Dear SMPers,
I would like to make a DCM analysis. However, I encounter some difficulties.
My experiment comprised two conditions, a memory task (the subjects
had to evoke past memories) and a control condition.
I would like to test, using DCM, the influence of left and right
frontal regions (FTl + FTr) on two areas of the medial temporal lobes
(PHl + PHr) during memory evocation.
In the literature, most of studies have used DCM analysis in the
context of factorial design, e.g., visual processing modulated by
attentional processes.
At first, I would like to know whether the use of DCM is appropriate to:
1. Basic designs without factorial modulation (in this case, B matrix = 0)?
2 the study of memory processes?
Secondly, I would like to estimate a model (DCM1) that comprised
intrinsic connections from FTl to PHl and PHr ; and from FTr to PHl
and PHr. The memory functions were entered into the DCM through FTl
and FTr.
I observed a warning message during the estimation (?the matrix is
singular?), indeed it seems difficult to distinguish the influence of
the connection FTl --> PHr from the influence of the connection FTr
--> PHr. Do I have to separate DCM1 into two parts (DCM2: connections
from FTl to PHl and PHr / DCM3 model FTr to PHl and PHr)?
Finally, independently of the estimated model (DCM1, DCM2 and DCM3), I
encountered two problems. In the sequel, values are given for DCM2.
1. For C, we obtained: DCM.C = [0.0554 0 0] and DCM.pC = [1.0e-03 *
0.2869 NaN NaN]
Could you explain why the posterior probability (1.0e-03 * 0.2869) is
so low? Why do I have NaN for the probabilities of the non estimated
parameters?
1. For DCM.A, I obtained the following values:
[-1 0 0
0.4867 -1 0
0.6482 0 -1]
What is the meaning of -1?
For DCM.pA, I obtained:
[NaN NaN NaN
0.9986 NaN NaN
1 NaN NaN]
Why do I have NaN for the probabilities of the non estimated parameters?
Thank you very much in advance,
Regards,
Anne
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