Identification of faulty DTI-based sub-networks in autism using network regularized SVM

Hai Li, Zhong Xue, Timothy M. Ellmore, Richard E. Frye, Stephen T. Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Neural imaging studies of autism spectrum disorders (ASD) have consistently demonstrated deficits in connectivity. In this paper, we propose a new network regularized support vector machines (SVM) method to identify the faulty subnetworks associated with ASD using diffusion tensor imaging (DTI). After constructing the bram connectivity network of each subject using DTI, the SVM-recursive feature elimination (RFE) algorithm is adopted to identify the faulty sub-networks in order to distinguish ASD from typical developing (TD) controls. Since connections in the network are not independent of each other, their topological proximities are incorporated as the network regulanzation of SVM-RFE by using the graph Laplacian to obtain robust sub-networks. Experiments on both simulated and clinical datasets showed a better performance of the proposed method in faulty sub-network identification, compared with the traditional SVM-RFE method.

Original languageEnglish (US)
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Pages550-553
Number of pages4
DOIs
StatePublished - Aug 15 2012
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: May 2 2012May 5 2012

Other

Other2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
CountrySpain
CityBarcelona
Period5/2/125/5/12

Keywords

  • Autism
  • bram connectivity
  • DTI
  • network regulanzation
  • SVM

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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