Freesurfer

freesurfer@nmr.mgh.harvard.edu

1 participants
30089 discussions

Abstract submission deadline extended - 1st International Workshop on Predictive Processing
by James Hartzell 12 Apr '18

12 Apr '18

We would like to inform you that the abstract submission deadline to PREDICTIVE PROCESSING has been extended. The new deadline is April 16th. The conference dates are June 20-22, 2018 Dear Researcher, We are happy to announce the new discussion arena fully centred on predictive processing. The Workshop on Predictive Processing (WoPP) will take place in San Sebastian (Spain) on June 20-22, 2018. The goal of the workshop is to address the role of predictive processing in cognition. Some of the crucial issues in this timely research topic are the extent to which prediction is a fundamental mechanism of brain function, the role of prediction in learning, and, how predictive processing is expressed across distinct cognitive domains. This workshop will gather experts from different fields in cognitive neuroscience including sensory processing, attention and memory, to work alongside the community of language processing, with the aim of furthering our understanding of the role of predictive processing in cognition. We invite submissions from researchers genuinely interested in discussing their data within the framework of the prediction view on neurocognition and brain functioning. The conference will include keynote speakers, regular talks, symposiums and poster sessions. Keynote speakers will be the main sources of discussion: Sophie Scott, University College London Moshe Bar, Bar-Ilan University Pascal Fries, Ernst Strüngmann Institute (ESI) Each keynote will be followed by a symposium on a related topic. Each symposium will be co-organized by two experts in the field, and will last 2 hours. Symposium organizers are Gina Kuperberg, Matt Davis, Craig Richter, Julien Vezoli, Lucia Amoruso and Ruth De Diego Balaguer. For further information please visit: http://www.bcbl.eu/events/prediction-2018/en/ We look forward to seeing you at the conference. Yours sincerely, The Organizing Committee Manuel Carreiras, Clara Martin, Nicola Molinaro & David Soto -- James Hartzell, PhD Basque Center on Cognition, Brain and Language (BCBL), Donostia, Spain Center for Mind/Brain Sciences (CIMeC), The University of Trento, Italy Center for Buddhist Studies, Columbia University, USA

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3T flag recon-all
by Backhausen, Lea 12 Apr '18

12 Apr '18

Hi all, Would you recommend using the 3t flag for recon-all when analyzing data from a 3t scanner? I read about it in some papers but not everybody seems to use it. Also, do you know whether the flag can be used in the longitudinal stream as well or does it only apply to the crosssectional procession and for the BASE and LONG steps I would have to let it go? I will be performing analyses with FS 6.0.0. Thank you very much for sharing your opinion on this. Best, Lea ------------------------------ Lea Backhausen Research Assistant Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU Dresden, Germany http://www.uniklinikum-dresden.de<http://www.uniklinikum-dresden.de/>

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newbie trac-all -bedp question
by Krieger, Donald N. 12 Apr '18

