Dear DCM experts,
I am conducting a DCM study for ERP's . More specifically, one of the
things i would like to do is to link DCM (modulatory) parameters with
two temporal (early and late effect) ERP effects. I already fitted a
quite large model space (400 models). My idea of linking parameter to
these two effects was to calculate a mean (across subject) predicted
effect (channels) for every fitted model for both temporal effects.
Then, for each temporal effect, i would take 10% (or 50%) of the
models with the smallest predicted effect and 10% of the models with
the largest predicted effect (sort of 'data' driven post hoc family
partitioning). Than i would use BMA for these models with lowest and
highest effect. After I would simple use classical paired t-tests on
these BMA parameters to see which parameters are significantly
different.
Is this a valid analysis strategy? I would be grateful for
comments/advice regarding this issue.
Kind regards,
Frederik Van de Steen
|