Re: [caret-users] caret list posting query
Hi Jason, Which part of the five page Methods section didn't you understand? ;-) Seriously, though, was there a step or two that seemed particularly opaque? Here is my readers' digest condensed version: * Register each structural volume to wustl.edu's 711-2C space via affine transform (711-2C is based on the ICBM template, something about Lancaster, I think). * Segment structural volume using SureFit (now part of Caret); tessellate segmentation - midthickness 3D surface. * Generate cerebral hull volume: Dilate segmentation volume six times and erode by six times to fill sulci, but keep overall brain size same; tessellate hull. * Generate depth (midthickness node-scalar mapping): Find distance from fiducial surface node to closest cerebral hull node. * Flatten and register midthickness surface: Use Core6 landmarks to constrain spherical deformation. (Flattening provides an easy way to draw registration landmarks.) * Apply deformation map to depth - one depth column/file for each subject all on PALS_B12 standard mesh. * Average resulting depth columns/files. Segmentation is by far the most difficult, time-intensive step; it's downhill from there. Since the PALS_B12 paper, we have been using t-maps to look for anatomical differences across populations. We hope you will soon be reading about this in the Journal of Neuroscience, if I can ever complete some important enhancements to the depth generation algorithm -- important enough for us to rework all the figures (but not change the ROIs much). One important consideration for you is your choice of landmarks. Using the Core 6 landmarks will normalize away any differences in the central sulci, because the central sulcus is one of the landmarks. If you want to align the central sulci, this is good; if you're looking for cross-group differences there, this is bad. You can delete that landmark, but keep the others (and perhaps add another elsewhere), but this is something to think about. Importantly, you can run the registration both ways, using different deformation prefices (e.g., defCore6_ and defNoCeS_), and create average depth and/or t-maps using the respective results. Each result will be valid in context, but will tell you something different. Hope this helps. On 03/28/2006 06:08 PM, Jason D Connolly wrote: Dear Caret-users, Could someone please instruct me as to how the spherical and flattened maps were averaged in the van essen 05 paper? We hope to create an avg struct image with the pixel intesity reflecting the degree of overlap/similarity across anatomical datasets (see figs 2 and 6). The goal is to see how the central sulci line up across subjects. Many thanks, Jason. Jason D. Connolly, PhD Center for Neural Science, New York University 6 Washington Place Room 875, New York, NY 10003 cell:646.417.2937 lab:212.998.8347 fax:212.995.4562 http://www.psych.nyu.edu/curtislab/people/jasonconnolly.html ___ caret-users mailing list caret-users@brainvis.wustl.edu http://pulvinar.wustl.edu/mailman/listinfo/caret-users -- Donna L. Dierker (Formerly Donna Hanlon; no change in marital status -- see http://home.att.net/~donna.hanlon for details.)
Re: [caret-users] caret list posting query
I need to qualify something I said below: On 03/29/2006 08:12 AM, Donna Dierker wrote: Hi Jason, Which part of the five page Methods section didn't you understand? ;-) Seriously, though, was there a step or two that seemed particularly opaque? Here is my readers' digest condensed version: * Register each structural volume to wustl.edu's 711-2C space via affine transform (711-2C is based on the ICBM template, something about Lancaster, I think). * Segment structural volume using SureFit (now part of Caret); tessellate segmentation - midthickness 3D surface. * Generate cerebral hull volume: Dilate segmentation volume six times and erode by six times to fill sulci, but keep overall brain size same; tessellate hull. * Generate depth (midthickness node-scalar mapping): Find distance from fiducial surface node to closest cerebral hull node. * Flatten and register midthickness surface: Use Core6 landmarks to constrain spherical deformation. (Flattening provides an easy way to draw registration landmarks.) * Apply deformation map to depth - one depth column/file for each subject all on PALS_B12 standard mesh. * Average resulting depth columns/files. Segmentation is by far the most difficult, time-intensive step; it's downhill from there. Since the PALS_B12 paper, we have been using t-maps to look for anatomical differences across populations. We hope you will soon be reading about this in the Journal of Neuroscience, if I can ever complete some important enhancements to the depth generation algorithm -- important enough for us to rework all the figures (but not change the ROIs much). One important consideration for you is your choice of landmarks. Using the Core 6 landmarks will normalize away any differences in the central sulci Actually, it won't normalize true depth differences in the CeS. If the CeS is truly deeper in group A than in group B, the Core6 landmarks will detect that difference. What it won't detect is a shift (e.g., anterior or posterior) across groups, because it will align their respective CeS locations on the sphere. , because the central sulcus is one of the landmarks. If you want to align the central sulci, this is good; if you're looking for cross-group differences there, this is bad. You can delete that landmark, but keep the others (and perhaps add another elsewhere), but this is something to think about. Importantly, you can run the registration both ways, using different deformation prefices (e.g., defCore6_ and defNoCeS_), and create average depth and/or t-maps using the respective results. Each result will be valid in context, but will tell you something different. Hope this helps. On 03/28/2006 06:08 PM, Jason D Connolly wrote: Dear Caret-users, Could someone please instruct me as to how the spherical and flattened maps were averaged in the van essen 05 paper? We hope to create an avg struct image with the pixel intesity reflecting the degree of overlap/similarity across anatomical datasets (see figs 2 and 6). The goal is to see how the central sulci line up across subjects. Many thanks, Jason. Jason D. Connolly, PhD Center for Neural Science, New York University 6 Washington Place Room 875, New York, NY 10003 cell:646.417.2937 lab:212.998.8347 fax:212.995.4562 http://www.psych.nyu.edu/curtislab/people/jasonconnolly.html ___ caret-users mailing list caret-users@brainvis.wustl.edu http://pulvinar.wustl.edu/mailman/listinfo/caret-users -- Donna L. Dierker (Formerly Donna Hanlon; no change in marital status -- see http://home.att.net/~donna.hanlon for details.)
