Freesurfer

freesurfer@nmr.mgh.harvard.edu

17 participants
30130 discussions

Comparison of "segment_subregions" and "recon-all"
by Yoonho Hwang 06 Aug '24

06 Aug '24

External Email - Use Caution Dear Bruce Fischl and Douglas Greve, Hi all, After completing the "recon-all" process, I have completed the "segment_subregions" process. There is a difference between the total volume (hippocampus, amygdala, thalamus, brainstem) using "asegstats2table" and the total volume (hippocampus, amygdala, thalamus, brainstem) using "segment_subregions". Is there a standard for how much difference is acceptable? Thank you very much Sincerely, Yoonho -- *Hwang, Yoonho* *Research professor* *Institute of Human Genomic Study,* *College of Medicine, Korea University* *#123, Jeokgeum-ro, Danwon-gu, Ansan-si, Gyeonggi-do, **15355, Republic of Korea.* *Email: ken.lmstar11(a)gmail.com <ken.lmstar11(a)gmail.com>*

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Re: [Freesurfer] mri_easyreg --help error: zsh : illegal hardware instruction mri_easyreg --help
by fsbuild 05 Aug '24

05 Aug '24

External Email - Use Caution  Hello Yaozhi, You probably installed the Intel Mac binaries to run on your Silicon Mac with an M1, M2, or M3 arm64 chip.  But not all Intel binaries will run on the newer arm64 Macs.   If you want to run that command you will need to download and install the silicon Mac nightly freesurfer build. To remove your current freesurfer install, you can try running the uninstall script from the command line under what you set FREESURFER_HOME to, $ cd $FREESURFER_HOME$ sudo bash uninstall.sh Once you have removed your current freesurfer installation, you can download and try installing the arm64 pkg, https://surfer.nmr.mgh.harvard.edu/pub/dist/freesurfer/dev/freesu… Right click with the mouse on the .pkg file and select “Open with —> install.app (default)".  Then follow the prompts in the installer windows. - R. On Aug 5, 2024, at 21:45, Yang, Yaozhi <yangyaozhi(a)ufl.edu> wrote:        External Email - Use Caution        Hello FreeSurfer Developers, I am trying to use easyreg from the paper--- A ready‑to‑use machine learning tool for symmetric multi‑modality registration of brain MRI and the paper says that I can use freesurfer to get the easyreg function. But after I download freesurfur in my macbook, and run mri_easyreg –-help on the terminal, I get the error saying that zsh : illegal hardware instruction mri_easyreg --help. So can you help me with that? How can I use easyreg method? Thank you very much! Best,Yaozhi_______________________________________________Freesurfer mailing listFreesurfer@nmr.mgh.harvard.eduhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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mri_easyreg --help error: zsh : illegal hardware instruction mri_easyreg --help
by Yang, Yaozhi 05 Aug '24

05 Aug '24

External Email - Use Caution Hello FreeSurfer Developers, I am trying to use easyreg from the paper--- A ready‑to‑use machine learning tool for symmetric multi‑modality registration of brain MRI and the paper says that I can use freesurfer to get the easyreg function. But after I download freesurfur in my macbook, and run mri_easyreg –-help on the terminal, I get the error saying that zsh : illegal hardware instruction mri_easyreg --help. So can you help me with that? How can I use easyreg method? Thank you very much! Best, Yaozhi

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Questions about FreeSurfer's longitudinal pipeline prior to longitudinal analysis
by Yoonho Hwang 05 Aug '24

05 Aug '24

External Email - Use Caution Dear Martin Reuter and Bruce Fischl, Hi all, When we use the longitudinal pipeline of FreeSurfer, I know there are three steps: 1. cross-sectional, 2: base, 3: long. When we create the subject template, it uses median and intensity scaling in the second step ("base") based on the paper ("Within-subject template estimation ~"). Considering the base, many processes are initialized in the third step ("long"). Q1) I know when we run the base, isn't there a process to use a function such as mri_convert to adjust the image size for each time to be the same? Could creating the subject template based on the median and intensity scaling minimizes the impact of longitudinal analysis on MRI from different scanners and acquisition parameters? Q2) In the base, I encountered the following warning, but completed the entire process without errors. ******************************************************************************* WARNING: Image parameters differ across time, maybe due to aquisition changes? Consistent changes in, e.g., resolution can potentially bias a longitudinal study! You can check image parameters by running mri_info on each input image. Will continue in 10 seconds ... ******************************************************************************* There is matrix size for each time point: Baseline (scanner1, 512 x 512 x 244 or 512 x 512 x 180) 1st follow-up (scanner1, 512 x 512 x 180) 2nd follow-up (scanner2, 448 x 448 x 192 or 448 x 448 x 208) Could I ignore the warning? Or is there anything I need to fix before running the long)? Also, could you please give me any suggestions on longitudinal analyses? Thank you very much Sincerely, Yoonho -- *Hwang, Yoonho* *Research professor* *Institute of Human Genomic Study,* *College of Medicine, Korea University* *#123, Jeokgeum-ro, Danwon-gu, Ansan-si, Gyeonggi-do, **15355, Republic of Korea.* *Email: ken.lmstar11(a)gmail.com <ken.lmstar11(a)gmail.com>*

