Freesurfer

freesurfer@nmr.mgh.harvard.edu

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30132 discussions

TODAY: IronTract Challenge webinar and live demo!
by Yendiki, Anastasia 09 Oct '20

09 Oct '20

Dear FreeSurfers - Join us today at 12pm EDT / 6pm CET for the IronTract Challenge webinar/happy hour! Learn about this effort to assess tractography methods objectively and get up to speed on how to participate by the deadline of November 8. The webinar will include demos on how to submit your results to the challenge and how to share your challenge code on the QMENTA platform. Register here for zoom link to the webinar: https://us02web.zoom.us/webinar/register/WN_OVGWn3SIRF2qw_8Ujks-Hg See you soon! Anastasia. | Anastasia Yendiki, Ph.D. | Associate Professor, Harvard Medical School | Associate Investigator, Massachusetts General Hospital | Martinos Center for Biomedical Imaging | 149 13th St. Suite 2301 | Charlestown, MA 02129 | Tel: 617-726-9434 | Fax: 617-726-7422 | Web: http://scholar.harvard.edu/a-y

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A basic question: What does aparc, wmparc, aseg mean?
by Zhiyuan Yu 09 Oct '20

09 Oct '20

External Email - Use Caution Hi All, I have seen aparc, wmparc, aseg frequently but I do not know what they represent. Could anyone explain them for me? Thanks, Zhiyuan

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Re: [Freesurfer] question about mri_segment_hypothalamic_subunits
by Iglesias Gonzalez, Juan E. 08 Oct '20

08 Oct '20

Hi Victor, I highly recommend that you keep both installations separate. Cheers, /Eugenio Juan Eugenio Iglesias Senior research fellow CMIC (UCL), MGH (HMS) and CSAIL (MIT) http://www.jeiglesias.com From: <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of "Zeng, Victor (BIDMC - Keshavan - Psychiatry)" <vzeng(a)bidmc.harvard.edu> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Date: Thursday, October 8, 2020 at 17:54 To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Subject: [Freesurfer] question about mri_segment_hypothalamic_subunits Hi all, I installed the most recent dev version of FS7.1, and copied its files over the old FS7.1. I entered sudo mri_segment_hypothalamic_subunits into xquartz, and receive an error: error: freesurfer has not properly been sourced I am using mac os Catalina 10.15.6. How can I fix this? Victor Zeng Beth Israel Deaconess Medical Center Keshavan Lab -- ________________________________ This message is intended for the use of the person(s) to whom it may be addressed. It may contain information that is privileged, confidential, or otherwise protected from disclosure under applicable law. If you are not the intended recipient, any dissemination, distribution, copying, or use of this information is prohibited. If you have received this message in error, please permanently delete it and immediately notify the sender. Thank you.

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question about mri_segment_hypothalamic_subunits
by Zeng, Victor (BIDMC - Keshavan - Psychiatry) 08 Oct '20

08 Oct '20

?Hi all, I installed the most recent dev version of FS7.1, and copied its files over the old FS7.1. I entered sudo mri_segment_hypothalamic_subunits into xquartz, and receive an error: error: freesurfer has not properly been sourced I am using mac os Catalina 10.15.6. How can I fix this? Victor Zeng Beth Israel Deaconess Medical Center Keshavan Lab -- ________________________________ This message is intended for the use of the person(s) to whom it may be addressed. It may contain information that is privileged, confidential, or otherwise protected from disclosure under applicable law. If you are not the intended recipient, any dissemination, distribution, copying, or use of this information is prohibited. If you have received this message in error, please permanently delete it and immediately notify the sender. Thank you.

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Postdoc Positions at University of Pennsylvania
by Tisdall, Dylan 08 Oct '20

08 Oct '20

External Email - Use Caution Hi all, I'm recruiting postdoc follows for a couple of NIH-funded projects at the University of Pennsylvania. Projects include 1) development of motion-resilient methods for pediatric neuroimaging, and 2) ex-vivo/in-vivo 7T neuroimaging and analysis methods to find biomarkers for specific neurodegenerative diseases. Please see the job ads: http://www.dylantisdall.com/2020_pediatric_postdoc_ad.pdf http://www.dylantisdall.com/2020_ftdc_postdoc_ad.pdf and reach out to me if you have questions or would like to apply. Thanks, Dylan --- Dylan Tisdall, PhD Research Assistant Professor Department of Radiology Perelman School of Medicine University of Pennsylvania Room D406 Richards Medical Labs 3700 Hamilton Walk Philadelphia, PA 19104

