Freesurfer

freesurfer@nmr.mgh.harvard.edu

16 participants
30084 discussions

Oblique Slice Acquisition
by Katherine Reiter 13 Apr '16

13 Apr '16

Hello freesurfer experts, I am working with a dataset that was acquired with oblique slices aligned to each participant's ac-pc line. 1) I've run a handful of subjects through mri_convert and the recon stream and each subject has a slight abnormality in the ventral temporal regions. It's subtle but apparent in each acquisition (image attached and the abnormality is apparent at the red cross). We believed it to be an alignment issue due to the oblique slices. Is there any way to fix this abnormality? 2) Are there any different preprocessing steps that should be added to mri_convert or the recon stream? Thanks much in advance! Kate Katherine Reiter, M.S. Clinical Psychology Doctoral Student Marquette University Phone: (414) 288-3807 Email: katherine.reiter(a)marquette.edu

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Open Research Assistant position at the VA
by Leslie Nordstrom 13 Apr '16

13 Apr '16

Hi FreeSurfers, There is a research assistant position opening at the VA Boston Healthcare System in neuroimaging and aging (job announcement is attached). This would be a great position for someone who is interested in both the technical aspects of MRI data processing/analysis as well as in neuropsychology and aging research. Interested applicants should send a resume and contact information for two references via email to Leslie Nordstrom: lesliekn(a)bu.edu with RESEARCH ASSISTANT as the subject line. Please forward this message to anyone you think might be interested in the position!

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.license and tksurfer
by stdp82＠virgilio.it 13 Apr '16

13 Apr '16

Hi list, please see the error reported below. It occurs when I invoke tksurfer.Of note, I have completed without problem fs-fast analysis without error.The .license file is well placed and written.Thanks Stefano tksurfer fsaverage rh inflated -aparc -overlay subj/rest/fc.ROI.surf.rh/ROI/sig.nii.gz -fminmax 1.3 3 thresholds min=1.3 max=3 slope=0.588235 mid=2.15 subject is fsaverage hemi is rh surface is inflated surfer: current subjects dir: /Applications/freesurfer/subjects/subject_prova surfer: not in "scripts" dir ==> using cwd for session root surfer: session root data dir ($session) set to: surfer: /Applications/freesurfer/subjects/fMRI/con_run1 checking for nofix files in 'inflated' Reading image info (/Applications/freesurfer/subjects/subject_prova/fsaverage) Reading /Applications/freesurfer/subjects/subject_prova/fsaverage/mri/orig.mgz surfer: Reading header info from /Applications/freesurfer/subjects/subject_prova/fsaverage/mri/orig.mgz -------------------------------------------------------------------------- ERROR: FreeSurfer license file /Applications/freesurfer/.license not found. If you are outside the NMR-Martinos Center, go to http://surfer.nmr.mgh.harvard.edu to get a valid license file (it's free). If you are inside the NMR-Martinos Center, make sure to source the standard environment.

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Re: [Freesurfer] TRACULA
by Alan Francis 13 Apr '16

