rDCM toolbox in Python

Python implementation of the regression Dynamic Causal Modelling (rDCM) toolbox (v6.0.0).

Installation

python3 -m pip install git+https://github.com/jadecci/rDCM_py.git

Usage

from rdcmpy import RegressionDCM

rdcm = RegressionDCM(data, TR, drive_input=task_regressors, prior_a=SC)
rdcm.estimate()
params = rdcm.get_params()

A = params['mu_connectivity']
C = params['mu_driving_input']

Functions translated

1. Original ridge rDCM model
2. Works for both task and resting-state fMRI data
3. Works for real data

Functions yet missing

1. Sparse rDCM model
2. Option to use synthetic/simulated data
3. Option to create covaraince matrix
4. Option to predict signals (in time domain) and evaluate the prediction

References

1. Frässle, S., Lomakina, E.I., Razi, A., Friston, K.J., Buhmann, J.M., Stephan, K.E., 2017. Regression DCM for fMRI. NeuroImage 155, 406–421. doi: 10.1016/j.neuroimage.2017.02.090
2. Frässle, S., Lomakina, E.I., Kasper, L., Manjaly Z.M., Leff, A., Pruessmann, K.P., Buhmann, J.M., Stephan, K.E., 2018. A generative model of whole-brain effective connectivity. NeuroImage 179, 505-529. doi: 10.1016/j.neuroimage.2018.05.058

rDCM for resting-state fMRI

3. Frässle, S., Harrison, S.J., Heinzle, J., Clementz, B.A., Tamminga, C.A., Sweeney, J.A., Gershon, E.S., Keshavan, M.S., Pearlson, G.D., Powers, A., Stephan, K.E., 2021. Regression dynamic causal modeling for resting-state fMRI. Human Brain Mapping 42, 2159-2180. doi: 10.1002/hbm.25357

SPM DCM fMRI prior

4. Marreiros AC, Kiebel SJ, Friston KJ. 2008. Dynamic causal modelling for fMRI: a two-state model. Neuroimage 39, 269-78.
5. Stephan KE, Kasper L, Harrison LM, Daunizeau J, den Ouden HE, Breakspear M, Friston KJ. 2008. Nonlinear dynamic causal models for fMRI. Neuroimage 42, 649-662.