Dear Tim,
Many thanks for your e-mail. In fact, the intrinsic connectivity parameters for most of the neural mass and mean field models used in dynamic causal modelling are optimised during Bayesian inversion. As you probably know, these can have a profound influence on dynamics and modelled responses. The particular intrinsic coupling parameters that are allowed to change are specified in the appropriate Matlab routine that provides the priors. For example, for the standard ERP (Jansen) model, you will find the following lines in spm_erp_priors
% set intrinsic connectivity
%--------------------------------------------------------------------------
% [default] fixed parameters
%--------------------------------------------------------------------------
E = [1 1/2 1/8]*32; % extrinsic rates (forward, backward, lateral)
G = [1 4/5 1/4 1/4]*128; % intrinsic rates (g1 g2 g3 g4)
...
% test for free parameters on intrinsic connections
%--------------------------------------------------------------------------
try
end
Dear Dr. Litvak,
My name is Tim Kunze and I am a student of Biomedical Engineering at Ilmenau University of Technology with Prof. Haueisen. For my bachelors degree, I am currently investigating the influence of Jansen's Model within the DCM approach and have a question:
I assumed, that the intrinsic connectivity parameters (C1-C4, respectively G1-G4) are constant for the model calculation (Bayesian inference). Today I found, that one can actually display the posteriors of the intrinsic connectivity within the dialog "review priors" and that this are not the ratios I expected. Is it right, that the parameters of the intrinsic connectivity within one source are adjusted during the model calculation?
thank you very much for your response,
kind regards,
Tim Kunze