Freesurfer

freesurfer@nmr.mgh.harvard.edu

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30070 discussions

Request to Post Job Position on Freesurfer listserv
by Otero Ospina, Andrea 01 May '25

01 May '25

External Email - Use Caution [A close up of a logo Description automatically generated][UGWqtuoMAAAAASUVORK5CYII=] Department of Psychiatry and Behavioral Sciences Postdoctoral Associate Position in Human Neuroimaging University of Miami Miller School of Medicine, Miami, Florida, USA The Gabbay Laboratory within the Department of Psychiatry at the University of Miami Miller School of Medicine is seeking highly motivated postdoctoral scholars in the field of human neuroimaging. Available positions will focus on analyzing fMRI and proton MR spectroscopy data as well as acquiring or processing related MRI and biological measures. Candidates should either have completed or be near completing a PhD in neuroscience, psychology, cognitive neuroscience, radiology, engineering, biostatistics, or a related field, and should have a strong background in quantitative analysis. Proficiencies with at least one major neuroimaging software suite (e.g. FSL, AFNI, SPM, Freesurfer, Caret, Connectome Workbench, etc.) and Unix/Linux environment are expected. Familiarity with Matlab, Python, and/or R programming languages is a plus. The primary responsibilities for this postdoctoral fellow will be to run scans, analyze neuroimaging data from multiple ongoing and recently concluded research projects, and write manuscripts. Under the mentorship of Vilma Gabbay, MD and Benjamin Ely, PhD, postdoctoral scholars will be expected to advance clinical and methodological knowledge through their postdoctoral training. Our lab is dedicated to the study and treatment of mood, anxiety, and substance use disorders in adolescent and high-risk adult (e.g. HIV+) populations using innovative neuroimaging and immunological methodologies. The neuroimaging program features high-resolution data acquisition and analysis techniques modeled on the Human Connectome Project (HCP) and is committed to the highest standards for magnetic resonance imaging and spectroscopy. The lab is funded by four ongoing NIH R01 neuroimaging studies (R01MH120601, R01DA054885, R01MH128878, and R01MH131207) and several smaller grants, providing unparalleled funding for active and exploratory studies. Major research topics include longitudinal studies of adolescent mood and anxiety disorders; comorbidity of depression, cannabis use, and HIV infection; and an intervention study aimed at improving well-being among healthcare workers with parental responsibilities during the COVID-19 pandemic. Project abstracts with further details can be found on NIH RePORTER. Resources available on-campus and through long-standing collaborations include multiple research-dedicated 3T MRI scanners (Siemens Skyra/Vida/Prisma) with cutting-edge accelerated and multi-echo fMRI sequences, high-performance scientific computing clusters, and an extensive library of analytic and statistical software. The PIs are fully committed to fostering the trainee's research independence through publication of first-authored manuscripts in top journals and mentorship in grant applications. In addition to assisting with the PI’s proposals, the successful applicant will be highly encouraged to develop and pursue funding for their own research project through federal (e.g. NIH F32) or foundation grant programs by the end of their first year. Please send a CV, statement of interest, and contact information for three references to axo833(a)med.miami.edu<mailto:axo833@med.miami.edu>. All qualified candidates will be given equal consideration. Priority will be given to candidates available to start sooner. Compensation will be proportional to experience and will follow standard postdoctoral fellowship rates posted by University of Miami. Andrea Otero, M.D. Clinical Research Laboratory Supervisor Department of Psychiatry and Behavioral Sciences University of Miami — Miller School of Medicine Email: axo833(a)med.miami.edu<mailto:njh115@med.miami.edu> Phone: 305-243-7086 [Image]

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Inquiry about the gtmpvc --reg-identity flag
by Y Zeng 01 May '25

01 May '25

External Email - Use Caution Hi, I am currently using mri_gtmpvc from PETSURFER for partial volume correction. I have registered the PET image to nu.mgz to get the coregistered PET image (in 1mm T1 space). As I also want to get the final output after PVC in the same space as PET scans, I use the –reg-identity flag, and the coregistered PET scan rather than the original PET scan as input. However, as the gtmseg.mgz is in the 0.5 mm space, it did give me results after Muller-Gartner PVC without any errors (the mgx.gm file is in the 1mm T1 space), but I am not sure if it is correct or not. Can you help me with this issue? I attached the code I used: mri_gtmpvc \ --i ${PET_smoothed_image_coregistered} \ --seg ${GTMSEG_resampled_to_T1mm} \ --reg-identity \ --default-seg-merge \ --no-reduce-fov \ --mgx .1 \ --psf 6 \ --no-rescale \ --o ${ROI_folder}/${FS_SUBJECT_ID}_gtmpvc_noscale Thanks, Yuheng

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mris_flatten limitations
by Glen, Daniel (NIH/NIMH) [E] 28 Apr '25

28 Apr '25

External Email - Use Caution I would like to abuse mris_flatten with a non-cerebrum surface. Will this work? It works for a sphere, but if I make cuts on more complicated shapes, it fails after hundreds of iterations with a "Segmentation fault". Are there any special tricks? Each patch file of cuts requires a different ?h.smoothwm file or h?.smoothwm file. If I copy the original surface to that requested file, the program continues. Is this correct? How can I predict this? I did manage to get one to make a flat.3D file, but display in Freeview or info from mris_info fails for that output, first using up large amounts of memory then crashes with another segmentation fault . Thanks, Daniel

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Join the mailing list
by Y Zeng 23 Apr '25

