International Workshop on Machine Learning in Medical Imaging

MICCAI 2015: Machine Learning in Medical Imaging pp 170-177 | Cite as

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

  • Yan Jin
  • 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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yan Jin
    • 1
  • 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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