13 Apr '16

Hi Jasmin - When replying please cc the FS group so that they may weigh in on the situation. Looking at the log, they key problem appears to be this: niiRead(): error opening file Users/jasminalves/Desktop/data/DTI/40/DTI.nii.gz Tracula is not able to open your DTI.nii.gz Please check this file or pathway. best, Alan On Tue, Apr 12, 2016 at 11:17 AM, Jasmin Alves <jalves(a)usc.edu> wrote: > Hi Alan, > > Thank you for your reply. I changed the pathway but now have stumbled upon > a new error. I tried looking through the feeds and couldn't really find a > relevant solution. > > Jasmins-MacBook-Pro:data jasminalves$ trac-all -prep -c dmrirc.txt > > INFO: SUBJECTS_DIR is /Users/jasminalves/Desktop/data/FREESURFER > > INFO: Diffusion root is /Users/jasminalves/Desktop/data/FREESURFER > > Actual FREESURFER_HOME /Applications/freesurfer > > trac-preproc -c > /Users/jasminalves/Desktop/data/FREESURFER/40/scripts/dmrirc.local -log > /Users/jasminalves/Desktop/data/FREESURFER/40/scripts/trac-all.log -cmd > /Users/jasminalves/Desktop/data/FREESURFER/40/scripts/trac-all.cmd > > #------------------------------------- > > /Applications/freesurfer/bin/trac-preproc > > #------------------------------------- > > #@# Image corrections Tue Apr 12 08:04:37 PDT 2016 > > mri_convert Users/jasminalves/Desktop/data/DTI/40/DTI.nii.gz > /Users/jasminalves/Desktop/data/FREESURFER/40/dmri/dwi_orig.nii.gz > > mri_convert Users/jasminalves/Desktop/data/DTI/40/DTI.nii.gz > /Users/jasminalves/Desktop/data/FREESURFER/40/dmri/dwi_orig.nii.gz > > niiRead(): error opening file > Users/jasminalves/Desktop/data/DTI/40/DTI.nii.gz > > $Id: mri_convert.c,v 1.179.2.7 2012/09/05 21:55:16 mreuter Exp $ > > reading from Users/jasminalves/Desktop/data/DTI/40/DTI.nii.gz... > > Darwin Jasmins-MacBook-Pro.local 14.5.0 Darwin Kernel Version 14.5.0: Tue > Sep 1 21:23:09 PDT 2015; root:xnu-2782.50.1~1/RELEASE_X86_64 x86_64 > > trac-preproc exited with ERRORS at Tue Apr 12 08:04:37 PDT 2016 > > Your help would be appreciated. > > Thank you, > > Jasmin > > On Tue, Apr 12, 2016 at 7:26 AM, Alan Francis <alandarkenergy(a)gmail.com> > wrote: > >> Hi Jasmin - >> >> In your dmrirc file , the dcmlist refers to the dicom list. You have put >> the BVEC/BVAL files there. That is not correct. You need to place the >> actual pathway to your niftis in this position. >> >> set subjlist = (40 39 36) >> >> # Input diffusion DICOMs (file names relative to dcmroot) >> # If original DICOMs don't exist, these can be in other image format >> # but then bvecfile and bvalfile must be specified >> # >> set dcmroot = Users/jasminalves/Desktop/data/DTI >> set dcmlist = (40/DTI.nii.gz DTI.bvec DTI.bval 39/DTI.nii.gz DTI.bvec >> DTI.bval 36/DTI.nii.gz DTI.bvec DTI.bval) *<- Pathway to NIFTIS HERE* >> >> Hope this helps, >> >> best, >> >> Alan >> >> >> -- >> |~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~| >> >> *Alan N. Francis PhD* >> Instructor - Imaging Neuroscience >> Brain Imaging Center >> McLean Hospital >> Harvard Medical School >> 115 Mill Street, Belmont, MA 02478 >> alanf(a)bwh.harvard.edu <ezra_wegbreit(a)brown.edu> >> afrancis(a)mclean.harvard.edu >> >> >> |~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~| >> > > > > -- > Jasmin Alves > Predoctoral Student > Medical Biology Graduate Program > University of Southern California > jalves(a)usc.edu > > -- |~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~| *Alan N. Francis PhD* Instructor - Imaging Neuroscience Brain Imaging Center McLean Hospital Harvard Medical School 115 Mill Street, Belmont, MA 02478 alanf(a)bwh.harvard.edu <ezra_wegbreit(a)brown.edu> afrancis(a)mclean.harvard.edu |~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|~|

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DODS vs DOSS for correlation analysis
by Cassandra Wannan 13 Apr '16

13 Apr '16

Hi Doug, I am carrying out some correlations between cortical thickness and cognitive ability, and had a question about the use of DODS vs DOSS for my analysis with Qdec. From reading some of the forums, it seems as though DOSS should be used when there are no interactions present. I am including gender as a covariate (fixed factor) and age as a nuisance variable in my model. When I run this with DODS, it shows that the correlation between thickness and cognition does not differ between males and females - does this mean that I should run the correlation using DOSS? I only seem to find significant correlations using DOSS, however I do not want to report these if the analysis is not correct. Thanks, Cassie Cassandra Wannan *PhD Candidate/Research Assistant/Psychology Tutor* Melbourne Neuropsychiatry Centre -------------------------------------------- 0433 339 839 cwannan(a)student.unimelb.edu.au

