Hi Rik,

Let me have a go at your questions.
See interleaved text here under.

Le 17/03/2011 11:09, Rik Henson a écrit :

 

Dear DCMers –

 

A few, hopefully simple questions for DCM(10) for fMRI:

 

1. Does it make sense to have a model with 4 regions, in which 2 regions within each of 2 pairs are interconnected, but there are no connections across pairs (ie, two “isolated” subnetworks; eg, with regions E1<->E2 F1<->F2, DCM.a = [1 1 0 0; 1 1 0 0; 0 0 1 1; 0 0 1 1])?

Yes it does.
You're "simply" modelling 2 parrallel and independent processes a single model.

2. A related question to above: does it make sense to compare the free energy of two models, one in which a region is “isolated”  (ie has no connections apart from a self-connection) with one in which it has connections to other regions? And could this be used to ask whether that region needs to be in the model? (I realise one cannot use the free energy to compare models with different regions – ie different data – but wonder whether this approach could be a useful heuristic answer to that question?).

The residual term of the isolated region will be its own signal (no drive -> flat modelled activity).
So your question would rather be: is the activity in my last/isolated region better explained, or not, when it is driven by the rest of the network? The model comparison will thus be between a simple model where the activity of one area is not explained at all, and another one with an extra parameter and a bit more signal explained.
This doesn't really answer your question whether the region should (or not) be part of the network though...
I would say that choice of regions to include in the model is more empirical: you should include the areas necessary to build a model which models as accurately as possible (or  sufficiently realistically) the "brain function" you want to study.

 

3. Does it make sense to have a model with no modulations (eg, DCM.b = zeros(4,4,2), for two inputs)?

Yes.
You would then be comparing the possible different intrinsic connectivity of a network.

 

4. If the GLM in an SPM.mat file has two event-related regressors for conditions G and H (and a jittered SOA so that responses vs the inter-event baseline are estimated efficiently):

 

4.1 does it make sense to use one of these as a driving input (eg, to both of two regions, eg, DCM.c = [1 0; 1 0]) and the other as a modulatory input (eg, DCM.b(:,:,1) = zeros(2,2); DCM.b(:,:,2) = [0 1; 1 0])?

An event-type modulatory input would only modulate the intrinsic connectivity for a very brief instant, while neuronal activity lasts much longer. Mathematically it is ok but I don't think it makes much sense.
I would be happy to hear Klaas (or other experts) opinion about this.

 

4.2 if instead one wants to make the driving input both G and H (ie treating any event vs baseline equivalently), and modulate by just H, is it sufficient to set:

 

DCM.U.u        = [full(SPM.Sess(ses).U(1).u)+full(SPM.Sess(ses).U(2).u)   full(SPM.Sess(ses).U(2).u)];    

DCM.U.name = [sprintf('%s+%s',SPM.Sess(ses).U(1).name{1},SPM.Sess(ses).U(2).name{1})    SPM.Sess(ses).U(2).name];      

 

(and with DCM.c = [1 0; 1 0] and DCM.b(:,:,1) = zeros(2,2); DCM.b(:,:,2) = [0 1; 1 0], as above), rather than having to re-parameterise and refit the GLM? (ie, are there any other fields in the DCM structure that would be affected by this re-definition of inputs, hence causing different results compared to reparametrising and refitting the GLM?)

Yes from a coding point of view this seems ok. Only 1 driving input which are the 2 types of events put together, i.e. with the same drive on the network, and the 2nd input made of only one sort of events.
Though the same worry as previous comment applies here: Does it make sense to have an instantaneous modulation of connectivity ?

 

Apologies if some of the above questions illustrate an incorrect understanding of the DCM code.

Sounds rather like deep understanding of DCM machinery!

HTH,
Chris

 

BW,R