Freesurfer October 2019

freesurfer@nmr.mgh.harvard.edu

111 participants
180 discussions

imperfect segmentation in the occipital lobe in epilepsy patient
by Xiaoqian Yan 30 Oct '19

30 Oct '19

External Email - Use Caution Dear Freesurfer experts, I am trying to do the segmentation on a patient's T1 image. I did not get error messages running the 'recon-all' (recon-all -i T1.nii -subjid PatientName -all), but the segmentation over the right occipital lobe was imperfect, which caused failures to map the functional activations in this region onto the surface. I tried to manually edit the wm.mgz several times, but the output did not change a lot (please see the attached image). The patient had depth EEG electrodes implanted in this region before, so there is a thermal lesion which I thought could have caused the bad segmentation. Do you have any ideas about what I can do next, or it is possible for you to help me editing the data? Thanks in advance, best wishes Xiaoqian

4 8

nuisance factors
by Georgina Angelopoulou 30 Oct '19

30 Oct '19

External Email - Use Caution Thank you very much! That makes a lot of sense. However, when I tried to run the analysis using that contrast (0.5 0.5 -0.5 -0.5 0), the following error appears: dimension mismatch between X and contrast Contrasts/age_sim.mtx X has 4 cols, C has 5 cols My fsgd file contains the following info: Title CMTX Class patientmale Class patientfemale Class controlmale Class controlfemale Variables age Any ideas of how to resolve it? Thanks once again, Georgina

2 1

Matrix is ill-conditioned or badly scaled
by Benjamin Ineichen 30 Oct '19

30 Oct '19

External Email - Use Caution Hi, I am having trouble with the mri_glmfit with the following error message and I was not able to find a solution on the mail archive: ERROR: matrix is ill-conditioned or badly scaled, condno = 1e+08 -------------------------------- Possible problem with experimental design: Check for duplicate entries and/or lack of range of continuous variables within a class. If you seek help with this problem, make sure to send: 1. Your command line: mri_glmfit.bin --y NFLclasscov.thickness.00.mgh --fsgd GLM4.fsgd.txt dods --C contrast4.mtx.txt --surf fsaverage lh --cortex --glmdir NFLclasscov.glmdir 2. The FSGD file (if using one) 3. And the design matrix above Attempting to diagnose further SumSq: Min=0.383673 (col 10), Max=7.173570 (col 15) The scale is much different between columns 10 and 15, you may want to normalize by subtracting the mean and dividing by the standard deviation. I get this error with both the non-demeaned and demeaned FSGD file (as suggested at the end of the error message). Both the non-demeaned (GLM3) and the demeaned FSGD files (GLM4) are attached, contrast file attached. Not sure if you also need the design matrix depicted in the command line, it would be at the end of the email. My questions: 1.) Do you see where the problem is? 2.) Would the contrast be correct for testing cortical thickness between NFL-low and NFL-high. Many thanks for your support! 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2 1

registration
by Nader Razmara 30 Oct '19

30 Oct '19

External Email - Use Caution Instead of mri_cvs_register Which type of registration is more accurate or coparative to this. Best Regards Nader

1 0

nuisance factors
by Georgina Angelopoulou 30 Oct '19

30 Oct '19

External Email - Use Caution Dear FreeSurfer Developers, I am trying to design a matrix in order to run a group comparison analysis and I would like to verify if I am following the right steps. I have read a lot of things in the forum, but I got confused regarding how to treat nuisance factors. I have two groups (patients and controls) and I want to test if there is a significant difference in cortical thickness regressing out the effects of age and sex. My fsgd file is ControlMales ControlFemales PatientMales PatientFemales Again, based on what I have read so far, in order to use the age as a nuisance variable, I first tested the age slopes using the row values of age (meaning without centering them), is that correct? Since there is no significant difference in age slopes, that means that now I can run the analysis without including the age at all as a nuisance variable in the model? (what you usually refer as DOSS?) with the contrast matrix 0.5 0.5 -0.5 0.5 (meaning that I only control for the effect of sex)? Thank you in advance, Georgina

