Freesurfer

freesurfer@nmr.mgh.harvard.edu

4 participants
30076 discussions

postdoc in computational neuroimaging (method development)
by Martin Reuter 28 Dec '16

28 Dec '16

Hi all, I wish everyone a successful new year 2017! Please pass along this job offer for a postdoc position in my group in Bonn, Germany, to anyone interested and feel free to contact me with any questions. Thanks, Martin Reuter The German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany is looking for a Postdoc (f/m) in Computational Neuroimaging – Code 1427/2016/12 to join the research group of Prof. Martin Reuter. Research projects will focus on developing image analysis, geometry processing and machine learning methods for the automatic analysis of multi-modal human MRI data, primarily with the aim to extract imaging biomarkers that can be leveraged to both understand or diagnose neurodegenerative disease and to provide a rational framework for targeting and monitoring therapy. Prof. Martin Reuter, has recently joined the DZNE and simultaneously holds a faculty position at the Martinos Center, MGH, Harvard Medical School, Boston, USA with an affiliation at the Massachusetts Institute of Technology CSAIL. He is the principal developer of the longitudinal processing framework of the software package FreeSurfer, providing a suite of tools for the automated analysis of brain MRI data to a world-wide user base. He and his team have, furthermore, introduced novel approaches for neuroanatomical shape analysis and computer-aided diagnosis and prognosis of neurodegenerative disease. Your profile: We seek a postdoctoral candidate with a Ph.D. in computer science, engineering or mathematics with interest in image and geometry processing and with solid programming skills in C/C++, Python, Matlab, R, shell scripting, GPU (parallelization). The ideal candidate would have knowledge in computational neuroimaging or biomedical engineering and considerable expertise in methodological development of quantitative MRI analysis, e.g. registration, segmentation, Bayesian modeling. Experience with big data analysis, cloud computing and machine learning is an asset. Knowledge of state-of-the-art data analyses techniques and neuroimaging software, including FreeSurfer, ANTS, FSL is also appreciated. Highly motivated individuals with a record of productive research are encouraged to apply. The working language is English. We offer: Integration into the computational imaging environment at DZNE and interdisciplinary interactions with other institutes in Bonn and abroad (e.g. Harvard Medical School). Recently opened DZNE Research Facilities at Bonn Venusberg, hosting a state of the art neuroimaging center with 7T and 3T MRI scanners, as well as computational infrastructure supported by experienced IT specialists and physicists. A rich set of high-quality MR data and applied research questions through collaboration with the Rhineland Study, a prospective deeply phenotyped longitudinal cohort study with up to 30.000 participants. A strong mentoring program for career development is provided. An international environment characterized by a strong focus on science and research. Bonn, located at the Rhine river in proximity to the Cologne metropolitan area, has a very high quality of life, with an international student, research and business community. The position is initially available for 2 years. Employment, payment and social benefits are determined by the Public Sector Collective Agreement (Tarifvertrag für den öffentlichen Dienst – TVöD Bund). The DZNE is an equal opportunity employer and especially welcomes and encourages disabled individuals to apply. You will obtain special skills and knowledge for your scientific qualification during your activities at DZNE. Interested candidates should send their full application documents in a single PDF with subject CODE 1427/2016/12 via email to application(at)dzne.de . Please include a CV, contact information of two referees, a brief statement describing your personal qualifications and future research interests (up to 1 page), and a list of publications highlighting up to three most important ones. Informal enquiries may be directed to Martin Reuter (martin.reuter(at)dzne.de <>). Information about previous activities of the group can be found at http://reuter.mit.edu <http://reuter.mit.edu/> Applications are due January 25 2017

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Re: [Freesurfer] Brighter image at baseline vs. follow up using longitudinal processing stream
by Tamara Tavares 28 Dec '16

