Fused lasso regression for identifying differential correlations in brain connectome graphs

Donghyeon Yu; Sang Han Lee; Johan Lim; Guanghua Xiao; Richard Cameron Craddock; Bharat B. Biswal

doi:10.1002/sam.11382

Fused lasso regression for identifying differential correlations in brain connectome graphs

Donghyeon Yu, Sang Han Lee, Johan Lim, Guanghua Xiao, Richard Cameron Craddock, Bharat B. Biswal

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

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 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.

Original language	English (US)
Pages (from-to)	203-226
Number of pages	24
Journal	Statistical Analysis and Data Mining
Volume	11
Issue number	5
DOIs	https://doi.org/10.1002/sam.11382
State	Published - Oct 2018

Keywords

Gaussian graphical model
fMRI
functional connectivity
fusion penalty
partial correlation
penalized least squares
precision matrix

ASJC Scopus subject areas

Analysis
Information Systems
Computer Science Applications

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10.1002/sam.11382

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title = "Fused lasso regression for identifying differential correlations in brain connectome graphs",

abstract = "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 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.",

keywords = "Gaussian graphical model, fMRI, functional connectivity, fusion penalty, partial correlation, penalized least squares, precision matrix",

author = "Donghyeon Yu and Lee, {Sang Han} and Johan Lim and Guanghua Xiao and Craddock, {Richard Cameron} and Biswal, {Bharat B.}",

note = "Funding Information: information National Institute of Mental Health, R01MH101555, BRAINS R01 (R01MH101555). National Research Foundation of Korea, NRF-2015R1C1A1A02036312, NRF-2017R1A2B2012264.We would like to thank AE and reviewers for the constructive comments. Publisher Copyright: {\textcopyright} 2018 Wiley Periodicals, Inc.",

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doi = "10.1002/sam.11382",

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AU - Yu, Donghyeon

AU - Lee, Sang Han

AU - Lim, Johan

AU - Xiao, Guanghua

AU - Craddock, Richard Cameron

AU - Biswal, Bharat B.

N1 - Funding Information: information National Institute of Mental Health, R01MH101555, BRAINS R01 (R01MH101555). National Research Foundation of Korea, NRF-2015R1C1A1A02036312, NRF-2017R1A2B2012264.We would like to thank AE and reviewers for the constructive comments. Publisher Copyright: © 2018 Wiley Periodicals, Inc.

PY - 2018/10

Y1 - 2018/10

N2 - 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 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.

AB - 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 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.

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KW - fMRI

KW - functional connectivity

KW - fusion penalty

KW - partial correlation

KW - penalized least squares

KW - precision matrix

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Fused lasso regression for identifying differential correlations in brain connectome graphs

Abstract

Keywords

ASJC Scopus subject areas

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