Title
Fused lasso regression for identifying differential correlations in brain connectome graphs
Abstract
AbstractIn 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 ℓ2 penalty on partial correlations and an ℓ1 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.
Year
DOI
Venue
2018
10.1002/sam.11382
Periodicals
Keywords
Field
DocType
fMRI,functional connectivity,fusion penalty,Gaussian graphical model,partial correlation,penalized least squares,precision matrix
Graph,Partial correlation,Regression,Computer science,Connectome,Lasso (statistics),Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
11
5
1932-1864
Citations 
PageRank 
References 
0
0.34
6
Authors
6
Name
Order
Citations
PageRank
Donghyeon Yu132.10
Sang Han Lee200.34
Johan Lim36310.95
Guanghua Xiao4569.63
Richard Cameron Craddock500.34
Biswal Bharat B620420.86