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DGM simulations and applications with real data

For questions about these data and analyses write to

How to cite this work

Schwab, S., Harbord, R., Zerbi, V., Elliott, L., Afyouni, S., Smith, J. Q., … Nichols, T. E. (2017). Directed functional connectivity using dynamic graphical models. bioRxiv. doi:10.1101/198887

Notebooks with results and figures

Reproducing full analysis

  • Clone this repository.
  • Install all packages suggested at the top of the notebook.
  • Adjust the PATH_HOME and PATH variables at the top of the notebooks.
  • You may (re)estimate all networks with DGM but the notebook will load the already computed networks.
  • As I will not provide the HCP and mouse time series, so time-series related chunks need to be disabled for the Notebook to completely run.

Obtaining the data

Network data

  • All network data (from simulations, human fMRI and mouse fMRI) can be produced with DGM or loaded from the RData containers in the results folder.

Raw time series

  • Simulation time series are based in the generative model described in [1] and are in this repository except sim1 and sim22 which can be obtained from the FMRIB NetSim Website. The Notebook will download these automatically and will extract them.
  • Human RSN time series can be obtained from the Human Connectome Project, Parcellation+Timeseries+Netmats (PTN) from here.
  • Mouse time-series is not open and must be requested from, but the network data is available.


  1. Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., et al. (2011). Network modelling methods for FMRI. NeuroImage, 54(2), 875–91. doi:10.1016/j.neuroimage.2010.08.063.

Additonal Analyses