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Matlab Matlab Runtime issue
by Wei Shao 08 Oct '20

08 Oct '20

External Email - Use Caution Hello, Freesurfer team My name is Wei Shao, FS user, I have a few questions regarding the Matlab package and the hippocampal subfield function with FS. I have followed the instruction from the website to install Matlab, sue the fs_install_mcr R2014b (Oct 01, 2020 14:41:05) Exiting with status 0 (Oct 01, 2020 14:41:05) End - Successful. Finished MCR v84 (R2014b) already exists in /usr/local/freesurfer/7.1.0-1 do you want to reinstall? [y/n] exiting without install *However*, when I try to use the hippocampal seg command: *segmentHA_T1.sh, it gives me this error* ERROR: cannot find Matlab 2014b runtime in location: /usr/local/freesurfer/7.1.0-1/MCRv84 The hippocampal/amygdala and brainstem modules require the (free) Matlab runtime. You will need to download the Matlab Compiler Runtime (MCR) for Matlab 2014b. To do so, please run the following commands (you might need root permissions): LINUX: cd /usr/local/freesurfer/7.1.0-1 curl " https://surfer.nmr.mgh.harvard.edu/fswiki/MatlabRuntime?action=AttachFile&d…" -o "runtime2014b.tar.gz" tar xvf runtime2014b.tar.gz Even after I did this as the following, it still gave me the same response when I tried segmentHA_T1.sh. Could you tell me how to fix this? Thanks! Best, Wei

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white matter surface extends too much into the cortex
by Caroline Chwiesko 07 Oct '20

07 Oct '20

External Email - Use Caution Dear Freesurfer team, when checking the output after recon-all i am having the issue that the white matter surface extends too much into the cortex. I observe this in most of my subjects (mostly in the posterior part of the brain) so it seems to be a systematic issue. I found in the archive that others had a similar problem and were asked to upload one subject to get a recommendation on how to change certain default parameters in recon-all to fix this problem. I was wondering if I could also upload a subject and get a recommendation for my data set on how to fix this issue ? Thank you very much in advance! Carolin

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Re: [Freesurfer] Hippocampal subfields error
by Emily K Clarke 07 Oct '20

