Dear Karl, Klaas and colleagues,
I am puzzled by the output from spm_dcm_average. I understand from your
message 024483 that it does not give the average of models in the sense of
the arithmetic means of each of the connections/modulations in matrices A,
B and C (although this approach has been advocated on the list by
yourself and Will in the past, suggesting that one perform one-sample
t-tests on the non-zero values of interest in matrices A,B,C).
In contrast, spm_dcm_average uses a Bayesian FFX analysis across the group,
to estimate the overall posterior mean for each
connection/modulation. But, how does this explain the following
discrepancy, in a very simple model (two regions X and Y, connected
reciprocally and with intrinsic self connections. Area X receives input
from an external visual stimulus. No moderator variables)
From matrices C, in 18 subjects, we estimated the strength of the
influence of the visual input on the area X. For all subjects, pC were
1.000. The actual value in C (for input onto area X) ranged from -0.06 to
+0.40, mean +0.07. Positive values in 15 / 18 subjects for this connection.
But, spm_dcm_average value for this connection was -0.03, pC 1.000 . I do
not understand how the model average could have suggested a negative value
for this connection, when the individual subjects nearly all showed
positive effects (and the nature of the task and simple model would lead
one to also expect a positive value).
Any ideas?
thanks in advance,
James Rowe
(PS, this question arose with a more realistic complex model with bilinear
inputs etc, but these are more tedious to describe in text - the issue is
still seen with the most basic model outlined above)
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