Freesurfer June 2016

freesurfer@nmr.mgh.harvard.edu

152 participants
213 discussions

build fsaverage
by shi yao wang 03 Jun '16

03 Jun '16

Hello ! I work for the department of radiology, Emory University. We're planning to use the FreeSurfer software suite (or more precisely, the surface-based stream ) in a project that aims to compare cortical thickness between treatment and nontreatment patients. We have finished recon-all for all patients. And next will do group analysis. We need to rebuild fsaverage for data reconstruction. However, we cannot find any information about that process. Is anybody kindly provide some suggestions about that? Thank you very much! Lawrence Wang Radiology Department, Emory University

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Problem with FreeSurfer template and control subject
by Mahtab Farahbakhsh 03 Jun '16

03 Jun '16

Dear Freesurfer, The data that I'm using as a part of my study contains only the patients' T1 images and we have not collected any control subjects yet. I want to compare the cortical thickness of these subjects with a template (instead of a control subject), but I'm confused which volume I need to choose. When I run recon-all, does it transform the slices based on the fsaverage? Is fsaverage based on Talairach template or MNI or something else? Would you please guide me with the question and inform me what template volume is the best for me to use instead of my control subjects? Best Regards, Mahtab.

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Two functional connectivity methods
by Rodriguez-Thompson, Anais 02 Jun '16

02 Jun '16

Hi FreeSurfer experts, I have two different ways to do functional connectivity, and was wondering which one is optimal since they produce different results. The first is handed down to me from a previous coordinator. However a few of the steps seemed overly complicated, so I looked into a more direct method. Both methods use single vertex as a seed, so we smooth in order to include enough vertexes for fcseed-sess. Option 1 (the old version): Creating the mask, smoothing, and binarizing it: ~ mri_label2label --s fsaverage --regmethod surface --hemi lh --srclabel $SUBJECTS_DIR/$subject/label/$label.label --trglabel $SUBJECTS_DIR/$subject/label/junk.label--outmask $SUBJECTS_DIR/$subject/mri/{$label}_sm00.mgh ~ mris_fwhm --i $SUBJECTS_DIR/$subject/mri/{$label}_sm00.mgh --fwhm 4 --smooth-only --o $SUBJECTS_DIR/$subject/mri/{$label}_sm04.mgh --s fsaverage --hemi lh ~ mri_binarize --i $SUBJECTS_DIR/$subject/mri/${label}_sm04.mgh --min 10e-10 --o $SUBJECTS_DIR/$subject/mri/{$label}_sm04.10e-10.bin.mgh Switching to dev in order to convert the mask into a volume: ~ source /usr/local/freesurfer/nmr-dev-env ~ mri_surf2vol --so $SUBJECTS_DIR/fsaverage1/surf/lh.pial $SUBJECTS_DIR/$subject/mri/${label}_sm04.10e-10.bin.mgh --subject fsaverage --o $SUBJECTS_DIR/$subject/mri/{$label}_sm04.10e-10.bin.vol.mgz We then move back into 5.3 and use this volume as segmentation in fcseed-config and -sess Option 2: Turning label into a volume, smoothing it, and binarizing it: ~ mri_label2vol --label $SUBJECTS_DIR/fsaverage1/label/{$label} --temp $SUBJECTS_DIR/fsaverage1/orig.mgz --regheader $SUBJECTS_DIR/fsaverage1/orig.mgz --hemi lh --subject fsaverage --proj frac 0 1 .1 --o $SUBJECTS_DIR/{$subject}/{$label}.vol.mgz ~ mri_fwhm --i $SUBJECTS_DIR/{$subject}/{$label}.vol.mgz --fwhm 4 --smooth-only --o $SUBJECTS_DIR/{$subject}/{$label}.sm4.vol.mgz ~ mri_binarize --i $SUBJECTS_DIR/{$subject}/{$label}.sm4.vol.mgz --min 10e-10 --o $SUBJECTS_DIR/{$subject}/{$label}.sm4.bin.vol.mgz We then use this volume as segmentation in fcseed-config and -sess. I would prefer to use the second method since it is more straightforward, but I wanted to make sure that a) it is sound and b) it is more optimal than option 1. Thank you for your help! Anais Rodriguez-Thompson Clinical Research Coordinator Brain Genomics Laboratory Martinos Center for Biomedical Imaging Massachusetts General Hospital Phone: (617) 643-3215

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mri_convert segfault error
by Joshua Lee 02 Jun '16

02 Jun '16

On freesurfer-Linux-centos4_x86_64-stable-pub-v5.1.0 installed on Ubuntu 16.04 LTS mri_convert is seg faulting >>ls brain* > brain.finalsurfs.mgz brainmask.auto.mgz brainmask.mgz brain.mgz > > mri_convert ./brain.mgz --in_type mgz --out_type nii -o ./brain.nii > > $Id: mri_convert.c,v 1.179.2.2 2011/05/16 20:53:47 greve Exp $ > reading from ./brain.mgz... > Segmentation fault (core dumped) > Any ideas? Thanks Joshua