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Inquiry on Calculation Methods for Folding Index and Intrinsic Curvature Index in mris_anatomical_stats
by littlepotato 05 Aug '24

05 Aug '24

External Email - Use Caution Dear FreeSurfer Support Team, I am writing to inquire about the specific methods and formulas used by the mris_anatomical_stats function in Freesurfer to calculate the Folding Index and Intrinsic Curvature Index for each label based on annotation files. Could you please provide detailed information on: the algorithms or mathematical formulas used to calculate the Folding Index and Intrinsic Curvature Index for each label. If there are any available documentation, publications, or example commands illustrating these calculations, I would greatly appreciate your sharing them. Thank you for your assistance. Best regards, Amber Lin

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Issues with parallel processing trac-all bedp step
by De Giorgi, Sara 05 Aug '24

05 Aug '24

Hello, I am having some issues with running the commands from trac-all -bedp -c $SUBJECTS_DIR/trac_cfg_longitudinal.sh -jobs commands_list_bedp Specifically, bedpostx_preproc.sh does not find the bedxpostX folder structure and the rest then fails. If I run trac-all -bedp -c $SUBJECTS_DIR/trac_cfg_longitudinal.sh (i.e. without the -jobs flag) it works fine with the same configuration file. I am using FSL 6.0.7.4 and Freesurfer 7.4.1. Could you help me with this? Thanks a lot in advance, Sara

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Longitudinal analyses using Repeated measures anova
by Josefine Tingdal Taube Danielsen 05 Aug '24

05 Aug '24

External Email - Use Caution Hi Freesurfer support, I'm using Freesurfer version 7.4.1 and Ubuntu 22_x86_64. freesurfer-linux-ubuntu22_x86_64-7.4.1-20230614-7eb8460. I'm trying to run longitudinal analysis using Repeated Measures Anova (https://surfer.nmr.mgh.harvard.edu/fswiki/RepeatedMeasuresAnova) My study has 2 groups (MM=patient group and HC=healthy controls) and 2 time points (1 and 2). I am looking at cortical thickness and cortical volume. Here is some information: We have been using these Contrast files: # Create 1-vs-2 contrast file echo "0 0 1" > 1-vs-2.mtx # Create group-effect contrast file echo "0 1 0" > group-effect.mtx # Create interaction contrast file echo "0 1 -1" > interaction.mtx # Create mean contrast file echo "1 0 0" > mean.mtxfree And then followed this: Preproc using FSGD mris_preproc --target fsaverage --hemi lh --meas thickness --out lh.thickness.mgh --fsgd rmanova.fsgd Smooth data mri_surf2surf --s fsaverage --hemi lh --fwhm 5 --sval lh.thickness.mgh --tval lh.thickness.sm05.mgh Run GLM fit mri_glmfit --glmdir lh.rmanova --y lh.thickness.sm05.mgh --fsgd rmanova.fsgd doss \ --C 1-vs-2.mtx --C group-effect.mtx --C interaction.mtx --C mean.mtx \ --surface fsaverage lh This was our results: INFO: gd2mtx_method is doss Reading source surface /mnt/c/CogimmunStudy/FS/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 7.4.1 cwd /mnt/c/CogimmunStudy/FS cmdline mri_glmfit --glmdir lh.rmanova --y lh.thickness.sm05.mgh --fsgd rmanova.fsgd doss --C 1-vs-2.mtx --C group-effect.mtx --C interaction.mtx --C mean.mtx --surface fsaverage lh sysname Linux hostname D56979 machine x86_64 user josdan FixVertexAreaFlag = 1 UseMaskWithSmoothing 1 OneSampleGroupMean 0 y /mnt/c/CogimmunStudy/FS/lh.thickness.sm05.mgh logyflag 0 usedti 0 FSGD rmanova.fsgd labelmask /mnt/c/CogimmunStudy/FS/fsaverage/label/lh.cortex.label maskinv 0 glmdir lh.rmanova IllCondOK 0 ReScaleX 1 DoFFx 0 SigUseDouble 1 Creating output directory lh.rmanova Loading y from /mnt/c/CogimmunStudy/FS/lh.thickness.sm05.mgh ... done reading. INFO: gd2mtx_method is doss Saving design matrix to lh.rmanova/Xg.dat Computing normalized matrix Normalized matrix condition is 1 Matrix condition is 3.15 Found 149955 points in label. Pruning voxels by thr: 1.175494e-38 Found 149955 voxels in mask Saving mask to lh.rmanova/mask.mgh Reshaping mriglm->mask... search space = 74612.577841 DOF = 123 Starting fit and test Fit completed in 0.0220333 minutes Computing spatial AR1 on surface Residual: ar1mn=0.986733, ar1std=0.003725, gstd=3.501824, fwhm=8.246166 Writing results 1-vs-2 maxvox sig=-2.06583 F=7.13254 at index 102162 0 0 seed=1720706961 Computing efficiency group-effect maxvox sig=114.013 F=8295.3 at index 155689 0 0 seed=1720706961 Computing efficiency interaction maxvox sig=106.775 F=6297.15 at index 64378 0 0 seed=1720706961 Computing efficiency mean maxvox sig=133.337 F=17231.6 at index 155689 0 0 seed=1720706961 Computing efficiency mri_glmfit done Now our questions: 1. Have we been using the right contrasts? 2. Do the results look realistic? 3. Is it possible to have some more information about results (e.g. summary, areas being significant, visualization, sig.mgh, t-values, p-values not i -log10)? Thank you! Best, Josefine Danielsen

