Freesurfer

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Re: [Freesurfer] Freesurfer Digest, Vol 188, Issue 13
by Juan Rivas 09 Oct '19

09 Oct '19

External Email - Use Caution Hi, thank you for your answer. This file does exist, it contains 59.3 MB. JC. Message: 10 Date: Tue, 8 Oct 2019 22:23:11 +0000 From: "Yendiki, Anastasia" <AYENDIKI(a)mgh.harvard.edu> Subject: Re: [Freesurfer] Tracula To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Message-ID: < BL0PR04MB4833DA61A1A5E068536924AF8A9A0(a)BL0PR04MB4833.namprd04.prod.outlook.com > Content-Type: text/plain; charset="us-ascii" Does this file exist? niiRead(): error opening file /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz Hi, I have been trying to process my diffusion images, but I have a new problem: I wrote this dmrirc: # FreeSurfer SUBJECTS_DIR # T1 images and FreeSurfer segmentations are expected to be found here # setenv SUBJECTS_DIR /autofs/cluster/neuromod/rivas/imagenes/DICOM # Output directory where trac-all results will be saved # Default: Same as SUBJECTS_DIR # set dtroot = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc # Subject IDs # set subjlist = (esq-15-hc) # Default: Run analysis on all subjects # set runlist = (1) # Input diffusion DICOMs (file names relative to dcmroot) # If original DICOMs don't exist, these can be in other image format # but then bvecfile and bvalfile must be specified (see below) # set dcmroot = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc set dcmlist = (dwi.nii.gz) # Diffusion gradient table # Must be specified if inputs are not MGH DICOMs # Three-column format, one row for each volume in the diffusion data set # Default: Read from DICOM header # set bvecfile = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc/processing/31190669_bvecs.dat # Diffusion b-value table # Must be specified if inputs are not MGH DICOMs # Single-column format, one value for each volume in the diffusion data set # Default: Read from DICOM header # set bvalfile = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc/processing/31190669_bvals.dat # Perform registration-based B0-inhomogeneity compensation? # Default: 0 (no) # # set dob0 = 1 # Input B0 field map magnitude DICOMs (file names relative to dcmroot) # Only used if dob0 = 1 # Default: None # # set b0mlist = (huey/fmag/XXX-1.dcm dewey/fmag/XXX-1.dcm louie/fmag/XXX-1.dcm) # Input B0 field map phase DICOMs (file names relative to dcmroot) # Only used if dob0 = 1 # Default: None # # set b0plist = (huey/fphas/XXX-1.dcm dewey/fphas/XXX-1.dcm louie/fphas/XXX-1.dcm) # Echo spacing for field mapping sequence (from sequence printout) # Only used if dob0 = 1 # Default: None # set echospacing = 0.7 # Perform registration-based eddy-current compensation? # Default: 1 (yes) # set doeddy = 1 # Rotate diffusion gradient vectors to match eddy-current compensation? # Only used if doeddy = 1 # Default: 1 (yes) # set dorotbvecs = 1 # Fractional intensity threshold for BET mask extraction from low-b images # This mask is used only if usemaskanat = 0 # Default: 0.3 # set thrbet = 0.5 # Perform diffusion-to-T1 registration by flirt? # Default: 0 (no) # set doregflt = 0 # Perform diffusion-to-T1 registration by bbregister? # Default: 1 (yes) # set doregbbr = 1 # Perform registration of T1 to MNI template? # Default: 1 (yes) # set doregmni = 1 # MNI template # Only used if doregmni = 1 # Default: $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz # set mnitemp = $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz # Perform registration of T1 to CVS template? # Default: 0 (no) # set doregcvs = 0 # CVS template subject ID # Only used if doregcvs = 1 # Default: cvs_avg35 # set cvstemp = donald # Parent directory of the CVS template subject # Only used if doregcvs = 1 # Default: # set cvstempdir = $FREESURFER_HOME/subjects # Use brain mask extracted from T1 image instead of low-b diffusion image? # Has no effect if there is no T1 data # Default: 1 (yes) # set usemaskanat = 1 # Paths to reconstruct # Default: All paths in the atlas # set pathlist = ( lh.cst_AS rh.cst_AS \ lh.unc_AS rh.unc_AS \ lh.ilf_AS rh.ilf_AS \ fmajor_PP fminor_PP \ lh.atr_PP rh.atr_PP \ lh.ccg_PP rh.ccg_PP \ lh.cab_PP rh.cab_PP \ lh.slfp_PP rh.slfp_PP \ lh.slft_PP rh.slft_PP ) # Number of path control points # It can be a single number for all paths or a different number for each of the # paths specified in pathlist # Default: 7 for the forceps major, 6 for the corticospinal tract, # 4 for the angular bundle, and 5 for all other paths # set ncpts = (6 6 5 5 5 5 7 5 5 5 5 5 4 4 5 5 5 5) # List of training subjects # This text file lists the locations of training subject directories # Default: $FREESURFER_HOME/trctrain/trainlist.txt # set trainfile = $FREESURFER_HOME/trctrain/trainlist.txt # Number of "sticks" (anisotropic diffusion compartments) in the bedpostx # ball-and-stick model # Default: 2 # set nstick = 2 # Number of MCMC burn-in iterations # (Path samples drawn initially by MCMC algorithm and discarded) # Default: 200 # set nburnin = 200 # Number of MCMC iterations # (Path samples drawn by MCMC algorithm and used to estimate path distribution) # Default: 7500 # set nsample = 7500 # Frequency with which MCMC path samples are retained for path distribution # Default: 5 (keep every 5th sample) # set nkeep = 5 # Reinitialize path reconstruction? # This is an option of last resort, to be used only if one of the reconstructed # pathway distributions looks like a single curve. This is a sign that the # initial guess for the pathway was problematic, perhaps due to poor alignment # between the individual and the atlas. Setting the reinit parameter to 1 and # rerunning "trac-all -prior" and "trac-all -path", only for the specific # subjects and pathways that had this problem, will attempt to reconstruct them # with a different initial guess. # Default: 0 (do not reinitialize) # set reinit = 0 And I gave this command: [shiraz:processing] (nmr-stable6-env) trac-all -prep -c dmrirc-esq-15-hc But there is an error: INFO: SUBJECTS_DIR is /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing INFO: Diffusion root is /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc Actual FREESURFER_HOME /autofs/cluster/freesurfer/centos7_x86_64/stable6 trac-preproc -c /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/dmrirc.local -log /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/trac-all.log -cmd /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/trac-all.cmd #------------------------------------- /usr/local/freesurfer/stable6/bin/trac-preproc #------------------------------------- #@# Image corrections Tue Oct 8 17:53:12 EDT 2019 mri_convert --bvec-voxel /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz mri_convert.bin --bvec-voxel /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz niiRead(): error opening file /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz... Linux shiraz.nmr.mgh.harvard.edu 3.10.0-957.1.3.el7.x86_64 #1 SMP Thu Nov 29 14:49:43 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux trac-preproc exited with ERRORS at Tue Oct 8 17:53:12 EDT 2019 > > ------------------------------ > > >