12 Apr '18

I am getting the following output with error on this command: trac-all -bedp -s HDFT1001 -i ep2d_diff_SliceAcc_b1k_64_768x768.23 INFO: SUBJECTS_DIR is /media/portable/home/kriegerd/Contrib/Freesurfer_6.0/subjects INFO: Diffusion root is /media/portable/home/kriegerd/Contrib/Freesurfer_6.0/subjects Actual FREESURFER_HOME /media/portable/home/kriegerd/Contrib/Freesurfer_6.0 WARN: Running bedbostx locally - this might take a while WARN: It is recommended to run this step on a cluster bedpostx_mgh -n 2 /media/portable/home/kriegerd/Contrib/Freesurfer_6.0/subjects/HDFT1001/dmri /home/kriegerd/Contrib/Freesurfer/bin/bedpostx_mgh: 131: /home/kriegerd/Contrib/Freesurfer/bin/bedpostx_mgh: Syntax error: "(" unexpected I apologize for not finding the answer in the list archive. It's probably there. I'm running under ubuntu 14.04. bedpostx_mgh is a shell script which calls for /bin/sh. On my machine, that is a symlink to /bin/bash If I change it to be explicitly /bin/bash, it runs a bit further. Here is the full output: INFO: SUBJECTS_DIR is /media/portable/home/kriegerd/Contrib/Freesurfer_6.0/subjects INFO: Diffusion root is /media/portable/home/kriegerd/Contrib/Freesurfer_6.0/subjects Actual FREESURFER_HOME /media/portable/home/kriegerd/Contrib/Freesurfer_6.0 WARN: Running bedbostx locally - this might take a while WARN: It is recommended to run this step on a cluster bedpostx_mgh -n 2 /media/portable/home/kriegerd/Contrib/Freesurfer_6.0/subjects/HDFT1001/dmri subjectdir is /media/portable/home/kriegerd/Contrib/Freesurfer_6.0/subjects/HDFT1001/dmri Making bedpostx directory structure Queuing preprocessing stages /home/kriegerd/Contrib/Freesurfer/bin/fsl_sub_mgh: 470: /home/kriegerd/Contrib/Freesurfer/bin/fsl_sub_mgh: Syntax error: "(" unexpected (expecting ";;") Queuing parallel processing stage 0 slices processed /home/kriegerd/Contrib/Freesurfer/bin/fsl_sub_mgh: 470: /home/kriegerd/Contrib/Freesurfer/bin/fsl_sub_mgh: Syntax error: "(" unexpected (expecting ";;") Queuing post processing stage /home/kriegerd/Contrib/Freesurfer/bin/fsl_sub_mgh: 470: /home/kriegerd/Contrib/Freesurfer/bin/fsl_sub_mgh: Syntax error: "(" unexpected (expecting ";;") /home/kriegerd/Contrib/Freesurfer/bin/bedpostx_mgh: line 439: 26461 Terminated ${subjdir}.bedpostX/monitor Thanks - Don

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CFP: The first International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2018)
by MLCN Workshop 12 Apr '18

12 Apr '18

**** Apologize for multiple posting ***Dear Colleagues,We are delighted to invite you to join us for the MLCN 2018 workshop as a satellite event of MICCAI 2018 conference, Granada, Spain. -------------------------------------------------------------------------------------------------------------------CALL FOR PAPERS:Recent advances in neuroimaging and statistical machine learning provide an exceptional opportunity for investigators and physicians to discover complex relationships between brain, behaviors, and mental and neurological disorders. MLCN 2018 <https://mlcn2018.com/> workshop, as a satellite event of MICCAI 2018 <http://www.miccai2018.org/en/>, aims to bring together researchers in both theory and application from various fields in domains of spatial statistics, pattern recognition in neuroimaging, and predictive clinical neuroscience. Topics of interests include but are not limited to: - Applications of spatio-temporal modeling in predictive clinical neuroscience - Spatial regularization in decoding clinical neuroimaging data - Spatial statistics in neuroimaging - Learning with structured inputs and outputs in clinical neuroscience - Multi-task learning in analyzing structured neuroimaging data - Deep learning in analyzing structured neuroimaging data - Model stability and interpretability in clinical neuroscience-------------------------------------------------------------------------------------------------------------------CONFIRMED SPEAKERS:Christos Davatzikos (University of Pennsylvania)Gaël Varoquaux (Parietal team, INRIA)Jian Kang (University of Michigan)-------------------------------------------------------------------------------------------------------------------SUBMISSION PROCESS:The workshop seeks high quality, original, and unpublished work on algorithms, theory, and applications of machine learning in clinical neuroimaging and spatially structured data analysis. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style of up to 8-pages papers using the CMT system at https://cmt3.research.microsoft.com/MLCN2018 <https://cmt3.research.microsoft.com/MLCN2018>. This workshop uses a double-blind review process in the evaluation phase, thus authors must ensure anonymous submissions. Accepted papers will be published in a joint proceeding with the MICCAI conference.-------------------------------------------------------------------------------------------------------------------IMPORTANT DATES:Paper submission deadline: June 11, 2018Notification of Acceptance: July 16, 2018Camera-ready Submission: July 23, 2018Workshop Date: September 20, 2018* *-------------------------------------------------------------------------------------------------------------------Best regards,MLCN 2018 Organizing Committee,Email: mlcnworkshop(a)gmail.com <mlcnworkshop(a)gmail.com>Website: https://mlcn2018.com/ <https://mlcn2018.com/>twitter: @MLCN2018 <https://twitter.com/@MLCN2018>*

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Postdoctoral Position
by siya sherif 12 Apr '18

