Freesurfer

freesurfer@nmr.mgh.harvard.edu

17 participants
30086 discussions

Freesurfer vs 6 Cortical Surfaces and vs 7.1.1 Subcortical areas
by Annelies Vant Westeinde 25 Sep '20

25 Sep '20

External Email - Use Caution Hello list, I noticed that I find the quality of the neocortical segmentation of vs 6 better than vs 7.1.1., but the subcortical segmentation of 7.1.1 better than vs 6. Is it valid to do analyses of neocortical and subcortical areas based on different Freesurfer versions? Even when, say, correlating them to each other? Or, in line with this to use the hippocampal and amygdala subfields from vs 7.1.1 in combination with neocortex (eg thickness estimates) of vs 6? Thank you! Annelies Annelies van't Westeinde | PHD Candidate Department of Women's and Children's Health | Karolinska Institutet 171 77 Solna | Karolinskavägen 37A +46 760967499 annelies.vant.westeinde(a)ki.se | ki.se<http://ki.se/en/startpage> ______________________________________ Karolinska Institutet – a medical university När du skickar e-post till Karolinska Institutet (KI) innebär detta att KI kommer att behandla dina personuppgifter. Här finns information om hur KI behandlar personuppgifter<https://ki.se/medarbetare/integritetsskyddspolicy>. Sending email to Karolinska Institutet (KI) will result in KI processing your personal data. You can read more about KI’s processing of personal data here<https://ki.se/en/staff/data-protection-policy>.

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FreeSurfer - Close path and fill label?
by Sanchez, Justin 24 Sep '20

24 Sep '20

Hi FreeSurfer team - Hope all is well with you these days! I am trying to pull off the following: Given a list of vertices that mark the outline of a new surface ROI (e.g., a path file, attached) and other FS surface files, 1. Make a closed path through these vertices 2. "Fill" the closed path to create a new label 3. Save the new label This is basically the process outlined here: https://surfer.nmr.mgh.harvard.edu/fswiki/tksurfer_labeledit#Actuallycreati…, except that the initial boundary vertices/path file are identified automatically (by an algorithm) rather than manually clicked. This works great if I load the surface in tksurfer, then load the path, then do custom fill, etc. But I’d love to be able to do this automatically, for many subjects, without having to load tksurfer. Since this functionality is implemented in tksurfer it seems there must be code for it, but I can’t find it in the FS docs or mailing list history. Can you help with this? Thanks a bunch, Justin

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parameterization template and the vertex correspondence in the pial surface
by Pam Garcia 24 Sep '20

24 Sep '20

External Email - Use Caution Hi FreeSufer team. I have some questions related to the parameterization template and the vertex correspondence in the pial surface. 1. In the parameterization template what is the reason for using the mean and variance of curvature for WM surface using the inflated and smoothed surface? What independent information do these measures supply? 2. Related to the vertex correspondence. How is the spherical surface mapped to the pial surface to achieve vertex correspondence between the WM and pial surfaces? Thanks a lot for the responses best Pam

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Re: [Freesurfer] virtual disk image of freesurfer 7.1.1
by fsbuild 23 Sep '20

23 Sep '20

External Email - Use Caution Hello Gonzalo, The virtual box setup on Mac and Windows is listed here, https://surfer.nmr.mgh.harvard.edu/fswiki/VM_67 The actual link to the compressed disk image file is, https://drive.google.com/file/d/1bPvF9m2z4yRDIOv51B5bihU1VN_o2J5x/… If you already know how to use virtualbox, then you can just create a new machine using the *.vdi file after downloading an unzipping the disk image (otherwise read thru the instructions for setting up virtualbox on Windows or Mac - it should work the same way to setup and run the virtual Ubuntu machine on a different Linux distribution like CentOS, Fedora, etc. ). That VM image has the 7.1.0 release however, so once you have the virtual machine up and running, just update it with the 7.1.1 archive found here, freesurfer-linux-centos7_x86_64-7.1.1.tar.gz - R.. On Sep 23, 2020, at 19:45, Gonzalo Rojas Costa <gonzalo.rojas.costa(a)gmail.com> wrote:        External Email - Use Caution        Hi:  Where can I find the virtual disk image of freesurfer 7.1.1 ?  Sincerely,Gonzalo Rojas Costa_______________________________________________Freesurfer mailing listFreesurfer@nmr.mgh.harvard.eduhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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virtual disk image of freesurfer 7.1.1
by Gonzalo Rojas Costa 23 Sep '20

23 Sep '20

External Email - Use Caution Hi: Where can I find the virtual disk image of freesurfer 7.1.1 ? Sincerely, Gonzalo Rojas Costa

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Viewing labels in freeview
by Bronwyn Overs 22 Sep '20

22 Sep '20

External Email - Use Caution Dear freesurfer mailing list, I generated my own label file from a linear mixed effects model and now I want to view this label file on the surface of fsaverage using freeview. I am able to load the surface and label files successfully from the command line using the following: freeview -f $SUBJECTS_DIR/fsaverage_mod/surf/lh.inflated:label=lh.volume.stack.R3B.fwhm20.lmem.final.200817.gender.FDR-0001.label However, once freeview is open and the label file is loaded, I cannot see the label displayed on the surface (screenshot attached). I am currently using freesurfer version 5.3.0. Can someone please tell me how to make the label file visible on the surface of fsaverage? Kind regards, Bronwyn Overs Research Assistant Neuroscience Research Australia Margarete Ainsworth Building Barker Street Randwick Sydney NSW 2031 Australia M 0411 308 769 T +61 2 9399 1725 [ http://neura.edu.au/ | neura.edu.au ] [ https://twitter.com/neuraustralia | Twitter ] | [ https://www.facebook.com/NeuroscienceResearchAustralia | Facebook ] | [ http://www.neura.edu.au/help-research/subscribe | Subscribe ]

