Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder

Liye Wang, Chong Yaw Wee, Xiaoying Tang, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Abstract: In this paper, we propose a novel framework for ASD diagnosis using structural magnetic resonance imaging (MRI). Our method deals explicitly with the distributional differences of gray matter (GM) and white matter (WM) features extracted from MR images. We project linearly the GM and WM features onto a canonical space where their correlations are mutually maximized. In this canonical space, features that are highly correlated with the class labels are selected for ASD diagnosis. In addition, graph matching is employed to preserve the geometrical relationships between samples when projected onto the canonical space. Our evaluations based on a public ASD dataset show that the proposed method outperforms all competing methods on all clinically important measures in differentiating ASD patients from healthy individuals.

Original languageEnglish
Pages (from-to)33-40
Number of pages8
JournalBrain Imaging and Behavior
Issue number1
Publication statusPublished - 2016 Mar 1

Bibliographical note

Funding Information:
This work was supported partially by NIH grant (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599), and National Natural Science Foundation of China (NSFC) Grants (61473190, 81471743).

Publisher Copyright:
© 2015, Springer Science+Business Media New York.


  • Diagnosis of autism spectrum disorder
  • Magnetic resonance imaging (MRI)
  • Multi-task feature selection

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience
  • Radiology Nuclear Medicine and imaging
  • Behavioral Neuroscience


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