Freesurfer

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30065 discussions

Volume are different in asegstats vs matlab
by Lee Subin Kristine 23 Mar '18

23 Mar '18

Hi, I have a question about volume info from freesurfer vs matlab. I am looking at, say, the left hippocampus volume from asegstats, which was 3778.2 mm^3. When I try to get the same volume by loading aseg.nii on matlab, however, I get a different number, 3945. For matlab, I used the following command, which counts the number of voxels equal to the left hippocampus segment id: length(find(aseg.img==17)) This was done after I converted the original aseg.mgz file into native space, using 2 different methods: (1) mri_convert -rl rawavg.mgz -rt nearest -i aseg.mgz -o bert_aseg.nii (2) mri_label2vol --seg aseg.mgz --temp rawavg.mgz --o bert_aseg.nii --regheader aseg.mgz In both cases, the output was 3945. The rawavg.mgz used here is also 1mm isovoxel, so I don't think it is due to the difference in voxel dimensions. What could be the cause of the difference in volume? Thanks, Kristine Sent from Mail<https://go.microsoft.com/fwlink/?LinkId=550986> for Windows 10

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Positions available in Dr. Zeynep Saygin's lab at OSU
by Saygin, Zeynep M. 23 Mar '18

23 Mar '18

TECHNICAL ASSISTANT, The Ohio State University, Dept. of Psychology – Dr. Zeynep Saygin’s Lab, to assist with neuroscience research on the functional and structural architecture of the human brain in adults and children. The Z-lab studies the functional differentiation of the human brain across early development and adulthood, and the plasticity of the brain in response to disorders and injury (see zeynepsaygin.com<http://zeynepsaygin.com/> for recent publications). The primary responsibilities will include programming, running, and analyzing behavioral and brain-imaging experiments using functional magnetic resonance imaging (fMRI), diffusion weighted imaging (DWI), functional connectivity, and behavioral evaluations of infants, children, and adults. This position is an excellent opportunity for individuals considering future graduate studies. We are seeking an aspiring, self-motivated researcher with strong coding/programming skills who is excited about the developing brain, cognitive neuroscience, and/or clinical disorders and demonstrates evidence of a serious interest in a career in the field. Required: strong mathematical, statistical, and coding skills (e.g., MATLAB, Python, shell scripting); strong interpersonal skills for interacting with a diverse group of people including children and parents; a bachelor's degree in psychology, neuroscience, cognitive science, computer science, data science, engineering, physics, or math; and strong organizational skills. Interested individuals should contact saygin.3(a)osu.edu<mailto:zsaygin@mit.edu> with a CV and names of 2-3 references. POSTDOCTORAL FELLOW, The Ohio State University, Dept. of Psychology – Dr. Zeynep Saygin’s Lab. We seek an outstanding scientist with a recent Ph.D. in cognitive neuroscience, developmental psychology, or related area, who has experience with neuroimaging methods (MRI, fMRI, DWI, and/or functional connectivity) and who is interested in any or all of the following: understanding the functional and structural architecture of the brain, how it develops in healthy individuals, and how it changes with disorders and brain injury in both adults and children. See zeynepsaygin.com<http://zeynepsaygin.com/> for recent publications. The fellow will be involved in conducting and planning behavioral and neuroimaging research studies in neonates, infants, children, and adults. Proficiency in programming is necessary. This position is fully funded. Initial hire is for one year with additional years contingent on fit and progress. OSU is located in Columbus, Ohio and is home to a vibrant community of cognitive neuroscientists, computational modelers, and vision scientists in the Psychology Department and Center for Cognitive and Brain Sciences. Fellows will have access to state-of-the-art MRI and EEG facilities located inside the Psychology building. Interested individuals should contact saygin.3(a)osu.edu<mailto:zsaygin@mit.edu> with a CV, names of 3 references, and a statement of research interests. --- Zeynep M. Saygin, Ph.D. Assistant Professor of Psychology The Ohio State University 205 Psychology Bldg. 1835 Neil Ave Columbus, OH 43210 614-292-3059 zlab<http://zeynepsaygin.com>

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Problem running mri_convert
by Jay Edelman 23 Mar '18

23 Mar '18

Dear Freesurfers, I am running the program "mri_convert" within the AFNI script, @SUMA_Make_Spec_FS, and am receiving the error below. I just installed freesurfer from your website and am running this on a Mac running Yosemite. I've looked at the online forums and see this addressed in other contexts, but unfortunately I can't find any fix. Thanks So Much! Jay Edelman Dept. of Biology, The City College of New York mri_convert.bin -ot nii mri/wm.seg.mgz /Users/jedelman/Dropbox/freesurfer/asim/./SUMA/wm.seg.nii $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from mri/wm.seg.mgz... TR=0.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) writing to /Users/jedelman/Dropbox/freesurfer/asim/./SUMA/wm.seg.nii... -- running 'mris_convert lh.smoothwm lh.smoothwm.asc' Trace/BPT trap dyld: lazy symbol binding failed: Symbol not found: ___emutls_get_address Referenced from: /Applications/freesurfer/bin/../lib/gcc/lib/libgomp.1.dylib Expected in: /usr/lib/libSystem.B.dylib dyld: Symbol not found: ___emutls_get_address Referenced from: /Applications/freesurfer/bin/../lib/gcc/lib/libgomp.1.dylib Expected in: /usr/lib/libSystem.B.dylib ?