28 Dec '16

Thank you for your reply Dr. Reuter. I have about 3 scans in which the acquisition parameters are slightly different between time 1 and time 2 which probably contributes to these differences. Do you recommend that I do not use these scans? For my other scans, sometimes the differences in intensity are very subtle. For example, I have one scan where the contrast between the GM and WM are more apparent at one time point vs. the other time point. In another example, the thalamus looks a bit darker at time 1 vs. time 2. Again these differences are very subtle and only noticeable if you are looking for them. I am just wondering at what point do these differences become problematic? I should also note that sometimes these differences are not readily apparent in the original T1 scan. Thank you again for your help. Best, Tamara

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Defining labels and Gordon 2014 annotation file
by Ladan Shahshahani 27 Dec '16

27 Dec '16

Dear experts, I want to use ROIs from Power et al. 2011 paper (Functional network organization of the human brain). An excel file is provided with the center coordinates. I wonder if it's possible to use this file to automatically create parcels. Is there any way of getting Gordon's 2014 parcellation annotation file in fsaverage space? Best regards, Ladan

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mri_cvs_register
by N Saf 26 Dec '16

26 Dec '16

Dear Freesurfers, I try to register my subject with cvs atlas in mni space I use this command mri_cvs_register --mov subject.nii --mni I got this error: Using m3z type morph files. amir : at least one mean and/or Gaussian curvature file is missing (.H, .K) /home/nazanin/freesurfer/subjects/amir/surf: No such file or directory. should I first do some processing with freesurfer for my subject and then use this command ??? and if so what is the minimum processing I should run before using mri_cvs_register command?I saw a similar question in mailing list but I did not find the answer ,in advance I apologize if it can be repetitive.another question is that if it's possible to register my subject with the probability map of cvs atlas ?? Best Regards, Nazanin

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From SUMA Dset file to Freesurfer file( overlay an gii file on fsaverage brain)
by Fatemeh.Molaei-Vaneghi 23 Dec '16

23 Dec '16

Dear FreeSurfers, I am doing some thing similar to the post from Zhiwei (https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg40540.html) I want to map visual topographic probabilistic maps (http://scholar.princeton.edu/napl/resources) from Princeton university onto the fsaverage. After converting these maps from SUAM 1d.dset format to FreeSurfer .gii format, I tried to map them onto fsaverage6 as annotation files in tksurfer. The following is the error message I got in tksurfer: mrisReadGIFTIfile: malformed data array [0] in file /Volume/DATA/Studies/FS/SubjectData/fsaverage6/maxprob_surf_lh.gii: num_vertices=198812 num_cols=1 expected nvertices=40962, num_cols=1 The probabilistic map (maxprob_surf_lh.gii) is composed of two columns, the first one being node index ranging from 1 to 198812 and the second one being node value (nodes/vertices that belong to a certain surface ROI get a certain value, the rest of the nodes get the value of zero in the second column). These maps have been created in icosahedron surface space (using SUMA's MapIcosahedron -ld 141) using Buckner40 dataset. Taking into account that fsaverage is also derived from this dataset I don't understand why there is no vertex correspondence between these two surface files. One should note that these files are 1d vectors, and therefore, no auxiliary information such as label and parent surface, etc is preserved in these files. With this intro, my question is how project these maps onto the fsaverage so they can be easily visualised in FreeSurfer? Best, Fatemeh Molaeii Max Planck Institute for Biological Cybernetics Spemannstr. 41 72076 Tübingen Germany

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Regarding: Segmentation violation detected (FS version dev6)
by Dr Sampada Sinha 23 Dec '16

23 Dec '16

Dear freesurfer experts, I am trying to so the brainstem segmentation with freesurfer version *freesurfer-Darwin-OSX-stable-v6-beta-20161116-5037eae. *I incorporated the MCR in the fs dev directory. When I run the command recon-all -s foo -brainstem-structures. I am getting the following error: MATLAB is exiting because of fatal error: /usr/local/freesurfer/bin/run_SegmentSubject.sh: line 38: 10488 Killed: 9 "${exe_dir}"/segmentSubject.app/Contents/MacOS/segmentSubject $args. Will you please let me know I do I fix this? My subjects were processed with: freesurfer-Linux-centos6_x86_64-dev-20160211. Presently I am working with freesurfer-Darwin-OSX-stable-v6-beta-20161116-5037eae. I am incorporating the matlab crash dump file with mail. Your help will be greatly appreciated. -- Sampada Research medical scientist (B) Department of Geriatric Mental Health (DGMH) King George Medical University Lucknow-226003 , Uttar Pradesh India

