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

1 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)
Journal	Statistical Analysis and Data Mining
DOIs	https://doi.org/10.1002/sam.11382
State	Accepted/In press - Jan 1 2018

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Keywords

FMRI
Functional connectivity
Fusion penalty
Gaussian graphical model
Partial correlation
Penalized least squares
Precision matrix

ASJC Scopus subject areas

Analysis
Information Systems
Computer Science Applications

Cite this

Yu, D., Lee, S. H., Lim, J., Xiao, G., Craddock, R. C., & Biswal, B. B. (Accepted/In press). Fused lasso regression for identifying differential correlations in brain connectome graphs. Statistical Analysis and Data Mining. https://doi.org/10.1002/sam.11382

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KW - Penalized least squares

KW - Precision matrix

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10.1002/sam.11382