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 easiest to match up with the analyses I've already conducted using these masks outside of fs/fsfast. 

The only problem is that there doesn't seem to be an obvious way to use these masks with func2roi-sess...I can produce labels from my masks, but they are in native functional space, and as I understand it func2roi-sess expects the .label file to be in the subject's 1mm^3 anatomical space (or tal). It seems I could "trick" func2roi-sess into using my masks as if they are functional contrast stat maps, but will this work? I.e., something like this:

func2roi-ses -maskanalysis DummyMaskAnalysis -maskcontrast Mask1 -maskmap Mask1.nii.gz -maskthresh 0.5 [...etc.]

Since I've set the threshold at 0.5 and the mask is binary, only my voxels should pass through. I tested this out and it actually ran with this "dummy" directory structure properly formed...Is there any obvious problem with this? (slash, obviously better way to do this?) 

Thanks a lot for your help.

Best,
Tim


On Fri, Aug 20, 2010 at 4:34 PM, Douglas N Greve <greve@nmr.mgh.harvard.edu> wrote:
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 voxels while ensuring that they will not show up in multiple ROIs?

Thanks again,
Tim


On Fri, Aug 20, 2010 at 4:15 PM, Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> wrote:

   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
       (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 more
       appropriate (or what other method you would suggest)...

       Method 1: Resample aparc+aseg.mgz into native functional space
       using
       mri_label2vol --seg aparc_aseg.mgz --fillthresh 0.5 [...plus
       the rest of the appropriate inputs such as subject's
       bold/register.dat]

       Then I just parse the resulting image (using matlab or python
       code) into separate binary masks for each unique identifier
       that I'm using...E.g., for right IPL I load this image and
       create a new volume [newVol = (oldVol==2008)] and save that out.

       Method 2: Create a label file from aparc+aseg.mgz for each
       unique identifier that I'm using, and then use mri_label2vol
       to produce a binary mask in native functional space:
       mri_cor2label --i aparc+aseg.mgz --id 2008 --l rIPL.label
       mri_label2vol --label rIPL.label --fillthresh 0.5 [... plus
       the rest of the required inputs, same as those used in Method 1]

       Even though these seem like they should be equivalent to me,
       and although the masks produced agree for the most part, I
       generally get several more voxels per ROI using Method 2 than
       I do using Method 1 (and not complete overlap otherwise). For
       instance, for one subject, Method 1 yields 308 voxels in rIPL,
       but Method 2 yields 316 voxels; disagreement between the two
       occurs in a total of 26 of those voxels, so it isn't just a
       matter of Method 2 being more generous. The discrepancy seems
       to be proportional to the size of the ROI, so I get just a
       handful of disagreements for smaller ROIs (but it seems to
       happen almost all the time).
       Thanks for any advice on which method is better, or a
       suggestion of a better method.

       Best,
       Tim
       ------------------------------------------------------------------------

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   --     Douglas N. Greve, Ph.D.
   MGH-NMR Center
   greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>

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--
Douglas N. Greve, Ph.D.
MGH-NMR Center
greve@nmr.mgh.harvard.edu
Phone Number: 617-724-2358 Fax: 617-726-7422

Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
FileDrop: www.nmr.mgh.harvard.edu/facility/filedrop/index.html