Dear all,
I have a question regarding the correct manner to perform a between-group
comparison within the framework of DCM network discovery.
In the framework of BMS, the suggested approach in the presence of more than one
experimental group (or family), when the winning models for the different groups
differ with respect to parameters/connections, is to use Bayesian Model
Averaging (Penny et al. 2010). This provides weighted summary coupling
parameters e.g. over the entire model space of each group, and it then allows to
perform between-group comparisons, thus avoiding conservative assumptions about
any particular model.
In one experiment, I have now used DCM network discovery, instead of BMS, to
identify a group-specific optimum model for two different experimental groups.
The resulting optimum models have different parameter/connection configurations
between the two groups, and it is therefore difficult to perform between-group
comparisons on the coupling parameters.
The problem is that in this case BMA does not seem to be a viable solution
since, strictly speaking, I only have one model per group (i.e. the winning
model), instead of a model space constituted by several models whose parameters
can be averaged.
Are there any recommended approaches to overcome this problem in this case?
I could e.g. manually specify and estimate a set (or even the full set?) of
alternative models to the optimum model, then perform BMS to actually verify
that the optimum model is indeed the winning model, and, contextually, also
calculate BMA. But this does not seem a very efficient solution. Also, I would
need to specify families to calculate BMA, and I do not think that there is any
particular rationale to do so here.
Another solution that I have explored is, given that for each subject I have
both the fully connected model entered in the DCM network discovery and the
output optimum model, I can in principle perform BMS entering the two models for
each subject and arbitrarily assigning the fully connected model to family 1 and
the optimum model to family 2, and in such a way calculate BMA.
But I am not sure that this is a sensible solution.
In addition, in one of my two experimental groups, the optimum model has the
same number of connections/parameters as the fully connected model (though e.g.
DCM.F differs between the two models). Therefore, BMS/BMA seems to make even
less sense to me.
Any help would be warmly appreciated!
Thank you and best wishes,
Marco
--
Marco Tettamanti, Ph.D.
Nuclear Medicine Department & Division of Neuroscience
San Raffaele Scientific Institute
Via Olgettina 58
I-20132 Milano, Italy
Phone ++39-02-26434888
Fax ++39-02-26434892
Email: [log in to unmask]
Skype: mtettamanti
http://scholar.google.it/citations?user=x4qQl4AAAAAJ
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