Freesurfer July 2021

freesurfer@nmr.mgh.harvard.edu

91 participants
121 discussions

Problem with mri_glmfit-sim
by juergen.haenggi＠upd.unibe.ch 15 Jul '21

15 Jul '21

External Email - Use Caution Dear FS experts I have run stats using mri_glmfit (FS 7.1.1 on Mac) with the following command mri_glmfit --y IPS_2groups_n135_lh_thickness.10.mgh --fsgd IPS_fsgd_2groups.txt --glmdir IPS_2groups_n135_Thickness_LH.glmdir --surf fsaverage lh --C IPS_2groups.mat --cortex and everything worked fine. When trying to run simulations with mri_glmfit-sim with the following command mri_glmfit-sim --glmdir IPS_2groups_n135_Thickness_LH.glmdir --sim mc-z 500 1.302 lh_mc-z.abs_500p1.3 --sim-sign abs --overwrite --cwpvalthresh 0.25 the following error occurred Loading y from /Applications/freesurfer/subjects/average_IPS_FS7_Linux/stats/glm_group_n135/--perm-force error: mri_read(): couldn't determine type of file /Applications/freesurfer/subjects/average_IPS_FS7_Linux/stats/glm_group_n135/--perm-force ... done reading. ERROR: loading y /Applications/freesurfer/subjects/average_IPS_FS7_Linux/stats/glm_group_n135/--perm-force The entire output is shown below. I do not understand why after the —y option in the command reported by FS in the terminal, i.e. mri_glmfit --C IPS_2groups_n135_Thickness_LH.glmdir/tmp.mri_glmfit-sim-96372/IPS_2groups.mtx --sim mc-z 500 1.302 IPS_2groups_n135_Thickness_LH.glmdir/csd/lh_mc-z.abs_500p1.3.j001 --sim-sign abs --fwhm 14.598750 --fsgd IPS_2groups_n135_Thickness_LH.glmdir/y.fsgd dods --mask IPS_2groups_n135_Thickness_LH.glmdir/mask.mgh --y --perm-force --surf fsaverage lh white the input file (IPS_2groups_n135_lh_thickness.10.mgh) is missed and hence the following option —perm-force is interpreted as input file. How can I fix this problem? Thanks in advance for any advise Regards Jürgen --------------------------------------------------- entire output: [upddkp224:average_IPS_FS7_Linux/stats/glm_group_n135] jurgenhanggi% mri_glmfit-sim --glmdir IPS_2groups_n135_Thickness_LH.glmdir --sim mc-z 500 1.302 lh_mc-z.abs_500p1.3 --sim-sign abs --overwrite --cwpvalthresh 0.25 cmdline mri_glmfit --y IPS_2groups_n135_lh_thickness.10.mgh --fsgd IPS_fsgd_2groups.txt --glmdir IPS_2groups_n135_Thickness_LH.glmdir --surf fsaverage lh --C IPS_2groups.mat --cortex SURFACE: fsaverage lh log file is IPS_2groups_n135_Thickness_LH.glmdir/lh_mc-z.abs_500p1.3.mri_glmfit-sim.log cd /Applications/freesurfer/subjects/average_IPS_FS7_Linux/stats/glm_group_n135 /Applications/freesurfer/bin/mri_glmfit-sim --glmdir IPS_2groups_n135_Thickness_LH.glmdir --sim mc-z 500 1.302 lh_mc-z.abs_500p1.3 --sim-sign abs --overwrite --cwpvalthresh 0.25 FreeSurferEnv.csh 7.1.1 Wed Jun 23 10:19:19 CEST 2021 Darwin upddkp224.unibe.ch<http://secure-web.cisco.com/1GJFlNOIRufFrF9EI_m9Z0M8ynVxVdXSTvLuBbjjKrTZTIr…> 19.6.0 Darwin Kernel Version 19.6.0: Thu May 6 00:48:39 PDT 2021; root:xnu-6153.141.33~1/RELEASE_X86_64 x86_64 jurgenhanggi setenv SUBJECTS_DIR /Applications/freesurfer/subjects FREESURFER_HOME /Applications/freesurfer Original mri_glmfit