07 Oct '20

External Email - Use Caution Hi Eugenio, No, I don't think so - we're running recon-all and using the hippocampal pipeline from FS 6.0. I bug tested with the FS 6.0dev version and found that that hippocampal pipeline worked okay, but since we're combining data with an older dataset we need to be able to extract the older hippocampal subfields, starting with the recon-all hippocampal subfields processing. Thanks, Emily Date: Wed, 9 Sep 2020 21:21:41 +0000 From: "Iglesias Gonzalez, Juan E." <JIGLESIASGONZALEZ(a)mgh.harvard.edu<mailto:JIGLESIASGONZALEZ@mgh.harvard.edu>> Subject: Re: [Freesurfer] Hippocampal subfields error To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>> Message-ID: <25DF95C0-873A-4906-9D51-A94CF022375D(a)mgh.harvard.edu<mailto:25DF95C0-873A-4906-9D51-A94CF022375D@mgh.harvard.edu>> Content-Type: text/plain; charset="utf-8" Hi Emily. Is it possibly that you may be mixing FS versions, by any chance? Cheers, /Eugenio Juan Eugenio Iglesias Senior research fellow CMIC (UCL), MGH (HMS) and CSAIL (MIT) https://urldefense.com/v3/__http://www.jeiglesias.com__;!!OToaGQ!54kYqoLJMa…<https://urldefense.com/v3/__http:/www.jeiglesias.com__;!!OToaGQ!54kYqoLJMaz…> From: <freesurfer-bounces(a)nmr.mgh.harvard.edu<mailto:freesurfer-bounces@nmr.mgh.harvard.edu>> on behalf of Emily K Clarke <ekc23(a)duke.edu<mailto:ekc23@duke.edu>> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>> Date: Wednesday, September 9, 2020 at 15:53 To: "freesurfer(a)nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>" <freesurfer(a)nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>> Subject: [Freesurfer] Hippocampal subfields error External Email - Use Caution Hi Eugenio, We?re still running into the issue where running recon-all with the hippocampal subfields tag isn?t actually generating any of the hippocampal files. I?ve included our full recon-all log below. We already examined the scans, and they look perfectly normal ? do you know why this might be happening? Thanks, Emily Clarke-Rubright Morey Neuroimaging Lab The whole log message: #-------------------------------------------- #@# Hippocampal Subfields processing (T1 only) left Mon Aug 10 18:54:21 EDT 2020 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/usr/local/packages/freesurfer_v6.0.0/MCRv80/runtime/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/bin/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/os/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/native_threads:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/server:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/client:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64:/usr/lib64/qt5/plugins/platforms:/usr/local/packages/ants-2.3.4/lib:/usr/lib64/openmpi/lib: Warning: application is running on a locale different from the original platform locale. Warning: No display specified. You will not be able to display graphics on the screen. Registering imageDump.mgz to hippocampal mask from ASEG This file does not contain MRI parameters $Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $ --mov: Using imageDump.mgz as movable/source volume. --dst: Using /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… as target volume. --lta: Output transform as trash.lta . --mapmovhdr: Will save header adjusted movable as imageDump_coregistered.mgz ! --sat: Using saturation 50 in M-estimator! reading source 'imageDump.mgz'... reading target '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. 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 ) MRIreorder() ----------- xdim=-1 ydim=3 zdim=-2 src 131 241 99, 0.250000 0.250000 0.250000 dst 131 99 241, 0.250000 0.250000 0.250000 Determinant after swap : 0.015625 Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241) Dst: (1, 1, 1)mm and dim (38, 40, 59) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 0 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (38, 40, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 40, 59) voxels. -- Resampled: (1, 1, 1)mm and (38, 40, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 Registration::computeMultiresRegistration - 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 1.1459029727155 0 1.0000000000000 0 -0.0876360601093 0 0 1.0000000000000 -2.9034875996918 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 20 30 ) T( 19 20 30 ) Iteration(f): 1 -- diff. to prev. transform: 7.11234 Iteration(f): 2 -- diff. to prev. transform: 5.92976 Iteration(f): 3 -- diff. to prev. transform: 7.00993 Iteration(f): 4 -- diff. to prev. transform: 4.91305 Iteration(f): 5 -- diff. to prev. transform: 0.781018 max it: 5 reached! Resolution: 0 S( 38 40 61 ) T( 38 40 61 ) Iteration(f): 1 -- diff. to prev. transform: 8.1296 Iteration(f): 2 -- diff. to prev. transform: 1.32542 Iteration(f): 3 -- diff. to prev. transform: 0.660013 Iteration(f): 4 -- diff. to prev. transform: 0.272219 Iteration(f): 5 -- diff. to prev. transform: 0.0943126 max it: 5 reached! - final transform: Tf = [ ... 