Freesurfer

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30132 discussions

ExploreDTI Workshop in May@Budapest
by Szabolcs David 08 Feb '19

08 Feb '19

External Email - Use Caution ExploreDTI Workshop - A course on processing, analyzing, and visualizing diffusion MRI data When: 27-29 May, 2019 Where: Budapest, Hungary Details: http://www.exploredti.com/workshop Cheers, Szabolcs >

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[PETSurfer] Intensity normalization of PET images without PVE correction
by Matthieu Vanhoutte 08 Feb '19

08 Feb '19

External Email - Use Caution Dear Douglas, Is it possible to use mri_gtmpvc command in order to intensity normalize PET images without applying PVE correction by setting --psf to 0 ? Best, Matthieu

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Re: [Freesurfer] Trouble Installing FreeSurfer {Disarmed}
by fsbuild 07 Feb '19

07 Feb '19

External Email - Use Caution Hello Rodrigo, You could also try installing freesurfer into you home directory and make sure that works - before trying to install into /usr/local. I am attaching a file that lists and explains the commands I used to install freesurfer into my home directory on an Ubuntu 16..04 machine. You should be able to cut and paste the commands with the exception of perhaps the location of the freesurfer 6.0.0 release archive and the license.txt file, which I put into my downloads directory ($HOME/downloads). - R. On Feb 1, 2019, at 18:04, fsbuild <fsbuild(a)contbay.com> wrote:        External Email - Use Caution        Hello Rodrigo,Please use capital -C and not lowercase -c to tell the tar command what directory to untar the files into; you used -C the first time when the file could not be found, but on the subsequent commands you changed it to -c - R.tar -C /eeg/local -xzvf freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0.tar.gzRodrigo Montenegro  at 13:42 2/01/2019        External Email - Use Caution        Hey! I have not received any feedback from that… Not sure if the mail was lost?...  Any help would be appreciated! Kind Regards, Rodrigo Montenegro+44(0)7503070807 From: Rodrigo Montenegro [mailto:rmontenegro11@gmail.com] Sent: 30 January 2019 20:39To: 'Freesurfer support list' <freesurfer@nmr.mgh.harvard.edu>Cc: 'rodrigo.montenegro(a)ndcn.ox.ac.uk' <rodrigo.montenegro@ndcn.ox.ac.uk>Subject: RE: [Freesurfer] Trouble Installing FreeSurfer {Disarmed}Importance: High Hi R, Thanks for the quick response. I have but still couldn’t go through the process. I have also read your e-mail but I have no idea what you refer to in your second e-mail. This is what I have got:  Kind Regards, Rodrigo Montenegro+44(0)7503070807 From: freesurfer-bounces@nmr.mgh.harvard.edu [mailto:freesurfer-bounces@nmr.mgh..harvard.edu] On Behalf Of fsbuildSent: 28 January 2019 23:17To: freesurfer@nmr.mgh.harvard..eduCc: rodrigo.montenegro@ndcn.ox.ac.ukSubject: Re: [Freesurfer] Trouble Installing FreeSurfer {Disarmed}         External Email - Use Caution        … Correction - with the v switch “tar -xzvf” it is going to list every fileit expands and then return to the “$” prompt. - R._______________________________________________Freesurfer mailing listFreesurfer@nmr.mgh.harvard.eduhttps://mail.nmr..mgh.harvard.edu/mailman/listinfo/freesurfer_______________________________________________Freesurfer mailing listFreesurfer@nmr.mgh.harvard.eduhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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Vertex-wise area anlaysis
by Klaas Bahnsen 07 Feb '19

