Decoding time-varying functional connectivity networks via linear graph embedding methods

Ricardo P. Monti*, Romy Lorenz, Peter Hellyer, Robert Leech, Christoforos Anagnostopoulos, Giovanni Montana

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project.

Original languageEnglish
Article number14
JournalFrontiers in Computational Neuroscience
Publication statusPublished - 20 Mar 2017


  • Brain decoding
  • Dynamic networks
  • Functional connectivity
  • Graph embedding
  • Visualization


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