Freesurfer August 2017

freesurfer@nmr.mgh.harvard.edu

145 participants
233 discussions

mri_vol2surf
by John Anderson 22 Aug '17

22 Aug '17

Dear FS experts, I ran voxel-wise analysis between two groups using randomise in FSL and the final statistical maps is as attached. I aim to to repeat the analysis using surface based analysis. I followed the steps as in wiki. I am not sure what is the correct value for projfrac int he command "mri_vol2surf" that is more appropriate to show the findings that I have in the voxel-wise (more int eh white matter) on surface. Thank you for any suggestion John

2 1

how to get aseg.mgz without transform
by Ritesh Mahajan 22 Aug '17

22 Aug '17

Hello Team, I have run "recon-all -i subject.nii -s subject -all -notalairach" command, i wanted to run it with notalairach. I am getting the following error: "No such file or directory mris_anatomical_stats: could not open atlas transform file /subject/mri/transforms/talairach.xfm" How can i get the aseg.mgz image without transform? Thanks, Ritesh Mahajan

2 1

mri_gtmpvc
by Joudy.carter 22 Aug '17

22 Aug '17

Dear experts, I have suv data and I want to do pve using pet surfer. I have the following questions and would appreciate your help: 1. The output of mgx is three maps mgx.{ctxgm,subctxgm,gm} these maps are pvc corrected. I am wondering where can I find pvc corrected maps for whole brain (white matter and gray matter)? 2. can I register these mgx files to standard space and run voxel-wise comparison using FSL randomise? 3. The output of RBV method is rbv.nii.gz. I want to concatenate all the rbv files for two groups and run voxel-wise analysis comparison. Is this possible? The size of these files is pretty large! Do you recommend better way to study the difference between two groups using these RBV files? 4. Are these RBV files for white matter and gray matter? 5. The suv data is an output of single frame PET data with no arterial blood sampling. Do I still need to re-scale over the pons? Thanks in advance Joudi

2 1

info on samseg routines
by Antonin Skoch 21 Aug '17

21 Aug '17

Dear experts, I would like to ask, whether I could obtain a basic general info on samseg routines - what specifically are they doing (publication reference) and how to use them? Regards, Antonin Skoch

2 1

Mean diffusivity on cortex?
by Shane Schofield 21 Aug '17

21 Aug '17

Hi DTI Experts, Is it sensible to do comparisons of MD on the cortex surface like a normal cortical thickness comparisons between group? If the answer is yes, are there recommended on smoothing ? DT_RECON has been completed in Freesurfer. Thank you.

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extract average thickness using label for every subject
by Patrícia Klobušiaková 21 Aug '17

21 Aug '17

Dear Freesurfer experts, I have labels representing clusters that I created using mri_surfcluster on sig.mgh file using a specific threshold. Now I want to use these labels to extract average thickness values for every subject. I tried to use mris_anatomical_stats -l $SUBJECTS_DIR/glm/analyza/cllabel-0001.label -t $SUBJECTS_DIR/s0110B/surf/rh.thickness.fwhm10.fsaverage.mgh s0110B rh to calculate it for one subject, but it doesn't work - I get error because the number of vertices in subject's file rh.thickness.fwhm10.fsaverage.mgh doesn't match surface. I should use fsaverage as a subject, right? So I tried mris_anatomical_stats -l $SUBJECTS_DIR/glm/analyza/cllabel-0001.label -t $SUBJECTS_DIR/s0110B/surf/rh.thickness.fwhm10.fsaverage.mgh fsaverage rh but then I get error, that it cannot read fsaverage/mri/wm.mgz file. The wm.mgz file isn't in fsaverage folder. How can I calculate this? Thanks for your help! Patricia

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Re: [Freesurfer] smoothing
by John Anderson 21 Aug '17

