Freesurfer

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30132 discussions

WM Parcellation - lobe mapping
by Brainiac . 07 Sep '11

07 Sep '11

Hi All, I need to classify WM into different lobes (temporal, parietal, frontal, occipital). I tried to do the lobe mapping using parcellations from wmparc.mgz. http://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation My problem is the "unsegmented white matter" in wmparc.mgz. My understanding is that these unsegmented areas are classified so because they are beyond 5mm from any pre-classified cortex area. My questions: (1) So in order to classify "unsegmented WM" into lobes, do I need to take each voxel in the unsegmented WM region and then assign it to the closest cortex area to classify it into respective parcellations. Then re-group into lobes. Is this correct? (2) Are there any tailor made function or any other easier way to do this? Is there any easier way to segment WM into lobes directly that I have missed? Thanks, Brain

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Ubuntu / CentOS compatability
by Alexopoulos, Dimitrios 07 Sep '11

07 Sep '11

We have processed many of our datasets using the centos4, version 5.0.0 freesurfer build (freesurfer-Linux-centos4_x86_64-stable-pub-v5.0.0) on our 64-bit centos5.6 server. We would like to modify these datasets (i.e. modify segmentations, regenerate surfaces and cortical parcellations, etc.) on our stand-alone linux boxes running 32-bit Ubuntu 11.04. Can we install the 32-bit Centos4 build on Ubuntu (freesurfer-Linux-centos4-stable-pub-v5.0.0) and not have to worry with inconsistencies/incompatibilities between OSs and versions. Thanks. The materials in this email are private and may contain Protected Health Information. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying, distribution or the taking of any action in reliance on the contents of this information is strictly prohibited. If you have received this email in error, please immediately notify the sender via telephone or return email.

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(newbie) recon-all error / result evaluation
by can atılgan 07 Sep '11

07 Sep '11

Hello, Firstly, I'm new to FreeSurfer. My objective is to get a thickness map of cerebral cortex. I converted a single slice of T1 dicom series in order to see whether i could get a proper result. I started the reconstruction progress simply with : recon-all -s 001 -all but after a long period of time the progress halted with a couple of error messages : ---------- unfolding failed - restoring original position -------------------- 0637: dt=0.000078, rms=0.365 (0.000%), neg=0, invalid=766 GCAMwrite(transforms/talairach.m3z): gzip encountered error. writing output transformation to transforms/talairach.m3z... GCAMwrite GCAMwrite:: m3z loop GCAMwrite:: the command is: gzip -f -c > transforms/talairach.m3z GCAMwrite:: the popen error code is: 12 mri_ca_register: GCAMwrite(transforms/talairach.m3z) failed ERROR: mri_ca_register with non-zero status 252 but continuing despite the error #-------------------------------------- #@# CA Reg Inv Wed Sep 7 06:14:11 EEST 2011 /home/socat/Desktop/can/001/mri mri_ca_register -invert-and-save transforms/talairach.m3z Loading, Inverting, Saving, Exiting ... Reading transforms/talairach.m3z ERROR: cannot find or read transforms/talairach.m3z ERROR: mri_ca_register with non-zero status 0 Linux hstkaddogpc-160-75.ege.edu.tr 2.6.9-55.ELsmp #1 SMP Fri Apr 20 17:03:35 EDT 2007 i686 i686 i386 GNU/Linux recon-all -s 001 exited with ERRORS at Wed Sep 7 06:14:12 EEST 2011 before the error messages showed up that "ulfolding failed - restoring original position" steps were repeated 3 times. About result evaluation; I digged through fs wiki to find out how i can get a visual cortical thickness map but I couldn't find anything but only average thickness value using mris_anatomical_stats command. How can I see such a map for example of sample data "bert"? Thanks in advance. Regards, Can

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Re: [Freesurfer] Memory allocation problem with mri_robust_template
by Jeff Sadino 06 Sep '11

