Freesurfer July 2019

freesurfer@nmr.mgh.harvard.edu

104 participants
165 discussions

label to asc
by Sam W. 29 Jul '19

29 Jul '19

External Email - Use Caution Hi, Is it possible to convert a label file like rh.cuneus.label into asc? I tried mris_convert --label rh.cuneus.label ctx-rh-cuneus rh.white rh.cuneus.asc but got an error: ERROR: unknown file annot file type specified for output: rh.cuneus.asc Thanks! Sam

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dcmunpack
by Gian D Pal 29 Jul '19

29 Jul '19

External Email - Use Caution Hi all, I have installed freesurfer and when I type in “dcmunpack -src . -scanonly scan.log”, I get “command not found.” Please advise. Thank you!

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Running FSL randomise on FS cortical thickness data
by Mark Wagshul 26 Jul '19

26 Jul '19

External Email - Use Caution Dear Freesurfer experts, Hi. We are analyzing cortical thickness data in Freesurfer, longitudinal data in 55 subjects using the linear mixed effects package. We are finding a number of siginificant clusters, and would like to correct for multiple comparisons with permutation testing. We have already run about 2000 permutations, and would like to use TFCE available in randomise rather than empirical methods to set parameters such as the cluster forming threshold. So, the simple question is, how do we convert our FS format data (these are F statistics at each vertex) into a format which can be read into randomise? Thanks for any advice you can provide. Best, Mark _______________________ Mark Wagshul, PhD Albert Einstein College of Medicine Bronx, NY Sent from my iPhone

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question regarding editing hippocampal subfield segmentation freesurfer 6.0
by Caroline Chwiesko 26 Jul '19

26 Jul '19

External Email - Use Caution Hello Freesurfer experts, I have a general question regarding the HIP subfield segmentation 6.0. Is it in general still recommended to edit the resulting HIP segmentation? I am asking because at 1mm resolution I am not really able to determine which voxel should be what subregion? If editing is still recommended what file and what method should be used for HIP editing? I attached two images as an example, which I think could benefit from some editing. Thank you very much in advance! [image: Screen Shot 2019-07-23 at 12.54.41 PM.png] [image: Screen Shot 2019-07-23 at 12.58.03 PM.png]

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Hippocampal and Amygdala Segmentation Error - Command not found
by Chen, Christine 26 Jul '19

26 Jul '19

External Email - Use Caution To whom it may concern, I am trying to run hippocampal and amygdala segmentation (https://surfer.nmr.mgh.harvard.edu/fswiki/HippocampalSubfieldsAndNucleiOfAm…) on FreeSurfer v6.0 (freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c). I was able to run recon-all, however I am unable to run segmentHA_T1.sh because the command doesn't exist in $FREESURFER_HOME/bin. Instead, there is segmentSF_T1.sh. I've tried searching the email listings for information comparing the two scripts, but haven't been able to find much. In order to run the hippocampal and amygdala segmentation, am I able to use segmentSF_T1.sh instead of segmentHA_T1.sh? Is there a more recent version of Freesurfer I have to install that includes segmentHA_T1.sh? Thank you in advance for the help. Best, CC ---------------------------------------------------------------------- Disclaimer: The materials in this e-mail are private and may contain Protected Information. Please note that e-mail communication is not encrypted by default. You have the right to request further emails be encrypted by notifying the sender. Your continued use of e-mail constitutes your acknowledgment of these confidentiality and security limitations. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying, distribution, or the taking of any action in reliance on the contents of this information is strictly prohibited. If you have received this e-mail in error, please immediately notify the sender via telephone or return e-mail.

