I see, then I get, but never done before. Interesting approach, normally
you do fc with regard to task-related areas or in resting state. So it
seems to be more ambitious approach. I'll think about.
On Aug 27 2011, Peter Michalsky wrote:
>> Why don't you want to get ts with variance related somehow to your
>> task(s) and regress out effects of no interest?
> functional connectivity analyisis is usually done this way in order to
> assure that the connectivity between regions isn't just driven by
> task-related activation patterns. What we are interested in is
> spontaneous synchronized brain activity.
>>> Yes I think for your purposes it would be the latter (zeros(1:11) 1).
>>> As Iwo pointed out, the "adjust data" option removes the null space of
>>> your F-contrast, i.e. the zeros, from your timeseries. See lines
>>> 143-153 in spm_regions.
> I've actually tried that but it gives me very odd results. The two
> regions I'm looking at are almost perfectly correlated (0.99) if I
> specify the F-Contrast like that. Another odd thing is that for both
> regions the eigenvariate captures roughly 99% of the variance. If I
> specify the contrast as (ones(1:11) 0) the eigenvariates capture a more
> reasonable 60% of the variance and the correlation between the
> time-courses of both regions is also in a much more plausible range. It
> really looks to me as if (ones(1:11) 0) were the right way to go but I
> would feel much more confident if I knew why.