2 1

question about qdec 'Generate Stats Data Tables'
by 艺 30 Oct '19

30 Oct '19

External Email - Use Caution Hello, We had a problem when using QDEC for group analysis. After generating relevant files, I press 'Load Data Tabel' button to load my file 'qdec.tabel.dat'. But when I press the 'Generate Stats Data Tables' button, a popup window shows an error indicating that: --------------------------------------------------------------------------------------------------------------------- ERROR: In /usr/pubsw/packages/KWWidgets/CVS-vtk560/KWWidgets/vtKWTkUtilities.cxx,line230 vtKWQdecApp (0x27fbfce0): Script: vtKTemp2 GenetateStatsDataTables Returened Error on line 1: Uncaught exception: command failed: asegstats2table --common-segs --meas volume --tablefile /usr/local/freesurfer/subjects/qdec/stats_tables/aseg.volume.stats.dat --statsfile=aseg.stats --subjects PT1_01 PT1_03 PT1_04 PT1_06 PT1_11 PT1_12 PT1_14 PT1_27 PT1_32 PT1_33 PT1_34 PT1_47 PT1_55 PT1_05 PT1_08 PT1_09 PT1_16 PT1_18 PT1_19 PT1_20 PT1_21 PT1_23 PT1_24 PT1_26 PT1_38 PT1_45 PT1_48 PT1_52 PT1_75 PT1_81 PT1_87 Stack trace: Uncaught exception: command failed: asegstats2table --common-segs --meas volume --tablefile /usr/local/freesurfer/subjects/qdec/stats_tables/aseg.volume.stats.dat --statsfile=aseg.stats --subjects PT1_01 PT1_03 PT1_04 PT1_06 PT1_11 PT1_12 PT1_14 PT1_27 PT1_32 PT1_33 PT1_34 PT1_47 PT1_55 PT1_05 PT1_08 PT1_09 PT1_16 PT1_18 PT1_19 PT1_20 PT1_21 PT1_23 PT1_24 PT1_26 PT1_38 PT1_45 PT1_48 PT1_52 PT1_75 PT1_81 PT1_87 while executing "vtkTemp2 GenerateStatsDataTables" --------------------------------------------------------------------------------------------------------------------- Before that we were able to successfully ‘Generate Stats Data Tables’  (for our data but not all of the above data). We don't know if it is a server environment or something else. What‘s wrong? Please, could you advise me? Best regards,   Adam

2 1

Mapping coordinates from sphere file to ?h.white
by Oscar Esteban 30 Oct '19

30 Oct '19

External Email - Use Caution Dear experts, In the context of the BIDS transforms extension and the ".x5" format we chatted about in the workshop we made in March at MIT, we are addressing the following question. We have a sphere of fsLR coordinates in fsaverage space (e.g., https://github.com/Washington-University/HCPpipelines/blob/master/global/te… - but the question extends to the right hemisphere and also resolutions other than 59k). We want to expand this sphere to the fsaverage's white surface (lh.white, in this example). Since coordinates on the fsLR sphere don't match any of fsaverage{,3,4,5,6}'s coordinates, we wonder whether FreeSurfer stores somewhere/somehow the analytical transform of this mapping. In other words, how one would map arbitrary coordinates on the surface to a particular fsaverage's white (or midthickness, or pial, etc.) surface. Otherwise, I guess I would pick fsaverage's surfaces (i.e., 7th degree icosahedron), and interpolate the displacement vectors that start at each sample on the surface and end at the corresponding location of that vertex index of the destination surface (lh.white for the example above). Is there any tool that actually does the _inverse transform of the surface-to-sphere mapping_? Thanks very much. Regards, Oscar -- *___________________________* *Oscar Esteban, Ph.D.* Postdoctoral Fellow, Poldrack Lab Stanford University +1 (650) 733 33 82

