Dear Maria
If I have understood your procedure correctly you didn't specify any inputs to your model. This means that your system is 'silent' since nothing is creating activity.
Hope this helps
Nia
On 23 Oct 2012, at 19:08, Maria Densmore <[log in to unmask]> wrote:
> Hello SPMers,
>
> We are having problems with our DCM analysis in that we are not getting
> results.
> Below we have listed the steps taken in hopes that the SPM community might
> notice where we have made our errors.
>
> Thanks for your time and attention.
> Look forward to your comments.
> Maria
>
>
>
> I set up the 6-Var-Design and specified the same model (M1) for each
> control participant as follows:
> 1. I collect the 6-Var SPM.mat file of the first control subject
> 2. I enter a name for the model (e.g. M1)
> 3. I collect VOIs (in this case 3 - LC, Thal, DMPFC) from the 6-Var-folder
> of each participant
> 4. Then I define the input specifications, i.e. I specify the variables
> included in the design (I do not include variables of no interest to this
> analysis but I include our variable EmDC)
> 5. I set the timing information to default (3 3 3), same with the Echo time
> TE =0.04,
> 6. I set the model options to default: bilinear modulatory effects, one
> state per region, no stochastic effects, no centre input
> 7. I then specify the intrinsic connections and choose all possible ones (3
> are already set, I choose the remaining 6).
> 8. I then specify the effects of EmDC on specific regions and
> connections. As we believe EmDC activates brainstem areas directly, I
> chose the LC only here and no effects on connections
> 9. I repeat this for all control participants
> 10. I use the spm_dcm_post_hoc command and include all 16 DCM_M1 models
> created before (Friston & Penny 2011: This routine searches over all
> possible reduced models of a full model (DCM) and uses post hoc model
> selection to select the best. Reduced models mean all permutations of free
> parameters (parameters with a non-zero prior covariance), where models are
> defined in terms of their prior covariance. The full model should be
> inverted prior to post hoc optimization. If there are more than 16
> free-parameters, this routine will implement a greedy search: This entails
> searching over all permutations of the 8 parameters whose removal
> (shrinking the prior variance to zero) produces the smallest reduction
> (greatest increase) in model evidence.
> This procedure is repeated until all 8 parameters are retained in the best
> model or there are no more parameters to consider.When several DCMs are
> optimized together (as in group studies),
> they are checked to ensure the same free parameters have been specified and
> the log-evidences are pooled in a fixed effects fashion. The outputs of
> this routine are graphics reporting the model reduction (optimization) and
> an optimized DCM (for every input DCM) that contains reduced conditional
> parameters estimates.)
> This gives me 16 DCM_opt_M1 files. I can load these files in Matlab and
> review the Ep.A, Ep.B and Ep.C values. Ep.B represents the intrinsic
> connections between brain ares. Here it should also be visible which
> connections have been removed. Unfortunatly, Matlab tells me they are all
> zero for DCM_opt_M1...
>
> <untitled-[2]>
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