12 Apr '18

*Postdoctoral position in the Team “Multimodal Neuroimaging and Lifestyle in Aging and Alzheimer’s disease” (Dr. Gaël Chételat , UMR-S U1237-PhIND, Caen, France) ** Duration: 2 years (1 year renewable 1 year). * Starting date: June 2018 * Research team: Multimodal Neuroimaging and Lifestyle in Aging and Alzheimer’s disease headed by Dr. Gaël Chételat; www.gael-chetelat.fr; Research unit: INSERM-UNICAEN-EFS UMR-S U1237-PhIND, GIP Cyceron, Boulevard Henri Becquerel, BP5229, 14 074 CAEN Cedex, France * Salary: according to experience *Job Description: *The aim of the project is to investigate vascular and white matter lesions across the Alzheimer’s disease (AD) continuum using two large neuroimaging datasets (IMAP+ and Silver Santé Study). The postdoc will define the optimal methodologies and implement the corresponding preprocessing pipelines to extract information related with WM lesions from multimodal neuroimaging data. He will apply these methods to data acquired in controls and patients. Then he will investigate the changes in normal aging and across the AD spectrum, and the relationships between WM lesions and other neuroimaging alterations as well as cognitive deficits in patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). The postdoctoral researcher will have access to cross-sectional and longitudinal cognitive and neuroimaging data including, but not limited to, FLAIR, T1, T2, ASL, resting-state and task-related fMRI, DTI, FDG-PET, and AV45-PET. *Requirements for applicants: *PhD Degree in neuroscience, psychology, engineering, or equivalent, and experience in neuroimaging are essential. The applicant should notably have a solid background in neuroimaging analyses related to vascular and white matter lesions. Programming skills are highly desirable though not necessary (Matlab, R, Python, C++, Perl). Experiences in the treatment of other imaging modalities (fMRI, PET) and /or experience in the field of normal and pathological aging will be appreciated, although not essential. *The lab:* The Multimodal Neuroimaging and Lifestyle in Aging and Alzheimer’s disease team, headed by Dr. Gaël Chételat, is organized around four main areas of interest: i) improving neuroimaging biomarkers for early Alzheimer’s disease diagnosis; ii) further the understanding of the physiopathological mechanisms of Alzheimer’s disease with multimodal imaging, iii) investigating the relationships between lifestyle factors and brain biomarkers and iv) developing non-pharmacological interventions to promote mental health and wellbeing in aging population. The team is currently involved in 2 large projects: Multimodality Imaging of Early-stage Alzheimer’s disease (IMAP+) and Silver Santé Study, a European study granted by the European Commission (www.silversantestudy.eu; H2020, 2015-PHC-22) assessing the effects of two 18-month interventions (English versus meditation training) on behavioral and biological markers of aging and Alzheimer’s disease. The team is included in the INSERM UNICAEN-EFS UMR-S U1237-PhIND directed by Pr. Denis Vivien and is based at the Centre Cyceron (Caen, France), a structure devoted to multimodal imaging. The structure provides a stimulating work environment as it groups several research units and several research instruments, such as a cyclotron, PET-CT and MRI. *To apply:* Send a CV, motivation letter and contact details of two academic referees to Gaël Chételat via silversantestudy(a)cyceron.fr. *Deadline: 15 May 2018*

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Re: [Freesurfer] Surface label to volume and then back to surface
by David Beeler 11 Apr '18