21 Aug '17

Dear Dr Greve, Thank you so much for the detailed explanation. I have additional question and I highly appreciate any clarification. By reviewing the log file "mri_gtmpvc.log" I see that this program apply smoothing FWHM=6 mm on the input SUVR maps and the output mgx images are smoothed according to this default feature. 1. What is the importance of smoothing in this case? 2. Can I turn this feature off? 3. I want to do PVC without smoothing, is this possible? Thanks John PVC will increase signal in some regions and will decrease it in others. Eg, for FDG it will increase it in GM and decrease it in WM. The reason you are seeing such a wide range is that the MG correction subtracts the WM then divides by the GM partial volume fraction (PVF, a number between 0 and 1). This requires a mask of some sort because there will be some voxels where the GM PVF is 0 (and you can't divide by 0). When you ran mri_gtmpvc with the --mgx option, you had to supply a threshold (eg, .01). This is the minimum allowed PVF before the voxel is masked out. If you used .01, then the multiplier could be as large as 1/.10 = 100, which agrees with the range you are seeing. Because of this problem, you must do the MG voxel-wise analysis on the surface where the GM PVF will be high. For subcortical analysis, you must use a mask of subcortical GM structures. The mask must be used when smoothing because you must only smooth within the mask (not such a problem on the surface). Note that there are many paper that use MG in a volume-based voxel-wise analysis; in my opinion, these studies are invalid. > -------- Original Message -------- > Subject: Re: pet surfer > Local Time: August 18, 2017 11:47 AM > UTC Time: August 18, 2017 3:47 PM > From: John.anderso(a)protonmail.com > To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> > > Hi Dr Greve, Thank you very much for the great answers. > > Kindly, I have one last question. > The range of the signal intensity within the voxels in the original SUV maps is min=0.00 and max=3.017629 > For the mgx images it is min=-43.74384 and max=88.468712 > > The difference in the range of signal intensity in the mgx images is largely wide. It seems that PVC increases the signal intensity. Is this correct? > Am I doing something wrong. I plan to include these images in voxel-wise analysis so I am curious about this difference between the images. > > Thanks for any clarification! > > On 8/18/17 10:20 AM, John Anderson wrote: > > Hi Dr Greve, > > I followed the steps in WIKI to do SUV-surface based analyses + PVC. I have the following questions and I highly appreciate your input: > > 1. Why the dimension of the images (mgx.gm, mgx.ctx.gm and mgx.ctx.subgm) is not like the original SUV image that has been fed to the pipeline. i.e. I start with image-dimensions 128X128X128 : 2X2X2 and end up with 79X113X102 : 2X2X2 > > Also FOV is different as well between the original SUV image /256/ and the output mgx images /158/. How this happen? I am I doing something wrong? > > I set up mri_gtmpvc to reduce the field of view to a bounding box around the head to reduce memory and computational loads. You can turn this off with --no-reduce-fov > > 2. Some voxels in the mgx images has negative signal intensity. is this normal? > > Yes. MG works by estimating the contribution of non-GM to GM and subtracting it out. If the estimate is too high, then it can cause negative values. > > Thank you for any clarification. > > John

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asegstats2table output meaning
by miracle ozzoude 21 Aug '17

21 Aug '17

Hello FreeSurfer, Where can I find the meaning of all of the outputs provided by asegstats and how some of the outputs are calculated (e.g. Total Gray matter etc.)? Thanks. Best, Paul

3 5

Resample to cropped FOV in anatomical space
by Michael Waskom 21 Aug '17

21 Aug '17

Hi Doug, I'd like to use the registration matrix from bbregister to transform functional data into a cropped FOV that corresponds with the anatomy. I thought that bbregister outputs a ras2ras transform, so if I could crop the anatomical volume with a correct header vox2ras, things should be fine. Is my thinking here wrong? Here is roughly what I have done: 1. Generate a registration matrix to the full anatomy bbregister --s subj --mov func.nii --reg reg.dat --t2 2. Crop the anatomy: mri_convert brain.mgz brain.nii fslroi brain.nii brain_crop.nii 50 150 50 150 50 150 (I know this is an fsl tool but I couldn't find an option to do exactly this in mri_convert) 3. Check the output -- looks fine so far tkregister2 --mov brain-crop.nii --regheader --s subj --surfs --reg /dev/null 4. Resample the functional volume mri_vol2vol --mov func.nii --targ brain_crop.nii --reg reg.dat --o func_xfm.nii 5. Check the output tkregister2 --mov func_xfm.nii --regheader --s subj --surfs --reg /dev/null The resulting functional image is shifted superior about ~20cm What am I missing in my understanding of the bbregister and header transformation matrices? Thanks! Michael

2 3

mris_anatomical_stats output
by stdp82＠virgilio.it 21 Aug '17

21 Aug '17

Hi list,how can I save the outputs of mris_anatomical_stats in xls file?Thanks Stefano

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Freesurfer August 2017