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Label created in the symmetric space
by Jose Graterol 09 Oct '19

09 Oct '19

External Email - Use Caution Dear Freesurfer Experts, I would appreciate your help. I will explain first what I have done and where my problem is. I want to measure the cortical thickness in stroke patients. Therefore I followed Douglas' instructions provided in this link <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=subj…>to join both affected hemispheres (left or right, depending on the patient) and to analyze them with xhemi. After mri_glmfit and mri_glmfit-sim (--cwpvalthresh 0,05 --cache 4 abs/neg) have run I obtained 2 significant clusters. One in the precentral area, which I am interested in. When I check the *abs/neg.y.ocn.dat file the values are all -0.XXXX. If I understood it correctly, those should be cortical thickness values in mm, making those values implausible. For that reason I made a label of that cluster using the autofill option from tksurfer. The idea was to obtain the mean cortical thickness using mris_anatomical_stats after mapping the label to the subjects. Now my questions, what would be the best method to map the label created from the fsaverage_sym space to the subject space? Or just simply, is this the right way to do this? or should I check why I am obtaining those values in the *y.ocn.dat file? Thanks in advance. Kind Regards José Graterol

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Questions re slice thickness, aseg and longitudinal analysis
by David Kamson 09 Oct '19

09 Oct '19

External Email - Use Caution Freesurfers, First of all, I'd like to express my gratitude to the community for the support that keeps researchers like myself afloat! I have a unique set of oncology patients that I want to evaluate for brain atrophy in a retrospective longitudinal analysis. I was thinking about using Aseg.auto results to assess longitudinal volume changes, but before I invest all the time I wanted to check with the community whether this makes any sense at all: The dataset that looks like this: - 22 patients (no control dataset [yet]) - 10-25 MRIs per patient acquired over 2-8 years in relatively uniform intervals - Patients had most of their scans on the same scanner, but scanners differed widely between patients - All patients have axial T1 post gadolinium scans of 1x1x5mm resolution (3D acquisition available in <10%) - About 80% of scans have an axial pre-contrast T1 sequence - All scans are skullstripped (third party algorithm) I'm looking for crude changes, no subtleties; volumes of interest are: - Whole brain volume - White matter volume - Ventricular volume (mainly lateral ventricle) - Subcortical gray matter volume (whole thalamus most importantly) I ran a few test analyses and to my surprise I was able to generate pretty acceptable surfaces, however, topology fixing took about 24H per scan, and I feel aseg.auto contained all the volumetric data I was really interested in. My concrete questions are: 1) Does the full autorecon pipeline affect Aseg.auto? If there is no benefit, I could reduce the per scan analysis time from 28 hours to 1-2 h. 2) Would this low-resolution dataset be accepted by reviewers if used for Aseg? Should I do any quantitative validation beyond a visual quality analysis of Aseg? 3) Can I perform a longitudinal analysis only for the Aseg results? 4) Is it OK to use T1-gad images for the analysis? I'd appreciate any input! Best regards, David O. Kamson, MD PhD Neuro-oncology fellow Johns Hopkins Hospital & National Institutes of Health

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PostDoc on structural MRI processing, Göttingen/Germany
by Focke, Niels 09 Oct '19

09 Oct '19

External Email - Use Caution We are currently offering a PostDoc position focused on structural (brain) MRI processing (100%, pay-grade TDL E13) in the context of a recently started "BigData" initiative on heart&brain imaging in Göttingen/Germany. Funding is currently available until 10/2022. Details can be found in the attached PDF. Applications and enquires should be directed to: Universitätsmedizin Göttingen Klinik für Klinische Neurophysiologie Prof. Dr. Niels Focke Robert-Koch Str. 40 Deutschland/Germany E-Mail: niels.focke(a)med.uni-goettingen.de<mailto:niels.focke@med.uni-goettingen.de>

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Notice about macOS Catalina Notarization and FreeSurfer
by Aditya Kulkarni 08 Oct '19

08 Oct '19

External Email - Use Caution Hello FreeSurfer team, With Apple’s recent release of macOS Catalina, I wanted to test its compatibility with FreeSurfer. Indeed the executables in the freesurfer/bin folder are subject to the “notarization” problem in the OS, such that every dependent executable that is used in a recon-all stream requires the user to allow it manually through security (a process which becomes cumbersome and interrupts recon-all runs with the many libraries it requires). I know you are all working very hard on the software’s development and just wanted to update with this information in case other users are updating to Catalina and going to run a FreeSurfer stream soon. Best, Aditya Kulkarni

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FS dev install error at mri_surf2vol on mac
by Jonathan DuBois 08 Oct '19

08 Oct '19

External Email - Use Caution Hi Freesurfer developers I’m trying to install the development package from github on my mac running osx 10.14.6. I ran CMake with the OSX package without any issue, although when I ran make I got the following error: [ 28%] Linking CXX executable surf2vol ld: warning: could not create compact unwind for _MatSetValuesLocal: stack subq instruction is too different from dwarf stack size … "vnl_nonlinear_minimizer::is_a() const", referenced from: vtable for fs_powell in libutils.a(numerics.cpp.o) ld: symbol(s) not found for architecture x86_64 clang: error: linker command failed with exit code 1 (use -v to see invocation) make[2]: *** [fem_elastic/surf2vol] Error 1 make[1]: *** [fem_elastic/CMakeFiles/surf2vol.dir/all] Error 2 make: *** [all] Error 2 The full error is copied in the attached text file. I have not been able to find anything useful on google so please let me know if you have any ideas. I don’t necessarily need MRI_surf2vol so I could exclude it from the install but I’m guessing that there are other programs that rely on it? Best, Jon

