I have analyzed 35 subjects perceptual decision making data using DCM. I used BMS to find the optimal model (model number 5) out of 10 defined models and also used BMA to interpret about the parameters by averaging over all the models over all the subjects. Each
of the models contains 4 areas e.g. a,b,c & d.
My questions are:
1). My winning model, model number 5 e.g. contains facial affect modulating from region a to b, a to c & a to d- using BMS.
whereas BMA results show there is no significant modulation by that facial affect from region a to b, a to c & a to d - using one way t-test on the parameters.
And similarly for significant intrinsic connections as well.
So is it always true that the connections/modulations on the winning model should match with the significant endogenous/modulatory parameters obtained from BMA ?
If not (this is what I understood from Dr. Friston & Dr. Stephan's paper- 'Ten simple rules for DCM' that we use BMS to infer about model structure & BMA to infer the parameters and BMA abandons the dependence of parameter inference on particular model chosen
and so it may not be matched.)
then whether to believe BMS results or BMA results and which one to report or both results can be reported in writing part with different inferences ?
2). What the minimum exceedance probability value is enough for a model to be selected as optimal (of course it should have maximum compared to other models but what value should be OK ?) ?
Thanks a lot !!
Regards,
Sahil Bajaj