Python implementation of the regression Dynamic Causal Modelling (rDCM) toolbox (v6.0.0).
python3 -m pip install git+https://github.com/jadecci/rDCM_py.git
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']
- Original ridge rDCM model
- Works for both task and resting-state fMRI data
- Works for real data
- Sparse rDCM model
- Option to use synthetic/simulated data
- Option to create covaraince matrix
- Option to predict signals (in time domain) and evaluate the prediction
- 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
- 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
- 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
- Marreiros AC, Kiebel SJ, Friston KJ. 2008. Dynamic causal modelling for fMRI: a two-state model. Neuroimage 39, 269-78.
- 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.