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Joint-connectivity-based sparse canonical correlation analysis of imaging genetics for detecting biomarkers of Parkinson’s disease

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Title
Joint-connectivity-based sparse canonical correlation analysis of imaging genetics for detecting biomarkers of Parkinson’s disease
Author(s)
Mansu Kim; Ji Hye Won; Jinyoung Youn; Hyunjin Park
Publication Date
2020-01
Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING, v.39, no.1, pp.23 - 34
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
Abstract: Imaging genetics is a method used to detect associations between imaging and genetic variables. Some researchers have used sparse canonical correlation analysis (SCCA) for imaging genetics. This study was conducted to improve the efficiency and interpretability of SCCA. We propose a connectivity-based penalty for incorporating biological prior information. Our proposed approach, named joint connectivity-based SCCA (JCB-SCCA), includes the proposed penalty and can handle multi-modal neuroimaging datasets. Different neuroimaging techniques provide distinct information on the brain and have been used to investigate various neurological disorders, including Parkinson’s disease (PD). We applied our algorithm to simulated and real imaging genetics datasets for performance evaluation. Our algorithm was able to select important features in a more robust manner compared to other multivariate methods. The algorithm revealed promising features of single-nucleotide polymorphisms and brain regions related to PD by using a real imaging genetic dataset. The proposed imaging genetics model can be used to improve clinical diagnosis in the form of novel potential biomarkers. We hope to apply our algorithm to cohorts such as Alzheimer’s patients or healthy subjects to determine the generalizability of our algorithm. © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
URI
https://pr.ibs.re.kr/handle/8788114/7021
ISSN
0278-0062
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > Journal Papers (저널논문)
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