Dear Ivan,
To my understanding (and from some limited experience with this), using a
mask image at the model estimation stage to reduce the search volume does
not affect the uncorrected Z-score estimate - the uncorrected p-values are
independently estimated for each voxel, and so they won't improve (or
change in any way) by changing the mask. The corrected p-values, on the
other hand, would benefit from reducing the search volume (although other
factors such as change in smoothness would also be relevant).
Regarding the practical side of it: perhaps one possibility to ensure the
mask doesn't have sharp edges (so that it doesn't decrease the smoothness
estimate) is to simply smooth it with a large kernel at some stage of its
creation.
In order to explicitly specify a mask at the individual model estimation
stage, you need to change several things in the SPMcfg.mat file
before esimation (this was described on the list some time ago). I
recently wrote a small program to do this automatically - it can be
downloaded from:
http://www-psych.stanford.edu/~kalina/SPM99/Tools/glm_specmask.html
The reason for writing this program was that we've noticed here that
on many occasions, the default mask (based on 80% of the global
signal) would not include regions in or near susceptibility areas -
especially for functional images collected at higher fields (e.g.,
3T) where differences in intensity between regions appear the be
larger. On the other hand, when an explicit mask based on the segmented
gray matter is used during model estimation, often times there appear to
be robust effects in regions that would have been excluded otherwise
because of low signal values.
I was wondering if anyone else has any similar observations and what other
approaches exist for dealing with this. As it appears to be potentially an
important issue (it may lead to claims about the absence of activation in
regions that were activated by the paradigm), I was wondering to what
extent it has been identified as an issue by other groups as well. Any
comments would be much appreciated.
Best regards,
Kalina
On Thu, 7 Dec 2000, Ivan Toni wrote:
> 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
>
>
_____________________________________________________________________________
Kalina Christoff Email: [log in to unmask]
Office: Rm.478; (650) 725-0797
Department of Psychology Home: (408) 245-2579
Jordan Hall, Main Quad Fax: (650) 725-5699
Stanford, CA 94305-2130 http://www-psych.stanford.edu/~kalina/
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