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

Brain Imaging and Behavior
Liye WangDinggang Shen

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.

References

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Feb 8, 2014·Medical Image Computing and Computer-assisted Intervention : MICCAI·Biao JieDinggang Shen

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Citations

Mar 24, 2017·Cerebral Cortex·Sarah M BurkeIvanka Savic-Berglund
Nov 5, 2019·Medical Physics·Charlems Alvarez-JimenezEduardo Romero
Sep 3, 2018·International Journal of Developmental Neuroscience : the Official Journal of the International Society for Developmental Neuroscience·Alex M PagnozziStephen E Rose
May 7, 2020·Human Brain Mapping·Barnaly Rashid, Vince Calhoun
Mar 29, 2018·Brain Imaging and Behavior·Bo ChengUNKNOWN Alzheimer’s Disease Neuroimaging Initiative
Sep 1, 2020·Reviews in the Neurosciences·Hidir Selcuk Nogay, Hojjat Adeli

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