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Dear Chris and Klaas,

 

thanks for your help and comments.

 

Spm_dcm_average actually estimates both the posterior mean and the posterior covariance and stores it to disk as Chris pointed out. I estimated both parameters for each group separately using spm_dcm_average. Now I want to look at specific DCM parameters and compare them across groups; for example, I want to find out if there is a between group difference of a task specific modulation of a forward connection from one region to another. Therefore, I entered these specific group parameters manually in the algorithm which spm_dcm_review uses for computing the posterior density of contrasts of connections and it worked quite well.

 

With spm_dcm_review I usually can only compare contrasts of connections with one group of subjects which I included in a single spm_dcm_average procedure (resulting in a single DCM_avg_* file). However, I want to do between group comparisons and this is/was related to my actual question if I can use spm_dcm_review also for between group comparisons (using parameters from group specific DCM_avg_* files).

 

Best, Sascha.

 

 

Von: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] Im Auftrag von Christophe Phillips
Gesendet: Mittwoch, 22. Juli 2009 13:33
An: [log in to unmask]
Betreff: Re: [SPM] DCM parameter comparison between groups

 


Dear Sasha and Klaas,

Here is a snippet of code from spm_dcm_average:

% Average models using Bayesian fixed effects analysis -> average Ep,Cp
%--------------------------------------------------------------------------
% averaged posterior covariance
final_iCp = sum(miCp,3);
Cp        = inv(final_iCp);
% averaged posterior mean
weighted_Ep = zeros(length(cwsel),1);
for model = 1:num_models,
    weighted_Ep = weighted_Ep + miCp(:,:,model)*mEp(:,model);
end
Ep = Cp*weighted_Ep;

% Copy contents of first DCM into the output DCM and insert averaged values into parameter & covariance vectors
DCM                 = DCM_first;
DCM.models          = P;
DCM.Ep(cwsel)       = Ep;
DCM.Cp(cwsel,cwsel) = Cp;

It does look like both posterior mean and covariance are estimated.

Sasha,
make sure you're using the latest updates for averaging as there used to be some problem with the averaging of the matrix D parameters, which only appear when dealing with non-linear models (effect of one area on the connection between 2 others).

Best,
Chris


Klaas Enno Stephan a écrit :

Dear Sasha,

If I understand your question correctly, then you would like to apply a linear contrast to the posterior densities of two groups that resulted from using the fixed effects Bayesian averaging routine in SPM.  In principle, this should be possible.  However, you would have to compute this manually.  As far as I remember, the SPM_DCM_average function only stores the posterior means in the DCM file it writes to disc, not the posterior covariances; you would need both, however, to compute the contrast. 

However, you should ask yourself whether for the particular question you are interested in a fixed effects or random effects analysis is more appropriate.  Ultimately, this should determine which analysis you are using at the second level.  There were numerous emails on this topic in the recent past which you should find easily in the archives. 

Best wishes,
Klaas

 

Von: Sascha Fruehholz <[log in to unmask]>
An: [log in to unmask]
Gesendet: Samstag, den 18. Juli 2009, 18:08:23 Uhr
Betreff: [SPM] DCM parameter comparison between groups


Dear SPMers,

 

I have two groups of subjects and I applied the same DCM model for both groups. I got DCM parameters for each group which are in accordance with our hypothesis. I want to do statistical comparisons for these parameters (i.e. DCM.B) between both groups. Therefore, I entered all single subject parameters in a classical statistical inference test (t-test). The resulting statistical group parameters are not the same as those which I  get when I use DCM averaging (spm_dcm_average) where group parameters are actually computed using a  Bayesian fixed effects analysis. More importantly, the group parameters which result from the classical inference approach did not fit with our hypothesis as good as those parameters from the Bayesian fixed effects analysis.

 

My question is: instead of using classical statistical inference tests for between group comparisons, is there a possibility to use for example the same algorithm which is implemented in spm_dcm_review (which computes the posterior density for contrasts of connections)  for between group comparisons? This algorithm obviously compares parameters within a DCM model using the DCM.Ep and DCM.Cp parameters. Can I also take the group specific DCM.Ep and DCM.Cp parameters and compare them by using the algorithm implemented in spm_dcm_review?

 

Any help is highly appreciated.

Best, Sascha.

 

 

 

 

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