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Summer School "Advanced Scientific Programming in Python" in Reading, UK, September 5--11, 2016
by Etienne B. Roesch 13 Apr '16

13 Apr '16

*Advanced Scientific Programming in Python* a Summer School by the G-Node, and the Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of Reading, UK Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are devised to be interactive and to give the students enough time to acquire direct hands-on experience with the materials. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python and of a version control system such as git, subversion, mercurial, or bazaar is assumed. Participants without any prior experience with Python and/or git should work through the proposed introductory material before the course. We are striving hard to get a pool of students which is international and gender-balanced. You can apply online: https://python.g-node.org *Application deadline: 23:59 UTC, May 15, 2016. Be sure to read the FAQ before applying. * Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Travel grants may be available. *Date & Location * September 5—11, 2016. Reading, UK *Program * - Best Programming Practices • Best practices for scientific programming • Version control with git and how to contribute to open source projects with GitHub • Best practices in data visualization - Software Carpentry • Test-driven development • Debugging with a debugger • Profiling code - Scientific Tools for Python • Advanced NumPy - Advanced Python • Decorators • Context managers • Generators - The Quest for Speed • Writing parallel applications • Interfacing to C with Cython • Memory-bound problems and memory profiling • Data containers: storage and fast access to large data - Practical Software Development • Group project *Preliminary Faculty * • Francesc Alted, freelance consultant, author of PyTables, Spain • Pietro Berkes, Enthought Inc., Cambridge, UK • Zbigniew Jędrzejewski-Szmek, Krasnow Institute, George Mason University, Fairfax, VA, USA • Eilif Muller, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland • Juan Nunez-Iglesias, Victorian Life Sciences Computation Initiative, University of Melbourne, Australia • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Bartosz Teleńczuk, European Institute for Theoretical Neuroscience, CNRS, Paris, France • Stéfan van der Walt, Berkeley Institute for Data Science, UC Berkeley, CA, USA • Nelle Varoquaux, Centre for Computational Biology Mines ParisTech, Institut Curie, U900 INSERM, Paris, France • Tiziano Zito, freelance consultant, Germany *Organizers * For the German Neuroinformatics Node of the INCF (G-Node) Germany: • Tiziano Zito, freelance consultant, Germany • Zbigniew Jędrzejewski-Szmek, Krasnow Institute, George Mason University, Fairfax, USA • Jakob Jordan, Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich GmbH, Germany For the Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of Reading UK: • Etienne Roesch, Centre for Integrative Neuroscience and Neurodynamics, University of Reading, UK *Website*: https://python.g-node.org *Contact*: python-info(a)g-node.org Kind regards, Etienne ----- Dr. Etienne B. Roesch Lecturer in Cognitive Science University of Reading

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Repeated measures of one group analysis question
by Martina Papmeyer 13 Apr '16

13 Apr '16

Dear FreeSurfer experts I have a question regarding the correct data analysis method for my study design and could unfortunately not find an example of a similar analysis on your mailing list or documentation on the wiki. I have repeated structural MRI data from one group (within-subjects design) at two time points (T0 and T1). Each subject has been randomly assigned to receive treatment or placebo either at T0 or T1 (i.e., half of them had treatment at T0 and placebo at T1 and half of them had placebo at T0 and treatment and T1). I am interested in the putative effects of treatment on cortical thickness. One major challenge I face stems from the fact that the scan interval (between T0 and T1) varies between subjects and, most importantly, that I expect a differential impact of treatment as compared to placebo at T0 on thickness at T1 (long-lasting increases). Thus, the time between scans and the order of the two conditions (treatment vs placebo) should be taken into account. Repeated measures ANOVA seems not appropriate. I have been thinking about conducting linear mixed effects models to analyse my data but also been thinking about the longitudinal two-stage model you describe on the wiki. I am unsure which method appears most appropriate and I am unsure how to model the time between scans correctly. Any suggestion regarding the most appropriate method and how to model the time between scans or reference to a similar data analysis question would be highly appreciated! Thank you very much for your help and all best wishes, Martina