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Demeaning in DODS and correlation
by Jennifer Szeto 22 Dec '16

22 Dec '16

Hi Freesurfer experts, I am trying to look at differences in cortical thickness between the following three groups : (1) MCI, (2) AD, and (3) control group using mri_glmfit (DODS), and want to control for age (continuous) and gender (categorical). I have read some other posts on the topic of demeaning age (i.e. subtracting each participant's age from the entire sample's mean age) but I am not sure if I will need it/am on the right track so would like to seek some guidance from you. 1) In order to decide whether I should demean age when including age as a covariate in the fsgd file (i.e. using demeaned age instead of participants' 'real' age) and control for gender, I should include gender as a categorical factor and check if there are any interactions between groups (MCI/AD/Control) x gender x age by running the following: Research question: Are the three groups different in terms of cortical thickness, when controlling for age and gender Groups: FemaleMCI MaleMCI FemaleAD MaleAD FemaleControl MaleControl Contrast: 0 0 0 0 0 0 0.5 -0.5 -0.5 0.5 0 0 for group (i.e. MCI versus AD) x gender x age interaction 0 0 0 0 0 0 0.5 -0.5 0 0 -0.5 0.5 for group (i.e. MCI versus control) x gender x age interaction, and 0 0 0 0 0 0 0 0 0.5 -0.5 -0.5 0.5 for group (i.e. AD versus control) x gender x age interaction If none of these is significant, then re-run the analyses using DODS/fsgd file with contrasts of interest e.g.: 0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 for differences between MCI and AD. However, if there's there're any significant interactions, I will have to demean age? Is this correct? Or do i have to check for interactions in the first place at all given my research question? Also, say if there's a gender effect, how should I control for it? 2) On a separate question, if I want to correlate disease groups (MCI/AD) with behavioural data such as depression - i.e. to see if cortical thickness in MCI/AD is correlated with depression, do i just have to create fsgd files with only one group (MCI OR AD) in each file and correlate with depression by running contrast [0 1]? Sorry about this silly question, just wanted to double check as I am new to freesurfer! Thanks for your help in advance! Regards, Jen

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cortical label mask fro mri_glmfit
by neuroimage analyst 22 Dec '16

22 Dec '16

Hi, I am just curious whether the mask.mgh file returned in glmdir folder created from lh.cortex.label? If so, how can I create my own surface mask from the label file? (mri_label2vol and mri_vol2surf ?) Second: How can I compare the subcortical volumes between the 2 groups using glm tools in freesurfer? Specifically, how do I call mris_preproc and mri_glmfit? I appreciate your response. Thank you Regards --VM

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Poorly Designed Matrix Two Stage Longitudinal Model
by Timothy Hendrickson 22 Dec '16

