Freesurfer September 2020

freesurfer@nmr.mgh.harvard.edu

111 participants
165 discussions

longitudinal study
by KennethSPrice 22 Oct '20

22 Oct '20

External Email - Use Caution Hello, I am running a longitudinal study and am examining the pial surface for my 2 timepoints. The surfaces are slightly offset. Will this effect my statistical analysis for volume and thickness? I have attached the pial surface for each time point to this email. Should I be examining timpoint1.long and timepoint2.long instead? Thanks, Ken Sent with [ProtonMail](https://protonmail.com) Secure Email.

3 2

Re: [Freesurfer] Right-side bias in thalamic segmentation pipeline?
by Iglesias Gonzalez, Juan E. 05 Oct '20

05 Oct '20

Dear Evan, The atlas is designed to be symmetric. Is it possible that the asymmetry is in your data? Another FS user (Chris Vriend, CCed) reported a similar effect (you can find his massage in the archive). Doug: any further ideas? Cheers, /E Juan Eugenio Iglesias Senior research fellow CMIC (UCL), MGH (HMS) and CSAIL (MIT) http://www.jeiglesias.com From: <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of "Gallagher, Evan (NGG)" <Evan.Gallagher2(a)pennmedicine.upenn.edu> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Date: Wednesday, September 30, 2020 at 14:18 To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Subject: [Freesurfer] Right-side bias in thalamic segmentation pipeline? External Email - Use Caution Hi all, I’ve been working on a project that involves the thalamic segmentation pipeline published by Iglesias et al. in 2018 in NeuroImage (“segmentThalamicNueclei.sh”). While the pipeline itself works great, we have noticed that the right hemisphere VOIs tend to have slightly larger volumes than the left hemisphere VOIs. This has been true in two separate datasets (n=24 and n=25), neither of which has an obvious physiological trait that would lead us to expect these sorts of hemisphere differences. This is my first time posting a question on this list, so I’m not sure if images are OK. If yes, there are a couple posted at the bottom of this message that should visualize what I’m talking about. If not, I can say that the volume differences are most pronounced in the largest VOIs (PuM, VPL, VLp, etc); in these regions, the right side is ~200-300 cmm larger than the left side. Additionally, the average volumes on both sides are reasonable based on Figure 8 of Iglesias et al (in the sense that our average volumes are well within the violin plots in that figure). I’m wondering if anyone has encountered this trend before, and/or if there’s a correction I might implement to equalize the volumes. We want to be understand—to the extent possible—the extent to which these differences are driven by biology vs image processing. Thanks so much, Evan Images follow: [cid:image001.png@01D69736.EDCFE020] [cid:image002.png@01D69736.EDCFE020] [cid:image003.png@01D69736.EDCFE020]

3 2

Error in selxavg3-sess and Xtmp.mat file
by Erica Israel 05 Oct '20

05 Oct '20

External Email - Use Caution Hi there, I am having trouble running the selxavg3-sess command, in which I am receiving the error: Error using svd Input to SVD must not contain NaN or Inf. Error in cond (line 35) s = svd(A); Error in fast_selxavg3b (line 394) XCond = cond(XtX); I assume it has something to do with my paradigm file, but was not able to figure out the main issue as I keep getting the same error after changing possible problems in the file (extra characters, changing to plain txt etc.) Looking into the Xtmp.mat file, I saw that only the 7th and 8th rows and columns consisted of NaN but am not sure how to interpret this. Below is what my paradigm file looks like and my Xtmp file is attached. 0.0000 0 20.00 1.0 rest 20.000 1 20.00 1.0 thumbab 40.000 0 20.00 1.0 rest 60.000 2 20.00 1.0 thumbflex 80.0000 0 20.00 1.0 rest 100.000 3 20.00 1.0 thumbrot 120.000 0 20.00 1.0 rest 140.000 4 20.00 1.0 index 160.000 0 20.00 1.0 rest 180.000 5 20.00 1.0 digits 200.000 0 20.00 1.0 rest 220.000 6 20.00 1.0 wristProSup 240.000 0 20.00 1.0 rest 260.000 7 20.00 1.0 elbowFlex 280.000 0 20.00 1.0 rest 300.000 8 20.00 1.0 wristFlex Additionally, here is an example of what I ran to make one of the analysis sessions: mkanalysis-sess -fsd left -stc up -surface self lh -fwhm 5 -event-related -paradigm ParadigmFile -nconditions 8 -spmhrf 0 -TR 1.08 -refeventdur 20 -nskip 4 -polyfit 2 -analysis left_arm.lh -per-session -force Thank you for the help! -- Erica Israel Senior Research Support Associate MIT Media Lab - Biomechatronics Group

2 4

Extraction of subject-wise values after permutation test !
by Martin Juneja 05 Oct '20

05 Oct '20

External Email - Use Caution Hello everyone, I have significant group differences in volume (controls > patients) for one of the clusters (say C1) - calculated after permutation test using PALM, which gives me following outputs: *_clustere_tstat_fwep.mgz (this is significant at p < 0.05, number of voxels = 1105) *_clustere_tstat.mgz *_dpv_tstat_fwep.mgz *_dpv_tstat_uncp.mgz *_dpv_tstat.mgz I was wondering if there is a way to calculate volume values of C1 for each subject (i.e., controls and patients). Thanks.