[caret-users] Macaque Segmentation
Hey All, Are there any special steps required outside of the standard tutorial for segmenting a Macaque brain? Or would the normal tutorial work correctly if followed? -Eric Faden
RE: [caret-users] Macaque Segmentation + AC/PC Align
I actually resampled them at .5mm already. The other problem I am having is that the way our monkeys are scanned they need to be rotated to the correct orientation (45 degrees or so). I tried to use the AC/PC align in Volume Attributes Editor. I selected the AC and PC then clicked align. After about 10 minutes I got a white cube where my volume used to be. Any thoughts on what this is? or what is a good way to AC/PC align my volume? -Eric -Original Message- From: Donna Dierker [mailto:[EMAIL PROTECTED] Sent: Wed 3/29/2006 10:58 AM To: Caret, SureFit, and SuMS software users Subject: Re: [caret-users] Macaque Segmentation The only difference that comes to mind is the voxdims. Resampling your macaque structural to cubic 0.5mm rather than cubic 1.0mm tends to work best (although I've seen 0.75mm work well). You definitely do NOT want to resample to cubic 1.0mm for monkeys. On 03/29/2006 09:53 AM, Faden, Eric (NIH/NIMH) [F] wrote: Hey All, Are there any special steps required outside of the standard tutorial for segmenting a Macaque brain? Or would the normal tutorial work correctly if followed? -Eric Faden ___ caret-users mailing list caret-users@brainvis.wustl.edu http://pulvinar.wustl.edu/mailman/listinfo/caret-users -- Donna L. Dierker (Formerly Donna Hanlon; no change in marital status -- see http://home.att.net/~donna.hanlon for details.) ___ caret-users mailing list caret-users@brainvis.wustl.edu http://pulvinar.wustl.edu/mailman/listinfo/caret-users winmail.dat
Re: [caret-users] Macaque Segmentation + AC/PC Align
Eric, The AC/PC align in volume attributes is something I recently added but has received only limited testing (I tried it on two volumes). I believe AFNI (afni.nimh.nih.gov) can AC-PC align a volume so you might try AFNI or another volume type program. Good luck. -- John Harwell [EMAIL PROTECTED] 314-362-3467 Department of Anatomy and Neurobiology Washington University School of Medicine 660 S. Euclid Ave.Box 8108 St. Louis, MO 63110 USA On Mar 29, 2006, at 10:57 AM, Faden, Eric ((NIH/NIMH)) [F] wrote: I actually resampled them at .5mm already. The other problem I am having is that the way our monkeys are scanned they need to be rotated to the correct orientation (45 degrees or so). I tried to use the AC/PC align in Volume Attributes Editor. I selected the AC and PC then clicked align. After about 10 minutes I got a white cube where my volume used to be. Any thoughts on what this is? or what is a good way to AC/PC align my volume? -Eric -Original Message- From: Donna Dierker [mailto:[EMAIL PROTECTED] Sent: Wed 3/29/2006 10:58 AM To: Caret, SureFit, and SuMS software users Subject: Re: [caret-users] Macaque Segmentation The only difference that comes to mind is the voxdims. Resampling your macaque structural to cubic 0.5mm rather than cubic 1.0mm tends to work best (although I've seen 0.75mm work well). You definitely do NOT want to resample to cubic 1.0mm for monkeys. On 03/29/2006 09:53 AM, Faden, Eric (NIH/NIMH) [F] wrote: Hey All, Are there any special steps required outside of the standard tutorial for segmenting a Macaque brain? Or would the normal tutorial work correctly if followed? -Eric Faden ___ caret-users mailing list caret-users@brainvis.wustl.edu http://pulvinar.wustl.edu/mailman/listinfo/caret-users -- Donna L. Dierker (Formerly Donna Hanlon; no change in marital status -- see http:// home.att.net/~donna.hanlon for details.) ___ caret-users mailing list caret-users@brainvis.wustl.edu http://pulvinar.wustl.edu/mailman/listinfo/caret-users winmail.dat ___ caret-users mailing list caret-users@brainvis.wustl.edu http://pulvinar.wustl.edu/mailman/listinfo/caret-users
