Dear Shoichi,

in response to your questions:

(1) No.  A value in DCM.pB reflects the 
conditional probability that this parameter, 
given the posterior mean and posterior variance 
of its estimate, exceeds a chosen 
threshold.  Your result means that you have only 
25% confidence that your parameter exceeds this threshold.

Furthermore, from your comments, I understand 
that you are using SPM2 where the threshold was 
set to be log(2)/4.  This was a choice that was 
motivated specifically for the "attention to 
motion" model shown in Friston et al. 2003.  We 
subsequently changed it to be zero, and this is 
what I would recommend for your case as well.  It 
means you are testing how confident you can be 
that the modelled effect exists.  Generally, I 
would recommend to use SPM5 for DCM analyses.

(2) You are correct that the numbers in DCM.B are 
arbitrary in the sense that they depend on the 
scaling of the input functions.  However, this 
scaling is linear and, because the PDF is 
Gaussian, it does not change any inference, 
neither at the single-subject nor at the group 
level.  I would say that you should always report 
the DCM.B values (together with a description of 
your input functions).  If it is a 
subject-by-subject analysis, the DCM.pB values 
are essential as well because this is what your 
inference is about.  If it is a group analyses, 
your inference is across subjects and whether or 
not DCM.pB is relevant depends on how this 
inference is obtained.  If you go for classical 
stats (e.g. t-test applied to the DCM.B values), 
the DCM.pB values are not relevant.  If you go 
for the Bayesian approach, you need the "average" 
function in DCM.  This will give you an "average" 
DCM.B value to report along with the associated 
DCM.pB for inference (see previous postings on 
this and on the caveats concerning 
interpretation).  Generally, never ever do any 
stats on the DCM.pB values themselves.

Best wishes
Klaas



At 02:05 17/01/2007, you wrote:
>Dear DCM experts,
>
>I studied DCM analysis, but I do not have confidence in my understanding.
>My comments (q1-q2) are correct?
>Please help me.
>
>I analyzed fMRI data with 5 ROIs and got the results:
>¥mean DCM.B (ROI1 to ROI2) = 0.0122
>¥mean DCM.pB(ROI1 to ROI2) = 0.2518
>¥DCM.T = 0.1733
>¥The mean DCM.B (ROI1 -> ROI2) was statistically significant using t-test
>(p<0.05).
>
>
>(q1) Because 0.2518 is larger than 0.17 (=log(2)/4), the modulation from
>ROI1 to ROI2 was significant with the probability of 95 %.  Because DCM
>uses Bayesian inference in each subject analysis (fix effect model), I
>cannot say that the significant level is P < 0.05.
>Is my comment correct?
>
>(q2) Because the DCM.B values are arbitrary, I should report the DCM.pB
>values not DCM.B value in my paper.
>Is my comment correct?
>
>Thank you in advance,
>
>Shoichi Ugai
>[log in to unmask]
>Department of system engineering
>Tokyo Metropolitan University
>Tokyo, JAPAN