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.
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Acknowledgments
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).
Conflict of interest
Liye Wang, Chong-Yaw Wee, Xiaoying Tang, Pew-Thian Yap, and Dinggang Shen declare that they have no conflicts of interest.
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Wang, L., Wee, CY., Tang, X. et al. Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder . Brain Imaging and Behavior 10, 33–40 (2016). https://doi.org/10.1007/s11682-015-9360-1
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DOI: https://doi.org/10.1007/s11682-015-9360-1