Freesurfer

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30088 discussions

Re: [Freesurfer] Longitudinal subfields segmentation single time point
by Iglesias Gonzalez, Eugenio 18 Jan '19

18 Jan '19

External Email - Use Caution Thanks a lot for your email, Saskia. Does this happen for more than one subject? Can you reproduce the error? Cheers, /E -- Juan Eugenio Iglesias ERC Senior Research Fellow Centre for Medical Image Computing (CMIC) University College London Research staff Martinos Center for Biomedical Imaging Massachusetts General Hospital Research Affiliate Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Lecturer on Radiology Harvard Medical School http://www.jeiglesias.com From: <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of "Koch, S.B.J. (Saskia)" <S.Koch(a)donders.ru.nl> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Date: Friday, 18 January 2019 at 09:46 To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Subject: [Freesurfer] Longitudinal subfields segmentation single time point External Email - Use Caution Dear FreeSurfer Developers, I am attempting to run the longitudinal hippocampal subfields segmentation, using the development version of FreeSurfer. This works perfect for subjects with multiple data time points, but I get the following error for subjects for which I have a single data time point (see attachment for the error log): ******************* Global iteration 2 ******************* ***** Estimating subject-specific atlas ***** Error using kvlGEMSMatlab /autofs/space/panamint_005/users/iglesias/software/freesurfer/GEMS2/Matlab/kvlMatlabObjectArray.cxx:82: itk::ERROR: MatlabObjectArray(0x7f8686741c40): Trying to access non-existing object (25) Error in kvlGetOptimizer (line 11) Error in SegmentSubfieldsT1Longitudinal (line 1155) Is it possible to run this longitudinal module on subjects with a single time point? Any thoughts on how to troubleshoot this? FreeSurfer version: freesurfer-Linux-centos7_x86_64-dev-20180118-d073f7e Platform: CentOS Linux 7 (Core) Uname -a: Linux mentat003.dccn.nl 3.10.0-693.17.1.el7.x86_64 #1 SMP Thu Jan 25 20:13:58 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux Thanks for your help! Saskia

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Longitudinal subfields segmentation single time point
by Koch, S.B.J. (Saskia) 18 Jan '19

18 Jan '19

External Email - Use Caution Dear FreeSurfer Developers, I am attempting to run the longitudinal hippocampal subfields segmentation, using the development version of FreeSurfer. This works perfect for subjects with multiple data time points, but I get the following error for subjects for which I have a single data time point (see attachment for the error log): ******************* Global iteration 2 ******************* ***** Estimating subject-specific atlas ***** Error using kvlGEMSMatlab /autofs/space/panamint_005/users/iglesias/software/freesurfer/GEMS2/Matlab/kvlMatlabObjectArray.cxx:82: itk::ERROR: MatlabObjectArray(0x7f8686741c40): Trying to access non-existing object (25) Error in kvlGetOptimizer (line 11) Error in SegmentSubfieldsT1Longitudinal (line 1155) Is it possible to run this longitudinal module on subjects with a single time point? Any thoughts on how to troubleshoot this? FreeSurfer version: freesurfer-Linux-centos7_x86_64-dev-20180118-d073f7e Platform: CentOS Linux 7 (Core) Uname -a: Linux mentat003.dccn.nl 3.10.0-693.17.1.el7.x86_64 #1 SMP Thu Jan 25 20:13:58 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux Thanks for your help! Saskia

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Help with finding which functional areas house certain electrodes
by Sparsh Jain 17 Jan '19

