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Extraction of co-expressed discriminative features of schizophrenia in imaging epigenetics framework
Author(s): Yuntong Bai; Zille Pascal; Vince D. Calhoun; Yu-Ping Wang
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Paper Abstract

Integration of imaging and non-imaging data has been a heated topic in biomedicine. While functional magnetic resonance imaging (fMRI) can serve as endo-phenotype for mental disorders, many recent researches have confirmed the essential role played by epigenetic factors in the progress of various mental diseases including Schizophrenia(SZ), which fosters an emerging branch imaging epigenetics. In this study, we focus on the integration of fMRI and DNA methylation to have a deeper understanding of SZ: we applied a model combining Lasso with Canonical Correlation Analysis (CCA) for joint DNA methylation and fMRI analysis of 184 subjects (80 patients,104 healthy controls). In the model, the regression term focuses on extracting the discriminative features associated with the disease, while the CCA term incorporates the co-expression among extracted features.We succeeded in

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530X (15 March 2019); doi: 10.1117/12.2513024
Show Author Affiliations
Yuntong Bai, Tulane Univ. (United States)
Zille Pascal, Tulane Univ. (United States)
Vince D. Calhoun, The Mind Research Network (United States)
Univ. of New Mexico (United States)
Yu-Ping Wang, Tulane Univ. (United States)


Published in SPIE Proceedings Vol. 10953:
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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