command line: cmdline mri_glmfit --y IPS_2groups_n135_lh_thickness.10.mgh --fsgd IPS_fsgd_2groups.txt --glmdir IPS_2groups_n135_Thickness_LH.glmdir --surf fsaverage lh --C IPS_2groups.mat --cortex DoSim = 1 UseCache = 0 DoPoll = 0 DoPBSubmit = 0 DoBackground = 0 DiagCluster = 0 gd2mtx = dods fwhm = 14.598750 nSimPerJob = 500 1/1 Wed Jun 23 10:19:19 CEST 2021 mri_glmfit --C IPS_2groups_n135_Thickness_LH.glmdir/tmp.mri_glmfit-sim-96372/IPS_2groups.mtx --sim mc-z 500 1.302 IPS_2groups_n135_Thickness_LH.glmdir/csd/lh_mc-z.abs_500p1.3.j001 --sim-sign abs --fwhm 14.598750 --fsgd IPS_2groups_n135_Thickness_LH.glmdir/y.fsgd dods --mask IPS_2groups_n135_Thickness_LH.glmdir/mask.mgh --y --perm-force --surf fsaverage lh white INFO: ignoring tag Creator INFO: ignoring tag SUBJECTS_DIR INFO: ignoring tag SynthSeed simbase IPS_2groups_n135_Thickness_LH.glmdir/csd/lh_mc-z.abs_500p1.3.j001 FWHM = 14.598750 gdfRead(): reading IPS_2groups_n135_Thickness_LH.glmdir/y.fsgd WARNING: carriage returns have been detected in file IPS_2groups_n135_Thickness_LH.glmdir/y.fsgd Was it created on a Windows computer? This may cause an error in reading the FSGD file. If so, try running: cat IPS_2groups_n135_Thickness_LH.glmdir/y.fsgd | sed 's/\r/\n/g' > new.IPS_2groups_n135_Thickness_LH.glmdir/y.fsgd Then use new.IPS_2groups_n135_Thickness_LH.glmdir/y.fsgd INFO: gd2mtx_method is dods Reading source surface /Applications/freesurfer/subjects/fsaverage/surf/lh.white Number of vertices 163842 Number of faces 327680 Total area 65417.000000 AvgVtxArea 0.399269 AvgVtxDist 0.721953 StdVtxDist 0.195472 Surface smoothing by fwhm=14.598750, niters=157.000000 7.1.1 cwd /Applications/freesurfer/subjects/average_IPS_FS7_Linux/stats/glm_group_n135 cmdline mri_glmfit --C IPS_2groups_n135_Thickness_LH.glmdir/tmp.mri_glmfit-sim-96372/IPS_2groups.mtx --sim mc-z 500 1.302 IPS_2groups_n135_Thickness_LH.glmdir/csd/lh_mc-z.abs_500p1.3.j001 --sim-sign abs --fwhm 14.598750 --fsgd IPS_2groups_n135_Thickness_LH.glmdir/y.fsgd dods --mask IPS_2groups_n135_Thickness_LH.glmdir/mask.mgh --y --perm-force --surf fsaverage lh white sysname Darwin hostname upddkp224.unibe.ch<http://secure-web.cisco.com/1GJFlNOIRufFrF9EI_m9Z0M8ynVxVdXSTvLuBbjjKrTZTIr…> machine x86_64 user jurgenhanggi FixVertexAreaFlag = 1 UseMaskWithSmoothing 1 fwhm 14.598750 niters 157.000000 OneSampleGroupMean 0 y /Applications/freesurfer/subjects/average_IPS_FS7_Linux/stats/glm_group_n135/--perm-force logyflag 0 usedti 0 FSGD IPS_2groups_n135_Thickness_LH.glmdir/y.fsgd mask IPS_2groups_n135_Thickness_LH.glmdir/mask.mgh maskinv 0 glmdir (null) IllCondOK 0 ReScaleX 1 DoFFx 0 Loading y from /Applications/freesurfer/subjects/average_IPS_FS7_Linux/stats/glm_group_n135/--perm-force error: mri_read(): couldn't determine type of file /Applications/freesurfer/subjects/average_IPS_FS7_Linux/stats/glm_group_n135/--perm-force ... done reading. ERROR: loading y /Applications/freesurfer/subjects/average_IPS_FS7_Linux/stats/glm_group_n135/--perm-force [upddkp224:average_IPS_FS7_Linux/stats/glm_group_n135] jurgenhanggi%

