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Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder

Authors
Zhao, FengQiao, LishanShi, FengYap, Pew-ThianShen, Dinggang
Issue Date
8월-2017
Publisher
SPRINGER
Keywords
Autism spectrum disorder (ASD); Canonical correlation analysis (CCA); Feature fusion
Citation
BRAIN IMAGING AND BEHAVIOR, v.11, no.4, pp.1050 - 1060
Indexed
SCIE
SCOPUS
Journal Title
BRAIN IMAGING AND BEHAVIOR
Volume
11
Number
4
Start Page
1050
End Page
1060
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/82639
DOI
10.1007/s11682-016-9587-5
ISSN
1931-7557
Abstract
Early diagnosis of autism spectrum disorder (ASD) is critical for timely medical intervention, for improving patient quality of life, and for reducing the financial burden borne by the society. A key issue in neuroimaging-based ASD diagnosis is the identification of discriminating features and then fusing them to produce accurate diagnosis. In this paper, we propose a novel framework for fusing complementary and discriminating features from different imaging modalities. Specifically, we integrate the Fisher discriminant criterion and local correlation information into the canonical correlation analysis (CCA) framework, giving a new feature fusion method, called Supervised Local CCA (SL-CCA), which caters specifically to local and global multimodal features. To alleviate the neighborhood selection problem associated with SL-CCA, we further propose a hierarchical SL-CCA (HSL-CCA), by performing SL-CCA with the gradually varying neighborhood sizes. Extensive experiments on the multimodal ABIDE database show that the proposed method achieves superior performance. In addition, based on feature weight analysis, we found that only a few specific brain regions play active roles in ASD diagnosis. These brain regions include the putamen, precuneus, and orbitofrontal cortex, which are highly associated with human emotional modulation and memory formation. These finding are consistent with the behavioral phenotype of ASD.
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