06 Sep '11

Hello, I am having a problem with mri_robust_template using too much RAM and causing my computers to freeze. I am using fs510. I think I would like to try to convert the inputs to uchar as Martin suggested, but I am not sure exactly where to do this. Would I issue this command: For each timepoint: mv tp1/mri/norm.mgz tp1/mri/norm.orig.mgz mri_convert -i tp1/mri/norm.orig.mgz -o tp1/mri/norm/mgz -odt uchar recon-all -base -tp 1 -tp 2 -all Also, my computers have two AMD processors and 4GB of DDR RAM running at 333MHZ. I dropped them down so they each only do one job, but they are still freezing. Besides upgrading the entire cluster to something more current, would doubling the RAM to 8GB be worthwhile? As always, thank you so very much! Jeff Sadino Mahen Fri, 11 Feb 2011 09:16:27 -0800 To Martin, Thanks a bunch. I haven't tested out all the recommendations, but at least I know what's going on. If I did want to convert to uchar, how would I go about this? Is there a function included in the Freesurfer package, or would I have to write my own function to do so? It's not a concern, as I'm probably not going to explore this avenue, but if I get time, I might enjoy trying to write such a function... Regards, Mahen On Wed, Feb 9, 2011 at 7:07 AM, Martin Reuter <mreu...(a)nmr.mgh.harvard.edu>wrote: > Hi Mahen, > > 4.5 is based on flirt registration which does a pairwise registration to > the first image. Since 5.0 we use the mri_robust_template tool (as it > can ignore outlier regions and produces more accurate registrations, see > Reuter et al Neuroimage 2010 "..robust registration"), but it is more > memory hungry and time consuming. > > If your input images are large, it can happen that mri_robust_template > uses a lot of memory. I tested that tool in the longitudinal stream > where I successfully ran it on > 10 images on a 4 gig machine (those > images are probably smaller than yours). > > So you have the following options: > > 1. use only the best 2 input images. This has been recommended in the > past, 5 inputs are usually not improving anything (see old emails on > this list from Bruce) > > 2. pass the flag -flirt-motioncor to recon-all and it will revert back > to using flirt (but accuracy of the registration might suffer, since the > inputs are full head images with potentially a lot of differences in > jaw, tongue, neck, eyes, scalp and cropping planes). > > Another option would be to convert the inputs to uchar and see if that > fits into the memory. They will be converted to uchar anyway in the > conform step. > > Best, Martin > > On Tue, 2011-02-08 at 23:21 +0800, Mahen wrote: > > Hi all, > > > > Since upgrading to Freesurfer v5.0, I have been unable to complete the > > first step of recon-all ('recon-all -autorecon1 -subjid ...') when > > using 5 anatomical datasets (our typical load for surface > > reconstruction). The problem seems to come when the third dataset is > > being registered, at which point recon-all exits with the following > > error message: > > ========================= > > Resolution: 0 > > Iteration(single-prec): 1 (subsample 200) mri_robust_template(47536) > > malloc: *** mmap(size=921600) failed (error code=12) > > *** error: can't allocate region > > *** set a breakpoint in malloc_error_break to debug > > MRIalloc(480, 480, 248): could not allocate 921600 bytes for 207th > > slice > > > > Cannot allocate memory > > /Users/mahen/fMRI/Surfaces/cck_kmu > > \n > > mri_convert /Users/mahen/fMRI/Surfaces/cck_kmu/mri/rawavg.mgz > /Users/mahen/fMRI/Surfaces/cck_kmu/mri/orig.mgz --conform \n > > mri_convert /Users/mahen/fMRI/Surfaces/cck_kmu/mri/rawavg.mgz > /Users/mahen/fMRI/Surfaces/cck_kmu/mri/orig.mgz --conform > > mghRead(/Users/mahen/fMRI/Surfaces/cck_kmu/mri/rawavg.mgz, -1): could > > not open file > > $Id: mri_convert.c,v 1.166.2.2 2010/08/10 19:11:50 greve Exp $ > > reading from /Users/mahen/fMRI/Surfaces/cck_kmu/mri/rawavg.mgz... > > Darwin MahenWillEatYourChildren.local 10.6.0 Darwin Kernel Version > > 10.6.0: Wed Nov 10 18:13:17 PST 2010; > > root:xnu-1504.9.26~3/RELEASE_I386 i386 > > > > recon-all -s cck_kmu exited with ERRORS at Tue Feb 8 20:50:00 CST > > 2011 > > ===================== > > My machine has 4gb of RAM, and I've ssh'd into my advisor's machine > > (which has 16gb) and had the same problem. > > I tryed updating the binaries for recon-all and mri_robust_template > > (from > > > http://surfer.nmr.mgh.harvard.edu/pub/dist/freesurfer/misc/macos-leopard-in…) > but the problem remained unsolved. > > My advisor informs me that it is now running with version 4.5 (all 5 > > anatomicals); I'd still like to know what's going on. I have tried > > running it on version 5 with just 2 anatomical datasets, and it works > > just fine. > > > > Here is the output of bugr: > > > > FREESURFER_HOME: /Applications/freesurfer > > > > Build stamp: freesurfer-Darwin-leopard-i686-stable-pub-v5.0.0 > > > > Kernel info: Darwin 10.6.0 i386 > > > > Thanks in advance, > > Mahen > > _______________________________________________ > > Freesurfer mailing list > > Freesurfer(a)nmr.mgh.harvard.edu > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > _______________________________________________ Freesurfer mailing listFreesurfer@nmr.mgh.harvard.eduhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine athttp://www.partners.org/complianceline . 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Problem with fcseed-config and fcseed-sess
by SHAHIN NASR 06 Sep '11