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One year position, working with ADNI data.
by ROBERT CARY WELSH 14 Jul '21

14 Jul '21

External Email - Use Caution Hi, Please see this posting. We are looking for an appropriate match for a 1-year NIH funded position. We are working on statistical modeling with ADNI data. We need a person that can hit the ground running. Only apply if eligible to work in the US as the lab does not have the resources to do any type of sponsorship. https://secure-web.cisco.com/1_4U_hvUopqCVLJhHNv0etmvHG3zmPupbHEEI8ATazfl-0… Do not send inquiries directly, as all materials must go via the portal above. Robert Welsh

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Hiring a web developer
by Billah, Tashrif 14 Jul '21

14 Jul '21

The web developer will join a research laboratory at Harvard Medical School/Mass General Brigham. It is located in Boston, MA. Here is the job description: https://partners.taleo.net/careersection/ghc/jobdetail.ftl?job=3161646&tz=G… If you have any question, reach out to sylvain(a)bwh.harvard.edu. Otherwise, feel free to apply through the above. Best, Tashrif

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RAM per CPU for running Freesurfer 7.1
by Ajay Kurani 14 Jul '21

14 Jul '21

External Email - Use Caution Hi Freesurfer Experts, I am designing some new hardware for a mini-cluster running Freesurfer and other neuroimaging toolboxes and wanted to better understand requirements from Freesurfer. 1) Is there a minimum amount of ram per core that is needed/recommended or is the amount of ram only defined per subject. Most of the threads I found say you need a minimum of 4 GB ram but these were from a long time ago and may not apply to the OPENMP code. Our compute node will have between 52-64 cores so I wanted to know what is the ratio for Ram to CPU (multithreading turned off) that is recommended as opposed to Ram per subject. 2) AMD vs Intel We will run jobs on an existing cluster that has Dual Intel Xeon 6230R and for our new minicluster we are debating between AMD and Intel. While AMD offers higher core-count per CPU, I am worried that results could potentially differ based on the CPU/underlying libraries. We plan on using docker images so from your perspective do you have a recommendation ? Best, Ajay

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Average intensity from functional image within a label along the surface
by Erik Jahner 12 Jul '21

12 Jul '21

External Email - Use Caution I am interested in obtaining the average intensity within a label from each frame of a functional image that has been smoothed on the surface (I have generated the label). However, the functional connectivity pipeline seems to use only volume based seeds. It seems to me that in using the FreeSurfer software we would want to take advantage of the surface based smoothing and not return to getting the average of voxels in aparc+aseg. (1) Am I incorrect in my thinking here? (2) Is there a way to get the mean intensity of a label (I have created) at each frame along the surface to get a timcourse for a particular region? I see there is a way to do this with anatomical features such as thickness, but I do not see a way to do this with functional data across frames for signal intensity. Thanks ~Erik Jahner

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Tracula 7.1.1 - trac-all - stat
by Fanny Noury 12 Jul '21

12 Jul '21

External Email - Use Caution Hello FreeSurfer Developers, I am just started to use "the trac-all -stat" command of Tracula 7.1.1 to reconstruct a mean tract for each pathway for all the subjects of a group. Several files are created for each pathway in the "stat" directory. What are the differences between these two files created for the AF, MD AD and RD diffusion parameters : - TRACTNAME_PP.avg33_mni_bbr.**.txt - TRACTNAME_PP.avg33_mni_bbr.**_Avg.txt Thank You very much for your help. Best Regards, F. Noury ----------------------------------------- Fanny NOURY, Ph.D Associate Pr. Rennes 1 University LTSI UMR INSERM 1099 Université de Rennes 1 Campus Villejean - RDC Bât. 5 CS 34317 35043 Rennes Cedex France

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Re: [Freesurfer] Developer login
by fsbuild 12 Jul '21

