Freesurfer

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30132 discussions

Re: [Freesurfer] Extracting DTI/NODDI data from hippocampal subfields
by Iglesias Gonzalez, Eugenio 08 May '18

08 May '18

External Email - Use Caution Dear Mark, Just to make sure we’re on the same page: I’ll say that two images are in the same voxel space when they have the same size (in voxels, e.g., 256x256x256) and the same vox2ras matrix - the one that maps voxel coordinates to RAS (physical) coordinates. The subfield segmentation is indeed in the same RAS coordinates as your native T1, but not in the same voxel space. One option that you have is to convert your subfield mask to the voxel space of the T1, with the option –rl (“reslice like”) of mri_convert: For probabilistic masks: mri_convert subfield_prob.mgz subfield_prob_T1voxelSpace.mgz -rl norm.mgz -odt float For discrete masks or segmentations, you can use a similar command, but with nearest neighbor interpolation: mri_convert subfield_mask.mgz subfield_mask_T1voxelSpace.mgz -rl norm.mgz -odt float -rt nearest I hope this helps! Cheers, /Eugenio -- Juan Eugenio Iglesias ERC Senior Research Fellow Translational Imaging Group University College London http://www.jeiglesias.com http://cmictig.cs.ucl.ac.uk/ From: <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of Mark Wagshul <Mark.Wagshul(a)einstein.yu.edu> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Date: Wednesday, 2 May 2018 at 20:57 To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Subject: [Freesurfer] Extracting DTI/NODDI data from hippocampal subfields External Email - Use Caution Hi. I am just getting my feet wet with running my subjects through the Freesurfer processing stream, so the following question night be a bit trivial (I hope): I have run qvlSegmentHippocampal subfields on one of my subjects, and it completed just fine with what seem to be the appropriate outputs in the subject’s mri folder. I have another 100 subjects to run, but want to make sure I can accomplish what I would like to on this subject first. The images which I see are sub-images, i.e. these have fewer voxels compared to the original image, but in the native T1 space resolution (= 1 mm3). I would now like to register the subfields in the native space (my DTI/NODDI maps are already registered into this space), to obtain mean values within each subfield. How can I transform these sub-field images back into the native T1 space, or is there a method within Freesurfer to perform the math (e.g. similar to fslmaths)? Thanks, Mark ____________________ Mark Wagshul, PhD Associate Professor Gruss Magnetic Resonance Research Center Albert Einstein College of Medicine Bronx, NY 10461 Ph: 718-430-4011 FAX: 718-430-3399 Email: mark.wagshul(a)einstein.yu.edu<mailto:mark.wagshul@einstein.yu.edu> [instein-logo-rgb] This email message and any accompanying attachments may contain privileged information intended only for the named recipient(s). If you are not the intended recipient(s), you are hereby notified that the dissemination, distribution, and or copying of this message is strictly prohibited. If you receive this message in error, or are not the named recipient(s), please notify the sender at the email address above, delete this email from your computer, and destroy any copies in any form immediately.

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Non-parametric correlation between FC and neuropsychological/volumetric outcomes
by stdp82＠virgilio.it 08 May '18

08 May '18

Hi list, which the best way to perform a whole-brain correlation of the FC assessed by FS-FAST with the volumetic or neuropsychological outcomes? Please note that my question is specifically addressed to those factors (neuropsycholgical scores or volume of seed) did not show a normal distribution and that cannot be trasformed. Thanks Kind regards Stefano

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N3 kernel decrease
by Rebecca Ray 07 May '18

07 May '18

External Email - Use Caution Hello, I am working on T1 images from multi-sites. One site in particular is producing T1s that have small to large areas of temporal cortex being excluded from segmentation. It seems that there may be an issue with inhomogeneity of the bias field and subsequent over correction. I have 2 scans from each person and the 2nd scan is worse. The second scan from one subject refuses to finish the freesurfer pipeline. It seems to boom out right before segmentation. I was told to decrease the bias field correction smoothing kernel but have not found where to do that on the Wiki. How do I add that syntax? Thanks. Rebecca

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Re: [Freesurfer] transforming functional volumes to MNI without resampling
by David Beeler 07 May '18

