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On Fri, 1 Jul 2005 10:32:45 +0100, Michele Wessa <[log in to unmask]> wrote: >I have a question concerning the smoothing of contrast images. Obviously >there are some groups who smooth the contrast images after already having >smoothed the normalised images (for exemple smooth the normalised images >with a Kernel of 6mm and re-smooth the contrast images also with a kernel >of 6mm). I am not sure what are the reasons for doing this? I asked myself >if it is helpful to use this procedure, if I don't want to use a wide >smoothing for the normalised data because I want to see for example small >structures (as the amygdala) and then to re_smooth the original images to >reveal better random effects statistics (I have no idea if this makes >sense)? > >Happy to have suggestions and explanations on this topic - > >Michèle Smoothing is a linear operation on the data. The statistical step is also linear. Because of this, these steps "commute," i.e., can be done in either order. (This might not be quite true at the most fine level of detail, e.g. how SPM masks out voxels outside the brain, but it's close enough for this discussion.) So if there are no other steps aside from the ones you mention, then as far as the beta and con images (but not the spmT and spmF images!) it doesn't matter what order you do things in. Furthermore, smoothing twice is equivalent to smoothing once with a larger kernel. Of course, there could be other objections. For example, it might not be valid to use (e.g.) 10 different smoothing kernels and inspect the results 10 times, because that's another multiple comparison problem.