External Email - Use Caution
Hi anatasia, thank you so much. I made a correction on the file, and the dwi.nii.gz problem was solved. I tried again with the previous script, but now I have a new error announce: JC.
[shiraz:esq-15-hc] (nmr-stable6-env) trac-all -prep -c dmrirc-esq-15-hc 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 Wed Oct 9 12:25:33 EDT 2019 mri_convert --bvec-voxel /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/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/esq-15-hc_dwi.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.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/esq-15-hc_dwi.nii.gz... TR=0.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0.999334, 0.0365012) k_ras = (-0, -0.0365012, 0.999334) writing to /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz... cp /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/processing/31190669_bvecs.dat /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvecs cp /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/processing/31190669_bvals.dat /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvals mv -f /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs.tmp /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvecs mv -f /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvals.tmp /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvals orientLAS /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.nii.gz INFO: input image orientation is LAS INFO: input image determinant is -11.2683 mri_convert -oni 130 -onj 130 -onk 44 -oid -1 0 0 -ojd 0 0.999334 0.0365012 -okd 0 -0.0365012 0.999334 -oc 3.38982 26.1253 36.373 -rt nearest /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.nii.gz mri_convert.bin -oni 130 -onj 130 -onk 44 -oid -1 0 0 -ojd 0 0.999334 0.0365012 -okd 0 -0.0365012 0.999334 -oc 3.38982 26.1253 36.373 -rt nearest /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.nii.gz
normalizing out_j_direction: (0, 0.999334, 0.0365012) -> (0, 0.999334, 0.0365012) normalizing out_k_direction: (0, -0.0365012, 0.999334) -> (0, -0.0365012, 0.999334) $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/esq-15-hc/dmri/dwi_orig.nii.gz... TR=0.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0.999334, 0.0365012) k_ras = (-0, -0.0365012, 0.999334) Reslicing using nearest writing to /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.nii.gz... INFO: found /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvals, copying INFO: found /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvecs, converting to LAS mv -f /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.bvecs /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs mv -f /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.bvals /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvals eddy_correct /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.nii.gz 0 File "/usr/pubsw/packages/fsl/current/bin/imglob", line 78 print "Usage: $0 [-extension/extensions] <list of names>" ^ SyntaxError: Missing parentheses in call to 'print'. Did you mean print("Usage: $0 [-extension/extensions] <list of names>")?
Usage: fslmerge <-x/y/z/t/a/tr> <output> <file1 file2 .......> [tr value in seconds] -t : concatenate images in time -x : concatenate images in the x direction -y : concatenate images in the y direction -z : concatenate images in the z direction -a : auto-choose: single slices -> volume, volumes -> 4D (time series) -tr : concatenate images in time and set the output image tr to the final option value mv -f /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs.norot xfmrot /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.ecclog /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs.norot /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs grep: /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.ecclog: No such file or directory head: cannot open ‘/autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.ecclog’ for reading: No such file or directory wc: /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.ecclog: No such file or directory wc: /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.ecclog: No such file or directory ERROR: Transform file should be eddy_correct/eddy log file or .mat file 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 Wed Oct 9 12:28:15 EDT 2019
[shiraz:esq-15-hc] (nmr-stable6-env) [shiraz:esq-15-hc] (nmr-stable6-env)
# FreeSurfer SUBJECTS_DIR
# T1 images and FreeSurfer segmentations are expected to be found here #
setenv SUBJECTS_DIR /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing
# Output directory where trac-all results will be saved # Default: Same as SUBJECTS_DIR # set dtroot = /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/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/Diffusion_Processing/esq-15-hc
set dcmlist = (esq-15-hc_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/Diffusion_Processing/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/Diffusion_Processing/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
El mié., 9 oct. 2019 a las 12:01, freesurfer-request@nmr.mgh.harvard.edu escribió:
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Today's Topics:
- Re: Questions re slice thickness, aseg and longitudinal analysis (Martin Reuter)
- Re: Label created in the symmetric space (Jose Graterol)
- How to measure the cortical thickness in tumor patients? (???)
- Re: ECC or non-ECC Memory? (Sotiris Michos)
- Re: Freesurfer Digest, Vol 188, Issue 13 (Juan Rivas)
- Re: Brodmann area parcellation (sang ho shin)
- Re: [FSL] Hippocampus subregions question (Iglesias Gonzalez, Juan E.)
