Dear all,
I'm trying to use an anatomical mask on an EPI timeseries. Previous
messages in this list have addressed a related topic, i.e. the use of an
anatomica mask at the time of contrast estimation. However, I'm trying
to get a bit further than using a mask just for cosmetic reasons. My
goal is to try to constrain the volume assessed by the statistical
procedure, so that hopefully this a-priori information might improve the
statistical power. More precisely, I thought that masking a dataset with
a proper anatomical mask (let's say, a smoothed segmented gm), should
reduced the search volume and therefore the Z score might improve.
However, I don't know what is going to happen to the smoothness of the
image. If the smoothness of the residuals is reduced, then the
statistics would actually get worst.
My first question is the following: does anybody have any experience on
this topic, i.e. if you reduce the search volume to gray
matter with an anatomical mask (rather than to the usual 80% of global
signal), does this have a positive effect on the stat ? I vaguely
remember a paper of Woolsey where he claimed that sharp edges in a mask
image might actually reduce the image smoothness, with a negative effect
on the stat.
Second question: provided it is worth to implement this anatomical mask
at the time of model assessment, how do you tell SPM to do it ? I saw
there is a field xM, in spm_fmri_spm_ui, with a flag xM.I set to zero,
so that SPM is extracting its own mask from the actual EPI timeseries.
However, when I put this xM.I = 1, and the xM.VM = {['mymask.img']},
it's not very happy (I got an error like "improper index matrix
reference" in spm_spm line 689).
I also tried to induce SPM to mask the dataset by masking just one EPI
image of the timeseries with my anatomical mask. Since SPM is
considering only voxels common to the whole dataset, this should have
worked. However, in this case I got an error message at the time of
model estimation ( Error in spm_spm.m on line 689 ==> tM =
inv(xM.VM(i).mat)*M; %-Reorientation matrix
Error in spm_fmri_spm_ui.m on line 622 ==>
spm_spm(VY,xX,xM,F_iX0,Sess,xsDes);
Thanks in advance,
Yours,
Ivan Toni
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