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Mass-Univariate LME - help please!
by tom parker 26 Jul '19

26 Jul '19

External Email - Use Caution Dear all, I am currently running a Mass-Univariate LME analysis in Freesurfer. I have some doubts about the last steps and I was wondering whether someone could help me figure them out and also check what I have done so far (sorry for the long email). My study has 2 time points (baseline, follow-up), 3 groups (controls, patients without cognitive problems, patients with cognitive problems) and 2 covariates of no interest (age, sex). I am interested in looking at the interactions between group and time (mainly whether the 2 patient groups show more cortical thinning over time compared to controls). These are the commands I have ran so far through terminal or in matlab: 1) mris_preproc --qdec-long long.qdec.table.dat --target fsaverage --hemi lh --meas thickness --out lh.thickness.mgh mris_preproc --qdec-long long.qdec.table.dat --target fsaverage --hemi rh --meas thickness --out rh.thickness.mgh 2) mri_surf2surf --hemi lh --s fsaverage --sval lh.thickness.mgh --tval lh.thickness_sm15.mgh --fwhm-trg 15 --cortex --noreshape mri_surf2surf --hemi rh --s fsaverage --sval rh.thickness.mgh --tval rh.thickness_sm15.mgh --fwhm-trg 15 --cortex --noreshape 3) [Y,mri] = fs_read_Y('lh.thickness_sm15.mgh'); [Y,mri] = fs_read_Y('rh.thickness_sm15.mgh'); 4) lhsphere = fs_read_surf('fsaverage/surf/lh.sphere') rhsphere = fs_read_surf('fsaverage/surf/rh.sphere') lhcortex = fs_read_label('fsaverage/label/lh.cortex.label') rhcortex = fs_read_label('fsaverage/label/rh.cortex.label') 5) Create X matrix in matlab, here is a sample: intercept time group2 group2*time group3 group3*time age sex 1 0 0 0 0 0 41 1 1 2.42 0 0 0 0 41 1 1 0 1 0 0 0 53 1 1 2.5 1 2.5 0 0 53 1 1 0 0 0 0 0 54 2 1 5.25 0 0 0 0 54 2 5) lhstats = lme_mass_fit_vw(X, [1], Y, ni, lhcortex); rhstats = lme_mass_fit_vw(X, [1], Y, ni, rhcortex); 6) I entered 3 contrasts separately to check the interaction group*time. CM.C1 = [0 0 0 1 0 0 0 0]; this contrast will show if group 1 and 2 show a different cortical thinning pattern over time CM.C2 = [0 0 0 0 0 1 0 0]; this contrast will show if group 1 and 3 show a different cortical thinning pattern over time CM.C3 = [0 0 0 -1 0 1 0 0]; this contrast will show if group 2 and 3 show a different cortical thinning pattern over time 7) For each of the previous contrasts, i did the following: F_lhstats = lme_mass_F(lhstats, CM); F_rhstats = lme_mass_F(rhstats, CM); fs_write_fstats(F_lhstats, mri,' sigL.mgh', 'sig'); fs_write_fstats(F_rhstats, mri,' sigR.mgh', 'sig'); mri.volsz(4) = 1 [detvtx,sided_pval,pth] = lme_mass_FDR2(F_lhstats.pval,F_lhstats.sgn,lhcortex,0.05,0); [detvtx,sided_pval,pth] = lme_mass_FDR2(F_rhstats.pval,F_rhstats.sgn,rhcortex,0.05,0); 8) fs_write_Y(sided_pval,mri,'spval_lh.mgh'); fs_write_Y(sided_pval,mri,'spval_rh.mgh'); mri_surfcluster --subject fsaverage --hemi lh --in spval_lh.mgh --cwpvalthresh 0.05 --fdr 0.05 --sign pos --o lh_time_cluster --sum lh_time_cluster_sum mri_surfcluster --subject fsaverage --hemi rh --in spval_rh.mgh --cwpvalthresh 0.05 --fdr 0.05 --sign pos --o rh_time_cluster --sum rh_time_cluster_sum *My questions are:* - Is the order of the steps (1-8) correct? - How can I visualize the significant FDR-corrected results? - Is there any way I can apply a Monte Carlo correction using the generated files? Thank you so much!

