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Fused lasso regression for identifying differential correlations in brain connectome graphs
- Yu, Donghyeon;
- Lee, Sang Han;
- Lim, Johan;
- Xiao, Guanghua;
- Craddock, Richard Cameron;
- 외 1명
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4초록
In this paper, we propose a procedure to find differential edges between 2 graphs from high-dimensional data. We estimate 2 matrices of partial correlations and their differences by solving a penalized regression problem. We assume sparsity only on differences between 2 graphs, not graphs themselves. Thus, we impose an (2) penalty on partial correlations and an (1) penalty on their differences in the penalized regression problem. We apply the proposed procedure in finding differential functional connectivity between healthy individuals and Alzheimer's disease patients.
키워드
fMRI; functional connectivity; fusion penalty; Gaussian graphical model; partial correlation; penalized least squares; precision matrix; INVERSE COVARIANCE ESTIMATION; PRECISION MATRIX ESTIMATION; FUNCTIONAL CONNECTIVITY; VARIABLE SELECTION; ELASTIC NET; NETWORKS; MRI; MINIMIZATION; INSIGHTS; DISEASE
- 제목
- Fused lasso regression for identifying differential correlations in brain connectome graphs
- 저자
- Yu, Donghyeon; Lee, Sang Han; Lim, Johan; Xiao, Guanghua; Craddock, Richard Cameron; Biswal, Bharat B.
- 발행일
- 2018-10
- 유형
- Article
- 권
- 11
- 호
- 5
- 페이지
- 203 ~ 226