Hello,
Continuing my fixation with voxels that are missing data (i.e., voxels
with zeros), I was wondering how these voxels get treated going to a 2nd
level analysis (or similarly, how voxels that weren't actually acquired
at acquisition, due to limited slice coverage, get treated as the first-
level images are transformed into standard-space for the 2nd level
analysis).
In particular, are such 0's treated as just any other legitimate value,
or are they used to create a mask for the 2nd level images? And, if the
latter, does a single instance of a 0 in a voxel of one subject result
in a 0 at that voxel in the 2nd level results
Basically, I'm trying to determine if there is any common software
package for image statistics that can compute voxel-wise statistics
using the subset of subjects that have data available at that voxel in
standard-space, while just "ignoring" the subjects that don't have data
at the voxel -- i.e., allowing for varying d.f's across voxels in the
statistic computation. (For example, from what I can tell so far, SPM
doesn't appear to have this capability, as a NaN at a voxel in a single
subject results in a NaN at that voxel in the group level contrast).
I see that FEAT does have an option in Higher-level analysis to
automatically "de-weight" outliers, which seems quite similar in concept
(and perhaps could even be used to achieve the behavior that I seek?)
thanks,
-MH
--
Michael Harms, Ph.D.
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Conte Center for the Neuroscience of Mental Disorders
Washington University School of Medicine
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