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Research Assistant Position available at Brown University
by Hwamee Oh 26 Jul '19

26 Jul '19

External Email - Use Caution Dear colleagues: We are looking for a Research Assistant who will join a group of researchers on Aging and Alzheimer's disease at Brown University. 50% of time of the position will be devoted to behavioral and fMRI experiment preparation, data collection, and MRI/PET neuroimaging data analysis and the other 50% of time will be devoted to other types of data analysis and data management including online survey, biospecimen, and cognitive assessment. The successful candidate should have BS or BA in Psychology, Biology, Neuroscience, Computer Science, or closely related field, computer programming skills (e.g., Python, Unix), and prior experience or willingness to work with human participants and data in research or clinical settings. Knowledge on statistics and a career interest in pursuing age- and Alzheimer's disease-related fields are a plus. Interested applicants can submit applications through this website ( https://brown.wd5.myworkdayjobs.com/en-US/staff-careers-brown/job/Butler-Ho… ). For any inquiries on the research and the position, please contact Dr. Hwamee Oh at hwamee_oh@ <hwameeoh(a)gmail.com>brown.edu.

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Error running recon-all
by vin rasa 25 Jul '19

25 Jul '19

External Email - Use Caution Dear Freesurfer experts, Greetings! In the first minute of recon-all, we are getting following error: mghRead(/media/results/mri/rawavg.mgz): could not read 262144 bytes at slice 0 T1 data type is int16, data type 4 (looked using fslinfo) Looking forward to have your input! Thank you so much, Kind regards

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convert stl to .surf.gii
by Hanad Sharmarke 25 Jul '19

25 Jul '19

External Email - Use Caution Hello, I am attempting to convert a .stl file to a .surf.gii. I tried using mris_convert but that did not work. This is the command. mris_convert cb4mesh-dec250k_fixnMf6_Euler-448_scale001.stl cb4mesh-dec250k_fixnMf6_Euler-448_scale001.surf.gii This is the error I get. MRISreadSTLfile: error reading file cb4mesh-dec250k_fixnMf6_Euler-448_scale001.stl No such file or directory mris_convert: could not read surface file cb4mesh-dec250k_fixnMf6_Euler-448_scale001.stl No such file or directory I’m definitely in the right directory so I’m not sure why this error is occurring. Thank you, Hanad

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DPABISurf V1.1 Is Released
by YAN Chao-Gan 25 Jul '19