23 Apr '25

External Email - Use Caution Tsengyuheng1999(a)gmail.com

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mri_surf2vol error: mri_read(): code inconsistency (file type recognized but not caught)
by Loïc Daumail 20 Apr '25

20 Apr '25

External Email - Use Caution Dear all, I am trying to convert label files into volume ROI masks. This is my command below: mri_surf2vol --surfval /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/analysis/functional_surf_roi/sub-NSxLxYKx1964/sub-NSxLxYKx1964_hemi-L_space-fsnative_label-MT_mask.label --o /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/analysis/fonctional_vol_roi/sub-NSxLxYKx1964/sub-NSxLxYKx1964_hemi-L_space-fsnative_label-MT_mask.nii.gz --identity sub-NSxLxYKx1964 --hemi lh --fillribbon --template /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/derivatives/freesurfer/sub-NSxLxYKx1964/mri/brain.mgz I get this error: Reading surface /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/derivatives/freesurfer/sub-NSxLxYKx1964/surf/lh.white Done reading source surface Reading thickness /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/derivatives/freesurfer/sub-NSxLxYKx1964/surf/lh.thickness Done INFO: reading /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/analysis/functional_surf_roi/sub-NSxLxYKx1964/sub-NSxLxYKx1964_hemi-L_space-fsnative_label-MT_mask.label as unknown mri_read(): type = 32 error: mri_read(): code inconsistency (file type recognized but not caught) ERROR: could not read /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/analysis/functional_surf_roi/sub-NSxLxYKx1964/sub-NSxLxYKx1964_hemi-L_space-fsnative_label-MT_mask.label as unknown Any idea what would cause this? Is it because the labels weren't saved in the correct format? Thanks, Best, -Loïc

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problems processing of 7T mp2rage sequences
by Sabrina Turker 18 Apr '25

18 Apr '25

External Email - Use Caution Dear all, I'm new to freesurfer and I'm trying to preprocess a 7T mp2rage dataset (0.6mm³ isotropic, TR=6000ms, TE=2.05ms) with freesurfer 7.2.0. The images have been corrected for B1, for intensity inhomogeneity with ANTs N4 bias field removal and they were skull striped with FSL based on intensity. This was done bya colleague who analyzed this dataset in 2017 and I'm hoping to run our updated toolboxes on the preprocessed data. However, he ran into several problems while first analyzing the data: freesurfer did not like the noisy background of the mp2rage, which is why he skullstripped the data before preprocessing it with freesurfer. He then ran recon-all once with the default pipeline and once with skull strip deactivated. According to him, the default pipeline yielded better results. I'm using his skull-stripped data now for the preprocessing and was wondering whether not deactivating the skull strip will lead to inaccurate measures for cortical thickness and gray matter volume. Since this is crucial for our toolboxes, we really want to be sure that we're going for the best solution. A second problem is the high resolution of the data - it's not recommended to run the default recon-all on anything lower than 1mm³ (https://secure-web.cisco.com/1yYZ_CG0JlkOTN6kC9-IeDAZVMirVH7tWBx6OXS1Dwof-Q…) but most of the information I could find is from 2016 or 2017. At the time, he used the -hires flag but got very bad results, which made him use the default pipeline even if the mp2rage data got downsampled. I was wondering if anybody has feedback on how they analyzed a similar dataset (high resolution, problems with skullstripping etc.), general opinions on the best way to analyze this dataset and/or if there have been any developments (e.g., new flags for recon-all flags?) that I'm not aware of. We got feedback from one colleague that they did analyze their dataset successfully with the -hires flag and did not encounter any problems at the time. Any advice would be much appreciated! Best and thanks in advance, Sabrina -- Dr. Sabrina Turker (she/her) Postdoctoral researcher Brain and Language Lab Department of Behavioral & Cognitive Biology University of Vienna

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Unsubscribe
by Katarzyna Ciesla 17 Apr '25

17 Apr '25

External Email - Use Caution hi twice i unsscribed from the maiing list because the amount of emsils I get is just too much. I keep getting them So again, pless unsubscribe me. Dr Katarzyna Cieśla-Seifer The Baruch Ivcher Institute for Brain, Cognition & Technology, The Baruch Ivcher School of Psychology and the Ruth and Meir Rosental Brain Imaging Center, Reichman University, Herzliya, Israel, World Hearing Center, Warsaw, Poland

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SUVr in PET neurological images
by Gonzalo Rojas Costa 17 Apr '25

17 Apr '25

External Email - Use Caution Hi: Is it possible to compute SUVr in neurological PET images ? Using PETSurfer? How? Sincerely, Gonzalo Rojas Costa Adjunct Assistant Professor School of Medicine Finis Terrae University

2 1

clarification - recon-all output data
by Stefanie Bradley 17 Apr '25

17 Apr '25

External Email - Use Caution Hi there, I wanted to clarify about the recon-all output: is it in the subject's native space or is everything already mapped to fsaverage space? For example, is aparc.stats based on the brain in the subject's native space or has the cortical surface already been transformed to fsaverage space, and then stats (ex., cortical thickness) derived after that? Thanks, Stefanie

3 8

LME ROI group analysis
by Bondi, Daniel 16 Apr '25

16 Apr '25

External Email - Use Caution Hi FreeSurfer experts, I am using the univariate LME pipeline (lme_fit_FS & lme_F) to conduct cortical thickness analysis on a subset of D-K atlas ROIs between two groups. Although I am testing longitudinal group*time differences with this pipeline, I am wondering if it is valid to test group-level differences (irrespective of time) with this model as well. I have been doing so by specifying only the group covariate in the contrast matrix before running the model. Thank you, Daniel Bondi

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