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Qdec question when examining group differences with continuous covariate and nuisance factor
by James Allebone 22 Mar '18

22 Mar '18

Hi there, I am running an analysis to examine differences in cortical thickness between two groups (clinical vs control) and have entered ICV as a nuisance variable and age as a covariate. The analysis results in qdec do not appear to give a result that answers the question: does the average cortical thickness differ between clinical and control with both the nuisance factor and the covariate accounted for. The only result comparing average thickness between the groups lists ICV as a nuisance factor, and does not include age as a covariate. Other results are examining correlation between age and thickness accounting for group, or whether the thickness-age correlation differs between groups accounting for ICV. A second question: when i just include ICV as a nuisance factor and do not enter age as a covariate (groups mean age is almost identical), there are some areas of cortical thickening in the clinical group, which become much larger after correction for multiple comparisons using Monte Carlo Null-Z simulation (threshold 0.05). This seems counterintuitave to me as I would expect a reduction in significant clusters after correction. One of the main clusters traverses the cingulate gyrus, however, when I extract cortical thickness values for each individual subject and compare group means for subregions of the cingulate in SPSS, there are no significant differences on any of the subregions. In fact, they are almost identical. Could adding ICV as a nuisance variable account for this? Mean ICV values are almost identitical between groups. Or is there some additive affect at the cluster level? Many thanks for any clarity you can provide. Kind regards, James

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mri_glmfit-sim on fsaverage5
by Tuominen, Lauri Johannes 22 Mar '18

22 Mar '18

Hello freesurfers! My resting state data is in fsaverage5 space. mri_info gives dimensions of 3414 x 1 x 3 x 120 and voxelsize 1x1x1. I do the group analysis and mri_glmfit creates a sig.mgh file that is has those same dimensions and results look good. Then I say: mri_glmfit-sim --glmdir /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204 --3spaces --sim mc-z 10000 1.3 mc_1.3_3space --sim-sign abs —overwrite And I get an error message saying dimension mismatch between input volume and seg. below is the tail of the message. Thank you for your help! Lauri number of voxels in search space = 9354 Done loading source values (nvtxs = 10242) overall max = 4.53945 at vertex 1977 overall min = -3.95722 at vertex 473 surface nvertices 10242 metric props tot surface area 66661.796875 group_avg_vtxarea_loaded 1 masked surface area 79140.789062 Computing voxel-wise significance CSDpvalMaxSigMap(): found 347/10242 above 0, max=0.485948 NOT Adjusting threshold for 1-tailed test thminadj = 1.3 Searching for Clusters ... thmin=1.300000 (1.300000), thmax=-1.000000 (-1), thsignid=0, minarea=0.000000 Found 46 clusters Max cluster size 1840.913696 Pruning by CW P-Value 0.05 Saving thresholded output to /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204/intercept/mc_1.3_3space.sig.masked.mgh Saving cluster numbers to /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204/intercept/mc_1.3_3space.sig.ocn.mgh Saving cluster pval /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204/intercept/mc_1.3_3space.sig.cluster.mgh Constructing output annotation Writing annotation /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204/intercept/mc_1.3_3space.sig.ocn.annot mri_segstats --seg /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204/intercept/mc_1.3_3space.sig.ocn.mgh --exclude 0 --i /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204.gm.nii.gz --avgwf /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204/intercept/mc_1.3_3space.y.ocn.dat --sum /tmp/mri_glmfit-sim.junk.8849 $Id: mri_segstats.c,v 1.121 2016/05/31 17:27:11 greve Exp $ cwd cmdline mri_segstats --seg /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204/intercept/mc_1.3_3space.sig.ocn.mgh --exclude 0 --i /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204.gm.nii.gz --avgwf /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204/intercept/mc_1.3_3space.y.ocn.dat --sum /tmp/mri_glmfit-sim.junk.8849 sysname Linux hostname bee.nmr.mgh.harvard.edu machine x86_64 user ljt22 UseRobust 0 Loading /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204/intercept/mc_1.3_3space.sig.ocn.mgh Loading /autofs/space/bee_001/users/Lauri/test10.nosm.group/lh.surf.avg.rh.204.gm.nii.gz ERROR: dimension mismatch between input volume and seg input 3414 1 3 seg 10242 1 1