- Re: Freesurfer Digest, Vol 188, Issue 13 (Yendiki, Anastasia)
- Re: How to measure the cortical thickness in tumor patients? (Bruce Fischl)
- mri_convert no scaling (Ezequiel Mikulan)
- Re: mri_convert no scaling (Bruce Fischl)
Message: 1 Date: Wed, 09 Oct 2019 10:48:48 +0200 From: Martin Reuter mreuter@nmr.mgh.harvard.edu Subject: Re: [Freesurfer] Questions re slice thickness, aseg and longitudinal analysis To: Freesurfer support list freesurfer@nmr.mgh.harvard.edu Message-ID: < 7a4d059cb5cb42f390e03f523677a3362d17cb81.camel@nmr.mgh.harvard.edu> Content-Type: text/plain; charset="UTF-8"
Hi David,
I am not very optimistic:
5mm is too thick for FreeSurfer (recommendation is 1 up to 1.5). You will certainly get something, but it can be very unreliable and completely wrong. Especially longitudinally these thick slices will induce large variance due to different head positioning (and different slice angulations) in the scanner.
Furthermore, FreeSurfer does not take Gad-Enhanced images. Also it will not work if tumor lesions are present.
About your questions:
- Surfaces update the aseg, but if you are only interested in the
volumes, you can skip this expensive step (potentially at the cost of slightly higher noise levels in your measurements).
I think not (see above). 5mm is too low.
Theoretically yes, but I have never tested if the scripts will do
it. You could run up to the aseg in the cross, then create base (up to aseg) and then run the longs up to aseg. Not even sure you really need the base aseg. You might be able to just run the initial base registration step, obtain the transformations and median norm.mgz image, could be sufficient for the long runs.
- No. Gad images won't work.
Best, Martin
On Mon, 2019-10-07 at 18:12 +0000, David Kamson wrote:
External Email - Use CautionFreesurfers,
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:
- 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
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Message: 2 Date: Wed, 9 Oct 2019 11:34:59 +0200 From: Jose Graterol gpjosealberto13@gmail.com Subject: Re: [Freesurfer] Label created in the symmetric space To: Freesurfer support list freesurfer@nmr.mgh.harvard.edu Message-ID: <CAFMm1s5_X3SzSWv=+ wUKKH0z3HbVRceBQFG1v7SOA6_jWMbNYg@mail.gmail.com> Content-Type: text/plain; charset="utf-8"
External Email - Use CautionIt worked as intended. Thank you very much.
On Tue, Oct 8, 2019 at 6:43 PM Greve, Douglas N.,Ph.D. < DGREVE@mgh.harvard.edu> wrote:
In that case, run mris_preproc in the same way but don't include the paired-diff. Concatenate the two groups together to give you one file. Smooth as you did before. Now with that one file run mri_segstats --seg csdbase.sig.ocn.mgh --i youronefile.mgh --avgwf subject.hemi.cluster.dat --excludeid 0
The output file subject.hemi.cluster.dat will have a row for each subject and hemisphere (the order will be subject.lh, subject.rh, nextsubject.lh, nextsubject.rh) and a column for each cluster. The values will be the thickness.
On 10/8/19 12:22 PM, Jose Graterol wrote:
External Email - Use CautionAha, yes. That is what I want. The original thickness values.
