Collection of codes (MATLAB scripts) of the extended Dynamic Causal Modelling for phase coupling (eDCM PC): the archived version from the gitlab repository (https://gitlab.com/azayeld/edcmpc/). Extended Dynamical Causal Modelling for phase coupling (eDCM PC) (Yeldesbay et al. 2019) is an additional collection of scripts to Dynamical Causal Modelling (DCM) (Friston et al. 2003) that allows to estimate the coupling between oscillatory systems using the phase information extracted from the measured signals in the case of non-uniform phase distributions. This collection of scripts extends the version of the Dynamical Causal Modelling for phase coupling (Penny et al. 2003). Please refer to the following publication for details Yeldesbay et al. 2019. References: Yeldesbay, A., Fink, G. R., & Daun, S. (2019). Reconstruction of effective connectivity in the case of asymmetric phase distributions. Journal of Neuroscience Methods, 317(February), 94–107. https://doi.org/10.1016/j.jneumeth.2019.02.009. Friston, K.J., Harrison, L., Penny, W., 2003. Dynamic causal modelling. NeuroImage 19 (4), 1273–1302. https://doi.org/10.1016/S1053-8119(03)00202-7. Penny, W.D., Litvak, V., Fuentemilla, L., Duzel, E., Friston, K., 2009. Dynamic causal models for phase coupling. J. Neurosci. Methods 183 (1), 19–30. https://doi.org/10.1016/j.jneumeth.2009.06.029.

{"references": ["Yeldesbay, A., Fink, G. R., & Daun, S. (2019). Reconstruction of effective connectivity in the case of asymmetric phase distributions. Journal of Neuroscience Methods, 317(February), 94\u2013107. https://doi.org/10.1016/j.jneumeth.2019.02.009."]}