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Hi Stefan,
Which version of FSL are you using? I
believe 4.0 was a bit too conservative when generating the masks (see previous
posts on this matter), but the newer versions don’t really have a problem with
this.
But, regardless of what version you’re
using, once a voxel is gone, it’s gone for everyone. So, even if you have just
one bad mask in your dataset, it will mess up the others.
I think your approach to overcoming this
(i.e., lowering the %brain/background threshold) is fine. Just make sure you do
it for everyone.
Hope this helps,
David
--
David V. Smith
Graduate Student, Huettel Lab
Center for Cognitive
Duke
Lab phone: (919) 668-3635
Lab website: http://www.duke.edu/web/mind/level2/faculty/huettel/
From:
Sent: Wednesday, May 13, 2009 7:52
PM
To: [log in to unmask]
Subject: [FSL] problems due to
concatenated masks in GFEAT of a very large study - 3rd posting - no answer?
Dear FSL'ers,
I had posted that
earlier. This is the 3rd posting. It would be *very* helpful if someone could
give me some advice on that. Please also let me also know If the problem is not
described clearly enough or if there is no easy answer.
I am running FEAT on a
large fMRI dataset (n > 300). Each subject has 3 runs consisting of 16
blocks of a memory paradigm. After hundreds of hours of computing I have
processed all first- and second level-analyses (from here on referred to as
cross-runs). I have checked the registrations and individual as well as
cross-run activation-maps (retrieval versus fixation) for a subset of subjects
and all seems fine. As a next step I ran a “Single-Group Average” over all 300
cross-runs just to get an impression of the overall activation patterns. The
results were in line with previous studies but in the top slices of the brain
(horizontal slices) as well as in a rim covering the parts of the brain closest
to the skull there was no activation whatsoever. I found out that this was
probably due to the mask.nii of the gfeat. This group mask did not include the
top slices and the outer rim .
Subsequently I went back
and checked all cross-run mask.nii and identified a very few masks which were
missing several top slices (due to bad positioning in the scanner, I guess).
After deleting these subjects from the overall analysis my final results and
the “Single-Group Average” mask looks much better. However, the outer rim is
still missing.
It seems like FEAT
concatenates all cross-run masks and does not include voxels which have a
missing value in any single mask. Vince Calhoun told me on the phone that this
might be due to the fact that FSL smoothes relatively late in the processing
stream (in contrast to SPM). Concequently I went back and changed the
brain/background threshold (brain_thresh) from 10 to 1 and rerun a few subjects
which had slightly impaired cross-run masks. With the new threshold more voxels
get included. Do you think that is an approbriate approach? Has anybody
experienced that problem before? Are there other solutions?
Thank you so much for
your thoughts!
Stefan