17 Jan '19

External Email - Use Caution Good afternoon, We want to find the functional areas where each electrode is located in patients. At present, we are finding the electrode RAS coordinates manually using CT-MRI co-registration. We are then manually searching for these locations in Freeview using the aparc.a2009s+aseg ATLAS (generated during MRI reconstruction) on top of MRI volume. This is giving us the exact anatomical/functional area where the electrode belongs. However, this is a very tedious and time-consuming process and we need to do this for many patients. We were wondering if there is 1. A way to automate this process by possibly entering all coordinates and using the LUT and atlas files to get the areas easily for all points. 2. More importantly, could you tell us how to establish a method where we can dump the anatomical region coordinates to a file, and then find the nearest functional area to a particular electrode automatically? For example, if a point lies in a white matter area, can we implement a process to find which functional area (gray matter region, or say hippocampus for example) lies CLOSEST to this electrode point in the white matter region. Can we implement this automated process in an efficient manner for all the points at once? We usually have anywhere between 140 -170 sets of coordinates for each patient, and doing this by hand on freeview is not practical. Instead of finding where exactly all the points lie, we would like to create a 'table' of all anatomical areas and then find out which area of relevance lies closest to a particular point. Thank you Sparsh Jain

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Issues parcellating with a custom atlas
by Michael Sughrue 17 Jan '19

17 Jan '19

External Email - Use Caution Hello My interest is to create an aparc+aseg file with a combination of a cortical parcellation with a custom cortical atlas and the subcortical grey matter components from the standard aseg....basically i want to mask and extract signal from multiple areas in a series of pipelines in python. The commands below are called from python, but edited to make it easy to read. About 50% of the time the code below works fine, but the other 50% it drops two cortical parcellations. Interestingly it is always the same two parcellations, both at the end of the left hemisphere list, that get dropped when making aparc+aseg. My only guess is that something about the aparc and aseg combination is causing them to be dropped. I have read the annot files it makes and the parcellations are made in the mris_ca_label step and making the aparc+aseg. I have viewed them in freeview and they are not there when viewed in the LUT for this atlas and I have tried omittng and excluding all forms of mri_binarize -replace commands (added to try to eliminate segmentations of the ventricle and white atter which i am trying to mask out of a resting state image), and this doesnt make a difference in the cases where it is failing. I have tried using the cases where it worked to train a new gcs file without success. I have also rune mri_aparc2aseg with and without the ribbon option, with all varieties of aseg files...etc etc. If there is something obviously wrong please let me know. Alternately, what is the best way to get aparc+aseg.mgz to print a list of what areas made it in there..in that case i can likely create a way to make sure the lables it is using for an adjacency matrix atleast match the rows and columns accurately..i tried using segstats but this wasnt always an accurate list as it sometimes created a mask with areas not on any LUT's i have and thus i have been unable to view them in freeview or the segstats list and figure out what is going wrong the commands i invoke from python are below...thanks in advance recon-all -subjid sub1 -autorecon1 -openmp 6 recon-all -subjid sub1 -autorecon2 -openmp 6 'recon-all -subjid sub1 -sphere -surfreg -jacobian_white -avgcurv -open mp6 mris_ca_label -l $SUBJECTS_DIR/sub1/label/lh.cortex.label -aseg $SUBJECTS_DIR/sub1/mri/aseg.presurf.mgz sub1 lh lh.sphere.reg $SUBJECTS_DIR/sub1/parc/lh.hcp-mmp_6p0.gcs lh.aparc.annot mris_ca_label -l $SUBJECTS_DIR/sub1/label/rh.cortex.label -aseg $SUBJECTS_DIR/sub1/mri/aseg.presurf.mgz sub1 rh rh.sphere.reg $SUBJECTS_DIR/sub1/parc/rh.hcp-mmp_6p0.gcs rh.aparc.annot recon-all -subjid sub1 -pial -cortribbon -hyporelabel -openmp 6 mri_binarize --i mri/aseg.presurf.hypos.mgz --o mri/aseg.mgz --replace 2 0 --replace 4 0 --replace 5 0 --replace 7 0 --replace 14 0 --replace 15 0 --replace 24 0 --replace 30 0 --replace 31 0 --replace 41 0 --replace 43 0 --replace 44 0 --replace 46 0 --replace 62 0 --replace 63 0 --replace 77 0 --replace 85 0 --replace 251 0 --replace 252 0 --replace 253 0 --replace 254 0 --replace 255 0') mri_aparc2aseg --s sub1 mri_binarize --i mri/aparc+aseg.mgz --o mri/aparc+aseg.mgz --replace 41 0

