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Identification of Infants at Risk for Autism Using Multi-parameter Hierarchical White Matter Connectomes

  • Yan JinEmail author
  • Chong-Yaw Wee
  • Feng Shi
  • Kim-Han Thung
  • Pew-Thian Yap
  • Dinggang Shen
  • Infant Brain Imaging Study (IBIS) Network
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

Autism spectrum disorder (ASD) is a variety of developmental disorders that cause life-long communication and social deficits. However, ASD could only be diagnosed at children as early as 2 years of age, while early signs may emerge within the first year. White matter (WM) connectivity abnormalities have been documented in the first year of lives of ASD subjects. We introduce a novel multi-kernel support vector machine (SVM) framework to identify infants at high-risk for ASD at 6 months old, by utilizing the diffusion parameters derived from a hierarchical set of WM connectomes. Experiments show that the proposed method achieves an accuracy of 76%, in comparison to 70% with the best single connectome. The complementary information extracted from hierarchical networks enhances the classification performance, with the top discriminative connections consistent with other studies. Our framework provides essential imaging connectomic markers and contributes to the evaluation of ASD risks as early as 6 months.

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References

  1. 1.
    Prevalence of autism spectrum disorder among children aged 8 years, Centers for Disease Control and Prevention. Surveillance Summaries 63(2), 1–21 (2014)Google Scholar
  2. 2.
    Ozonoff, S., et al.: A prospective study of the emergence of early behavioral signs of autism. J. Am. Acad. Child Adolesc. Psychiatry 49(3), 256–266 (2010)Google Scholar
  3. 3.
    Wolf, J.J., et al.: Differences in white matter fiber tract development present from 6 to 24 months in infants with Autism. Am. J. Psychiatry 169(6), 589–600 (2012)CrossRefGoogle Scholar
  4. 4.
    Lewis, J.D., et al.: Network inefficiencies in autism spectrum disorder at 24 months. Transl. Psychiatry 4, e388 (2014). doi: 10.1038/tp.2014.24CrossRefGoogle Scholar
  5. 5.
    Zhu, X., et al.: A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. Neuroimage 100, 91–105 (2014)CrossRefGoogle Scholar
  6. 6.
    Li, J., Jin, Y., Shi, Y., Dinov, I.D., Wang, D.J., Toga, A.W., Thompson, P.M.: Voxelwise spectral diffusional connectivity and its applications to alzheimer’s disease and intelligence prediction. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 655–662. Springer, Heidelberg (2013)Google Scholar
  7. 7.
    Zhan, L., et al.: Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease. Front Aging Neurosci 7, 48 (2015). doi: 10.3389/fnagi.2015.00048CrossRefGoogle Scholar
  8. 8.
    Jin, Y., et al.: Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics. Neuroimage 100, 75–90 (2014)CrossRefGoogle Scholar
  9. 9.
    Wee, C.-Y., et al.: Diagnosis of autism spectrum disorders using regional and interregional morphological features. Hum. Brain Mapp. 35(7), 3414–3430 (2014)CrossRefGoogle Scholar
  10. 10.
    Ingalhalikar, M., et al.: Diffusion based abnormality markers of pathology: Toward learned diagnostic prediction of ASD. Neuroimage 57(3), 918–927 (2012)CrossRefGoogle Scholar
  11. 11.
    Shi, F., et al.: Infant brain atlases from neonates to 1- and 2-year-olds. PLos One 6(4), e18746 (2011). doi: 10.1371/journal.pone.0018746CrossRefGoogle Scholar
  12. 12.
    Rakotomamonjy, A., et al.: SimpleMKL. J. Mach. Learn. Res. 9, 2491–2521 (2008)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Yan Jin
    • 1
    Email author
  • Chong-Yaw Wee
    • 1
  • Feng Shi
    • 1
  • Kim-Han Thung
    • 1
  • Pew-Thian Yap
    • 1
  • Dinggang Shen
    • 1
  • Infant Brain Imaging Study (IBIS) Network
  1. 1.Department of Radiology and BRIC, School of MedicineUniversity of North Carolina at Chapel HillChapel HillUSA

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