06 Sep '11

Hi, I have faced a problem using ROI-based, fcseed-config and fcseed-sess I used either: fcseed-config -roi /cluster/tootell/pitcairn/1/shahin/good_subjects_anat/atou_anat/label/rh.V1.label -fcname mean.R_V1.dat -fsd bold -mean -cfg mean.R_V1.config OR: fcseed-config -roi /cluster/tootell/pitcairn/1/shahin/good_subjects_anat/fsaverage/label/rh.V1.label -fcname mean.R_V1.dat -fsd bold -mean -cfg mean.R_V1.config to generated the mean.R_V1.config file for right V1 area (ROI for V1 is generated automatically and I have not changed it). Then I use below command to generate mean.R_V1.dat for the session of interest (e.g. atou) fcseed-sess -s atou -cfg mean.R_V1.config -make-mask but I just get a "if: Expression Syntax." message and the expected dat file is not generated. Do you have any idea what is wrong? Any help will be highly appreciated. -- Shahin Nasr PhD in Cognitive Neuroscience Martinos Imaging Center, MGH Harvard Medical School

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Using different versions of FS for the same dataset (5.0 and 5.1)
by Alexander Lebedev 06 Sep '11

06 Sep '11

Dear Freesurfer users and developers, I apologize if this question sounds rhetorically or maybe it has been asked previously (I didn't find the answers in the archives). But here it is... I have a big dataset of images and several workstations. I started the analysis on the first one and afterwards I installed Freesurfer on the others. But just right now I have noticed that their versions are different (I have v5.0 on the first computer, whereas other ones have 5.1). Could you please tell me if I should redo all the analyses from the first workstation or if it is ok and I can upgrade my version and keep it going? Thank you very much beforehand! --- Best Regards, Alex

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questions about mapping volume data to brain surface and extracting cortical thickness
by ZhiLiangLong 06 Sep '11

06 Sep '11

Hi FS experts: I have two questions about FS. (1) Is there a way that can map volume data(.hdr and .img file) to brain surface in FS? (2) I find a way that can extract cortical thickness automatically,first,the statistical significant areas derived from group analysis on qdec are converted to annotation file using mri_surfcluster,then i use mri_annotation2label to convert the annotation file to label file,finally, mri_label2label and mri_anatomical_stats are used to map the label file to each subject and obtain cortical thickness. I want to know if the process is correct. any help appreciated

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Re: [Freesurfer] FS-FAST modeling question
by Benjamin Matthew Deen 06 Sep '11