0.9986737402682 -0.0371977207162 0.0355962086772 0.4749903609000 0.0194355513105 0.9126027300469 0.4083852549446 -14.0571487737101 -0.0476761978762 -0.4071517980857 0.9121153400046 9.9144950322154 0 0 0 1.0000000000000 ] - final iscale: If = 1 ********************************************************** * * WARNING: Registration did not converge in 5 steps! * Problem might be ill posed. * Please inspect output manually! * ********************************************************** Final Transform: Adjusting final transform due to initial resampling (voxel or size changes) ... M = [ ... -0.2496684350671 0.0088990521693 0.0092994301791 34.3717771918614 -0.0048588878276 0.1020963137361 -0.2281506825117 16.0960341057205 0.0119190494690 0.2280288350011 0.1017879495214 -5.4978401025085 0 0 0 1.0000000000000 ] Determinant : -0.015625 writing output transformation to trash.lta ... converting VOX to RAS and saving RAS2RAS... mapmovhdr: Changing vox2ras MOV header (to map to DST) ... To check aligned result, run: freeview -v /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… imageDump_coregistered.mgz Registration took 0 minutes and 1 seconds. Thank you for using RobustRegister! If you find it useful and use it for a publication, please cite: Highly Accurate Inverse Consistent Registration: A Robust Approach M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010. https://urldefense.com/v3/__http://dx.doi.org/10.1016/j.neuroimage.2010.07.…<https://urldefense.com/v3/__http:/dx.doi.org/10.1016/j.neuroimage.2010.07.0…> https://urldefense.com/v3/__http://reuter.mit.edu/papers/reuter-robreg10.pd…<https://urldefense.com/v3/__http:/reuter.mit.edu/papers/reuter-robreg10.pdf…> $Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $ --mov: Using imageDump.mgz as movable/source volume. --dst: Using /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… as target volume. --lta: Output transform as trash.lta . --mapmovhdr: Will save header adjusted movable as imageDump_coregistered.mgz ! --affine: Enableing affine transform! --sat: Using saturation 50 in M-estimator! reading source 'imageDump.mgz'... reading target '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. 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 ) MRIreorder() ----------- xdim=-1 ydim=3 zdim=-2 src 131 241 99, 0.250000 0.250000 0.250000 dst 131 99 241, 0.250000 0.250000 0.250000 Determinant after swap : 0.015625 Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241) Dst: (1, 1, 1)mm and dim (38, 40, 59) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 0 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (38, 40, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 40, 59) voxels. -- Resampled: (1, 1, 1)mm and (38, 40, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 Registration::computeMultiresRegistration - 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 1.1459028059189 0 1.0000000000000 0 -0.0876339827813 0 0 1.0000000000000 -2.9034861096682 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 20 30 ) T( 19 20 30 ) Iteration(f): 1 -- diff. to prev. transform: 7.39891 Iteration(f): 2 -- diff. to prev. transform: 6.07128 Iteration(f): 3 -- diff. to prev. transform: 6.45354 Iteration(f): 4 -- diff. to prev. transform: 9.61165 Iteration(f): 5 -- diff. to prev. transform: 6.45157 max it: 5 reached! Resolution: 0 S( 38 40 61 ) T( 38 40 61 ) Iteration(f): 1 -- diff. to prev. transform: 18.1397 Iteration(f): 2 -- diff. to prev. transform: 10.4781 Iteration(f): 3 -- diff. to prev. transform: 4.71708 Iteration(f): 4 -- diff. to prev. transform: 2.16703 Iteration(f): 5 -- diff. to prev. transform: 0.732237 max it: 5 reached! - final transform: Tf = [ ... 1.1003251237945 0.4596695415609 0.1319823678432 -15.3375075204562 -0.0809129505733 1.0958357713115 0.4294815397408 -16.7169936140805 -0.0871431123400 -0.3796938017010 0.9044729973197 10.3182069614882 0 0 0 1.0000000000000 ] - final iscale: If = 1 ********************************************************** * * WARNING: Registration did not converge in 5 steps! * Problem might be ill posed. * Please inspect output manually! * ********************************************************** Final Transform: Adjusting final transform due to initial resampling (voxel or size changes) ... M = [ ... -0.2750812479116 0.0329955927294 -0.1149173912565 38.1277797083535 0.0202282358422 0.1073703848787 -0.2739589559572 15.8057165631404 0.0217857755217 0.2261182492690 0.0949234628220 -5.6011779490725 0 0 0 1.0000000000000 ] Determinant : -0.0203612 Decompose into Rot * Shear * Scale : Rot = [ ... -0.9711353975406 0.1063152910440 -0.2135254048909 0.2376286779964 0.3535137619413 -0.9047434064484 0.0207038894236 0.9293681073764 0.3685732898005 ] Shear = [ ... 1.0000000000000 -0.0073421724799 0.1576683162397 -0.0067818833526 1.0000000000000 -0.0678198022274 0.1779196232492 -0.0828533754661 1.0000000000000 ] Scale = diag([ 0.2723989962754 0.2516119340902 0.3073865945084 ]) writing output transformation to trash.lta ... converting VOX to RAS and saving RAS2RAS... mapmovhdr: Changing vox2ras MOV header (to map to DST) ... To check aligned result, run: freeview -v /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… imageDump_coregistered.mgz Registration took 0 minutes and 1 seconds. Thank you for using RobustRegister! If you find it useful and use it for a publication, please cite: Highly Accurate Inverse Consistent Registration: A Robust Approach M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010. https://urldefense.com/v3/__http://dx.doi.org/10.1016/j.neuroimage.2010.07.