07 May '18

External Email - Use Caution Hi Doug, Ok so if I am understanding correctly, SPM or FSL will be better at registering functional data to CVS or MNI space for group analyses than freesurfer will be because they have the capability of doing nonlinear registrations. If that's the case I may give SPM a shot and compare the results. But if I were to use freesurfer to do this registration, is there something better than the following commands?: bbregister --s MNI152_FS --mov funcVol --reg func2mni.lta --bold mri_vol2vol --mov funcVol --reg func2mni.lta --o funcVolMNI --fstarg --no-resample --nearest Adding --12 to bbregister and using the same mri_vol2vol as above actually makes the transformation much worse, is there something I'm missing? I will use SPM if I have to, but I feel like there is probably something smarter I could be doing with freesurfer. And for clarification, is the --12 flag in bbregister making the registration affine (ie accounting for translation, rotation, scaling, and sheers)? Thanks again for your help, much appreciated! DB ------------------------------------------------------------------------ Douglas N. Greve wrote: If that is really what you want to do, you should use a tool appropriate for this type of analysis (eg, SPM and FSL have non-linear intersubject registration) ------------------------------------------------------------------------ On 05/04/2018 01:13 PM, David Beeler wrote: > > > Hi Doug, > > funcVol is an individual functional resting state scan. We are doing > some clustering analyses and trying to visually compare the clustering > across subjects (I would be a bit sketched out trying to quantify the > overlap between subjects due to individual differences in anatomy, > etc, but just looking at it might give us a clue on what is going on). > We do all our analyses in the individual subject's native space, but > it's hard to see what the similarities / differences are. > > If not bbregister, what would be the correct way to align all subjects > to a common space? > > Thanks, > DB > ------------------------------------------------------------------------ > Douglas N. Greve wrote: > What is funcVol? An individual functional? If so, then use --12 (12 dof > to account for scaling). In general, we don't recommend doing cross > subject registration with BBR as it is really not appropriate for that > kind of thing. > ------------------------------------------------------------------------ > On 05/03/2018 01:30 PM, David Beeler wrote: > > Hi, > > I'm trying to register and transform some raw functional data > (108x108x72x175, 2mm iso voxels) to MNI space, while keeping the low > res dimensions. > > I have tried: > bbregister --s MNI152_FS --mov funcVol --reg func2mni.lta --init-fsl > --bold > mri_vol2vol --mov funcVol --reg func2mni.lta --o funcVolMNI --fstarg > --no-resample --nearest > mri_vol2vol --mov funcVol --reg func2mni.lta --o funcVolMNI --fstarg > --no-resample > > And this looks pretty good, but not perfect... the functional data is > oriented correctly but the brain seems to be a centimeter or two > smaller in multiple dimensions, see: > > http://web.mit.edu/dsbeeler/www/images/bbregister-mni.png > > I'm not too surprised since the MNI brain is blurry and oddly round, > but presumably there is a way to do this transformation more > accurately. I've run mri_cvs_register for this subject using the --mni > flag: > > mri_cvs_register --openmp 8 --mni --mov $subjID --outdir $mniDir > > And for other applications I use this m3z file to transform volumes > between MNI orig.mgz and individual subject orig.mgz, but is there a > way to use it to transform functional data to MNI space while keeping > the functional dimensions? > > I could do: > > mri_vol2vol --mov funcVol --targ $SUBJECTS_DIR/$subjID/mri/orig.mgz > --reg register.dof6.lta --nearest --o template-in-anat.nii.gz > > mri_vol2vol --noDefM3zPath --targ $SUBJECTS_DIR/$subjID/mri/orig.mgz > --mov template-in-anat.nii.gz --m3z > $mniDir/final_CVSmorph_tocvs_avg35_inMNI152.m3z --o > template-in-MNI-m3z.nii.gz --nearest > > But not only is this unideal because it requires upsampling the > functional data to 256x256x256, it also gets these funky wavy distortions: > > http://web.mit.edu/dsbeeler/www/images/m3z-mni.png > > > Any thoughts are appreciated, thanks! > > -DB > > > _______________________________________________ > Freesurfer mailing list > Freesurfer(a)nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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Query regarding creation of atlas directly from probabilistic information
by Mageshwar Selvakumar 07 May '18

07 May '18

External Email - Use Caution Dear All and Bruce, This mail is on top of the query I had a couple of weeks back where I misinterpreted that I had binary mask of each structure as individual nifti volume files but on closer look, the nifti files had the probability that the structure is present at that certain location. On looking into the freesurfer website, I came across this documentation of creation of Atlas, https://surfer.nmr.mgh.harvard.edu/fswiki/SurfaceLabelAtlas SurfaceLabelAtlas - Free Surfer Wiki<https://surfer.nmr.mgh.harvard.edu/fswiki/SurfaceLabelAtlas> Overview. This document provides an overview of two related topics: How the FreeSurfer pipeline automatically labels a subject's regions (producing an annotation file) by comparing the subject's curvature data to an "atlas" ("gcs") file. surfer.nmr.mgh.harvard.edu In this documentation the process is done by looping through N subjects to get the probability information, and these subjects are registered to the registration template derived from average of reference templates. I would like to know if I could use these probability information directly and since the atlas information is already in the MNI152 space, can I use the registration template which is already available with freesurfer, and if yes, can someone guide me on which registration files I must use. Secondly, I tried using mri_vol2surf and tried to convert the volume data to a surface data before creating labels, but on doing so I had errors when creating labels from the surface files. To convert into volume I ran the below code: *mri_vol2surf --mov ID076_LL_PHGa_l_HvOx_prob.mgz --ref ID076_LL_PHGa_l_HvOx_prob.mgz --out ID076_LL_PHGa_l_HvOx_prob_surf.mgz --hemi lh --mni152reg --projdist-max -2 0 .1 --reshape --interp nearest* Then I to convert this surface file into label I ran *mri_cor2label --i ID080_CPuP_l_HvOx_prob_surf.mgz --l area1.label --id 076* And I got the error *Found 0 label voxels ERROR: found no voxels matching id 76* But the process runs fine if I run the mri_cor2label on the volume file. It would be nice if someone can help me out on where I'm going wrong here. Thank you, Kind regards Mageshwar Selvakumar, M.Sc Department of Pharmakologische Bildgebung und Bildanalyse Friedrich Alexander Universität Erlangen Nürnberg Office: Fahrstr. 17, 91054 Erlangen Phone: + 49-9131-8522898

