Inferring functional connectivity in MRI using Bayesian network structure learning with a modified PC algorithm

Swathi P. Iyer, Izhak Shafran, David Grayson, Kathleen Gates, Joel T. Nigg, Damien Fair

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Resting state functional connectivity MRI (rs-fcMRI) is a popular technique used to gauge the functional relatedness between regions in the brain for typical and special populations. Most of the work to date determines this relationship by using Pearson's correlation on BOLD fMRI timeseries. However, it has been recognized that there are at least two key limitations to this method. First, it is not possible to resolve the direct and indirect connections/influences. Second, the direction of information flow between the regions cannot be differentiated. In the current paper, we follow-up on recent work by Smith et al. (2011), and apply PC algorithm to both simulated data and empirical data to determine whether these two factors can be discerned with group average, as opposed to single subject, functional connectivity data. When applied on simulated individual subjects, the algorithm performs well determining indirect and direct connection but fails in determining directionality. However, when applied at group level, PC algorithm gives strong results for both indirect and direct connections and the direction of information flow. Applying the algorithm on empirical data, using a diffusion-weighted imaging (DWI) structural connectivity matrix as the baseline, the PC algorithm outperformed the direct correlations. We conclude that, under certain conditions, the PC algorithm leads to an improved estimate of brain network structure compared to the traditional connectivity analysis based on correlations.

Original languageEnglish (US)
Pages (from-to)165-175
Number of pages11
StatePublished - Jul 15 2013


  • Bayesian network
  • Directed functional connectivity
  • Effective connectivity
  • FMRI
  • PC algorithm

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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