…<https://urldefense.com/v3/__http:/dx.doi.org/10.1016/j.neuroimage.2010.07.0…> https://urldefense.com/v3/__http://reuter.mit.edu/papers/reuter-robreg10.pd…<https://urldefense.com/v3/__http:/reuter.mit.edu/papers/reuter-robreg10.pdf…> Reading contexts of file /usr/local/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt Constructing image-to-world transform from header information (asegMod.mgz) Returning the following transform: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 422 Debug: Off Object Name: Observers: none Matrix: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Offset: [132.04, -111.801, 144.75] Center: [0, 0, 0] Translation: [132.04, -111.801, 144.75] Inverse: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Singular: 0 Constructing image-to-world transform from header information (/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… ) Returning the following transform: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 579 Debug: Off Object Name: Observers: none Matrix: 0.275081 -0.0329956 0.114917 0.0217858 0.226118 0.0949235 -0.0202282 -0.10737 0.273959 Offset: [-38.0874, -33.402, 1.94475] Center: [0, 0, 0] Translation: [-38.0874, -33.402, 1.94475] Inverse: 3.54297 -0.162038 -1.43002 -0.38743 3.81537 -1.15946 0.109759 1.48336 3.09018 Singular: 0 Transform before cropping: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 581 Debug: Off Object Name: Observers: none Matrix: -0.275081 0.0329956 -0.114917 0.0202282 0.10737 -0.273959 0.0217858 0.226118 0.0949235 Offset: [170.128, 142.806, 78.3988] Center: [0, 0, 0] Translation: [170.128, 142.806, 78.3988] Inverse: -3.54297 1.43002 -0.162038 0.38743 1.15946 3.81537 -0.109759 -3.09018 1.48336 Singular: 0 WorldToImageTransform before cropping: AffineTransform (0x2b5e6a47e8f0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 583 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [132.04, 144.75, 111.801] Center: [0, 0, 0] Translation: [132.04, 144.75, 111.801] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 m_BoundingBoxSize: [131, 241,99] minimalMappedCoordinate: [123.105, 115.958,78.3988] maximalMappedCoordinate: [178.047, 171.204,144.802] Cropping with min [118 110 72] and max [184 177 151] m_OriginalImageRegion before checking margins: ImageRegion (0x2b5e69a829b8) Dimension: 3 Index: [118, 110, 72] Size: [67, 68, 80] m_CroppedImageRegion before checking margins: ImageRegion (0x2b5e69a82980) Dimension: 3 Index: [0, 0, 0] Size: [67, 68, 80] m_OriginalImageRegion after checking margins: ImageRegion (0x2b5e69a829b8) Dimension: 3 Index: [118, 110, 72] Size: [67, 68, 80] m_CroppedImageRegion after checking margins: ImageRegion (0x2b5e69a82980) Dimension: 3 Index: [0, 0, 0] Size: [67, 68, 80] Transform after cropping: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 740 Debug: Off Object Name: Observers: none Matrix: -0.275081 0.0329956 -0.114917 0.0202282 0.10737 -0.273959 0.0217858 0.226118 0.0949235 Offset: [52.1278, 32.8057, 6.39882] Center: [0, 0, 0] Translation: [52.1278, 32.8057, 6.39882] Inverse: -3.54297 1.43002 -0.162038 0.38743 1.15946 3.81537 -0.109759 -3.09018 1.48336 Singular: 0 WorldToImageTransform after cropping: AffineTransform (0x2b5e6a47e8f0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 741 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [14.0404, 34.7505, 39.8009] Center: [0, 0, 0] Translation: [14.0404, 34.7505, 39.8009] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Careful here: the applied transformation will turn positive tetrahedra into negative ones. -0.275081 0.0329956 -0.114917 0.0202282 0.10737 -0.273959 0.0217858 0.226118 0.0949235 determinant: -0.0203612 Starting to swap the point assignments of each tetrahedron... Warning: This concatenation operation includes an empty array with an incorrect number of rows. Concatenation including empty arrays will require all arrays to have the same number of rows in a future release. [ > In segmentSubjectT1_autoEstimateAlveusML at 325] 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 segmentSubjectT1_autoEstimateAlveusML (line 348) The vector of prior probabilities in the mesh nodes must always sum to one over all classes #-------------------------------------------- #@# Hippocampal Subfields processing (T1 only) right Mon Aug 10 18:54:37 EDT 2020 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/usr/local/packages/freesurfer_v6.0.0/MCRv80/runtime/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/bin/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/os/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/native_threads:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/server:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/client:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64:/usr/lib64/qt5/plugins/platforms:/usr/local/packages/ants-2.3.4/lib:/usr/lib64/openmpi/lib: Warning: application is running on a locale different from the original platform locale. Warning: No display specified. You will not be able to display graphics on the screen. Registering imageDump.mgz to hippocampal mask from ASEG This file does not contain MRI parameters $Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $ --mov: Using imageDump.mgz as movable/source volume. --dst: Using /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… as target volume. --lta: Output transform as trash.lta . --mapmovhdr: Will save header adjusted movable as imageDump_coregistered.mgz ! --sat: Using saturation 50 in M-estimator! reading source 'imageDump.mgz'... reading target '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 4 Type Target : 3 ensure both FLOAT (3) Reordering axes in mov to better fit dst... ( 1 3 -2 ) MRIreorder() ----------- xdim=1 ydim=3 zdim=-2 src 131 241 99, 0.250000 0.250000 0.250000 dst 131 99 241, 0.250000 0.250000 0.250000 Determinant after swap : 0.015625 Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241) Dst: (1, 1, 1)mm and dim (38, 43, 57) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 4 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 43, 57) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 Registration::computeMultiresRegistration - 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 0.1781192630761 0 1.0000000000000 0 1.0539982530706 0 0 1.0000000000000 -3.1974090629449 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 21 30 ) T( 19 21 30 ) Iteration(f): 1 -- diff. to prev. transform: 13.2959 Iteration(f): 2 -- diff. to prev. transform: 9.20966 Iteration(f): 3 -- diff. to prev. transform: 6.22883 Iteration(f): 4 -- diff. to prev. transform: 8.12577 Iteration(f): 5 -- diff. to prev. transform: 6.11784 max it: 5 reached! Resolution: 0 S( 38 43 61 ) T( 38 43 61 ) Iteration(f): 1 -- diff. to prev. transform: 5.83495 Iteration(f): 2 -- diff. to prev. transform: 1.34307 Iteration(f): 3 -- diff. to prev. transform: 0.589867 Iteration(f): 4 -- diff. to prev. transform: 0.289573 Iteration(f): 5 -- diff. to prev. transform: 0.137676 max it: 5 reached! - final transform: Tf = [ ... 0.9980802661280 -0.0059694736544 0.0616453384301 -1.7131111872539 -0.0239991870640 0.8803011391816 0.4738079182283 -13.5066607583463 -0.0570948455303 -0.4743777711274 0.8784679327523 13.0909553444701 0 0 0 1.0000000000000 ] - final iscale: If = 1 ********************************************************** * * WARNING: Registration did not converge in 5 steps! * Problem might be ill posed. * Please inspect output manually! * ********************************************************** Final Transform: Adjusting final transform due to initial resampling (voxel or size changes) ... M = [ ... 0.2495200665320 0.0154113346075 0.0014923684136 0.7292410424572 -0.0059997967660 0.1184519795571 -0.2200752847954 16.2081470043947 -0.0142737113826 0.2196169831881 0.1185944427819 -4.6804480513511 0 0 0 1.0000000000000 ] Determinant : 0.015625 writing output transformation to trash.lta ... converting VOX to RAS and saving RAS2RAS... mapmovhdr: Changing vox2ras MOV header (to map to DST) ... To check aligned result, run: freeview -v /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… imageDump_coregistered.mgz Registration took 0 minutes and 1 seconds. Thank you for using RobustRegister! If you find it useful and use it for a publication, please cite: Highly Accurate Inverse Consistent Registration: A Robust Approach M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010. https://urldefense.com/v3/__http://dx.doi.org/10.1016/j.neuroimage.2010.07.…<https://urldefense.com/v3/__http:/dx.doi.org/10.1016/j.neuroimage.2010.07.0…> https://urldefense.com/v3/__http://reuter.mit.edu/papers/reuter-robreg10.pd…<https://urldefense.com/v3/__http:/reuter.mit.edu/papers/reuter-robreg10.pdf…> $Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $ --mov: Using imageDump.mgz as movable/source volume. --dst: Using /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… as target volume. --lta: Output transform as trash.lta . --mapmovhdr: Will save header adjusted movable as imageDump_coregistered.mgz ! --affine: Enableing affine transform! --sat: Using saturation 50 in M-estimator! reading source 'imageDump.mgz'... reading target '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 4 Type Target : 3 ensure both FLOAT (3) Reordering axes in mov to better fit dst... ( 1 3 -2 ) MRIreorder() ----------- xdim=1 ydim=3 zdim=-2 src 131 241 99, 0.250000 0.250000 0.250000 dst 131 99 241, 0.250000 0.250000 0.250000 Determinant after swap : 0.015625 Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241) Dst: (1, 1, 1)mm and dim (38, 43, 57) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 4 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 43, 57) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 Registration::computeMultiresRegistration - 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 0.1781175809123 0 1.0000000000000 0 1.0539986543476 0 0 1.0000000000000 -3.1974083471847 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 21 30 ) T( 19 21 30 ) Iteration(f): 1 -- diff. to prev. transform: 9.84742 Iteration(f): 2 -- diff. to prev. transform: 7.72517 Iteration(f): 3 -- diff. to prev. transform: 8.94618 Iteration(f): 4 -- diff. to prev. transform: 7.30294 Iteration(f): 5 -- diff. to prev. transform: 5.69484 max it: 5 reached! Resolution: 0 S( 38 43 61 ) T( 38 43 61 ) Iteration(f): 1 -- diff. to prev. transform: 11.7013 Iteration(f): 2 -- diff. to prev. transform: 3.26787 Iteration(f): 3 -- diff. to prev. transform: 1.06278 Iteration(f): 4 -- diff. to prev. transform: 0.347277 Iteration(f): 5 -- diff. to prev. transform: 0.0826464 max it: 5 reached! - final transform: Tf = [ ... 1.1636664104160 -0.3014738824441 0.0004403516595 4.0520187720971 -0.0480158583329 1.0596278475093 0.4954259490783 -17.9010148715481 -0.2453688431946 -0.4779665918288 0.8802303084621 16.7816318160186 0 0 0 1.0000000000000 ] - final iscale: If = 1 ********************************************************** * * WARNING: Registration did not converge in 5 steps! * Problem might be ill posed. * Please inspect output manually! * ********************************************************** Final Transform: Adjusting final transform due to initial resampling (voxel or size changes) ... M = [ ... 0.2909164812245 0.0001100842157 0.0753684565922 -3.0302464159984 -0.0120039592287 0.1238564808785 -0.2649069144859 17.7887142169889 -0.0613421845898 0.2200575304725 0.1194916267047 -1.6040021052477 0 0 0 1.0000000000000 ] Determinant : 0.0216399 Decompose into Rot * Shear * Scale : Rot = [ ... 