Fused lasso regression for identifying differential correlations in brain connectome graphs

被引：4

作者：

Yu, Donghyeon ^{[1
]}

Lee, Sang Han ^{[2
,3
]}

Lim, Johan ^{[4
]}

Xiao, Guanghua ^{[5
]}

Craddock, Richard Cameron ^{[6
]}

Biswal, Bharat B. ^{[7
]}

机构：

[1] Inha Univ, Dept Stat, Incheon, South Korea

[2] Nathan S Kline Inst Psychiat Res, Ctr Biomed Imaging & Neuromodulat, 140 Old Orangeburg Rd, Orangeburg, NY 10962 USA

[3] NYU, Sch Med, Dept Child & Adolescent Psychiat, New York, NY 10003 USA

[4] Seoul Natl Univ, Dept Stat, Seoul, South Korea

[5] Univ Texas Southwestern Med Ctr Dallas, Dept Clin Sci, Dallas, TX 75390 USA

[6] Univ Texas Austin, Dell Med Sch, Dept Diagnost Med, Austin, TX 78712 USA

[7] New Jersey Inst Technol, Dept Biomed Engn, Newark, NJ 07102 USA

来源：

STATISTICAL ANALYSIS AND DATA MINING | 2018年 / 11卷 / 05期

基金：

新加坡国家研究基金会;

关键词：

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;

D O I：

10.1002/sam.11382

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：203 / 226

页数：24

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