12 Jul '21

External Email - Use Caution Hello Zachary, Sending some info to your private email shortly. - R. On Jul 12, 2021, at 16:33, Zachary Wickliffe <zswickliffe(a)gmail.com> wrote:        External Email - Use Caution        Hey there, setting up virtualbox to run freesurfer and it's asking for a developer login. Can you help?Thank you,Zachary Wickliffe+1-727-871-1733zswickliffe(a)gmail.comOn Mon, Jul 12, 2021, 4:28 PM Zachary Wickliffe <zswickliffe(a)gmail.com> wrote:Hey there, setting up virtualbox to run freesurfer and it's asking for a developer login. Can you help?Thank you,Zachary Wickliffe+1-727-871-1733zswickliffe@gmail.com_______________________________________________Freesurfer mailing listFreesurfer@nmr..mgh.harvard.eduhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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Re: [Freesurfer] Developer login
by Zachary Wickliffe 12 Jul '21

12 Jul '21

External Email - Use Caution Hey there, setting up virtualbox to run freesurfer and it's asking for a developer login. Can you help? Thank you, Zachary Wickliffe +1-727-871-1733 zswickliffe(a)gmail.com On Mon, Jul 12, 2021, 4:28 PM Zachary Wickliffe <zswickliffe(a)gmail.com> wrote: > Hey there, setting up virtualbox to run freesurfer and it's asking for a > developer login. Can you help? > > Thank you, > > Zachary Wickliffe > +1-727-871-1733 > zswickliffe(a)gmail.com >

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Re: [Freesurfer] mri_glmfit DOSS
by Dr. Cornelius Kronlage 12 Jul '21

12 Jul '21

External Email - Use Caution Thank you for your reply! Using DOSS is precisely what I was trying to do, but mri_glmfit doesn't let me. I worked around by creating the design matrix in matlab and specify it to mri_glmfit directly, but I was wondering whether this might be bug in the parsing of the FSGD file. As a minimal example to reproduce, see the attached file. Trying to run mri_glmfit (doesn't even need more input specified), I get: $ mri_glmfit --fsgd dosstest.fsgd doss gdfRead(): reading dosstest.fsgd INFO: DeMeanFlag keyword not found, DeMeaning will NOT be done. Continuous Variable Means (all subjects) 0 C 32.1111 8.50417 1 D 0.555556 0.496904 2 E 5.44444 3.49956 Class Size and Means of each Continuous Variable 1 A 1 50.0000 1.0000 4.0000 2 B 8 29.8750 0.5000 5.6250 ERROR: Class A has 1 members. With 3 variables and using DODS, you need at least 4 members dosstest.fsgd: GroupDescriptorFile 1 Title DOSSTest DOSS Class A Class B Variables C D E Input SUBJECT1 A 50 1 4 Input SUBJECT2 B 30 0 12 Input SUBJECT3 B 29 0 5 Input SUBJECT4 B 31 1 3 Input SUBJECT5 B 20 1 4 Input SUBJECT6 B 42 1 6 Input SUBJECT7 B 33 0 11 Input SUBJECT8 B 30 0 1 Input SUBJECT9 B 24 1 3 Concerning demeaning: I am only going to use a contrast vector for differences in intercept between the groups [-1 1 0 0 0] and had the understanding that demeaning would't be necessary in this case. Am I mistaken or is there other reasons to do this especially for eTIV? Thank you very much and with best wishes, Cornelius > Date: Fri, 9 Jul 2021 14:05:31 -0400 > From: "Douglas N. Greve" <dgreve(a)mgh.harvard.edu> > Subject: Re: [Freesurfer] mri_glmfit DOSS > > You cannot use a DODS in this design as it tries to fit an offset and a > slope for each class, which can't be done when the class has only one > member. Try using DOSS instead. You should probably demean and rescale > the eTIV to be between 0 and 1. > > On 6/29/2021 11:19 AM, Dr. Cornelius Kronlage wrote: > > > > ????????External Email - Use Caution > > > > Dear freesurfers, > > > > for a morphometry study, I am comparing a single subject at a time to > > a group of controls. I would like to include age, gender (as a > > continuous variable for the sake of simplicity) and eTIV as ?nuisance? > > regressors in the GLM. The FSGD looks like this: > > > > GroupDescriptorFile 1 > > ?? ?Title SUBJECT1_vs_allControls > > ?? ?Class Test > > ?? ?Class Controls > > ?? ?Variables Age Gender eTIV > > > > Input SUBJECT1 Test 50 1 1497422.2500 > > Input SUBJECT2 Controls 30 0 1709039.536815 > > Input SUBJECT3 Controls 29 0 1423097.816203 > > Input SUBJECT4 Controls 31 1 1524690.026713 > > ... > > > > When I try to run mri_glmfit I get the following error even though I > > specify DOSS: > > > > $ mri_glmfit --fsgd SUBJECT1_vs_allControls.fsgd doss [...] > > > > gdfRead(): reading SUBJECT1_vs_allControls.fsgd > > WARNING: variable 1 is "Gender" which is often a discrete factor > > ? The proper way to handle discrete factors is to create classes. > > ? See https://secure-web.cisco.com/1pPOfqP7ALbHwAeCTJZowgLS8JJNUeA6-Z2RJlgx_6oCnQ… > > INFO: DeMeanFlag keyword not found, DeMeaning will NOT be done. > > Continuous Variable Means (all subjects) > > 0 Age 28.6875 6.36611 > > 1 Gender 0.46875 0.499022 > > 2 eTIV 1.51444e+06 125811 > > Class Size and Means of each Continuous Variable > > 1 Test? 1? 50.0000?? 1.0000 1497422.2500 > > 2 Controls 31? 28.0000?? 0.4516 1514993.8589 > > ERROR: Class Test has 1 members. With 3 variables and using DODS, you > > need at least 4 members > > > > How do I get mri_glmfit to use DOSS, or do I have to specify the > > design matrix by hand in this case? > > > > Thank you! > > > > Best wishes > > Cornelius > > > >