0.9808313831675 0.1119800663090 0.1594686882846 0.0897438323260 0.4668219029177 -0.8797859714247 -0.1729619679387 0.8772330224665 0.4478240524369 ] Shear = [ ... 1.0000000000000 -0.1069794540990 0.0987385001868 -0.0910164402411 1.0000000000000 -0.0348388469911 0.0999837864599 -0.0414655431721 1.0000000000000 ] Scale = diag([ 0.2948725983257 0.2508729779031 0.2985915205121 ]) writing output transformation to trash.lta ... converting VOX to RAS and saving RAS2RAS... mapmovhdr: Changing vox2ras MOV header (to map to DST) ... To check aligned result, run: freeview -v /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… imageDump_coregistered.mgz Registration took 0 minutes and 1 seconds. Thank you for using RobustRegister! If you find it useful and use it for a publication, please cite: Highly Accurate Inverse Consistent Registration: A Robust Approach M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010. https://urldefense.com/v3/__http://dx.doi.org/10.1016/j.neuroimage.2010.07.…<https://urldefense.com/v3/__http:/dx.doi.org/10.1016/j.neuroimage.2010.07.0…> https://urldefense.com/v3/__http://reuter.mit.edu/papers/reuter-robreg10.pd…<https://urldefense.com/v3/__http:/reuter.mit.edu/papers/reuter-robreg10.pdf…> Reading contexts of file /usr/local/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt Constructing image-to-world transform from header information (asegMod.mgz) Returning the following transform: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 422 Debug: Off Object Name: Observers: none Matrix: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Offset: [132.04, -111.801, 144.75] Center: [0, 0, 0] Translation: [132.04, -111.801, 144.75] Inverse: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Singular: 0 Constructing image-to-world transform from header information (/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… ) Returning the following transform: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 579 Debug: Off Object Name: Observers: none Matrix: -0.290916 -0.000110086 -0.0753684 -0.0613422 0.220058 0.119492 0.012004 -0.123856 0.264907 Offset: [49.0706, -22.4049, 0.96176] Center: [0, 0, 0] Translation: [49.0706, -22.4049, 0.96176] Inverse: -3.37777 -0.432721 -0.765819 -0.81721 3.51948 -1.82003 -0.229025 1.66513 2.95866 Singular: 0 Transform before cropping: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 581 Debug: Off Object Name: Observers: none Matrix: 0.290916 0.000110086 0.0753685 -0.012004 0.123856 -0.264907 -0.0613422 0.220058 0.119492 Offset: [82.9698, 143.789, 89.396] Center: [0, 0, 0] Translation: [82.9698, 143.789, 89.396] Inverse: 3.37777 0.765819 -0.432721 0.81721 1.82003 3.51948 0.229025 -2.95866 1.66513 Singular: 0 WorldToImageTransform before cropping: AffineTransform (0x2b82ec807bf0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 583 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [132.04, 144.75, 111.801] Center: [0, 0, 0] Translation: [132.04, 144.75, 111.801] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 m_BoundingBoxSize: [131, 241,99] minimalMappedCoordinate: [82.9698, 116.267,81.4215] maximalMappedCoordinate: [128.201, 173.514,153.92] Cropping with min [78 111 74] and max [133 179 161] m_OriginalImageRegion before checking margins: ImageRegion (0x2b82ec809218) Dimension: 3 Index: [78, 111, 74] Size: [56, 69, 88] m_CroppedImageRegion before checking margins: ImageRegion (0x2b82ec8091e0) Dimension: 3 Index: [0, 0, 0] Size: [56, 69, 88] m_OriginalImageRegion after checking margins: ImageRegion (0x2b82ec809218) Dimension: 3 Index: [78, 111, 74] Size: [56, 69, 88] m_CroppedImageRegion after checking margins: ImageRegion (0x2b82ec8091e0) Dimension: 3 Index: [0, 0, 0] Size: [56, 69, 88] Transform after cropping: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 740 Debug: Off Object Name: Observers: none Matrix: 0.290916 0.000110086 0.0753685 -0.012004 0.123856 -0.264907 -0.0613422 0.220058 0.119492 Offset: [4.96976, 32.7887, 15.396] Center: [0, 0, 0] Translation: [4.96976, 32.7887, 15.396] Inverse: 3.37777 0.765819 -0.432721 0.81721 1.82003 3.51948 0.229025 -2.95866 1.66513 Singular: 0 WorldToImageTransform after cropping: AffineTransform (0x2b82ec807bf0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 741 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [54.0404, 33.7505, 37.8009] Center: [0, 0, 0] Translation: [54.0404, 33.7505, 37.8009] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Warning: This concatenation operation includes an empty array with an incorrect number of rows. Concatenation including empty arrays will require all arrays to have the same number of rows in a future release. [ > In segmentSubjectT1_autoEstimateAlveusML at 325] 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 segmentSubjectT1_autoEstimateAlveusML (line 348) The vector of prior probabilities in the mesh nodes must always sum to one over all classes -------------- next part -------------- An HTML attachment was scrubbed... URL: https://urldefense.com/v3/__http://mail.nmr.mgh.harvard.edu/pipermail/frees…<https://urldefense.com/v3/__http:/mail.nmr.mgh.harvard.edu/pipermail/freesu…> ------------------------------ _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> https://urldefense.com/v3/__https://mail.nmr.mgh.harvard.edu/mailman/listin…<https://urldefense.com/v3/__https:/mail.nmr.mgh.harvard.edu/mailman/listinf…> End of Freesurfer Digest, Vol 199, Issue 17 *******************************************

