Hi all,
I am using "con" images for a "second level analysis" using a multivariate
method. Based on my early results, I feel that the original data was
inadequately smoothed for this purpose, and I would like to smooth the data
more. There are two options for this...either go back to the beginning,
resmooth the data with a larger kernel and estimate all of the models over
again, or simply smooth the con images by a small kernal. Certainly
smoothing existing con images would be faster and would allow me to
experiment with several additional levels of smoothing
I am wondering, therefore, whether these two methods equivalent (except of
course for a rim of "edge" voxels near the brain mask border, whose
neighbors are NaN)?
Also, how would you calculate how much extra smoothing to add to con images
to achieve a given level of "original smoothing" had I smoothed the raw data
by the desired amount in the first place?
thanks!
Amit
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