Oh, I need to give you an updated mri_binarize. Here is one for centos6.
If that does not work for you, let me know what your platform is.
ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/mri_binarize
On 11/25/2013 03:38 PM, cel...@nmr.mgh.harvard.edu wrote:
> Hi Doug and freesurfe
Hi Doug and freesurfer team
We just realized that the output framemask created by mri_binarize does
not have the same dimension than the input file y.mgh, which is a
concatenated file created through mris_preproc.
so the dimension of our y.mgh is: 163842 x 1 x 1 x 33
and by running this command (
Yes thanks so much, it worked very fine
Celine
>
> In that case, I think you will want to change the design at each voxel
> based on which subjects are present. I have not tried to do this with
> pvr, but there is no reason it should not work. To do this, create a
> binary volume from your data th
In that case, I think you will want to change the design at each voxel
based on which subjects are present. I have not tried to do this with
pvr, but there is no reason it should not work. To do this, create a
binary volume from your data that has the 0s in it, something like
mri_binarize --i
you will want to use the -no-prune flag.If thisis a group analysis, you
should be very, very careful that you know what you are doing. In
general, setting values to 0 prior to group analysis will create biased
results. What you really need to do is have adifferent model for each
voxel that inc
Hi,
When using glm_fit with an input volume that has many voxels with a 0
value (due to a previous thresholding for quality reasons), and the voxel
removes might be distributed randomly across our population, is there a
way not to take into account those voxels in the glm_fit model? I saw the
--pun