2 1

Resting state fMRI - volume analysis
by Wenzhen Zhao 07 Oct '20

07 Oct '20

External Email - Use Caution Dear Freesurfer Developers, I have a question regarding volume analysis of fMRI data. I wanted to perform volume analysis in MNI305 space, and I used following command after creating a seed; $mkanalysis-sess -analysis lh.pcc.mni305.vol -mni305 2 -stc siemens -fwhm 0 -notask -taskreg lh.pcc.dat 1 -nuisreg vcsf.dat 5 -nuisreg wm.dat 5 -mcextreg -polyfit 5 -nskip 10 -fsd rest -hpf 0.01 -TR 3 -per-run $selxavg3-sess -s sess01 -a lh.pcc.mni305.vol -no-preproc -overwrite The resulting analysis seems only showing subcortical regions, not the whole brain. (Shown in the attachment) I read on the page that volume analysis is only for subcortical regions, is this the reason that I only see subcortical regions? Another problem is that in the preprocessing step, I could not smooth the volume. If I do, it seems to distort the image and only gives something similar to the picture shown. Could this be the reason for the distorted analysis? Thanks for your help. Best, Wenzhen

3 5

Recon-all Results for fMRI
by goktekin.durusoy＠boun.edu.tr 07 Oct '20

07 Oct '20

External Email - Use Caution Hi, I am using freesurfer 5.3.0. I would like to ask whether it is possible or not to have different results for destrieux atlas parcellation for the same data. (aparc.a2009s+aseg.mgz file). It seems there is a little difference in terms of parcellation locations and their volumes. Thank you, Goktekin Durusoy

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