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Motion Parameter on the Cortical Surface
by Pfannmoeller, Joerg Peter 07 May '18

07 May '18

Dear all, in fMRI analyses the translation of the the brains center and the rotation of the associated coordinate system are determined. Did anybody of you ever write a tool to determine the corresponding motion of the cortical surfaces? What I mean is the total shift due to translation and rotation for each time point in the fMRI time series and for each point on the white matter or pial surface. Yours Joerg

2 1

vertex wise cortical thickness within ROIs
by Fujiwara, Hisako 07 May '18

07 May '18

External Email - Use Caution Hello FreeSurfer Developers, I'm attempting to compute absolute cortical thickness for each vertices within anatomical ROIs using apac.annot. I have successfully output the whole brain vertex wise cortical thickness using: mris_convert -c <subject>/surf/?h.thickness <subject>/surf/?h.org <subject>/surf/?hthickenss.asc Since this creates huge numbers (> e.g., 140,000) for each hemisphere for each participants, I would like to narrow out focus on the specific ROIs for relevant to our study? Is this achievable to add options to do? Thank you for your help in advance. Best regards, Hisako

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fmriprep to fsfast interface
by Boggess, Matthew Jozsef 07 May '18

07 May '18

Hello, I've been working on taking the outputs of the fmriprep BOLD preprocessing tool (https://fmriprep.readthedocs.io/en/latest/index.html) and coercing them into the fsfast structure so that first levels can be run with mkanalysis-sess and selxavg3b-sess. It's been a bit trickier than anticipated and I was hoping to get some help verifying my current approach (attached to this email). Question 1: fmriprep outputs the preprocessed BOLD data in the T1 space (mni305 is not an option). However, the typical spatial normalization workflow uses a registration matrix from the bold to T1 to transform to talairach. Is there a way to do this transformation without having to specify a registration matrix given the functional data is already in the T1 space? My current approach is to use mri_convert with the --apply_transform flag pointing to the talairach.xfm registration matrix in the subject's recon folder, but I am not sure if this is a reasonable workaround. Question 2: fsfast normally calculates the global mean value of the raw data to be used for intensity normalization. However, with fmriprep I only have access to the preprocessed data in T1 space. Would extracting the global mean value for intensity normalization from the preprocessed BOLD in T1 space be valid? Question 3: Is there a way to do first levels in the native subcortical space? If so, what is the filename structure expected (maybe mni305.2mm gets replaced with the subject name as with the surface data)? I've only seen examples transforming to mni305 space, but ideally an option would be to just transfer the preprocessed BOLD in T1 space and run first levels staying in the native T1 space. Thanks for your help! Matt

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wmparc.mgz transformation
by Rongxiang Tang 07 May '18

07 May '18

External Email - Use Caution Dear All, I would like to transform individual wmparc.mgz into MNI space for each of my subjects. I have tried using this command:mri_vol2vol --targ /data/mri/wmparc.mgz --mov /usr/local/fsl/data/standard/MNI152_T1_2mm.nii.gz --inv --reg /usr/local/freesurfer/subjects/fsaverage/mri/transforms/reg.mni152.2mm.dat --o test.mgz However, when I compare this output file with my FSL normalized functional data (MNI space), they didn't really match well - not a one-to one exact match (orientation slightly different), although the dimension and space are all in MNI 2mm. Do I need to use bbregister or other commands to transform it better? Thanks!Catherine

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Complete CSF value computation
by M Janani 07 May '18

07 May '18

External Email - Use Caution Hi Team, We would like to compute the CSF value from the MRI data. We have both T1 and T2 weighted images. Have processed the T1 images and obtained the CSF value(summation of ventricular system and CSF value). But it's very low since it doesn't include the sulcal CSF value. We are interested in computing the complete CSF value. Any help from your people end would be really grateful. Regards, Janani ::DISCLAIMER:: -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The contents of this e-mail and any attachment(s) are confidential and intended for the named recipient(s) only. E-mail transmission is not guaranteed to be secure or error-free as information could be intercepted, corrupted, lost, destroyed, arrive late or incomplete, or may contain viruses in transmission. The e mail and its contents (with or without referred errors) shall therefore not attach any liability on the originator or HCL or its affiliates. Views or opinions, if any, presented in this email are solely those of the author and may not necessarily reflect the views or opinions of HCL or its affiliates. Any form of reproduction, dissemination, copying, disclosure, modification, distribution and / or publication of this message without the prior written consent of authorized representative of HCL is strictly prohibited. If you have received this email in error please delete it and notify the sender immediately. Before opening any email and/or attachments, please check them for viruses and other defects. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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