 |
 |
On Tue, Oct 1, 2013 at 10:39 AM, boris suchan ruhr universität < [log in to unmask]> wrote: > Dear all, > we ae analyzing currently a data set where subjects have learned two > categories and its members. What we want to do is to show, that two > regions show a functional connectivity which increases over time. > But we have the following two problems: > > PPI is looking for a relation from on region to all possible parts of > the brain... > > If we would use DCM we cannot extract the time series at the beginning > from the experiment as the regions that we are interested in show no > activaton... > > What we have alreday done is to use the activation cluster from the > learned condition, extracted the signal changes during learning and looked > than for correlations between the signal changes of both areas but this > seems not to be the best way... > Any help or comments? > Use the gPPI toolbox. Generate the connectivity for each task at baseline and then at followup. Compare the connectivity using group models. gPPI is automated, so you can quickly loop through multiple seed regions. > Many thanx > boris >