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 journalArticlepeer-review

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 languageEnglish (US)
Pages (from-to)203-226
Number of pages24
JournalStatistical Analysis and Data Mining
Volume11
Issue number5
DOIs
StatePublished - Oct 2018

All Science Journal Classification (ASJC) codes

  • Analysis
  • Information Systems
  • Computer Science Applications

Keywords

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

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