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  • [IEEE Trans Med Imaging.] Joint-Connectivity-Based Sparse Canonical Correlation Analysis of Imaging Genetics for Detecting Biomarkers of Parkinson's Disease.파킨슨병 바이오마커 검출을 위한 영상유전학의 조인트 연결성 기반 스파스 정형상관해석

    기초과학연구원 / 김만수, 박현진*

  • 출처
    IEEE Trans Med Imaging.
  • 등재일
    2020 Jan
  • 저널이슈번호
    39(1):23-34. doi: 10.1109/TMI.2019.2918839. Epub 2019 May 24.
  • 내용

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    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 with 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.

     

     

    Kim M, Won JH, Youn J, Park H.

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