07 Feb '19

External Email - Use Caution Dear Freesurfer Experts, First of all: Thank you so much for your work and your fantastic product. I turn to you with a short question, I hope it is not too unnecessary and can be answered quickly. My interest lies in vertex wise longitudinal investigation of cortex surface area. The paper 'False positive rates in surface-based anatomical analysis' (Greve, Fischl 2018) briefly describes how Freesurfer calculates cortex surface areas: First the transfer to the target atlas with "Jacobian Correction" and second the division by eTIV (ICV) to include the factor of different head sizes. Finally, a smoothing should be applied. My question is where these data are stored in the Freesurfer data structure or where they are generated. My understanding is that first recon-all stores this data per scan under SubjectID/surf/?h.area. These could then be picked up by mris_preproc and smoothed with mri_surf2surf. This smoothed file could then be used for example for a GLM model (mri_glmfit). Are the steps described in the paper covered with this workflow? Or must a special option be selected at some point, e.g. to perform the division by the eTIV. I'm a bit unsure at the moment because I also found a collection of questions from previous courses on the FreeSurfer Wiki page. In course 2014 Copenhagen the question is asked which units are used in the output files which are calculated by mris_preproc. Part of the answer states that the data in ?h.area.mgh on vertex level do not have much meaning, because they are dependent on the triangle mesh. Hopefully you can help me and hopefully I won't steal you too much time with my question. With kind regards, Klaas Bahnsen

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Maximum vertex value
by Marina Fernández 07 Feb '19

07 Feb '19

External Email - Use Caution Dear Freesurfer Team, How can I get the maximum vertex value for each subject in a particular cluster? Thank you very much. Marina.

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Functional connectivty walkthrough step 8 error
by Amrita Bedi 07 Feb '19

07 Feb '19

External Email - Use Caution Hello Freesurfer developers, I was following the functional connectivity walkthrough for resting stage BOLD data and I got the attached error when I ran step 8 (selxavg3-sess). Does anyone have any thoughts on how to trouble shoot this? Not sure if it’s of any use but I ran this using Freesurfer 6.0 on Centos 7.4 Thank You. Best Amrita

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Combination of multiple PET projection: "mri_vol2surf --projfrac" vs. "mris_expand"
by Matthieu Vanhoutte 07 Feb '19

07 Feb '19

External Email - Use Caution Dear Freesurfer’s experts, I have read the paper from Marcoux, et al. « An Automated Pipeline for the Analysis of PET Data on the Cortical Surface ». Frontiers in Neuroinformatics 12 (10 décembre 2018). https://doi.org/10.3389/fninf.2018.00094 <https://doi.org/10.3389/fninf.2018.00094>. In this paper, they propose a robust PET signal projection based on weighting multiple PET projections at different fractions of the cortical thickness. They used « mris_expand" instead of "mri_vol2surf --projfrac" based on the following comments: "We mainly rely on the mris_expand function of FreeSurfer rather than on vol2surf. The vol2surf function allows the user to project volume data onto the cortical surface by sampling along the normal of the white surface, by steps corresponding to fractions of the cortical thickness. We observed that the main issue with this approach is its lack of accuracy and robustness, as there is no constraint to ensure that the last sample will be on the corresponding vertex of the pial surface. Another solution proposed by FreeSurfer is to use the normal fromthemid surface, starting on the side of the white surface and going toward the pial surface, sampling by steps proportional to the thickness. Here again we observed that, because of different folding patterns, sampling points could be located outside the cortical ribbon. Our approach, using deformable surfaces obtained with mris_expand, ensures that the seven surfaces that are used to project the signal follow the folding patterns and stay within the cortical ribbon." Is "mris_expand" more accurate and robust than "mri_vol2surf --projfrac" according to you ? Best, Matthieu

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zero values when run recon-all with T2pial flag (V6.0)
by Morgan Botdorf 07 Feb '19

07 Feb '19

External Email - Use Caution Dear all, I recently ran recon-all on data using the T2pial flag to improve the pial surface (using version 6.0). The T2 images seemed to improve the pial surfaces; however, when I extract stats using the Destrieux atlas, I get zero values for several regions and for BrainSegVolNotVent and eTIV. If I run recon-all without the T2 image, I get data for these regions, BrainSegVolNotVent, and eTIV. Has anyone run into similar issues using the T2pial flag or have an idea of what may be causing this problem? Thank you in advance for your help! Morgan

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problem using recon-all
by Shachar Gal 07 Feb '19

