Dear Klaas and DCM experts,

I appreciate your help very much.

>1) The Bayesian model comparison tells you that DCM_control is a lot
>better than DCM_test.  If, as you say, that is a consistent finding
>across subjects, then there is no reason to stick to DCM_test.

Some DCM works built several models in their papers, and decided which 
model was optimal based on their Bayes factors. However, they did not 
always compare one model to the special model such as DCM_control (no 
intrinsic connection, no modulatory effects). Is it necessary that the 
Bayes factor of the "optimal" model is larger than that of the spcecial 
model?

Thanks you in advance for your advice,

Shoichi

>2) The overall Bayes factor is mentioned at the bottom of the MATLAB
>output, in your example 414.24.  See this paper for detailed
>explanations on model accuracy and model complexity:
>Penny WD, Stephan KE, Mechelli A & Friston KJ (2004)
>Comparing dynamic causal models.
>NeuroImage 22: 1157-1172.
>
>3) Given a flat prior p(m) on the models, the posterior probability
>of the model is identical to the model evidence, i.e. p(m|y) =
>p(y|m).  You can derive this mathematically from Bayes theorem.
>
>Best wishes,
>Klaas
>
>
>
>At 06:52 08/12/2006, you wrote:
>>Dear DCM experts,
>>
>>I analyzed fMRI data with DCM, but I have trouble with the interpretation
>>of the results. Now I want to check whether my model (DCM_test) is
>>reasonable or not. Based on the previous message, I compared the model to
>>the other model (DCM_control) which have no intrinsic connections (i.e.,
>>DCM.a = [0 0 0;0 0 0;0 0 0], DCM.b = [0 0 0;0 0 0;0 0 0], DCM.c = [1 1
>>1]'). We got following result with the subject 1:
>>         -------------------------------------------------
>>         Model 1: D:\DATA\sub01\DCM_control.mat
>>                             versus
>>         Model 2: D:\DATA\sub01\DCM_test.mat
>>
>>         All costs are in units of binary bits
>>         Region  ROI_1: relative cost  = -0.00, BF= 1.00
>>         Region  ROI_2: relative cost  = -0.11, BF= 1.08
>>         Region  ROI_3: relative cost  = 0.07, BF= 0.95
>>         AIC Penalty = -8.66, BF = 403.43
>>         BIC Penalty = -24.60, BF = 25412184.00
>>         AIC Overall = -8.69, BF = 414.24
>>         BIC Overall = -24.64, BF = 26093096.24
>>
>>         Consistent evidence in favour of model 1
>>         Bayes factor >= 414.24
>>         -------------------------------------------------
>>The parameter of the DCM_control and DCM_test are as follows:
>>DCM_control
>>DCM.A  =  [-1 0 0; 0 -1 0; 0 0 -1];
>>DCM.B  =  [0 0 0; 0 0 0; 0 0 0];
>>DCM.C  =  [0.0167, 0.0060, -0.0018]
>>
>>DCM_test
>>DCM.A  =  [-1.0000  -0.0000  0.0000; 0.0098  -1.0000  0.0000; -0.0508  -
>>0.0000  -1.0000]
>>DCM.B  =  1.0e-005 *[0  0  -0.2646; 0  0  -0.5407; 0  0  0]
>>DCM.C  =  [0.0171  0  0]
>>         -------------------------------------------------
>>
>>(1) Should I give up DCM_test model?  I calculated the data of ten
>>subjects, and found that all the results were similar. Should I not 
compare
>>DCM_test model to DCM_control which is the special case of the DCM?
>>
>>(2) In recent DCM papers, they compared two models and they reported their
>>Bayes factors (for example, Smith et al., Neuron 49, 631-638, 2006, page
>>637, Figure 3 legend; Each subject's BF was more than 7). In the upper
>>case, there are a lot of BFs. Which is my Bayes factor.
>>
>>(3)Could you tell me how to extract the posterior pobability value.
>>
>>I am sorry for my basic questions, but I hope someone helps me.
>>
>>
>>Sincerely,
>>
>>
>>Shoichi Ugai
>>[log in to unmask]
>>Department of system engineering
>>Tokyo Metropolitan University
>>Tokyo, JAPAN
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