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Exporting mean curvature indices
by Martin Juneja 17 Jan '19

17 Jan '19

External Email - Use Caution Hi Freesurfer experts, I am interested in estimating mean curvature index for each of the 7 networks (Yeo7 atlas) for my data of healthy controls. I am able to estimate this subjectwise, and the output stats file for each subject looks like following: # ColHeaders StructName NumVert SurfArea GrayVol ThickAvg ThickStd MeanCurv GausCurv FoldInd CurvInd FreeSurfer_Defined_Medial_Wall 8952 5993 1906 0.916 1.370 0.082 0.026 85 7.4 7Networks_1 23231 15309 34881 2.139 0.685 0.125 0.028 261 25.2 7Networks_2 20682 12735 35481 2.511 0.746 0.101 0.022 167 17.7 7Networks_3 14062 9362 24591 2.423 0.569 0.107 0.021 124 11.4 7Networks_4 12798 8490 25886 2.776 0.656 0.109 0.023 115 11.4 7Networks_5 10915 7302 26939 3.047 0.899 0.122 0.030 133 12.6 7Networks_6 15373 10259 28312 2.539 0.698 0.123 0.027 164 15.9 7Networks_7 32448 21779 70662 2.773 0.695 0.124 0.028 375 36.0 I have 123 such files from a set of 123 subjects. I was just wondering how can I save mean curvature indices from all the subjects in one file (text or excel)? I defined my Subjects.txt file with subject IDs of each subject. I am not sure how to proceed from there to save curvature indices. I tried to run following command, but that does not work: aparcstats2table --subjectsfile Subjects.txt --hemi lh --meas white.H -t MCI.txt I would really appreciate any help.

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convert surface-based label to volumetric space...mri_label2vol?
by Stephanie Hare 17 Jan '19

17 Jan '19

External Email - Use Caution Good afternoon, I'm new to Freesurfer; my background is resting-state fMRI using SPM. Years back, left and right cortical thickness measures were calculated for our sample of schizophrenia patients using the Desikan-Killiany atlas and were input to an SPSS spreadsheet. In an exploratory analysis of the data, I found that behavioral performance on a skills assessment was correlated with thickness of lh_rostralmiddlefrontal and lh_parstriangularis regions. I'd like to perform a follow-up resting-state fMRI analysis, using these two surface-based parcellations ( lh_rostralmiddlefrontal and lh_parstriangularis) as seed regions and use those behavioral performance scores as a regressor. Is there a simple way to convert these two 2D-surface-based atlas measurements to masks in nifti format in 3D-volumetric space? I was doing a quick google search and came across the mri_label2vol command. Would this be helpful? Sorry for my naiveté. I appreciate your time. Best, Stephanie Hare Postdoctoral Fellow Neuroimaging Research Program Maryland Psychiatric Research Center

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Brain Twitter Conference 2019 #brainTC
by Kivisaari Sasa 17 Jan '19

17 Jan '19

External Email - Use Caution Dear colleagues, The 3rd Annual Brain Twitter Conference 2019 (#brainTC) will take place on March 14th, 2019 and the abstract submission is now open! This year the keynote tweeters include Russ Poldrack (@russpoldrack‏) and Jay van Bavel (@jayvanbavel)! For more information about the conference and how to submit an abstract, please visit our website https://brain.tc and follow @RealBrainTC. What is Twitter Conference? A Twitter conference is meant for scientists to present and discuss their research on social media. During the conference presenters will be allocated a 15 min time slot, during which they will present a series of 6 tweets or less, marked with the #brainTC tag and an ordinal number of the tweet. A Twitter conference allows for scientific discussions without the hassle of travel; it’s therefore a carbon-neutral, time-efficient and family-friendly way to share you work and to network. In the last two years #brainTC has a reached an average of ~ 0.5 million Twitter users! On behalf of the organizing committee, best wishes, Sasa Kivisaari Post-doctoral Scientist Aalto University Finland