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Re: [Freesurfer] mri_glmfit-sim using custom stat outputs
by Chung, Yoonho 02 Jun '16

02 Jun '16

Thanks Doug - so the residual file would be [number of subjects x number of vertices] matrix? Does it have to be in special file format? > So you will generate the sig.mgh file? I would recommend just running > mri_glmfit using a design close to whatever you did to generate the > directory structure, then just replace the sig.mgh file with the file of > your choosing. The one snag may be the fwhm.dat file. This is a measure > of the spatial correlation in the residual. Since it works off the > residual, it depends on the actual design. If your analysis produces a > residual, you can run mris_fwhm to get the fwhm.dat file, and then just > run mri_glmfit-sim. > doug > On 06/02/2016 02:56 PM, Chung, Yoonho wrote: > > Dear experts, > > > This is a follow-up question from my previous post. > > > http://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg47287.html > > > If I am running stats with my own stats code to generate p-values and > other parameters, what other files > > do I need to generate in order to run 'mri_glmfit-sim'? I do not have > the glmdir file (since I am not running mri_glmfit) so I would like to > know which files need to be supplied with which flag/option, in place > of the '--glmdir', for mri_glmfit-sim to run the cluster-based > correction for my vector of p-values. An example command would be > extremely helpful. Thank you in advance! > > > Best, > > Yoon

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mri_glmfit-sim using custom stat outputs
by Chung, Yoonho 02 Jun '16

02 Jun '16

Dear experts, This is a follow-up question from my previous post. http://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg47287.html If I am running stats with my own stats code to generate p-values and other parameters, what other files do I need to generate in order to run 'mri_glmfit-sim'? I do not have the glmdir file (since I am not running mri_glmfit) so I would like to know which files need to be supplied with which flag/option, in place of the '--glmdir', for mri_glmfit-sim to run the cluster-based correction for my vector of p-values. An example command would be extremely helpful. Thank you in advance! Best, Yoon

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glmdir question
by Clara Kühn 02 Jun '16

02 Jun '16

Dear FreeSurfer Experts, when running the Monte Carlo correction on a GLM I specify this command: mri_glmfit-sim --glmdir lh.gender_age.glmdir --cache 4 neg --cwp 0.05 --2spaces This assumes that my mgh file that I used as input for the GLM is in lh.gender_age.glmdir. That means though, that everytime I calculate a GLM and create a new folder for this GLM I have to copy the mgh file into the glmdir to run the Monte Carlo correction. Is there a way to specify where that mgh is located so that I don't have a bunch of copies of it in all my glmdirs? Thanks for your help! Cheers Clara -- Clara Kühn, Phd Student Max-Planck-Institute for Human Cognitive and Brain Science Department of Neuropsychology Stephanstrasse 1A 04103 Leipzig, Germany Phone: +49 341 - 9940 2271 Fax: +49 341 - 9940 2260 Web: www.cbs.mpg.de E-Mail: ckuehn(a)cbs.mpg.de

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epidewarp.fsl radian phase range error
by Milde, Christopher 02 Jun '16

02 Jun '16

Dear FS experts, I also encountered this problem when trying to apply epidewarp.fsl: ERROR: input phase image exceeds allowable phase range. Allowable range is 6.283 radians. Image range is: 12.5633 radians I read that there is an updated version of epidewarp.fsl which might solve this problem. Can I rescale the phase images? I appreciated any support. Best, Christopher

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Downsampling data and writing mgh file
by Nandita Vijayakumar 02 Jun '16

02 Jun '16

Dear FreeSurfer Experts, I'm trying to downsample the thickness info of subjects (with longitudinal data) to the same number of vertices as fsaverage6, and would like to save this output as an mgh file for use in Matlab. I was able to downsample a single subject using the following command: mri_surf2surf --hemi lh --srcsubject 'subjID' --srcsurfval thickness --src_type curv --trgsubject fsaverage6 --trgsurfval 'output name' --trg_type mgh However, it won't allow me to enter multiple subject IDs into the same command. I'm wondering what's the best way to do this, with the aim of getting a single mgh file that has the thickness data for all subjects. Do I have to create a separate downsampled mgh file for each subject and then merge them? Thanks, Nandi

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About TRACULA
by yangfuxing 02 Jun '16

02 Jun '16

Dear professor, I'm a postgraduate of neurosurgery. Recently, I'm interested in cortical parcellation and subcortical segmentation using freesurfer. In my study, all patients have brain tumors. So I begin to wonder if "recon-all" can still be applied to these subjects, or in other word, is it possible that freesurfer's automated brain segmentation could be applied to brain tumor patients because their normal structures were changed? If I just run it regularly according to fswiki, would the outcome be accurate? Second question is about automatic tractography using TRACULA. Similarly, I'd like to use "trac-all" to reconstruct arcuate fasciculus in patients with brain tumor, is it possible? How can I set up the configuration? Hope to hear from you soon. Best wishes! Sincerely

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Freesurfer June 2016