11 Apr '18

Hi Doug, thanks for the explanation! Also --projfrac-max -.1 1 .1 is a big improvement, I think it should be good for my purposes. Feeling good about this now! Cheers, DB _______________________________________ On 04/11/2018 12:44 PM, David Beeler wrote: > Hi Doug, thanks for the response! So it looks like my initial > transform from surface label to anatomical volume wasn't working > because my template.nii.gz and brainmask were in a different space > than the functional data I was processing. This happened because I was > taking the raw f.nii.gz, doing some transformations first, and saving > it to a file called f_reg.nii.gz. I was then trying to run > preproc-sess using the -i flag to specify this f_reg.nii.gz, but when > preproc runs mktemplate-sess it still uses f.nii.gz to make the > template, regardless of what the -i or -mcin flags say. I just added a > line in the if($DoTemplate) section of preproc-sess to have the option > of adding a -funcstem and it worked fine. I have now updated my > preprocessing to run all the commands individually instead of using > preproc-sess at all, and I'm probably going to send another message to > this list later making sure I'm doing that correctly. Additionally, > Re: "I think you could do it with a single call to mri_label2vol using > --regheader and specifying the func template." This works great! > Thanks for the shortcut. I got the surface label direct to functional > volume transform working with: mri_vol2vol --regheader --mov > $OUTPUTDIR/rh.MT.volume.anat.nii.gz --targ > $MEANFUNCDIR/meanfunc.nii.gz --o > $OUTPUTDIR/rh.MT.volume.func-regheader.nii.gz --nearest But I'm still > not entirely sure exactly what information is contained in regheader > (or where I can view this information). The reason I have been using > regheader instead of register.dof6.lta is that my functional data is > aligned to the anatomical prior to running preprocessing, and my > understanding was that using regheader would just downsample (similar > to mri_convert) and I wouldn't need to specify a registration file. Is > this a flawed understanding of regheader? Regheader assumes that the two volumes "share a RAS space". This is a little hard to explain. Each volume has some geometry information in it that determines where a voxel is in RAS (right, anterior, superior) space. --regheader assumes that the RAS space is the same, so, if the RAS for voxel in volume 1 is the samseg as that of a voxel in volume2, then they must represent the same place anatomically. If you have already resampled your functional data into the 1mm, 256^3 anatomical space, then --regheader should work ok > So transforming to functional space seems to be okay now, but > transforming back to the surface is still not perfect. I tried > mri_vol2surf --mov $OUTPUTDIR/rh.MT.volume.func-regheader.nii.gz > --regheader $SUBJID --projfrac 0.5 --interp nearest --hemi rh --o > $OUTPUTDIR/regheadersurface.nii.gz and also bbregister --s $SUBJID > --reg $OUTPUTDIR/testreg.lta --mov $MEANFUNCDIR/meanfunc.nii.gz > --init-fsl --bold mri_vol2surf --mov > $OUTPUTDIR/rh.MT.volume.func-regheader.nii.gz --reg > $OUTPUTDIR/testreg.lta --projfrac 0.5 --interp nearest --hemi rh --o > $OUTPUTDIR/testregsurface.nii.gz Which both give essentially the same > result: Original Label: > http://web.mit.edu/dsbeeler/www/images/MTlabel.png Back to Surface > Overlay: http://web.mit.edu/dsbeeler/www/images/MTpatchy.png This is > actually much better than it was, but is there a way to get rid of the > patchiness? The patchiness is caused by something else. When you run vol2surf, you can try --projfrac-max -.1 1 .1 That should reduce the patchiness. If it is still not tolerable, then let me know > Again, thanks for all the useful info! > David > ______________________________ > Hi David, I've tried to answer your questions below. > > doug > On 03/30/2018 01:48 PM, David Beeler wrote: > > Hi all, > > Sorry in advance, this is a bit of an annoying one! > > > > I am trying to use freesurfer's automatically generated MT label as a > > parcel to constrain