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Interpreting LME results with multi-row contrasts
by Dan Levitas 05 Aug '24

05 Aug '24

External Email - Use Caution I am performing a longitudinal LME analysis in which participants were scanned anywhere from 2-6 times over the course of several years. There are 4 groups denoting participants: Controls (C, coded as 0), group 1 (G1, coded as 1), group 2, (G2, coded as 2), and group 3 (G3, coded as 3). Additionally, I have included two covariates (age and number of previous concussions). With a single random effect (intercept, all 1s), my design matrix is: *Yij = intercept + time + age + numPrevConcussion + G1 + time*G1 +G2 + time*G2 + G3 + time*G3* I am primarily interested in time*group interaction differences between G1 and G2 (G1 - G2) and G1 and G3 (G1 - G3). I first tried single row contrasts separately (i.e. CM.C = [0 0 0 0 0 1 0 -1 0 0] for G1-G3, and CM.C = [0 0 0 0 0 1 0 0 0 -1] for G1 - G3), which produced no significant results. I then created a multi-row contrast of the two contrasts above (i.e. CM.C = [zeros(2,5) [-1 0 1 0 0;-1 0 0 0 1]]), which also did not reveal significant findings. Only when I created a 3-row contrast (CM.C = [zeros(3,5) [1 0 0 0 0;1 0 -1 0 0;1 0 0 0 -1]]) did I get significant results. I'm a bit unsure how to properly interpret these contrasts and wanted to get clarification on my thinking. Is the 3-row contrast assessing the null hypothesis of no group differences in the rate of change over time among the four groups? If so, does the lack of findings in the 2-row and single row contrasts indicate that there are no interaction differences between G1 and G2, and between G1 and G3? Thank you

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Inquiry on Calculation Methods for Folding Index and Intrinsic Curvature Index in mris_anatomical_stats
by littlepotato 02 Aug '24

02 Aug '24

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FreeSurfer in fmriprep
by Tengwen Fan 01 Aug '24

