Hi Doug,
OK, just one last question! I've decided I'd rather use the smaller masks
(generated by resampling aparc+aseg.mgz to functional space, as in my Method
1), since the difference is small (~2%), but there's absolutely no overlap
in this case. Thus it's both the most conservative approach and
you can play around with the fill threshold
doug
Timothy Vickery wrote:
> I see, thanks for the quick clarification. So in the Method 2 there is
> a chance that some voxels will show up in multiple ROIs, right? Is
> there a modification of Method 2 that maximizes that number of labeled
> voxel
I see, thanks for the quick clarification. So in the Method 2 there is a
chance that some voxels will show up in multiple ROIs, right? Is there a
modification of Method 2 that maximizes that number of labeled voxels while
ensuring that they will not show up in multiple ROIs?
Thanks again,
Tim
On
The difference is the partial volume correction is different if there
are a bunch of other labels there (aparc+aseg) vs only one label
doug
Timothy Vickery wrote:
> Hi all,
>
> I'm creating binary mask volumes in a subject's native functional
> space from the segmented brain in aparc+aseg.mgz (
Hi all,
I'm creating binary mask volumes in a subject's native functional space from
the segmented brain in aparc+aseg.mgz (FS v 4.5). I have found that doing
this two different ways produces different results, and I'm wondering if
anyone can illuminate why this might occur and which method is mor