06 Sep '11

Hi, I have an update to this query. I tried using mkanalysis-sess instead of the ".new" version, and now I do seem to be able to get rid of intensity normalization and prewhitening (or at least, adding these flags now has an effect on betas/t-stats, and in the expected directions). The code I'm using is mkanalysis-sess -analysis ToMLoc-1 -TR 2 -p para.para -dt blocked -fsd bold -funcstem f -runlistfile runlist1 -polyfit 0 -nc 2 -spmhrf 0 -tw 40 -no-inorm -nowhiten -noautostimdur # mkcontrast stuff here... selxavg3-sess -sf sessid -df sessdir -analysis ToMLoc-1 However, I have a few remaining questions about the results. 1) When I look at the regressors used in the analysis, they have a surprisingly large magnitude -- the HRF peaks at a value of ~20. Is there any way to alter the analysis such that these peaks are comparable to the values used in FSL/SPM, which is around .8, so that beta values will be directly comparable across analyses? If not, I can just scale the betas by the relevant factor, but it would be nice if this were possible. 2) When I compare the results of this analysis to results obtained with FSL or SPM, I find that the spatial pattern of beta/t-stats is very similar, but the t-stats have a noticeably reduced magnitude across the brain. Is there anything that may differ about FS-FAST's implementation of the GLM, such that t-stats would differ? 3) I saw in a ppt presentation on FS-FAST that the option -hpf can be used to add a highpass temporal filter to the model. However, when I use this option with mkanalysis-new, the script says that the flag isn't recognized. Is there another way to add such a filter? Thanks in advance for any help. Cheers, Ben ________________________________________ Date: Sun, 4 Sep 2011 13:15:51 -0400 From: Benjamin Matthew Deen <bdeen(a)mit.edu> Subject: [Freesurfer] FS-FAST modeling question To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Message-ID: <D0755B22EA5ED64C941ED568237AEC8D1764DCC5E3(a)EXPO11.exchange.mit.edu> Content-Type: text/plain; charset="us-ascii" Hi, I'm trying to run a model in fs-fast using no intensity normalization and no autocorrelation correction, for the purpose of comparing results with other software packages, and comparing results with/without autocorrelation correction. However, I haven't been able to get this to work so far. I've tried using the -noinorm and -nowhiten flags for mkanalysis-sess.new, but these don't seem to have any effect on the results: noinorm doesn't change the beta values, and nowhiten doesn't change either betas or t-stats, at all. The commands I'm using are: mkanalysis-sess.new -analysis ToMLoc-1 -TR 2 -p para.para -dt blocked -fsd bold -funcstem f -runlistfile runlist1 -polyfit 0 -nc 2 -spmhrf 0 -tw 40 -noinorm -nowhiten mkcontrast-sess -sf sessid -df sessdir -analysis ToMLoc-1 -contrast cope1 -a 1 -c 2 mkcontrast-sess -sf sessid -df sessdir -analysis ToMLoc-1 -contrast cope2 -a 2 -c 1 mkcontrast-sess -sf sessid -df sessdir -analysis ToMLoc-1 -contrast cope3 -a 1 -a 2 -c 0 mkcontrast-sess -sf sessid -df sessdir -analysis ToMLoc-1 -contrast cope4 -a 0 -c 1 -c 2 selxavg3-sess -sf sessid -df sessdir -analysis ToMLoc-1 Any idea what might be going wrong, or how I can get this to work? Thanks, Ben ------------------------------

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Regarding Segmentation of Hippocampal Subfield
by mohan boddu 06 Sep '11

06 Sep '11

Hello, When I segmented the Hippocampal Subfields (using the process stated in http://www.freesurfer.net/fswiki/HippocampalSubfieldSegmentation ) it generated two files by name *"posterior_Left-Hippocampus.mgz"* and * "posterior_Right-Hippocampus.mgz*". I would like to know what actually are those since they are not actual Hippocampus. When I am generating the crisp images do I need to use those files also, if so what are the labels of those regions. Also, what about the *right_alveus, right_fornix, left_alveus, left_fornix*regions. They haven't generated in the segmentation. Is there a way to generate those regions also. Any help regarding would be appreciated. Thank you. -- With Thanks & Regards, Mohan Boddu

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two questions: asymmetry and controlling for global thickness abnormality
by Tetiana Dadakova 05 Sep '11

05 Sep '11

Dear list, I have two question: 1. Is it possible to do the asymmetry analysis in FS, I mean to compare left and right hemispheres within one group? 2. Is it necessary to somehow control for global abnormality of thickness while looking at regional abnormality, e.g. if one group has thinner brain overall, but also thinner brain in some regions? (Similar to dividing by total brain volume in volumetric analysis) Thank you and best wishes, Tanja.

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