activity measured during a functional MT > > localizer. I am currently doing my analysis in the volume and I'd like > > to transform the MT surface label into the subject's volume functional > > space (i.e. the same space as the sig.nii.gz for a particular > > contrast), take the voxels in sig.nii.gz that are within the MT label, > > and project them back onto the surface (preferably to be viewed as an > > overlay with freeview). I will eventually run the analysis on the > > surface as well (making this much less roundabout, but I also use > > volume parcels so understanding how to correctly go between the volume > > and surface is important to me). > > > > In my pipeline I start off by running preproc-sess, then I register > > and transform my preprocessed functional data (fmcpr.nii.gz) to the > > subject's anatomical (without upsampling). After doing mkanalysis and > > mkcontrast, I run selxavg on this volume. So the functional data is > > aligned with the anatomical, but still has low res dimensions. > > > > If it's not too much to ask, would it be possible to provide the > > specific commands to do all these transformations properly? I'll > > provide some of the commands I've tried below so you can see where I'm > > going wrong: > > > > First I want to transform the MT label to the functional volume. I've > > tried: > > > > mri_label2vol --subject $SUBJID --label > > $SUBJECTS_DIR/$SUBJID/label/rh.MT.label --o > > $OUTPUTDIR/rh.MT.volume.anat.nii.gz --temp > > $SUBJECTS_DIR/$SUBJID/mri/orig.mgz --identity --fill-ribbon --hemi rh > > > > ...which I would expect to put the label in high resolution anatomical > > volume space (orig.mgz), but it doesn't fill the graymatter ribbon > > nicely at all. > I would have expected this to work. Can you send a pic? I just ran it > myself, and it seemed to do ok. > > > > I then use mri_vol2vol to transform to functional/sig.nii.gz space (I > > could also just downsample with mri_convert since I've already aligned > > my sig.nii.gz to my anatomical, this gives me the same result). I am > > using regheader here, is this okay? > > > > mri_vol2vol --regheader --mov $OUTPUTDIR.rh.MT.volume.anat.nii.gz > > --targ meanfunc.nii.gz --o rh.MT.volume.func.nii.gz --nearest > > > > ...and this works pretty well, but it's a little sparse so I may need > > to dilate it with mri_morphology. I think this step is fine. > Why do this extra step? I think you could do it with a single call to > mri_label2vol using --regheader and specifying the func template. Why > are you using --regheader here instead of the register.dof6.lta? > > > > Next I use matlab to take the voxels in sig.nii.gz that are also in > > rh.MT.volume.func.nii.gz and I save that as a new volume, say > > constrainedsig.nii.gz. > You can do this with mri_mask sig.nii.gz rh.MT.volume.func.nii.gz > constrainedsig.nii.gz > > Now I want to transform constrainedsig.nii.gz to the surface. I have > > tried going directly using the register.dof6.lta generated from > > preproc-sess: > > > > mri_vol2surf --mov constrainedsig.nii.gz --reg register.dof6.lta > > --projfrac 0.5 --interp nearest --hemi rh --o > > $OUTPUTDIR/constrainedsig_surface.nii.gz > This should have worked. Try doing directly on the rh.MT.volume.func.nii.gz > > > > ... and this is in the right area, but its shape doesn't match the > > shape of the original label. So I've also tried backtracking and doing > > another vol2vol and then vol2surf with regheader: > > > > mri_vol2vol --regheader --mov constrainedsig.nii.gz --targ > > $SUBJECTS_DIR/$SUBJID/mri/orig.mgz --o > > $OUTPUTDIR/constainedsig_anatvolume.nii.gz > > > > mri_vol2surf --mov constrainedsig_anatvolume.nii.gz --hemi rh > > --regheader S02 --o $OUTPUTDIR/constrainedsig_anatsurface > > > > ...and while this ends up in the right place, it is really patchy > > (looking kind of like twisty zebra stripes). Seems to me like it has > > the wrong dimensions or something, but mri_info says it's the same > > dimensions as the volume I originally created with mri_label2vol. > > > > Any help is much appreciated, thanks! > > David

