Aymeric Stamm
    • 1
  • Elisabeth Le Rumeur
    • 2
  • Catherine Allaire
    • 3
  • Jean-Christophe Ferré
    • 1
    • 2
  • Clément de Guibert
    • 4
    • 5
  • Christian Barillot
    • 1
  1. 1.Inria, INSERM, VisAGeS U746 Unit/ProjectRennesFrance
  2. 2.Department of NeuroradiologyUniversity HospitalRennesFrance
  3. 3.Centre Toul-arC’hoatChateaulinFrance
  4. 4.LAS-EA 2241, European University of Brittany-Rennes 2RennesFrance
  5. 5.Regional center for language and learning disordersUniversity HospitalRennesFrance

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