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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초록

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

fMRIfunctional connectivityfusion penaltyGaussian graphical modelpartial correlationpenalized least squaresprecision matrixINVERSE COVARIANCE ESTIMATIONPRECISION MATRIX ESTIMATIONFUNCTIONAL CONNECTIVITYVARIABLE SELECTIONELASTIC NETNETWORKSMRIMINIMIZATIONINSIGHTSDISEASE
제목
Fused lasso regression for identifying differential correlations in brain connectome graphs
저자
Yu, DonghyeonLee, Sang HanLim, JohanXiao, GuanghuaCraddock, Richard CameronBiswal, Bharat B.
DOI
10.1002/sam.11382
발행일
2018-10
유형
Article
저널명
Statistical Analysis and Data Mining
11
5
페이지
203 ~ 226