Hello,
For reasons to convoluted to get into, I'm trying to analyze a (synthetic)
subject in their native space, then normalize afterwards for a group
analysis in SPM5. I know that there are problems with the fact that the
contrasts are masked after the estimation, and padded with NaN. I know that
theoretically, normalizing before and after should give the same results.
I'm not getting the same results, and I'm having some problems with my image
masks. Here's what I did:
1) Outside of SPM, I generated a binary mask for the EPI's (I'll call that
"mask_original")
2) Case 1: Normalize First - I normalized the first EPI to the EPI template,
and applied the transformation to the EPI time series, and my mask_original
to create "mask_normalized". I then ran the model, using "mask_normalized"
as my explicit mask. SPM created a mask that is the union of
mask_normalized and the internally derived implicit mask. I'll call that
union mask "normalized_mask_A", since it's already in MNI space.
3) Case 2: Normalize After - I took my raw EPI images and modeled them,
using "mask_original" as my explicit mask. SPM created a mask that is the
union of "mask_original" and the implicit mask. I'll call that "mask_B".
Then, I normalized the first EPI to the EPI template, and applied the
transformation to the con images and to "mask_B" to produce
"normalized_mask_B". Through trial and error, I've figured out that this
produces a con image that need to be masked, and the subsequent masked con
contains the zero values that have to be converted to NaN before it will
work properly in the group model, but I don't think that's the root of the
problem.
Now, my question is this: Shouldn't "normalized_mask_A" and
"normalized_mask_B" be the same? They aren't! Am I doing something
incorrectly here?
My other question is, why should the results of the normalize first vs. the
normalize second analysis be different? I don't think it can be accounted
for just by the voxels on the edge of the brain in the con image that won't
get interpolated properly in the transformation.
Thank you for your help in solving my rather convoluted problem - I'm
posting via the website, and sometimes it seems to post multiple times, so
I'm sorry in advance if that happens.
Thanks!
Allison
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