Hi Kosuke,
I can't give an authoritative answer, but I think thresholding/masking
first level con images for a second level analysis should be
unnecessary, and may be a bad idea.
Say I was looking at a two sample t-test of some con images, and at a
particular region, sample B were all large and positive, while sample
A where either all large and negative, or all very close to zero. In
either case, I would want B>A to return significance, not for A to be
masked out of the analysis.
> An appropriate mask can also be obtained by setting absolute threshold
> to "none," as it sets xM.TH to -Inf. However, data analyzed in this way
> (sometimes but not always) causes problems with smoothness estimation.
> I am not sure why, but it may be due to zero-valued pixels that survive.
This sounds to me like a problem that should be investigated and
solved itself, rather than worked around by thresholding contrasts.
What kind of problems with smoothness estimation? (e.g. errors,
warnings, or unusually rough/smooth results, or something else?). Are
there NaNs in the first level contrasts, or do you have implicit
zero->NaN thresholding at the second level?
Best,
Ged.
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