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Extract random effects for correlation
by Lim Kai Wei, Joseph 13 Apr '16

13 Apr '16

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mri_surfcluster issues
by Antonin Skoch 12 Apr '16

12 Apr '16

Dear FreeSurfer experts, I am using mri_surfcluster to compare results from PALM and FreeSurfer cluster extent inference. I came across several issues in mri_surfcluster I cannot cope with. 1. I observed that --mask option in mri_surfcluster does not prevent cluster to growing outside the mask. When I run mri_surfcluster --in lh.thickness.fsaverage_masked.mgh --sum summary.txt --subject fsaverage --surf white --hemi lh --thmin 0 --ocn cluster_summary.mgz --mask ./mask.mgh the cluster is growing outside mask and covers all surface including non-cortical (masked) regions. Is this intended behavior? This behavior (allowing clusters leak outside mask) seems strange to me since it could possibly influence results of cluster-extent inference in case of mask-constrained analysis. 2. How behaves mri_glmfit-sim in permutation-based building cluster-extent null distribution with mask? Is in this case also allowed to clusters to leak outside mask? 3. Another issue is concerning reported cluster area. I tested mri_surfcluster on data where overlay values (lh.thickness.fsaverage.mgh) were set to 0 outside cortex mask (they were non-zero also in some portion of non-cortical vertices). When I set thmin to non-zero value in mri_surfcluster mri_surfcluster --in lh.thickness.fsaverage_masked.mgh --sum summary.txt --subject fsaverage --surf white --hemi lh --thmin 0.00001 --ocn cluster_summary.mgz --mask ./mask.mgh I get cluster comprising (almost) all cortex, not leaking to non-cortical areas. But what is strange, the reported cluster size is far larger than area of the whole cortical surface. number of voxels in search space = 149953 Done loading source values (nvtxs = 163842) overall max = 5 at vertex 817 overall min = 0 at vertex 8 surface nvertices 163842 surface area 65417.097656 surface area 65416.648438 NOT Adjusting threshold for 1-tailed test Searching for Clusters ... thmin=0.000100 (0.000100), thmax=-1.000000 (-1), thsignid=0, minarea=0.000000 Found 2 clusters Max cluster size 76431.562500 Saving cluster numbers to pok.mgz Here is output of mri_surfcluster: # ClusterNo Max VtxMax Size(mm^2) MNIX MNIY MNIZ NVtxs 1 5.000 817 76431.56 -25.8 4.6 -37.5 149874 2 2.670 144205 0.53 -15.8 -40.4 -3.8 1 How it is possible? 4. Does the fsaverage/surf/lh.area correspond to area of particular vertices on fsaverage/surf/lh.white? I expect yes. 5. How it is possible to calculate area of specified vertex of surface? I tried to do that by creating .label file with particular vertex number and running mri_surfcluster --in lh.thickness.fsaverage_masked.mgh --sum summary.txt --subject fsaverage --surf white --hemi lh --thmin 0.00001 --clabel myVertex.label I indeed got report of 1 cluster with 1 vertex, with VtxMax value identical to vertex index in myVertex.label but the area value in summary.txt is different that value in fsaverage/surf/lh.area. Thank you very much in advance for clarification, Antonin Skoch

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FSFAST with reoriented reconstruction
by Julio Alberto González Torre 12 Apr '16

12 Apr '16

Hi Freesurfer users. I performed the recomended reconstruction of 82 subjects with Freesurfer. The images used for this reconstruction were previously reoriented to AC-PC position, because they were previously used for SPM purposes. Now I'm trying to perform the FSFAST workflow, and I had to unpack the original functional DICOMs for each subject. Those images are not reoriented because they were not used until now. The question is: The functional images should be previously reoriented to the same position as the anatomical used in Freesurfer? Should I perform the recomended reconstruction again with the original DICOMS? Or it doesn't matter the reorientation of the functional/anatomical images and Freesurfer would perform correctly the FSFAST? (I hope so, so I could skip the reconstruction, which involves many hours of processing) Thanks.

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