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Re: [Freesurfer] Skull stripping error - too much brain lost - solved {Disarmed}
by Matthew Grecsek 22 Mar '18

22 Mar '18

Doug, your added text would have been enough to keep me out of the weeds. Thanks. Douglas Greve <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from…> Thu, 22 Mar 2018 13:28:02 -0700 <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date…> I added some text to clarify in that wiki page. See if you think it issufficient (and suggest some wording if you don't:). On 3/22/18 4:22 PM, Bruce Fischl wrote: Hi Matt it's not that trivial. There are frequently multiple T1-weighted protocols, and the naming is hardly standardized. We would need to run some type of MR simulator to decide if something was T1-weighted in the most general sense, and even then it could be mag-trans or with contrast or something. cheers Bruce On Thu, 22 Mar 2018, Matthew Grecsek wrote: Hi guys, The problem was garbage in - garbage out: 1. The issue manifested in the skull stripping step but was caused by bad input 2. I am a data science guy (not neurology), new to freesurfer, andfollowed the instructions at https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferBeginnersGuide recon-all \ -i <one slice in the anatomical dicom series> \ 3. When I issued recon-all, I gave it a -i of an image file from my mri data. I assumed that it would find the T1- weighted data from my collection automatically as in dcmunpack. 4. It seems that the -i requires specifically the T1-weighted data, because when I looked at the tkmedit brainmask images they were inverted (maybe from T2 or something else). 5. I re-ran the recon-all on the proper T1 data and it ran to completion with no errors. I received several offline requests from other users, so it seems like this may be a common mistake. I recommend making the requirement for input of T1-weighted data moreexplicit in the wiki page. As a feature request, it seems pretty easy to use dcmunpack to find the correct data from a collection and make it part of initial recon-all. I had uploaded my original data to the ftp site per reply below, but feel free to delete and close the issue as solved. Cheers, -Matt ------------ Hi Matt no idea what that would happen. If you upload the dataset we will take a look cheers Bruce On Mon, 5 Mar 2018, Matthew Grecsek wrote: Following your advice below, it was clearly apparent that the output from the skull stripping step was missing a lot. I tried adjusting the -wsthresh flag from 50 -> 200 and it still left major parts of the brain out. I have a good quality mri and subject with no known anatomical abnormalities. The log file indicates "surface validation has detected possible Error" Does this suggest a more meaningful defect in the base image or something that can be corrected via other parameters or manual editing? Log file attached. Thanks, -Matt Bruce Fischl Fri, 02 Mar 2018 08:00:11 -0800 Hi Matt check the output of the skull stripping. This error usually means that too much (everything?) has been erased and no plausible cc could be found cheers Bruce On Thu, 1 Mar 2018, Matthew Grecsek wrote: My recon-all ended prematurely with the following message: final transformation (x=136.0, yr=-10.054, zr=-0.114): 0.98464 0.00200 -0.17457 15.51372; -0.00197 1.00000 0.00035 17.22442; 0.17457 0.00000 0.98464 -17.83761; 0.00000 0.00000 0.00000 1.00000; mri_cc: no WM voxels found with norm > 40 -- check skull stripping No such file or directory Linux centos-7-1 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 recon-all -s matt2 exited with ERRORS at Thu Mar 1 04:35:09 UTC 2018 I am running: freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c My full log file is attached. Please let me know suggestions for correcting and continuing the process. Thanks, -Matt _______________________________________________ Freesurfer mailing listFreesurfer@nmr.mgh.harvard.eduhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer Reply via email to [image: The Mail Archive] <https://www.mail-archive.com/> - The Mail Archive home <https://www.mail-archive.com/> - freesurfer - all messages <https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/> - freesurfer - about the list <https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/info.html> - Expand <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=subj…> - Previous message <https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg57403.html> - Next message <https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg57404.html#>

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Skull stripping error - too much brain lost - solved
by Matthew Grecsek 22 Mar '18