25 Jul '19

External Email - Use Caution Dear Colleagues, We are pleased to announce the release of DPABISurf V1.1! DPABISurf is a surface-based resting-state fMRI data analysis toolbox evolved from DPABI/DPARSF, as easy-to-use as DPABI/DPARSF. DPABISurf is based on fMRIPprep 1.4.1 (Esteban et al., 2018)(RRID:SCR_016216), and based on FreeSurfer 6.0.1 (Dale et al., 1999)(RRID:SCR_001847), ANTs 2.2.0 (Avants et al., 2008)(RRID:SCR_004757), FSL 5.0.9 (Jenkinson et al., 2002)(RRID:SCR_002823), AFNI 20160207 (Cox, 1996)(RRID:SCR_005927), SPM12 (Ashburner, 2012)(RRID:SCR_007037), PALM alpha112 (Winkler et al., 2016), GNU Parallel (Tange, 2011), MATLAB (The MathWorks Inc., Natick, MA, US) (RRID:SCR_001622), Docker (https://docker.com) (RRID:SCR_016445), and DPABI V4.1 (Yan et al., 2016)(RRID:SCR_010501). DPABISurf provides user-friendly graphical user interface (GUI) for pipeline surface-based preprocessing, statistical analyses and results viewing, while requires no programming/scripting skills from the users. <http://www.rfmri.org/dpabi> The DPABISurf pipeline first converts the user specified data into BIDS format (Gorgolewski et al., 2016), and then calls fMRIPprep 1.4.1 docker to preprocess the structural and functional MRI data, which integrates FreeSurfer, ANTs, FSL and AFNI. With fMRIPprep, the data is processed into FreeSurfer fsaverage5 surface space and MNI volume space. DPABISurf further performs nuisance covariates regression (including ICA-AROMA) on the surface-based data (volume-based data is processed as well), and then calculate the commonly used R-fMRI metrics: amplitude of low frequency fluctuation (ALFF) (Zang et al., 2007), fractional ALFF (Zou et al., 2008), regional homogeneity (Zang et al., 2004), degree centrality (Zuo and Xing, 2014), and seed-based functional connectivity. DPABISurf also performs surface-based smoothing by calling FreeSurfer’s mri_surf2surf command. These processed metrics then enters surfaced-based statistical analyses within DPABISurf, which could perform surfaced-based permutation test with TFCE by integrating PALM. Finally, the corrected results could be viewed by the convenient surface viewer DPABISurf_VIEW, which is derived from spm_mesh_render.m. <http://www.rfmri.org/dpabi> DPABISurf is designed to make surface-based data analysis require minimum manual operations and almost no programming/scripting experience. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies. DPABISurf is open-source and distributed under GNU/GPL, available with DPABI at http://www.rfmri.org/dpabi. It supports Windows 10 Pro, MacOS and Linux operating systems. You can run it with or without MATLAB. 1. With MATLAB. 1.1. Please go to http://www.rfmri.org/dpabi to download DPABI. 1.2. Add with subfolders for DPABI in MATLAB's path setting. 1.3. Input 'dpabi' and then follow the instructions of the "Install" Button on DPABISurf. 2. Without MATLAB. 2.1. Install Docker. 2.2. Terminal: docker pull cgyan/dpabi 2.3. Terminal: docker run -d --rm -v /My/FreeSurferLicense/Path/license.txt:/opt/freesurfer/license.txt -v /My/Data/Path:/data -p 5925:5925 cgyan/dpabi x11vnc -forever -shared -usepw -create -rfbport 5925 /My/FreeSurferLicense/Path/license.txt: Where you stored the FreeSurferLicense got from https://surfer.nmr.mgh.harvard.edu/registration.html. /My/Data/Path: This is where you stored your data. In Docker, the path is /data. 2.4. Open VNC Viewer, connect to localhost:5925, the password is 'dpabi'. 2.5. In the terminal within the VNC Viewer, input "bash", and then input: /opt/DPABI/DPABI_StandAlone/run_DPABI_StandAlone.sh ${MCRPath} Now please enjoy the StandAlone version of DPABISurf with GUI! If you don't want to run with GUI, you can also call the compiled version of DPABISurf_run. E.g., docker run -it --rm -v /My/FreeSurferLicense/Path/license.txt:/opt/freesurfer/license.txt -v /My/Data/Path:/data cgyan/dpabi /bin/bash /opt/DPABI/DPABI_StandAlone/run_DPABISurf_run_StandAlone.sh ${MCRPath} /data/DPABISurf_Cfg.mat New features of DPABISurf_V1.1_190725 (download at http://rfmri.org/dpabi) 1. New module for Surface-Based Temporal Dynamic Analysis (DPABI_TDA_Surf) was added. Dynamic regional indices (ALFF, fALFF, ReHo, Degree Centrality and Global Signal Correlation) and dynamic functional connectivity could be automatically calculated by one click through DPABI_TDA_Surf (with DPABISurf preprocessed data). The statistics maps (CV, Mean and SD) of the dynamic regional indices would also be generated by DPABI_TDA_Surf. A neuroimaging index which measures the concordance of the dynamic regional indices is incorporated into DPABI_TDA_Surf. Please see more details at: Yan, C.