1 0

Tracula
by Juan Rivas 08 Oct '19

08 Oct '19

External Email - Use Caution Hi, I have been trying to process my diffusion images, but I have a new problem: I wrote this dmrirc: # FreeSurfer SUBJECTS_DIR # T1 images and FreeSurfer segmentations are expected to be found here # setenv SUBJECTS_DIR /autofs/cluster/neuromod/rivas/imagenes/DICOM # Output directory where trac-all results will be saved # Default: Same as SUBJECTS_DIR # set dtroot = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc # Subject IDs # set subjlist = (esq-15-hc) # Default: Run analysis on all subjects # set runlist = (1) # Input diffusion DICOMs (file names relative to dcmroot) # If original DICOMs don't exist, these can be in other image format # but then bvecfile and bvalfile must be specified (see below) # set dcmroot = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc set dcmlist = (dwi.nii.gz) # Diffusion gradient table # Must be specified if inputs are not MGH DICOMs # Three-column format, one row for each volume in the diffusion data set # Default: Read from DICOM header # set bvecfile = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc/processing/31190669_bvecs.dat # Diffusion b-value table # Must be specified if inputs are not MGH DICOMs # Single-column format, one value for each volume in the diffusion data set # Default: Read from DICOM header # set bvalfile = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc/processing/31190669_bvals.dat # Perform registration-based B0-inhomogeneity compensation? # Default: 0 (no) # # set dob0 = 1 # Input B0 field map magnitude DICOMs (file names relative to dcmroot) # Only used if dob0 = 1 # Default: None # # set b0mlist = (huey/fmag/XXX-1.dcm dewey/fmag/XXX-1.dcm louie/fmag/XXX-1.dcm) # Input B0 field map phase DICOMs (file names relative to dcmroot) # Only used if dob0 = 1 # Default: None # # set b0plist = (huey/fphas/XXX-1.dcm dewey/fphas/XXX-1.dcm louie/fphas/XXX-1.dcm) # Echo spacing for field mapping sequence (from sequence printout) # Only used if dob0 = 1 # Default: None # set echospacing = 0.7 # Perform registration-based eddy-current compensation? # Default: 1 (yes) # set doeddy = 1 # Rotate diffusion gradient vectors to match eddy-current compensation? # Only used if doeddy = 1 # Default: 1 (yes) # set dorotbvecs = 1 # Fractional intensity threshold for BET mask extraction from low-b images # This mask is used only if usemaskanat = 0 # Default: 0.3 # set thrbet = 0.5 # Perform diffusion-to-T1 registration by flirt? # Default: 0 (no) # set doregflt = 0 # Perform diffusion-to-T1 registration by bbregister? # Default: 1 (yes) # set doregbbr = 1 # Perform registration of T1 to MNI template? # Default: 1 (yes) # set doregmni = 1 # MNI template # Only used if doregmni = 1 # Default: $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz # set mnitemp = $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz # Perform registration of T1 to CVS template? # Default: 0 (no) # set doregcvs = 0 # CVS template subject ID # Only used if doregcvs = 1 # Default: cvs_avg35 # set cvstemp = donald # Parent directory of the CVS template subject # Only used if doregcvs = 1 # Default: # set cvstempdir = $FREESURFER_HOME/subjects # Use brain mask extracted from T1 image instead of low-b