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Creating average surface for fMRI subjects and extracting cortical thickness
by Arsenije Subotic 11 Apr '18

11 Apr '18

Dear experts, I would like to create an average surface template of fMRI activation for two groups, and then determine areas of low cerebrovascular reactivity in my disease group, create a ROI, and extract the cortical thickness in this region and see how it compares to a control group by translating this ROI onto the control surface template. Do you have any tips or suggestions on how this workflow should be done? I've currently been able to create an average surface for both groups, and I know how to create labels using tksurfer and to extract cortical thickness using mri_anatomical_stats. Is it possible however to create an ROI by only considering areas of low CVR/low activation in my disease group and calculating cortical thickness for this area, and then visualize it somehow and then translate this label onto the control group and doing the same? I know it's a lot of questions, but going through the tutorial (https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/MultiModalFmriGroup_tk…) it seems that I wasn't able to find exactly what I'm looking for. Thank you for your help, Arsenije

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mri_surf2surf results in large area with zero values
by Frauke Beyer 11 Apr '18

11 Apr '18

Dear experts, I have an issue with mri_surf2surf (i suspect). I have resampled T1-values to the fsaverage template using mri_vol2surf and a --projfrac of 0.25 with the default white matter surface. Then I loaded these values in Matlab using MRIread, inverted them and saved them back into a file using MRIwrite. In Matlab, there were no suspicious data points (e.g. the range of R1 values excluded 0, minimum was 0.25). This was also true when loading the R1values as an overlay in Freeview. Now I wanted to smooth this image using mri_surf2surf with target and source subject being identical to fsaverage and fwhm of 10mm. exact command was: mri_surf2surf --srcsubject fsaverage --trgsubject fsaverage --hemi lh --sval /nobackup/aventurin4/data_fbeyer/flica_myelin/flica_inputs/round2/t1maps/lh.t1map.proj0.25.fsavg_inv.mgh --tval /nobackup/aventurin4/data_fbeyer/flica_myelin/flica_inputs/round2/t1maps/lh.t1map.proj0.25.fsavg_inv_smoothed10mm.mgh --fwhm-trg 10 But now, while it worked fine for some subjects in the 4D file for others large areas of 0 emerged in the smoothed file. Those were mostly found in inferior temporal/orbitofrontal lobe. Does anybody have an idea what could cause this strange behaviour? Thank you so much for your help, Frauke

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Running matlab-based components of freesurfer on windows 10 linux shell
by Savio Wong 11 Apr '18

11 Apr '18

Hi everyone, I have installed freesurfer on windows 10 linux shell following this link: http://nuclear-imaging.info/site_content/2016/09/12/installing-running-free… Recon-all and freeview run perfectly on the system. However, when I tried to run mkcontrast, I encountered the following error: An Error has occurred while trying to initialize the MCR. The error is: Fatal error loading library /usr/local/freesurfer/MCRv83/bin/glnxa64/libmwmclbase.so Error: libmwblas.so: cannot enable executable stack as shared object requires: Invalid argument Error:mclmcr initialization failed ERROR running mkcontrast It seems that the windows 10 linux shell lacks some libraries to run the matlab: https://www.mathworks.com/matlabcentral/answers/308911- can-i-install-matlab-in-bash-on-ubuntu-on-windows Does anyone have any experience in this error? is there any solution for that? Any advice is much appreciated. best..................savio

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Re: [Freesurfer] Surface label to volume and then back to surface
by David Beeler 11 Apr '18