2 1

recon-all -skullstrip issues
by Adam Rytina 30 Oct '19

30 Oct '19

External Email - Use Caution Hello all, I´m struggling with running "recon-all -skullstrip", in general with "recon-all". I added NIfTI series file to a new directory "QWE" (/usr/local/freesurfer/subjects/QWE) and running the skull stripping "mri_watershed /usr/local/freesurfer/subjects/QWE/ser.nii /usr/local/freesurfer/subjects/QWE/brainwithout.nii" without any proble. However, when trying to adjust watershed parameters the problem occurs. "recon-all -skullstrip -wsthresh 5 -clean-bm -subjid QWE". There are afterwards some errors occuring: "mv: cannot stat '/usr/local/freesurfer/subjects/QWE/mri/brainmask.mgz': No such file or directory", "mghRead(/usr/local/freesurfer/subjects/QWE/mri/nu.mgz, -1): could not open file mri_em_register: could not open input volume nu.mgz." I´m working with image series "ser.nii" and "brainwithout.nii", not with "brainmask.mgz" and "nu.mgz" in the tutorial. Please, where is the problem? Thanks a lot Adam [https://ipmcdn.avast.com/images/icons/icon-envelope-tick-round-orange-anima…]<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campai…> Bez virů. www.avast.com<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campai…>

2 3

mri_glmfit ill-conditioned matrix question
by Nillo, Ryan Michael R 30 Oct '19

30 Oct '19

Hello, I ran mri_glmfit with the following fsgd file: GroupDescriptorFile 1 Title CJD Class MM1_MV1 Class MV2_VV2 Variables frontal Input 7693 MV2_VV2 2.930667 Input 11985 MV2_VV2 2.410333 Input 13082 MV2_VV2 2.529333 Input 13425 MV2_VV2 2.593667 Input 13667 MM1_MV1 2.628667 Input 13900 MM1_MV1 2.675333 Input 14094 MM1_MV1 2.549333 Input 15566 MV2_VV2 2.666667 Input 15999 MM1_MV1 2.641 Input 16714 MV2_VV2 2.643 Input 16848 MV2_VV2 2.63 Input 16952 MV2_VV2 2.516 Input 7693a MV2_VV2 3.054 Input 11985a MV2_VV2 2.42 Input 13082a MV2_VV2 2.454 Input 13425a MV2_VV2 2.602 Input 13667a MM1_MV1 2.604333 Input 13900a MM1_MV1 2.601333 Input 14094a MM1_MV1 2.523333 Input 15566a MV2_VV2 2.713333 Input 15999a MM1_MV1 2.584333 Input 16714a MV2_VV2 2.673333 Input 16848a MV2_VV2 2.533333 Input 16952a MV2_VV2 2.486 My two contrast matrices are: one.mtx = [ 0 0 1 0 ] two.mtx = [ 0 0 0 1 ] When running the mri_glmfit command "mri_glmfit --glmdir new.frontal.lgi --fsgd new.frontal.fsgd.txt --fwhm 30 --surf fsaverage lh --y lh.pial_lgi.fwhm0.mgh --C one.mtx --C two.mtx”, I get the following output: INFO: gd2mtx_method is dods Saving design matrix to new.frontal.lgi/Xg.dat Computing normalized matrix Normalized matrix condition is 12820.7 Design matrix ------------------ 0.00000 1.00000 0.00000 2.93067; 0.00000 1.00000 0.00000 2.41033; 0.00000 1.00000 0.00000 2.52933; 0.00000 1.00000 0.00000 2.59367; 1.00000 0.00000 2.62867 0.00000; 1.00000 0.00000 2.67533 0.00000; 1.00000 0.00000 2.54933 0.00000; 0.00000 1.00000 0.00000 2.66667; 1.00000 0.00000 2.64100 0.00000; 0.00000 1.00000 0.00000 2.64300; 0.00000 1.00000 0.00000 2.63000; 0.00000 1.00000 0.00000 2.51600; 0.00000 1.00000 0.00000 3.05400; 0.00000 1.00000 0.00000 2.42000; 0.00000 1.00000 0.00000 2.45400; 0.00000 1.00000 0.00000 2.60200; 1.00000 0.00000 2.60433 0.00000; 1.00000 0.00000 2.60133 0.00000; 1.00000 0.00000 2.52333 0.00000; 0.00000 1.00000 0.00000 2.71333; 1.00000 0.00000 2.58433 0.00000; 0.00000 1.00000 0.00000 2.67333; 0.00000 1.00000 0.00000 2.53333; 0.00000 1.00000 0.00000 2.48600; -------------------------------- ERROR: matrix is ill-conditioned or badly scaled, condno = 12820.7 -------------------------------- Possible problem with experimental design: Check for duplicate entries and/or lack of range of continuous variables within a class. If you seek help with this problem, make sure to send: 1. Your command line: mri_glmfit --glmdir new.frontal.lgi --fsgd new.frontal.fsgd.txt --fwhm 30 --surf fsaverage lh --y lh.pial_lgi.fwhm0.mgh --C one.mtx --C two.mtx 2. The FSGD file (if using one) 3. And the design matrix above Attempting to diagnose further SumSq: Min=2.828427 (col 1), Max=10.485683 (col 4) ... could not determine the cause of the problem Can anyone give me some insight into what might be going wrong? Thanks so much, Ryan Michael Nillo Staff Research Associate I University of California San Francisco Department of Radiology and Biomedical Imaging External Email - Use Caution