diffusion image? # Has no effect if there is no T1 data # Default: 1 (yes) # set usemaskanat = 1 # Paths to reconstruct # Default: All paths in the atlas # set pathlist = ( lh.cst_AS rh.cst_AS \ lh.unc_AS rh.unc_AS \ lh.ilf_AS rh.ilf_AS \ fmajor_PP fminor_PP \ lh.atr_PP rh.atr_PP \ lh.ccg_PP rh.ccg_PP \ lh.cab_PP rh.cab_PP \ lh.slfp_PP rh.slfp_PP \ lh.slft_PP rh.slft_PP ) # Number of path control points # It can be a single number for all paths or a different number for each of the # paths specified in pathlist # Default: 7 for the forceps major, 6 for the corticospinal tract, # 4 for the angular bundle, and 5 for all other paths # set ncpts = (6 6 5 5 5 5 7 5 5 5 5 5 4 4 5 5 5 5) # List of training subjects # This text file lists the locations of training subject directories # Default: $FREESURFER_HOME/trctrain/trainlist.txt # set trainfile = $FREESURFER_HOME/trctrain/trainlist.txt # Number of "sticks" (anisotropic diffusion compartments) in the bedpostx # ball-and-stick model # Default: 2 # set nstick = 2 # Number of MCMC burn-in iterations # (Path samples drawn initially by MCMC algorithm and discarded) # Default: 200 # set nburnin = 200 # Number of MCMC iterations # (Path samples drawn by MCMC algorithm and used to estimate path distribution) # Default: 7500 # set nsample = 7500 # Frequency with which MCMC path samples are retained for path distribution # Default: 5 (keep every 5th sample) # set nkeep = 5 # Reinitialize path reconstruction? # This is an option of last resort, to be used only if one of the reconstructed # pathway distributions looks like a single curve. This is a sign that the # initial guess for the pathway was problematic, perhaps due to poor alignment # between the individual and the atlas. Setting the reinit parameter to 1 and # rerunning "trac-all -prior" and "trac-all -path", only for the specific # subjects and pathways that had this problem, will attempt to reconstruct them # with a different initial guess. # Default: 0 (do not reinitialize) # set reinit = 0 And I gave this command: [shiraz:processing] (nmr-stable6-env) trac-all -prep -c dmrirc-esq-15-hc But there is an error: INFO: SUBJECTS_DIR is /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing INFO: Diffusion root is /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc Actual FREESURFER_HOME /autofs/cluster/freesurfer/centos7_x86_64/stable6 trac-preproc -c /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/dmrirc.local -log /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/trac-all.log -cmd /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/trac-all.cmd #------------------------------------- /usr/local/freesurfer/stable6/bin/trac-preproc #------------------------------------- #@# Image corrections Tue Oct 8 17:53:12 EDT 2019 mri_convert --bvec-voxel /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz mri_convert.bin --bvec-voxel /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz niiRead(): error opening file /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz... Linux shiraz.nmr.mgh.harvard.edu 3.10.0-957.1.3.el7.x86_64 #1 SMP Thu Nov 29 14:49:43 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux trac-preproc exited with ERRORS at Tue Oct 8 17:53:12 EDT 2019 What may I do? Thanks, JC.