22 Mar '18

Hi guys, The problem was garbage in - garbage out: 1. The issue manifested in the skull stripping step but was caused by bad input 2. I am a data science guy (not neurology), new to freesurfer, and followed the instructions at https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferBeginnersGuide recon-all \ -i <one slice in the anatomical dicom series> \ 3. When I issued recon-all, I gave it a -i of an image file from my mri data. I assumed that it would find the T1- weighted data from my collection automatically as in dcmunpack. 4. It seems that the -i requires specifically the T1-weighted data, because when I looked at the tkmedit brainmask images they were inverted (maybe from T2 or something else). 5. I re-ran the recon-all on the proper T1 data and it ran to completion with no errors. I received several offline requests from other users, so it seems like this may be a common mistake. I recommend making the requirement for input of T1-weighted data more explicit in the wiki page. As a feature request, it seems pretty easy to use dcmunpack to find the correct data from a collection and make it part of initial recon-all. I had uploaded my original data to the ftp site per reply below, but feel free to delete and close the issue as solved. Cheers, -Matt ------------ Hi Matt no idea what that would happen. If you upload the dataset we will take a look cheers Bruce On Mon, 5 Mar 2018, Matthew Grecsek wrote: Following your advice below, it was clearly apparent that the output from the skull stripping step was missing a lot. I tried adjusting the -wsthresh flag from 50 -> 200 and it still left major parts of the brain out. I have a good quality mri and subject with no known anatomical abnormalities. The log file indicates "surface validation has detected possible Error" Does this suggest a more meaningful defect in the base image or something that can be corrected via other parameters or manual editing? Log file attached. Thanks, -Matt Bruce Fischl Fri, 02 Mar 2018 08:00:11 -0800 Hi Matt check the output of the skull stripping. This error usually means that too much (everything?) has been erased and no plausible cc could be found cheers Bruce On Thu, 1 Mar 2018, Matthew Grecsek wrote: My recon-all ended prematurely with the following message: final transformation (x=136.0, yr=-10.054, zr=-0.114): 0.98464 0.00200 -0.17457 15.51372; -0.00197 1.00000 0.00035 17.22442; 0.17457 0.00000 0.98464 -17.83761; 0.00000 0.00000 0.00000 1.00000; mri_cc: no WM voxels found with norm > 40 -- check skull stripping No such file or directory Linux centos-7-1 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 recon-all -s matt2 exited with ERRORS at Thu Mar 1 04:35:09 UTC 2018 I am running: freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c My full log file is attached. Please let me know suggestions for correcting and continuing the process. Thanks, -Matt

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Problems displaying pial_lgi in dec
by Dusan Hirjak 22 Mar '18

22 Mar '18

Dear Freesurfer Experts, I would like to display my results after running the LGI command on my subjects (via Freesurfer 6.0, MacOS Sierra). Although I created the .Qdecrc (text file) file with MEASURE1 = pial_lgi and put it in my home directory as well as in the folder where my study subjects are, it is still not working. I can not see "pial_lgi" in the Qdec drop down menu. I would be very thankful for any suggestions. Dusan

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PetSurfer: NaNs in MGX volumes
by Elijah Mak 22 Mar '18

22 Mar '18

Hi Doug, Reposting this on the FS mailing list: After MGX-PVC and MRTM2, I sometimes get NaNs in the voxelwise corrected PET data when I load it on Freeview. Error message looks like this WARNING: 19 NaNs found in volume Is this happening because the MGX GM threshold is too high (I am using 0.2) or could there be something else happening? Appreciate your help. Please let me know if you need more information. Thanks. Best Wishes, Elijah

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Re: [Freesurfer] Hippocampal subfields
by Iglesias Gonzalez, Eugenio 22 Mar '18

22 Mar '18

Dear Arsenije The atlas is encoded in a tetrahedral mesh, so there is no nifti file for it. Having said that, it is possible to rasterize the atlas at a target resolution. The command is the following: kvlRasterizeAtlasMesh $FREESURFER_HOME/average/HippoSF/atlas/AtlasMesh.gz $FREESURFER_HOME/average/HippoSF/atlas/AtlasDump.mgz X -1 where X is the label number (see $FREESURFER_HOME/average/HippoSF/atlas/compressionLookupTable.txt for a listing; 0 is cortex, 1 is cerebral white matter, etc). -1 indicates the reference position of the atlas, so do not modify that. This command will create a file called rasterized.mgz. You can convert it to nifty with mri_convert, eg. mri_convert rasterized.mgz probability_label_X.nii.gz You can do this with all the different hippocampal labels. And if you want a probability for the whole hippocampus, which is probably what you need for the registration with your manual mask, you can sum the probabilities of the different subfields into a single volume (e.g., with fslmaths) I hope this helps, /Eugenio -- Juan Eugenio Iglesias ERC Senior Research Fellow Translational Imaging Group University College London http://www.jeiglesias.com http://cmictig.cs.ucl.ac.uk/ From: <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of Arsenije Subotic <arsenije.subotic1(a)ucalgary.ca> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Date: Thursday, 22 March 2018 at 15:15 To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Subject: [Freesurfer] Hippocampal subfields Hi all I would like to use subfield specific hippocampus maps that were produced Dr Iglesias's Lab: https://sites.google.com/site/jeiglesias/hippocampal-subfields These maps are available via FreeSurfer, however our lab is trying to segment hippocampus subregions from already manually extracted hippocampuses. In other words, we have manually traced hippocampus masks in a NIFTI format that we would like to co-register with these maps. How can I directly download subfield specific hippocampal atlases in a NIFTI format so that we can use non linear co registration after? Thank you very much. Look forward hearing from you, Arsenije

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