Dear SPM experts,
I want to study differences in fMRI resting-state effective connectivity between a group of healthy volunteers and a group of patients. I am focusing on 4 specific regions of interest and using the Parametric Bayes (PEB) model to evaluate group effects on parameters.
I ran the analysis but I would appreciate if someone could help me in understanding some parts of the results and in identifying the most useful information to report in the paper.
For each subjects, I specified the full model DCM and then collated all DCMs in a GCM file. The estimation of the models was performed using the spm_dcm_peb_fit.
I then estimated a second level PEB. I put the group mean as first regressor and the group difference (vector of 1 for the healthy volunteers and 2 for the patient group) as the second one. Since I am focusing on resting state data, I am interested in the matrix A. I then searched over nested PEB models using the function spm_dcm_peb_bmc (PEB). I did not have specific hypotheses, so I was interested in looking at all possible reduced models to prune away any parameters that do not contribute to the model evidence.
Please find a screenshot of the results that I got at this link.
I would really appreciate any help in interpreting the results.
The posterior probability of the models (left-most figure) is very low, so I suppose that this means that it is not possible to determine the winning model. Is that correct? On the right-most figure instead I have the results of the BMC. I interpreted the results like that: the best parameters describing the group effect (covariate 1) are those with a posterior probability of 1. So, can say that the model best describing the group effect is the one characterized by parameters with posterior probability of 1? And that the model best describing differences between group is the one whose parameters have posterior probability of 1?
Thank you very much for your help.