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) |
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Journal | Statistical Analysis and Data Mining |
DOIs | |
State | Accepted/In press - Jan 1 2018 |
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