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 journalArticle

1 Scopus citations


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)
JournalStatistical Analysis and Data Mining
StateAccepted/In press - Jan 1 2018



  • 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

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