11 Apr '18

Hi Doug, thanks for the response! So it looks like my initial transform from surface label to anatomical volume wasn't working because my template.nii.gz and brainmask were in a different space than the functional data I was processing. This happened because I was taking the raw f.nii.gz, doing some transformations first, and saving it to a file called f_reg.nii.gz. I was then trying to run preproc-sess using the -i flag to specify this f_reg.nii.gz, but when preproc runs mktemplate-sess it still uses f.nii.gz to make the template, regardless of what the -i or -mcin flags say. I just added a line in the if($DoTemplate) section of preproc-sess to have the option of adding a -funcstem and it worked fine. I have now updated my preprocessing to run all the commands individually instead of using preproc-sess at all, and I'm probably going to send another message to this list later making sure I'm doing that correctly. Additionally, Re: "I think you could do it with a single call to mri_label2vol using --regheader and specifying the func template." This works great! Thanks for the shortcut. I got the surface label direct to functional volume transform working with: mri_vol2vol --regheader --mov $OUTPUTDIR/rh.MT.volume.anat.nii.gz --targ $MEANFUNCDIR/meanfunc.nii.gz --o $OUTPUTDIR/rh.MT.volume.func-regheader.nii.gz --nearest But I'm still not entirely sure exactly what information is contained in regheader (or where I can view this information). The reason I have been using regheader instead of register.dof6.lta is that my functional data is aligned to the anatomical prior to running preprocessing, and my understanding was that using regheader would just downsample (similar to mri_convert) and I wouldn't need to specify a registration file. Is this a flawed understanding of regheader? So transforming to functional space seems to be okay now, but transforming back to the surface is still not perfect. I tried mri_vol2surf --mov $OUTPUTDIR/rh.MT.volume.func-regheader.nii.gz --regheader $SUBJID --projfrac 0.5 --interp nearest --hemi rh --o $OUTPUTDIR/regheadersurface.nii.gz and also bbregister --s $SUBJID --reg $OUTPUTDIR/testreg.lta --mov $MEANFUNCDIR/meanfunc.nii.gz --init-fsl --bold mri_vol2surf --mov $OUTPUTDIR/rh.MT.volume.func-regheader.nii.gz --reg $OUTPUTDIR/testreg.lta --projfrac 0.5 --interp nearest --hemi rh --o $OUTPUTDIR/testregsurface.nii.gz Which both give essentially the same result: Original Label: http://web.mit.edu/dsbeeler/www/images/MTlabel.png Back to Surface Overlay: http://web.mit.edu/dsbeeler/www/images/MTpatchy.png This is actually much better than it was, but is there a way to get rid of the patchiness? Again, thanks for all the useful info! David ______________________________ Hi David, I've tried to answer your questions below. doug On 03/30/2018 01:48 PM, David Beeler wrote: > Hi all, > Sorry in advance, this is a bit of an annoying one! > > I am trying to use freesurfer's automatically generated MT label as a > parcel to constrain activity measured during a functional MT > localizer. I am currently doing my analysis in the volume and I'd like > to transform the MT surface label into the subject's volume functional > space (i.e. the same space as the sig.nii.gz for a particular > contrast), take the voxels in sig.nii.gz that are within the MT label, > and project them back onto the surface (preferably to be viewed as an > overlay with freeview). I will eventually run the analysis on the > surface as well (making this much less roundabout, but I also use > volume parcels so understanding how to correctly go between the volume > and surface is important to me). > > In my pipeline I start off by running preproc-sess, then I register > and transform my preprocessed functional data (fmcpr.nii.gz) to the > subject's anatomical (without upsampling). After doing mkanalysis and > mkcontrast, I run selxavg on this volume. So the functional data is > aligned with the anatomical, but still has low res dimensions. > > If it's not too much to ask, would it be possible to provide the > specific commands to do all these transformations properly? I'll > provide some of the commands I've tried below so you can see where I'm > going wrong: > > First I want to transform the MT label to the functional volume. I've > tried: > > mri_label2vol --subject $SUBJID --label > $SUBJECTS_DIR/$SUBJID/label/rh.MT.label --o > $OUTPUTDIR/rh.MT.volume.anat.nii.gz --temp > $SUBJECTS_DIR/$SUBJID/mri/orig.mgz --identity --fill-ribbon --hemi rh > > ...which I would expect to put the label in high resolution anatomical > volume space (orig.mgz), but it doesn't fill the graymatter ribbon > nicely at all. I would have expected this to work. Can you send a pic? I just ran it myself, and it seemed to do ok. > > I then use mri_vol2vol to transform to functional/sig.nii.gz space (I > could also just downsample with mri_convert since I've already aligned > my sig.nii.gz to my anatomical, this gives me the same result). I am > using regheader here, is this okay? > > mri_vol2vol --regheader --mov $OUTPUTDIR.rh.MT.volume.anat.nii.gz > --targ meanfunc.nii.gz --o rh.MT.volume.func.nii.gz --nearest > > ...and this works pretty well, but it's a little sparse so I may need > to dilate it with mri_morphology. I think this step is fine. Why do this extra step? I think you could do it with a single call to mri_label2vol using --regheader and specifying the func template. Why are you using --regheader here instead of the register.dof6.lta? > > Next I use matlab to take the voxels in sig.nii.gz that are also in > rh.MT.volume.func.nii.gz and I save that as a new volume, say > constrainedsig.nii.gz. You can do this with mri_mask sig.nii.gz rh.MT.volume.func.nii.gz constrainedsig.nii.gz > Now I want to transform constrainedsig.nii.gz to the surface. I have > tried going directly using the register.dof6.lta generated from > preproc-sess: > > mri_vol2surf --mov constrainedsig.nii.gz --reg register.dof6.lta > --projfrac 0.5 --interp nearest --hemi rh --o > $OUTPUTDIR/constrainedsig_surface.nii.gz This should have worked. Try doing directly on the rh.MT.volume.func.nii.gz > > ... and this is in the right area, but its shape doesn't match the > shape of the original label. So I've also tried backtracking and doing > another vol2vol and then vol2surf with regheader: > > mri_vol2vol --regheader --mov constrainedsig.nii.gz --targ > $SUBJECTS_DIR/$SUBJID/mri/orig.mgz --o > $OUTPUTDIR/constainedsig_anatvolume.nii.gz > > mri_vol2surf --mov constrainedsig_anatvolume.nii.gz --hemi rh > --regheader S02 --o $OUTPUTDIR/constrainedsig_anatsurface > > ...and while this ends up in the right place, it is really patchy > (looking kind of like twisty zebra stripes). Seems to me like it has > the wrong dimensions or something, but mri_info says it's the same > dimensions as the volume I originally created with mri_label2vol. > > Any help is much appreciated, thanks! > David > > > _______________________________________________ > Freesurfer mailing list > Freesurfer(a)nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.

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