01 Aug '24

External Email - Use Caution Dear FreeSurfer Developers, I hope this email finds you well! I am using fMRIprep to do pre-processing. The fMRIprep combines methodology from AFNI, ANTs, Freesurfer, FSL, and Mindboggle to do preprocessing. An error was reported while running the fMRIprep. It seems that it was due to the structural data and it was about the registration by FreeSurfer. But I am new in the neuroimaging area and not very sure about what the error actually is about. I asked the fmriprep team in neurostars (https://neurostars.org/t/fmriprep-error-makeisotropic-warning-not-different…) and they are also not sure about this and recommended to ask the FreeSurfer. Although I am not sure if the FreeSurfer in fmriprep is what you also work with, I would like to ask and would really appreciate it if you could help explain what error it is. Thanks you so much! The error is attached below. Please let me know if you have any questions or if more information is needed! [https://neurostars.org/uploads/default/original/2X/1/1898e24a8821a606fad2dd…]<https://neurostars.org/t/fmriprep-error-makeisotropic-warning-not-different…> Fmriprep error: makeIsotropic WARNING: not different enough, won't reslice!<https://neurostars.org/t/fmriprep-error-makeisotropic-warning-not-different…> Summary of what happened: Hi, I am running fmriprep and getting the following error. I would really appreciate any help! Please let me know if more information should be provided! Thanks so much, tengwen Command used (and if a helper script was used, a link to the helper script or the command generated): fmriprep-docker /Users/Desktop/WL_fMRI/data/BIDS /Users/Desktop/WL_fMRI/data/derivatives/fmriprep participant --participant-label sub-WLfMRI035 --fs-license-file /Users/Desktop/WL_fMRI/licen... neurostars.org The Error is: Cmdline: mri_robust_register --satit --iscale --lta /tmp/work/fmriprep_23_2_wf/sub_WLfMRI033_wf/anat_fit_wf/surface_recon_wf/fsnative2t1w_xfm/T1_robustreg.lta --mov /out/sourcedata/freesurfer/sub-WLfMRI033/mri/T1.mgz --dst /tmp/work/fmriprep_23_2_wf/sub_WLfMRI033_wf/anat_fit_wf/anat_template_wf/denoise/mapflow/_denoise0/sub-WLfMRI033_ses-Session1_T1w_noise_corrected.nii.gz Stdout: 7.3.2 --satit: Will iterate with different SAT to ensure outliers below wlimit! --iscale: Enabling intensity scaling! --lta: Output transform as /tmp/work/fmriprep_23_2_wf/sub_WLfMRI033_wf/anat_fit_wf/surface_recon_wf/fsnative2t1w_xfm/T1_robustreg.lta . --mov: Using /out/sourcedata/freesurfer/sub-WLfMRI033/mri/T1.mgz as movable/source volume. --dst: Using /tmp/work/fmriprep_23_2_wf/sub_WLfMRI033_wf/anat_fit_wf/anat_template_wf/denoise/mapflow/_denoise0/sub-WLfMRI033_ses-Session1_T1w_noise_corrected.nii.gz as target volume. reading source '/out/sourcedata/freesurfer/sub-WLfMRI033/mri/T1.mgz'... reading target '/tmp/work/fmriprep_23_2_wf/sub_WLfMRI033_wf/anat_fit_wf/anat_template_wf/denoise/mapflow/_denoise0/sub-WLfMRI033_ses-Session1_T1w_noise_corrected.nii.gz'... Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 0 Type Target : 3 ensure both FLOAT (3) Reordering axes in mov to better fit dst... ( -1 -3 2 ) Determinant after swap : 1 Mov: (0.5, 0.5, 0.5)mm and dim (512, 512, 512) Dst: (0.5, 0.5, 0.5)mm and dim (360, 512, 512) Asserting both images: 0.5mm isotropic - reslicing Mov ... -- changing data type from 0 to 3 (noscale = 0)... - reslicing Dst ... -- Original : (0.5, 0.5, 0.5)mm and (360, 512, 512) voxels. -- Resampled: (0.5, 0.5, 0.5)mm and (512, 512, 512) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - WARNING: trg mri outside_val = 0 but suspected background: -0.00687514 If background is black or darkgray, ignore this. If white, pass --whitebgdst Registration::findSaturation - computing centroids - computing initial transform -- using translation info - Get Gaussian Pyramid Limits ( min size: 16 max size: -1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 4 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 4 ) - Max Resolution used: 3 -- gpS ( 64 , 64 , 64 ) -- gpT ( 64 , 64 , 64 ) - running loop to estimate saturation parameter: -- Iteration: 1 