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.
Bibliographical noteFunding 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).
© 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
- Psychiatry and Mental health
- Cellular and Molecular Neuroscience
- Cognitive Neuroscience
- Radiology Nuclear Medicine and imaging
- Behavioral Neuroscience