22 Dec '16

Just following up on the message below. I would appreciate a comment on this issue. -Tim FreeSurfer experts, I am attempting to use the two stage model for longitudinal analysis, however I keep receiving an error message that my design matrix is poorly scaled. I'm curious, I've used the same format of design matrix on cross sectional without problem. Anyway here is the command line input: mri_glmfit --glmdir rh.AllSite.gender.group.Age.thickness.fwhm10.twoStage.glmdir \ --X DOSSDS.twoStage_AllSite_Gender_Group_Age.dat \ --y rh.testretest.thickness-spc.stack.fwhm10.mgh \ --C DiffDiagnosis_ControlMore_AcctSite_AcctGender_AcctAge_pairedAnalysis.DOSSDS.mtx \ --label rh.testretest.fsaverage.cortex.label \ --surf fsaverage rh The error message is attached within file error_designMtx_12_19_16.txt Additionally I've included my analysis script, all timepoint qdec file, temporal average qdec file, fsgd file, and design matrix. Any help would be extremely beneficial. -Tim Timothy Hendrickson Department of Psychiatry University of Minnesota Office: 612-624-6441 Mobile: 507-259-3434 (texts okay) Design matrix ------------------ 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -1.878; 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -2.325; 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -1.967; 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.552 0.000; 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.121 0.000 0.000 0.000; 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.588 0.000 0.000; 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.101 0.000; 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0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.274 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -1.127; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.027 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.554 0.000 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.741 0.000 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.292 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.127 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.949 0.000 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.135 0.000 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.913 0.000; 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.819 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.887 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.527 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.166; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.244; 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.031 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.563 0.000 0.000; 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.239 0.000 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.123 0.000; 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.161 0.000 0.000; 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.738 0.000 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.095 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.246 0.000; 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.104 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -1.095 0.000 0.000; 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.231 0.000 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.099 0.000 0.000; 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.524; 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.265 0.000 0.000; -------------------------------- ERROR: matrix is ill-conditioned or badly scaled, condno = 1e+08 -------------------------------- Possible problem with experimental design: Check for duplicate entries and/or lack of range of continuous variables within a class. If you seek help with this problem, make sure to send: 1. Your command line: mri_glmfit --glmdir rh.AllSite.gender.group.Age.thickness.fwhm10.twoStage.glmdir --X DOSSDS.twoStage_AllSite_Gender_Group_Age.dat --y rh.testretest.thickness-spc.stack.fwhm10.mgh --X DOSSDS.twoStage_AllSite_Gender_Group_Age.dat --C DiffDiagnosis_ControlMore_AcctSite_AcctGender_AcctAge_pairedAnalysis.DOSSDS.mtx --label rh.testretest.fsaverage.cortex.label --surf fsaverage rh 2. The FSGD file (if using one) 3. And the design matrix above Timothy Hendrickson Department of Psychiatry University of Minnesota Office: 612-624-6441 Mobile: 507-259-3434 (texts okay)

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pial surface errors - V6beta
by Antonin Skoch 22 Dec '16

22 Dec '16

Dear experts, I noticed that my original mail did not appear in mail archive, therefore I am reposting, to prevent this request left unattended. The reason of omitting the original post from mail archive was probably the attachments (screenshots). Therefore this time I am reposting without attachments. They are included in the original mail. Best regards and merry Christmas, Antonin Skoch From: Antonin Skoch <ansk(a)ikem.cz> To: <freesurfer(a)nmr.mgh.harvard.edu> Sent: 12/21/2016 1:41 AM Subject: pial surface errors - V6beta Dear experts, in my data I quite often encounter errors in pial surface which is probably induced by errors of white surface, in some cases caused by juxtacortical lesions (even in healthy subjects). See screenshots for examples. I have tested this on freesurfer V6 beta but it is commonly also found in older versions. I have uploaded the subject pial_errors.tar.gz to your server. The error showed pial_error1.png in probably caused by small error in white surface shape in 18,-18,63 (RAS). Another similar problem is in 15,10,53 (RAS). This is probably caused by juxtacortical lesion. Another problematic spot with juxtacortical lesion is on -7,18,47 (RAS). I have observed such kind of errors in quite a large number of subjects, these are either healthy controls or subjects with psychiatric ilnesses, therefore no particular apparent macroscopic pathology should be present in these subjects. Do you think that there could be some improvement in the mris_make_surfaces code to prevent this kind of behaviour (i.e. very small error in white has gross effect on pial), or it is necessary in all these cases to manually intervene ? Especially, I do not see any particular problem in image in case of pial_error1.png. And, in case of editing of problems errors by juxtacortical lesions I find fastest way to edit wm (due to the fact that I can constrain the brush not to draw to existing wm) in contrast to editing aseg (i.e. label the spot as a lesion). Do you think that aseg editing in contrast to wm editing has some benefit in this case? Regards, Antonin Skoch

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