Greve, Douglas N.,Ph.D. <DGREVE@mgh.harvard.edu mailto:DGREVE@mgh.harvard.edu> schrieb am Di., 8. Okt. 2019, 17:36:
That all looks fine, I'm just not sure what problem you want me to solve. You said that the values in the y.ocn.dat file were all negative, it has to be this way because you specified only negative valueswhen
you ran glmfit-sim (and negative values are possible because youhave
computed left-right and negated half of your subjects). Do you want the original thickness values? On 10/8/19 11:14 AM, Jose Graterol wrote: > > External Email - Use Caution > > I will try to go back a bit, just to be sure I did not make a mistake. > I registered both hemispheres of every subject (affected and > non-affected) with xhemi. > > Then for the left-affected subjects I ran: > mris_preproc --target fsaverage_sym --hemi lh --xhemi--paired-diff
> --srcsurfreg fsaverage_sym.sphere.reg --meas thickness --out > leftlesionsubjects.lh.lh-rh.thickness.sm00.mgh --s sub-xxx > > For the right-affected: > mris_preproc --target fsaverage_sym --hemi lh --xhemi--paired-diff
> --srcsurfreg fsaverage_sym.sphere.reg --meas thickness --out > rightlesionsubjects.lh.lh-rh.thickness.sm00.mgh --s sub-xxx > > Then I changed the sign of the right-affected subjects: > fscalc rightlesionsubjects.lh.lh-rh.thickness.sm00.mgh mul -1 -o > rightlesionsubjects.lh.rh-lh.thickness.sm00.mgh > > Then I concatenated them: > > mri_concat leftlesionsubjects.lh.lh-rh.thickness.sm00.mgh > rightlesionsubjects.lh.rh-lh.thickness.sm00.mgh --o > allsubjects.lh.lesion-healthy.thickness.sm00.mgh > > Then I smoothed the file: > mris_fwhm --s fsaverage_sym --hemi lh --cortex --smooth-only --fwhm 10 > --i allsubjects.lh.lesion-healthy.thickness.sm00.mgh --o > allsubjects.lh.lesion-healthy.thickness.sm10.mgh > > Then glmfit: > mri_glmfit --y allsubjects.lh.lesion-healthy.thickness.sm10.mgh > --glmdir glmdir.allsubjects.lh.lesion-healthy.thickness.sm10--osgm
> --surf fsaverage_sym lh > > and finally the correction for multiple comparisons: > > mri_glmfit-sim --yallsubjects.lh.lesion-healthy.thickness.sm10.mgh
> --glmdir glmdir.allsubjects.lh.lesion-healthy.thickness.sm10 > --cwpvalthresh 0.05 --cache 4 neg > > > Hopefully this helps. > > > > > On Tue, Oct 8, 2019 at 4:45 PM Greve, Douglas N.,Ph.D. > <DGREVE@mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edu> <mailto:DGREVE@mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edu>>> wrote: > > The input has both positives and negatives, so is is not > surprising that the y.ocn.dat also has positive and negative. Not > sure what is going wrong here ... > > On 10/7/2019 5:48 PM, Jose Graterol wrote: >> >> External Email - Use Caution >> >> Ok, I uploaded it with the name "glmdir.jg.allsubjects.tar.gz". I >> had to log in as anonymous and not with my email, otherwiseit
>> would throw an error (503 Login with USER first. Login failed.). >> The mgh file is inside the gz file too. Thanks again foryour
>> time and help. >> >> On Mon, Oct 7, 2019 at 7:06 PM Greve, Douglas N.,Ph.D. >> <DGREVE@mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edu> <mailto:DGREVE@mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edu>>> wrote: >> >> ok, I still don't know what is going on. Can you uploadthe
>> glmdir and the input to mri_glmfit (ie, the argument ofthe
>> --y flag). You can upload it using these instructions: >> >> From the linux command line, >> Create the file you want to upload, eg, >> cd $SUBJECTS_DIR >> tar cvfz subject.tar.gz ./subject >> Now log into our anonymous FTP site: >> ftp surfer.nmr.mgh.harvard.edu <http://surfer.nmr.mgh.harvard.edu> >> <http://surfer.nmr.mgh.harvard.edu> >> It will ask you for a user name: use your email address >> It will ask you for a password: use "anonymous" (noquotes)
>> cd transfer/incoming >> put subject.tar.gz >> >> Send an email that the file has been and the name of the file. >> >> On 10/7/2019 11:53 AM, Jose Graterol wrote: >>> >>> External Email - Use Caution >>> >>> Hi Douglas, >>> >>> thanks for you answer. I have attached the file to the email. >>> >>> On Mon, Oct 7, 2019 at 5:24 PM Greve, Douglas N.,Ph.D. >>> <DGREVE@mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edu> <mailto:DGREVE@mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edu>>> wrote: >>> >>> Let's backup a moment. Can you send the y.ocn.datfile
>>> that has problematic values? >>> >>> On 10/4/2019 4:58 AM, Jose Graterol wrote: >>>> >>>> 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=s...
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 understoodit
>>>> 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 tothe
>>>> subject space? Or just simply, is this the right way to >>>> do this? or should I check why I am obtainingthose
>>>> values in the *y.ocn.dat file? >>>> >>>> Thanks in advance. >>>> >>>> Kind Regards >>>> >>>> Jos? Graterol >>>> >>>> _______________________________________________ >>>> Freesurfer mailing list >>>> Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>> >>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>> >>> _______________________________________________ >>> Freesurfer mailing list >>> Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> >>> <mailto:Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>> >>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>> >>> >>> _______________________________________________ >>> Freesurfer mailing list >>> Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>> >>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> >> <mailto:Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>> >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >> >> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>> >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>
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