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unsubscribe
by 230159460＠seu.edu.cn 16 Jan '19

16 Jan '19

External Email - Use Caution Dear Sir， I am wonder how to unsubscribe the mails like ， my mailbox is almost full and i can not recieve mails from my workmates Shaoxun Yuan, PhD Candidate State Key Laboratory of Bioelectronics School of Biological Science and Medical Engineering Southeast University, Nanjing, 210018, China Phone:(86)18761869223

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Longitudinal Hippocampal Subfields - error during segmentation
by Rachel Phillips 16 Jan '19

16 Jan '19

External Email - Use Caution Hello, I have run a longitudinal sample (up to 4 timepoints per subject) through the Freesurfer 6.0 longitudinal processing pipeline, and currently I am attempting to produce the longitudinal hippocampal subfield segmentation with the following command: longHippoSubfieldsT1.sh <baseID> [SubjectsDirectory]. I am receiving the following error: "Error using SegmentSubfeldsT1Longitudinal (line 441) The vector of prior probabilities in the mesh nodes must always sum to one over all classes". Below is an example of the output that precedes this error: [> In SegmentSubfieldsT1Longitudinal at 418] Reading contexts of file /usr/local/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt -------------- Making Lateral-nucleus map to reduced label 1 Making Paralaminar-nucleus map to reduced label 1 Making Basal-nucleus map to reduced label 1 Making Accessory-Basal-nucleus map to reduced label 1 Making Corticoamygdaloid-transitio map to reduced label 1 Making Central-nucleus map to reduced label 1 Making Cortical-nucleus map to reduced label 1 Making Medial-nucleus map to reduced label 1 Making Anterior-amygdaloid-area-AAA map to reduced label 1 -------------- Making alveus map to reduced label 2 Making Hippocampal_tail map to reduced label 2 Making HATA map to reduced label 2 Making fimbria map to reduced label 2 Making parasubiculum map to reduced label 2 Making hippocampal-fissure map to reduced label 2 -------------- Making Left-Cerebral-Cortex map to reduced label 3 -------------- Making Left-Cerebral-White-Matter map to reduced label 4 -------------- Making Left-Lateral-Ventricle map to reduced label 5 -------------- Making Left-choroid-plexus map to reduced label 6 -------------- Making Unknown map to reduced label 7 -------------- Making Left-VentralDC map to reduced label 8 -------------- Making Left-Putamen map to reduced label 9 -------------- Making Left-Pallidum map to reduced label 10 -------------- Making Left-Thalamus-Proper map to reduced label 11 -------------- Making Left-Accumbens-area map to reduced label 12 -------------- Making Left-Caudate map to reduced label 13 Error using SegmentSubfieldsT1Longitudinal (line 441) The vector of prior probabilities in the mesh nodes must always sum to one over all classes Any guidance on this error would be greatly appreciated. Thanks, Rachel

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Re: [Freesurfer] preproc-sess mask issue
by Dowling, Kevin Francis 16 Jan '19