07 Feb '19

External Email - Use Caution hello, I using the HCP FreeSurfer Pipeline (using FreeSurfer 5.3-HCP version) on ubuntu, and I encounter this error (copying from the recon-all.log) _______________________________________ #@# Talairach Sun Jan 13 17:35:07 IST 2019 /home/shachar/piano_hcp/102/102/mri mri_nu_correct.mni --n 1 --proto-iters 1000 --distance 50 --no-rescale --i orig.mgz --o orig_nu.mgz Linux maestro 4.15.0-43-generic #46-Ubuntu SMP Thu Dec 6 14:45:28 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux recon-all -s 102 exited with ERRORS at Sun Jan 13 17:35:08 IST 2019 _______________________________________ i'm a bit confused by the lack of error descriptions, and would appreciate any leads for solving it :) Shachar

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Advice re: cluster thresholding, smoothing, and best practices?
by Daniel Leopold 07 Feb '19

07 Feb '19

External Email - Use Caution Hi all, I'm hoping to get some input on clustering, multiple correction, smoothing preferences, and (if available) best practices. Please correct me if I'm wrong, but it seems that the amount of attention given to multiple correction and spatial dependencies in fMRI hasn't yet translated to MRI/SBM. Doug & Bruce's recent paper (#1) beautifully highlights the value of permutation-based analyses for control of FPR (despite some of its complexities), but I'm wondering what (if any) consensus or reasoning exists as to the optimal spatial smoothing and correction methods that can be applied during permutations-based analysis of surface inputs (i.e., surface area, thickness, NPC of the two for volume, LGI, etc.). 1) The FSL page on randomise ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/UserGuide) suggests that cluster-based thresholding based upon the null distribution of the maximum cluster size/extent is passé in comparison to using the maximum cluster mass. PALM offers both extent and mass options, but does this same sentiment for functional data apply to SBM? 2) My understanding is that LGI is already smoothed at ~ FWHM=15 during default processing of pial_lgi. If one were to look at area, thickness, and LGI, is there value to all variables being at the same level of smoothing (e.g., area @ 20mm, thickness @ 20mm, LGI @ 5mm)? 3) Whereas earlier papers (e.g., #2) advocated for p < .005 and cluster size > 10 voxels for functional analyses, for example, a number of folks have more recently suggested a high initial CFT (e.g., CFT=.001) and FWE = .05 (#s 3 & 4). Do any similar simulation results or suggestions exist for SBM rather than fMRI? 4) Does anyone know of guidance on FWE (or FDR or voxel/vertex-wise for that matter) thresholds for TFCE-corrected results? 5) Does anyone know of similar papers about FPRs in surface-based analyses that *also* consider false negative rates as well? 6) Have any journals made similar declarations about SBM/morphometry results like NeuroImage: Clinical did for functional data (#5)? Although I'm neither the first nor the last person to ask these questions, I wanted to gather my thoughts and hopefully tap into the collective wisdom of this listserv community. My apologies if this email is seen as incendiary or faux pas, but I'm new to this world and would love any and all suggestions. Thanks in advance for sharing any thoughts and helping rectify my naiveté! Dan 1: Greve, D. N., & Fischl, B. (2018). False positive rates in surface-based anatomical analysis. *NeuroImage*, *171*, 6-14. 2: Lieberman, M. D., & Cunningham, W. A. (2009). Type I and Type II error concerns in fMRI research: re-balancing the scale. *Social Cognitive and Affective Neuroscience, 4*(4), 423-428. 3: Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. PNAS, 113(28), 7900-7905. https://doi.org/10.1073/pnas.1602413113 4: Woo CW, Krishnan A, Wager TD (2014) Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations. *NeuroImage, 91*, 412–419. 5: Roiser, J. P., Linden, D. E., Gorno-Tempinin, M. L., Moran, R. J., Dickerson, B. C., & Grafton, S. T. (2016). Minimum statistical standards for submissions to Neuroimage: Clinical. *NeuroImage: Clinical*, *12*, 1045.

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