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[mri_cvs_register] Transform is zero
by Stefano Zappala 29 Oct '19

29 Oct '19

External Email - Use Caution Hi there, I am having problem reading the final_CVSmorph_toS131032017_Supine.m3z transform as output of the mri_cvs_register function. I used mri_convert final_CVSmorph_toS131032017_Supine.m3z final_CVSmorph_toS131032017_Supine.nii.gz to convert the transform file to nifti format but what I get it's just a map of zeros. I have checked any different source of error: * mri_convert: it works because if I convert /transforms/talairach.m3z to nifti the conversion is successful; * mri_cvs_register: it works because the two images are aligned very well and correspond perfectly; * final_CVSmorph_toS131032017_Supine: it is not empty because if I run mri_convert -at final_CVSmorph_toS131032017_Supine.m3z firstScan/mri/T1.mgz secondScan/mri/T1mine.mgz the result is morphed correctly. So the transform final_CVSmorph_toS131032017_Supine is actually not populated with zeros. So it could be a problem of type of data: the transform should be indeed populated with very small values, since the difference between the scans even if noticeable, it is very small. I don't know what to try anymore: I was wondering if you have any suggestion? In summary, the call to mri_cvs_register is successful giving me as output a transform in .m3z format which is not zero. When I try to convert it to nifti file, the transformation results full of zeros. 1) FreeSurfer version: freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c 2) Platform: Ubuntu 18.04.3 LTS 3) uname -a: Linux zappst-Q170M-C 5.0.0-29-generic #31~18.04.1-Ubuntu SMP Thu Sep 12 18:29:21 UTC 2019 x86_64 x86_64 x86_64 GNU/LinuxThu Aug 13 22:55:16 UTC 2015 x86_64 x86_64 x86_64 GNU/Linux 4) recon-all.log for only one of the two images registered: see attached 5) mri_cvs_register.log: see attached Many thanks, Stefano. Stefano Zappalá Stefano Zappalá PhD Student Myfyrwraig PhD School of Computer Science & Informatics Yr Ysgol Cyfrifiadureg a Gwybodeg Cardiff University Prifysgol Caerdydd 5 The Parade 5 The Parade Cardiff Caerdydd CF24 3AA CF24 3AA Tel: +44 (0)7<tel:+44%2029%202087%206070>943096049 Ffôn: +44 (0)79<tel:+44%2029%202087%206070>43096049

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