16 Jan '19

Hi Doug, Thanks so much for your note, that makes complete sense! To confirm, if I just delete the session/bold/masks and session/bold/RRR/masks and re-ran my preproc-sess command (-update) in v 6.0 new masks would be generated but my registration, stc, motion correction, (normalization?), would remain in 5.3. Is there any chance that version 6.0 might view the absence of register.dof6.lta files for my subjects (generated as register.dof6.dat files in 5.3) as cause for re-running the registration step and generating/using the register.dof6.lta files for use downstream of this step? Thank you again for your help! Best wishes, Kevin Date: Tue, 15 Jan 2019 21:47:37 +0000 From: "Greve, Douglas N.,Ph.D." <DGREVE(a)mgh.harvard.edu> Subject: Re: [Freesurfer] preproc-sess mask issue To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Message-ID: <5b4ea669-5ade-6be4-2435-aed05faf1454(a)mgh.harvard.edu> Content-Type: text/plain; charset="Windows-1252" Yea, the 5.3 stream used FSL brain masking to make the masks for the fmri. Version 6 uses the FS brain mask transferred into fmri space. Inevitably, you will have to do a lot of reprocessing. The simplest thing to do is to delete the mask files in session/bold/masks and sessoin/bold/RRR/masks (where RRR is the run number). When you re-run preproc-sess, it will figure out what needs to be re-run. It should not rerun motion correction, slice timing correction, or registration ________________________________ ________________________________ On 1/15/19 1:26 PM, Dowling, Kevin Francis wrote: > > Hello FreeSurfer Experts, > > > I'm writing to follow-up on my question about incomplete lhrh cortical > and mni305 subcortical masks in preproc-sess. My initial email is > copied below, but briefly when running WLS functional analyses via > mri_glmfit in FS 5.3, I noticed a number of large regions that were > pruned out?in my subcortical and cortical analyses. I traced these > pruning problems back to subjects' whose raw and fmcpr.siemens.nii.gz > data are fine for all functional runs, but for whom some pre-processed > runs had brain.mni305.nii.gz, brain.fsaverage.rh.nii.gz, and > brain.fsaverage.lh.nii.gz masks that were missing voxels/vertices (0 > values rather than 1s) that cause the pruning problem later on in > mri_glmfit.I tried pre-processing a number of these problematic > subjects in FS 6 and noticed that these missing voxels/vertices were > all fixed. However, as our analysis has ~200 subjects and is > multi-modal in nature, I would like to avoid re-doing all of our > analyses in FS 6.0 unless absolutely necessary. > > > Since the fmcpr.siemens.nii.gz functional data looks fine to me for > the problematic subject runs (as do the brain.nii.gz and > brain.e3.nii.gz masks), I thought something might possibly be amiss > with the registration in the mni305 mask generation command (below). > Specifically,when I look at the output of this command for the > problematic runs relative to the good runs, the mask > brain.mni305.2mm.nii.gz looks like it has been rotated away from the > normal orientation (see attached image, rotated_mask_questionable.png). > > > mri_vol2vol --mov > /autofs/cluster/roffman/users/Stable5_PerRun/GDDA161_Testing/bold/masks/brain.nii.gz > --reg > /autofs/cluster/roffman/users/Stable5_PerRun/GDDA161_Testing/bold/022/register.dof6.dat > --tal --talres 2 --talxfm talairach.xfm --nearest --no-save-reg --o > /autofs/cluster/roffman/users/Stable5_PerRun/GDDA161_Testing/bold/022/masks/brain.mni305.2mm.nii.gz > > > When I look at the analogous surface command (below), there are > similar problems, that result in a mask missing 50% of its vertices: > > > mri_vol2surf --mov > /autofs/cluster/roffman/users/Stable5_PerRun/GDDA161_Testing/bold/masks/brain.nii.gz > --reg > /autofs/cluster/roffman/users/Stable5_PerRun/GDDA161_Testing/bold/022/register.dof6.dat > --trgsubject fsaverage --interp nearest --projfrac 0.5 --hemi rh --o > /autofs/cluster/roffman/users/Stable5_PerRun/GDDA161_Testing/bold/022/masks/brain.fsaverage.rh.nii.gz > --noreshape --cortex --surfreg sphere.reg > > > Looking at the structural functional alignment for this subject with > tkregister-sess -s GDDA161_Testing -per-run -fsd bold, the > registration (from register.dof6.dat) seems fine (mincost .57 for this > problem run, but looks good visually).?Since the use of the > register.dof6.dat file is common to these two processes, and since FS > 6.0 uses register.dof6.lta rather than the .dat file, my? inclination > was to suspect there was a registration issue except that, again, > visualizing register.dof6.dat via tkregister-sess is fine. > > > Forgive this somewhat naive question, but beyond checking the input > (which seems fine) and output (which is bad) of this command along > with the subject's structural/functional registration, is there > anything else I could do to further troubleshoot this issue? Is there > a different registration file I should be looking at, or alternatively > am I looking too far downstream in the preproc-sess pipeline? I would > be most grateful for any additional troubleshooting ideas. > > > I have attached a .txt file containing the debug output for the > described subject, whom is an extreme example of the pruning/masking > issue. The problem run is 022. > > > Thank you again for any thoughts or suggestions you might have! > > > Best wishes, > > Kevin > > > *Kevin F. Dowling * > Clinical Research Coordinator > Brain Genomics Laboratory > Division of Psychiatric Neuroimaging > Martinos Center for Biomedical Imaging > Massachusetts General Hospital > 149 13th Street > Charlestown, MA, 02129 > (p) 617.643.3215 > He / Him / His > > > ------------------------------------------------------------------------ > *From:* Dowling, Kevin Francis > *Sent:* Tuesday, January 8, 2019 2:40 PM > *To:* freesurfer(a)nmr.mgh.harvard.edu > *Subject:* preproc-sess mask issue > > Hello FreeSurfer Experts, > > > I'm writing with a question about a potential problems that has come > up in my functional analysis. I'm running FS version 5.3 on a Linux > (at the Martinos Center - CentOS release 7.6.1810). > > > I was recently running osgm WLS analyses with mri_glmfit and the > --prune flag when I noticed that a large number of vertices in my > surface based analysis were pruned out due to no signal (about 40% of > the entire surface, see attached an example image of a group analysis > that includes one of the bad subjects: pruning_issue.png). My first > thought was that this result was potentially due to a poor > functional/structural alignment for some subjects. However, I > re-checked my functional and structural alignments, along with the > quality of the recons, and they all look good. Upon further > inspection, I observed similar erroneous pruning problems affecting > voxels in the mni305 space (for an example see pruning_issue2.png). > This issue stems from a modest number of subjects that have first > level analyses with large amounts of no signal. Indeed, removing these > problematic subjects from group surface based analyses resolves most > of the excessive surface pruning problems. > > > When I look at the fmcpr.siemens.nii.gz files for the runs in the > problematic subjects' directories the processed functional data looks > good, but when I visualize the fmcpr.siemens.sm5.mni305.nii.gz (and > ...fsaverage.lh.nii.gz / ...fsaverage.rh.nii.gz) files it is clear > that an inappropriate number of voxels (/vertices) have been masked > out. When I look at the brain.mni305.nii.gz, > brain.fsaverage.rh.nii.gz, and brain.fsaverage.lh.nii.gz masks I can > see they are missing a large number of voxels/vertices that should be > in the masks, which leads me to think that something may be going > wrong in the lh/rh/mni305 masking/mask generation step (though > brain.nii.gz and brain.e3.nii.gz masks look fine to me). I've looked > through the archives and wasn't able find a similar issue discussed > previously. > > > I was therefore wondering what might be causing this mask issue (given > the alignments appear to be good) and what might be the best way to go > about correcting this error. Would I need to manually edit all of > these masks to include the missing voxels/vertices if the automatic > generation failed or is there a better solution someone might be able > to suggest? > > > My preprocessing command is: preproc-sess -s SubjectID01 -surface > fsaverage lhrh -mni305 -d $SUBJECTS_DIR -fsd bold -stc siemens -fwhm 5 > -per-run > > > I would be happy to send along mask files/other files should they be > of help. > > > Thank you in advance for any suggestions you might be able to provide! > > Best wishes, > Kevin > > > *Kevin F. Dowling * > Clinical Research Coordinator > Brain Genomics Laboratory > Division of Psychiatric Neuroimaging > Martinos Center for Biomedical Imaging > Massachusetts General Hospital > 149 13th Street > Charlestown, MA, 02129 > (p) 617.643.3215 > He / Him / His

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