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Ill-Conditioned/Badly Scaled Matrix Error
by Lurie, Lucy 11 Jul '21

11 Jul '21

External Email - Use Caution Hello I am trying to a whole brain analysis with one group and five covariates. I am getting an error that the design matrix may be ill-conditioned or badly scaled. I checked over the fsgd file in TextEdit and it seems to be formatted correctly. I am using Freesurfer version 5.3.0 and my platform is macOS Big Sur version 11.4. I would appreciate any help further troubleshooting this error. Thank you! ERROR: matrix is ill-conditioned or badly scaled, condno = 13065 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 --y lh.eig_cgORCE_baselineORCE_teenCG.thickness.10.mgh --fsgd eig_cgORCE_baselineORCE_teenCG.fsgd dods --C eig_cgORCE3042_slope.mtx --C eig_cgORCEbaseline_slope.mtx --C eig_teenCGqual_slope.mtx --surf fsaverage lh --cortex --glmdir lh.eig_cgORCE_baselineORCE_teenCGagesex.glmdir 2. The FSGD file (if using one) 3. And the design matrix Design matrix ------------------ 1.000 0.000 16.660 2.950 2.200 0.000; 1.000 0.000 16.960 1.900 2.600 0.000; 1.000 0.000 17.390 2.450 2.800 0.000; 1.000 1.000 17.130 2.300 2.500 0.000; 1.000 1.000 16.980 2.400 2.300 0.000; 1.000 0.000 17.690 2.200 2.900 0.000; 1.000 1.000 16.870 2.750 2.900 1.000; 1.000 0.000 17.570 2.550 1.700 0.000; 1.000 0.000 17.130 2.150 1.928 0.000; 1.000 1.000 17.650 2.750 2.200 0.000; 1.000 1.000 17.210 2.450 2.500 1.000; 1.000 0.000 15.610 3.100 1.565 1.000; 1.000 0.000 16.440 2.650 2.300 0.000; 1.000 1.000 17.350 2.400 2.600 0.000; 1.000 0.000 16.800 1.600 1.400 0.000; 1.000 1.000 17.980 2.200 2.300 1.000; 1.000 1.000 16.890 2.800 1.600 1.000; 1.000 0.000 16.760 1.100 1.300 0.000; 1.000 1.000 16.790 2.500 1.400 0.000; 1.000 1.000 16.620 2.900 2.800 0.000; 1.000 1.000 16.500 2.500 1.200 0.000; 1.000 1.000 16.290 2.700 3.200 0.500; 1.000 0.000 16.440 3.100 2.500 1.000; 1.000 0.000 17.540 2.100 2.100 1.000; 1.000 1.000 16.810 1.900 2.600 1.000; 1.000 0.000 17.110 2.650 2.400 0.000; 1.000 1.000 16.520 2.000 2.300 0.000; 1.000 1.000 16.520 3.100 3.400 1.000; 1.000 0.000 16.320 3.300 2.800 0.500; 1.000 0.000 16.380 3.200 1.900 