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Fused lasso regression for identifying differential correlations in brain connectome graphs
Author(s) -
Yu Donghyeon,
Lee Sang Han,
Lim Johan,
Xiao Guanghua,
Craddock Richard Cameron,
Biswal Bharat B.
Publication year - 2018
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11382
Subject(s) - regression , lasso (programming language) , connectome , differential (mechanical device) , mathematics , computer science , regression analysis , pattern recognition (psychology) , artificial intelligence , statistics , functional connectivity , psychology , neuroscience , world wide web , engineering , aerospace engineering
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 ℓ 2 penalty on partial correlations and an ℓ 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.