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Nueroimaging Data Analyst at CU Boulder
by Lena Sherbakov 22 Sep '20

22 Sep '20

External Email - Use Caution Hi Everyone, We're looking to hire a Nueroimaging Data Analyst at CU Boulder. For more information, please see the posting on our website: https://jobs.colorado.edu/jobs/JobDetail/?jobId=27063&emailCampaignId=136 -- Lena Sherbakov, Ph.D. Data Scientist Intermountain Neuroimaging Consortium 1777 Exposition Dr., Boulder, CO, 80301 Office 182B p 650.269.1852 colorado.edu/mri

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Re: [Freesurfer] Freesurfer Digest, Vol 199, Issue 42 - Problem running infant FreeSurfer - failure to create symbolic link
by Fleur Warton 22 Sep '20

22 Sep '20

External Email - Use Caution Hi Tim, You're right, that was the issue - local computer running Ubuntu and a Windows server. Thanks very much! -- Fleur Warton Postdoctoral Research Fellow Division of Biomedical Engineering Faculty of Health Sciences University of Cape Town On Mon, Sep 21, 2020 at 6:00 PM <freesurfer-request(a)nmr.mgh.harvard.edu> wrote: > Send Freesurfer mailing list submissions to > freesurfer(a)nmr.mgh.harvard.edu > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > or, via email, send a message with subject or body 'help' to > freesurfer-request(a)nmr.mgh.harvard.edu > > You can reach the person managing the list at > freesurfer-owner(a)nmr.mgh.harvard.edu > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Freesurfer digest..." > > > Today's Topics: > > 1. Problem running infant FreeSurfer - failure to create > symbolic link (Fleur Warton) > 2. Re: Problem running infant FreeSurfer - failure to create > symbolic link (Tim Sch?fer) > 3. Re: Viewing labels in freeview (Wang, Ruopeng) > 4. Re: Masking surface overlays and spatial correlations > (Douglas N. Greve) > 5. Re: Functional connectivity preprocessing and seed analysis > (Douglas N. Greve) > 6. Re: recon-all 7.1.1 (James Hartzell) > 7. Re: Functional connectivity preprocessing and seed analysis > (Wenzhen Zhao) > 8. Re: Functional connectivity preprocessing and seed analysis > (Douglas N. Greve) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Mon, 21 Sep 2020 11:51:53 +0200 > From: Fleur Warton <fleur.warton(a)gmail.com> > Subject: [Freesurfer] Problem running infant FreeSurfer - failure to > create symbolic link > To: freesurfer(a)nmr.mgh.harvard.edu > Message-ID: > <CANGE1NGZK2QiKjwFBzVP4-wyyB6GoD= > HkzKdGwc_8_+KWNf1Xw(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > External Email - Use Caution > > Hi FreeSurfer team, > > I'm trying to run the infant freesurfer (version: > freesurfer-infant-20200205 ) and get the following error: > > ln: failed to create symbolic link 'brain.nii.gz': Operation not supported > > The command I used was: > > infant_recon_all --s 002B --age 0 > > I think this may have to do with the fact that my subjects_dir is located > on a server (not the computer I'm running FreeSurfer on), since when I ran > the command with the subjects_dir located on my local computer it ran fine > and completed without errors. Unfortunately for space reasons it isn't > possible for me to do that for my entire dataset. > > Thanks, > > Fleur > -- > > Fleur Warton >

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Re: [Freesurfer] Hippocampal subfields error
by Emily K Clarke 21 Sep '20

21 Sep '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> Subject: Re: [Freesurfer] Hippocampal subfields error To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Message-ID: <25DF95C0-873A-4906-9D51-A94CF022375D(a)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… From: <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of Emily K Clarke <ekc23(a)duke.edu> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Date: Wednesday, September 9, 2020 at 15:53 To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)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://reuter.mit.edu/papers/reuter-robreg10.pd… $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://reuter.mit.edu/papers/reuter-robreg10.pd… 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://reuter.mit.edu/papers/reuter-robreg10.pd… $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://reuter.mit.edu/papers/reuter-robreg10.pd… 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

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mri_glmfit-sim error CSDs have the same seed
by Meaghan Perdue 21 Sep '20

21 Sep '20

External Email - Use Caution Hello Freesurfer experts, We're receiving the following error message when trying to run mri_glmfit-sim: ERROR: CSDmerge: CSDs have same seed This only occurs when we try to run the permutation on the LH for thickness. We've been able to run the simulation successfully on LH surface area and RH thickness and area. How can we troubleshoot this problem? Thank you, Meaghan Perdue -- Meaghan Perdue she | her | hers PhD Student Developmental Psychology Neurobiology of Language University of Connecticut Bousfield A302 Visit my website <https://mvperdue16.wixsite.com/meaghanperdue>

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