1.000; 1.000 0.000 16.570 2.800 2.000 0.000; 1.000 1.000 16.250 2.200 1.600 0.000; 1.000 1.000 16.820 2.900 2.600 1.000; 1.000 0.000 16.580 2.600 2.800 0.000; 1.000 1.000 16.730 2.850 3.150 0.000; 1.000 0.000 16.310 2.600 1.900 0.000; 1.000 1.000 17.010 1.700 2.400 0.000; 1.000 1.000 16.360 3.200 3.200 0.000; 1.000 1.000 16.490 2.400 1.525 0.000; 1.000 0.000 16.300 2.400 1.450 1.000; 1.000 0.000 16.060 2.200 1.300 0.000; 1.000 1.000 16.600 2.200 2.000 0.000; 1.000 0.000 16.720 3.300 2.500 0.000; 1.000 1.000 16.160 2.300 2.200 0.000; 1.000 1.000 16.140 2.800 1.850 0.000; 1.000 1.000 16.550 3.100 2.950 1.000; 1.000 1.000 17.300 3.450 2.700 1.000; 1.000 0.000 16.940 3.000 2.200 1.000; 1.000 1.000 16.550 2.700 2.850 0.000; 1.000 0.000 17.170 3.000 2.900 1.000; 1.000 0.000 17.150 2.800 2.450 0.000; 1.000 0.000 16.650 3.100 1.900 0.500; 1.000 0.000 16.070 2.700 2.900 1.000; 1.000 1.000 16.720 1.800 1.450 1.000; 1.000 0.000 16.150 2.900 1.000 0.000; 1.000 1.000 16.120 2.400 2.200 1.000; 1.000 1.000 17.540 2.600 2.600 0.000; 1.000 1.000 16.840 2.900 1.100 0.000; 1.000 1.000 15.790 3.100 1.600 1.000; 1.000 1.000 16.430 2.800 1.500 0.000; 1.000 0.000 16.390 3.300 2.700 1.000; 1.000 0.000 16.480 3.350 3.000 0.000; 1.000 1.000 16.930 3.000 1.000 0.000; 1.000 1.000 15.550 2.200 2.600 0.000; 1.000 1.000 17.080 2.900 1.500 0.000; 1.000 0.000 16.850 2.500 2.200 0.500; 1.000 1.000 16.700 2.200 1.800 0.000; 1.000 1.000 15.930 2.600 2.000 1.000; 1.000 1.000 16.080 2.600 1.200 0.500; 1.000 0.000 16.130 3.150 1.400 1.000; 1.000 1.000 16.250 3.100 1.400 1.000; 1.000 0.000 16.040 3.250 3.200 1.000; 1.000 0.000 16.040 3.500 2.900 1.000; 1.000 0.000 16.070 1.400 1.000 1.000; 1.000 0.000 16.550 3.200 2.300 1.000; 1.000 0.000 16.120 2.800 2.000 0.500; 1.000 1.000 16.450 3.150 1.400 1.000; 1.000 0.000 15.910 3.400 1.600 1.000; 1.000 1.000 15.700 3.000 2.400 1.000; Best, Lucy Lurie (she/her/hers) Doctoral Student, Clinical Psychology Department of Psychology and Neuroscience University of North Carolina at Chapel Hill CIRCLE Lab<https://secure-web.cisco.com/18i_Rf4fzTTcqPA2OQAm3_CnM6qhNKl6D-xG6RO1PJGAuL…>

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Freesurfer July 2021