Since you know the spatial distance between your peaks and the size of your smoothing kernel, you could simply compute the degree of dependency in terms of the overlap of the two Gaussians. Also, you could run the DCM on the smoothed data first, and then apply it to the unsmoothed data, hopefully producing parameter estimates that are not too dissimilar?
Von: Tali Bitan <[log in to unmask]>
An: [log in to unmask]
Gesendet: Montag, den 13. Juli 2009, 11:58:58 Uhr
Betreff: [SPM] distance between VOIs in DCM
Dear DCM experts
We would like to use DCM with regions that are pretty close together (~16mm
between peaks at the group and individual level), while our smoothing kernel is
Would it be reasonable to use 5mm radius VOIs based on these peaks
(resulting in a distance of only 6 mm between voxels in the perimeters of
Or is it better to use unsmoothed data? or alternatively - pick a different
(weaker) coordinate for one of the VOIs?
Thanks a lot