trying sat: 16 min sat: 0 ( -1 ), max sat: 16 ( 0.12101 ), sat diff: 16, (wlimit=0.16) -- Iteration: 2 trying sat: 8 min sat: 8 ( 0.324589 ), max sat: 16 ( 0.12101 ), sat diff: 8, (wlimit=0.16) -- Iteration: 3 trying sat: 12 min sat: 12 ( 0.193726 ), max sat: 16 ( 0.12101 ), sat diff: 4, (wlimit=0.16) -- Iteration: 4 trying sat: 14 min sat: 12 ( 0.193726 ), max sat: 14 ( 0.15173 ), sat diff: 2, (wlimit=0.16) -- Iteration: 5 trying sat: 13 min sat: 13 ( 0.17074 ), max sat: 14 ( 0.15173 ), sat diff: 1, (wlimit=0.16) -- Iteration: 6 trying sat: 13.5 - final SAT: 14 ( it: 6 , weight check 0.15173 <= 0.16 ) Registration::computeMultiresRegistration - computing centroids - computing initial transform -- using translation info - Get Gaussian Pyramid Limits ( min size: 16 max size: -1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 1.6982521922295 0 1.0000000000000 0 -1.1595074345649 0 0 1.0000000000000 -1.1415599519338 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 4 S( 32 32 32 ) T( 32 32 32 ) Iteration(f): 1 -- intensity log diff: abs(-0.85599) -- diff. to prev. transform: 1.62699 Iteration(f): 2 -- intensity log diff: abs(-0.0720915) -- diff. to prev. transform: 0.761125 Iteration(f): 3 -- intensity log diff: abs(-0.00194135) -- diff. to prev. transform: 0.0383579 Iteration(f): 4 -- intensity log diff: abs(-0.00123869) -- diff. to prev. transform: 0.0128975 Iteration(f): 5 -- intensity log diff: abs(-0.000750416) <= 0.001 :-) -- diff. to prev. transform: 0.00518321 <= 0.01 :-) Resolution: 3 S( 64 64 64 ) T( 64 64 64 ) Iteration(f): 1 -- intensity log diff: abs(0.00702447) -- diff. to prev. transform: 0.977321 Iteration(f): 2 -- intensity log diff: abs(9.94659e-05) <= 0.001 :-) -- diff. to prev. transform: 0.0258922 Iteration(f): 3 -- intensity log diff: abs(1.21569e-05) <= 0.001 :-) -- diff. to prev. transform: 0.00163623 <= 0.01 :-) Resolution: 2 S( 128 128 128 ) T( 128 128 128 ) Iteration(f): 1 -- intensity log diff: abs(-0.000545933) <= 0.001 :-) -- diff. to prev. transform: 0.356881 Iteration(f): 2 -- intensity log diff: abs(-7.53945e-05) <= 0.001 :-) -- diff. to prev. transform: 0.0881187 Iteration(f): 3 -- intensity log diff: abs(-5.00529e-05) <= 0.001 :-) -- diff. to prev. transform: 0.0253314 Iteration(f): 4 -- intensity log diff: abs(-2.0764e-05) <= 0.001 :-) -- diff. to prev. transform: 0.00744419 <= 0.01 :-) Resolution: 1 S( 256 256 256 ) T( 256 256 256 ) Iteration(f): 1 -- intensity log diff: abs(0.00127177) -- diff. to prev. transform: 0.268975 Iteration(f): 2 -- intensity log diff: abs(0.000348898) <= 0.001 :-) -- diff. to prev. transform: 0.0666025 Iteration(f): 3 -- intensity log diff: abs(8.4156e-05) <= 0.001 :-) -- diff. to prev. transform: 0.016444 Iteration(f): 4 -- intensity log diff: abs(2.77798e-05) <= 0.001 :-) -- diff. to prev. transform: 0.00492133 <= 0.01 :-) Resolution: 0 S( 512 512 512 ) T( 512 512 512 ) Iteration(f): 1 -- WARNING: mem usage large: 4668.08Mb mem + 6 MRI Maybe use --subsample <int> Stderr: makeIsotropic WARNING: not different enough, won't reslice! Killed Traceback: Traceback (most recent call last): File "/opt/conda/envs/fmriprep/lib/python3.10/site-packages/nipype/interfaces/base/core.py", line 398, in run runtime = self._post_run_hook(runtime) File "/opt/conda/envs/fmriprep/lib/python3.10/site-packages/niworkflows/interfaces/freesurfer.py", line 248, in _post_run_hook fix_lta_length(lta_file) File "/opt/conda/envs/fmriprep/lib/python3.10/site-packages/niworkflows/interfaces/freesurfer.py", line 399, in fix_lta_length lines = Path(lta_file).read_text().splitlines(keepends=True) File "/opt/conda/envs/fmriprep/lib/python3.10/pathlib.py", line 1134, in read_text with self.open(mode='r', encoding=encoding, errors=errors) as f: File "/opt/conda/envs/fmriprep/lib/python3.10/pathlib.py", line 1119, in open return self._accessor.open(self, mode, buffering, encoding, errors, FileNotFoundError: [Errno 2] No such file or directory: '/tmp/work/fmriprep_23_2_wf/sub_WLfMRI033_wf/anat_fit_wf/surface_recon_wf/fsnative2t1w_xfm/T1_robustreg.lta' Best, Tengwen

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