2 7

How to draw streamlines and their corresponding ROIs using freeview
by Zhiyuan Yu 04 Oct '20

04 Oct '20

External Email - Use Caution Hi, I have extracted fibers and the corresponding ROIs. I would like to know how can I draw them together using freeview. Thank you, Zhiyuan

2 6

grey matter thickness map and SNR
by miracle ozzoude 02 Oct '20

02 Oct '20

External Email - Use Caution Hello Experts, I have a question regarding grey matter thickness map and signal to noise ratio. Does smoothing of the map increase signal to noise during surface based cortical thickness analyses? if yes or no, why? This question was asked by a reviewer in one of our manuscript because he/she thinks that one of the reasons to perform smoothing is to comply with random field theory hypotheses, when controlling for multiple testing. We did apply multiple comparison corrections using monte carlo simulation with cluster threshold of 2, 5000 iterations, and bonferroni. Thanks alot. best, Paul

3 5

Tracula error
by Hasimoglu, Yasemin 01 Oct '20

01 Oct '20

Hi, I am trying to run trac-all using a configuration file for 30 subjects with longitudinal data, and keep getting the following errors: Too many ('s. ERROR: must specify as many DWI dicoms as subjects I looked through my file line by line and looked at similar posts on the archives but could not find a solution. I'm attaching the configuration file here. There's no trac-all.log file since this is happening at the very beginning. Any help would be greatly appreciated! Best, Yasemin Yasemin Hasimoglu Research Technician Department of Neurology Massachusetts General Hospital 149 13th St, Suite 10130B Charlestown, MA 02129

2 2

Understanding Talairach failure detection
by Devavrat Vartak 30 Sep '20

30 Sep '20

External Email - Use Caution Hello freesurfer experts, My recon-all finished without errors, but I noticed a 'Talairach registration error' in the recon_all log. Freesurfer Ver: 7.1.1 MacOS: Mojave I followed the instructions on page '' https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/Talairach_freeview". 1) I used: freeview -v /Users/.../sub-09/freesurfer/recon_all/mri/T1.mgz -v /Users/.../sub-09/freesurfer/recon_all/mri/brainmask.mgz:reg=/Users/.../sub-09/freesurfer/recon_all/mri/transforms/talairach.xfm I am trying to understand what Talairach registration does...(See image) > should the T1 and the brainmask (lowered opacity) completely overlap one another and not be displaced? [image: Screen Shot 2020-09-29 at 22.29.01.png] 2) When I try: tkregisterfv --s sub-09/freesurfer/recon_all --fstal --surf orig > This generates the error: ERROR: cannot find /Users/.../sub-09/freesurfer/recon_all/surf/orig Is the tkregisterfv command correct? Any suggestions on how I can explore if the registration went ok and if not how to initialize tkregisterfv? Thank you. - Dev

1 0

Right-side bias in thalamic segmentation pipeline?
by Gallagher, Evan (NGG) 30 Sep '20

30 Sep '20

External Email - Use Caution Hi all, I've been working on a project that involves the thalamic segmentation pipeline published by Iglesias et al. in 2018 in NeuroImage ("segmentThalamicNueclei.sh"). While the pipeline itself works great, we have noticed that the right hemisphere VOIs tend to have slightly larger volumes than the left hemisphere VOIs. This has been true in two separate datasets (n=24 and n=25), neither of which has an obvious physiological trait that would lead us to expect these sorts of hemisphere differences. This is my first time posting a question on this list, so I'm not sure if images are OK. If yes, there are a couple posted at the bottom of this message that should visualize what I'm talking about. If not, I can say that the volume differences are most pronounced in the largest VOIs (PuM, VPL, VLp, etc); in these regions, the right side is ~200-300 cmm larger than the left side. Additionally, the average volumes on both sides are reasonable based on Figure 8 of Iglesias et al (in the sense that our average volumes are well within the violin plots in that figure). I'm wondering if anyone has encountered this trend before, and/or if there's a correction I might implement to equalize the volumes. We want to be understand-to the extent possible-the extent to which these differences are driven by biology vs image processing. Thanks so much, Evan Images follow: [cid:image001.png@01D69732.FB8CB570] [cid:image002.png@01D69732.FB8CB570] [cid:image004.png@01D69732.FB8CB570]

1 0

Question about ICV analysis !
by Martin Juneja 30 Sep '20

30 Sep '20

External Email - Use Caution Hello FreeSurfer experts, I have this interesting situation. I understand its important to include ICV as a covariate while conducting cortical volume (CV) analysis. So for my clinical sample (CL) vs healthy-controls (HCs): 1. If I do *network-wise* analysis (i.e., after parcellating the whole brain into 17 networks), I get highly significant differences in CV (MANCOVA: CL < HCs) for a specific network (say N1) - *if I include ICV as a covariate*, but not otherwise. 2. If I do *whole-brain vertex-wise* analysis, I get highly significant differences in CV (CL < HCs) for a specific cluster (which is overlapping with an area of N1) - *if I do not include ICV as a covariate*, but not otherwise. I am not sure how to interpret this i.e., ICV as a covariate for network-wise analysis plays an opposite role i.e., makes my findings stronger compared to ICV as a covariate for whole-brain vertex-wise analysis i.e., makes my findings weaker. Does ICV as a covariate add some kind of noise during whole-brain vertex-wise analysis? I would really appreciate any help in understanding this.

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Freesurfer September 2020