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Dynamic Causal Modelling for MNE and the nipy ecosystem
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Dynamic Causal Modelling in MNE-python


Dynamic Causal Modelling (DCM) is a framework for modelling (primarily) neuroimaging data signals using biophysically-based neural population models, with an emphasis on (small) networks and estimation of effective connectivity. DCM lives inside SPM, somewhat confusingly spread across multiple sub-toolboxes, and following the standard SPM convention of being cryptically coded and poorly (if at all) documented. This project aims to liberate DCM from these squalid matlab shackles, and (to mix metaphors somewhat) unleash unto it the beast that is the mne-python and broader nipy development community. A subsidiary aim is to make some of the better parts of the DCM optimization machinery available to the neurophysiological modelling and neuroinformatics platform The Virtual Brain (TVB).

There’s really two components to DCM – the neural modelling part, which (for M/EEG) are generally variants on the second-order linear filter-type model of Jansen & Rit, and the model estimation part, which use Variational Bayes E-M type algorithms. For this project we’ll focus on the neural models described in spm_dcm_erp.m, and the generic VB-inversion routine spm_nlsi_N.m, plus the related helper functions above and below these two. We will push hard, and aim to have a functioning MNE-Python ERP-DCM implementation by the end of the hackathon. The acid test shall be to be able to run the DCM ERP tutorial in the SPM manual.

There should be many interesting questions that come up along the way, to do with improvements and general integration with other nipy and generic python libraries. Can we make use of other python libraries such as scikit-learn for parts of this? Would the VB-inversion routine be useful for fitting non-DCM models in MNE? Etc. etc. I’m hoping to get lots of feedback and good ideas from the imaging community on these Qs.


The main thing we need to do is port several matlab functions to python. Including:

spm_fx_erp.m (Jansen-Rit-David model equations for ERP simulation) spm_int_L.m (integrator with explicit Jacobian) spm_int_ode.m (generic ode integrator) spm_nlsi_N.m (nonlinear system identification - i.e. model fitting)

Each of these do have lots of other dependencies that will also need to be dealt with.

Getting started

If you are new to SPM and/or DCM, I suggest the following to get going:

  1. Download SPM from the website; or (easier), clone the neurodebian github repository

git clone

  1. Play around with the DCM demos.

Start up matlab & initialize spm

addpath(spm_folder) spm eeg

Close GUI windows.

Look for 'DEMO' files in the 'Neural Models' and 'dcm_meeg' toolboxes, such as this one

run DEMO_model_reduction_ERP.m

Browse through the code and check out the functions calls and function calls therein, etc.

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