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nu_correct: crashed while running nu_estimate_np_and_em (termination status=512)
by Durgerian, Sally 08 Oct '19

08 Oct '19

External Email - Use Caution Hi all, This post is a follow-up to a question I asked a long time ago. I finally figured out a way around my problem, and wanted to share it with anyone else who is getting this error. I was reading this thread, which referred to problems people were having using virtual machines: https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg45472.html Though not using a virtual machine, I was trying to run freesurfer on data that were housed on a different computer via a mounted partition. The above thread gave me the idea to try copying my data from the mounted partition to the local machine, and when I did that everything ran just fine. Sally

1 0

Re: [Freesurfer] ECC or non-ECC Memory?
by fsbuild 08 Oct '19

08 Oct '19

External Email - Use Caution Hello Sotiris, My understanding is that ECC memory gives you something like a parity check across memory writes/reads.  Or there is some extra bit(s) in hardware that contribute to being able to determine if the value in memory to be read actually matches what was written there.  So for number crunching on Wall Street, suppose there was a RAM failure that manifests itself by resulting in a *wrong* financial calculation that a human being would not detect from monitoring the results.  If that error was from a bit mistakenly flipping in memory, than ECC can help.  ECC can detect a memory error in 2 bits but can only fix 1 bit of whatever is in memory.  Depending upon what bit(s) have mistakenly flipped, it could be a very small or very large error. For this to work, I think the (hardware) memory controller and the CPU need to support ECC as well.  So if the motherboard you are spec'ing out does not explicitly say it supports ECC, then I would assume it does not.  ECC memory is 2X to 3X more expensive than non-ECC memory. I don't know how motherboard prices compare that do/do not support ECC.  Users who are building machines for supper fast gaming, or audio/video production and streaming typically won't spend extra money on ECC as they want to max out on the CPU, memory, and buss/bandwidth. I have seen unsubtle RAM and chip failures cause blatant problems, e.g., the kernel running on an idle server that is on 24/7 all of a sudden starts to panic because of a RAM failure.  I have seen the sign bit mistakenly flip on an ALU chip because it was too close to the heat sink of another chip such that every positive number became negative inside the ALU.  This was obvious after the motherboard heated up (and ECC would not have fixed this).  The BIOS you can run on motherboards have various diagnostics which I think include memory tests, e.g., writing and reading a repeating pattern in/out of memory multiple times to check for consistency.  You could look for what diagnostics are provided in the BIOS program supported by the motherboard you choose. Some RAM manufacturers have fixed warranties or limited lifetime warranties. I've always had good experience with Crucial memory (who make both non-ECC and ECC RAM). As Matt said, you should run some diagnostic test on the RAM when it is new - and then periodically.   Once the warranty expires, you are stuck with any defective RAM no matter why it failed.. I don't think ECC memory is going to have much effect upon how efficiently a program utilizes multiple cores and threads.  I have not compared the clock speeds, but I could imagine ECC memory might be a bit slower than non-ECC memory.  If you have the budget to spec out a machine with a motherboard and CPU you like that supports ECC memory, then I would try it.  It's also good to look online to see who else has already built a machine with the motherboard and CPU you want to use. - R. Glasser, Matthew October 6, 2019 at 16:04         External Email - Use Caution         If you use non-ECC memory, it is a good idea to run something like memtest86 to ensure that it is error free (and return any sticks with errors to the manufacturer).  ECC and non-ECC RAM should be similar assuming the non-ECC RAM doesn’t have errors 😉   Matt.   From: <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of Sotiris Michos <sotmihos(a)gmail.com> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Date: Sunday, October 6, 2019 at 3:01 PM To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Subject: [Freesurfer] ECC or non-ECC Memory?           External Email - Use Caution         Dear Freesurfer community,   I am interested in building a PC with the goal of running Freesurfer (about 2000-2500 Euros). I have carefully read this very informative webpage. However, I still have some questions and I would be glad if some assistance could be provided.   I suspect that a proper PC would be placed towards the workstation side of the spectrum, since from what I read in the above webpage, Freesurfer can utilize multiple cores and is quite demanding in RAM memory per instance.   What I would like to ask is whether ECC Memory is critical for this application or if the corruption coming from a non-ECC module has any significant effect on the end result. Also, any advice or suggestions about the system's buildup would be more than welcome.   Best Regards, Sotiris Michos   The materials in this message are private and may contain Protected Healthcare Information or other information of a sensitive nature. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited. If you have received this email in error, please immediately notify the sender via telephone or return mail. _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer Sotiris Michos October 6, 2019 at 16:01         External Email - Use Caution        Dear Freesurfer community, I am interested in building a PC with the goal of running Freesurfer (about 2000-2500 Euros). I have carefully read this very informative webpage. However, I still have some questions and I would be glad if some assistance could be provided. I suspect that a proper PC would be placed towards the workstation side of the spectrum, since from what I read in the above webpage, Freesurfer can utilize multiple cores and is quite demanding in RAM memory per instance. What I would like to ask is whether ECC Memory is critical for this application or if the corruption coming from a non-ECC module has any significant effect on the end result. Also, any advice or suggestions about the system's buildup would be more than welcome. Best Regards,Sotiris Michos _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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Main effect of sex for total surface area, but not for summed vertex-wise surface area
by Linn Christin Bonaventure Norbom 08 Oct '19

08 Oct '19

External Email - Use Caution Hello all Freesurfer experts! Through my analysis I found that, if I plot the "white surface total area" found in subjid/stats/?h.aparc.stats by sex, I as expected find that males have overall larger surface area as compared to females. When plotting the summed verticies of ?h.area for each person on the other hand (albeit after mapping the ?h.area to fsaverage), this main association with sex is not at all present. I am a bit puzzled by this and I am wondering if some sort of global scaling is performed when computing vertex-wise surface area? So that corresponding differences in vertex-wise area are relative sex differences? I understand that area at a single vertex does not hold biological relevance in the same way that for instance thickness does, but unfortunately I still don't quite understand the differing sex associations between aparcs total surface area and the summed vertex-wise surface area. Thanks so much in advance! Kind regards Linn

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