-G., Yang, Z., Colcombe, S.J., Zuo, X.-N., Milham, M.P., 2017. Concordance among indices of intrinsic brain function: insights from inter-individual variation and temporal dynamics. Sci Bull 62, 1572-1584. 2. The calculation of degree centrality now considers a vertex's correlation to both left and right hemispheres. 3. Standardization considers bilateral hemispheres. 4. Smooth function after Standardization was added. 5. If ICA-AROMA was chosen, no head motion realign parameters would be regressed out. 6. Fixed compatibility issues with old matlab versions. 7. The default surface-based smoothing kernel changed to 6mm instead of 10mm. 8. The DPABISurf results organizing function was added to the R-fMRI Maps Project. 9. Output an excel table for the volume of subcortical structures (calculated by freesurfer): {WorkingDir}/Results/AnatVolu/Anat_Segment_Volume.tsv. 10. DPABISurf_VIEW, the surface-based viewer now has a function to yoke between different viewers. 11. Docker updated basing on fMRIPrep 1.4.1. Besides pull from docker hub, the docker file can be also downloaded form here <ftp://ftpdownload:FTPDownload@lab.rfmri.org/sharing/DPABI/Docker/dpabi41docker.tar.gz> . 12. Besides the stand alone version of DPABI (with GUI), the compiled version of DPABISurf_run was also added to docker. Users can run DPABISurf_run with scripting. E.g., docker run -it --rm -v /My/FreeSurferLicense/Path/license.txt:/opt/freesurfer/license.txt -v /My/Data/Path:/data cgyan/dpabi /bin/bash /opt/DPABI/DPABI_StandAlone/run_DPABISurf_run_StandAlone.sh ${MCRPath} /data/DPABISurf_Cfg.mat Tips for Linux or Mac O: please start matlab from terminal in order to reach docker in DPABI (e.g., Linux: matlab; Mac: open /Applications/MATLAB_R2018a.app/). References: - Ashburner, J. (2012). SPM: a history. *Neuroimage*, 62(2), 791-800, doi:10.1016/j.neuroimage.2011.10.025. - Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C. (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. *Med Image Anal*, 12(1), 26-41, doi:10.1016/j.media.2007.06.004. - Cox, R.W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. *Comput Biomed Res*, 29(3), 162-173. - Dale, A.M., Fischl, B., Sereno, M.I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. *Neuroimage*, 9(2), 179-194, doi:10.1006/nimg.1998.0395. - Esteban, O., Markiewicz, C.J., Blair, R.W., Moodie, C.A., Isik, A.I., Erramuzpe, A., Kent, J.D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S.S., Wright, J., Durnez, J., Poldrack, R.A., Gorgolewski, K.J. (2018). fMRIPrep: a robust preprocessing pipeline for functional MRI. *Nat Methods*, doi:10.1038/s41592-018-0235-4. - Gorgolewski, K.J., Auer, T., Calhoun, V.D., Craddock, R.C., Das, S., Duff, E.P., Flandin, G., Ghosh, S.S., Glatard, T., Halchenko, Y.O., Handwerker, D.A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B.N., Nichols, T.E., Pellman, J., Poline, J.B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J.A., Varoquaux, G., Poldrack, R.A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. *Sci Data*, 3, 160044, doi:10.1038/sdata.2016.44. - Jenkinson, M., Bannister, P., Brady, M., Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. *Neuroimage*, 17(2), 825-841. - Tange, O. (2011). Gnu parallel-the command-line power tool. *The USENIX Magazine*, 36(1), 42-47. - Winkler, A.M., Ridgway, G.R., Douaud, G., Nichols, T.E., Smith, S.M. (2016). Faster permutation inference in brain imaging. *Neuroimage*, 141, 502-516, doi:10.1016/j.neuroimage.2016.05.068. - Yan, C.G., Wang, X.D., Zuo, X.N., Zang, Y.F. (2016). DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. *Neuroinformatics*, 14(3), 339-351, doi:10.1007/s12021-016-9299-4. - Zang, Y., Jiang, T., Lu, Y., He, Y., Tian, L. (2004). Regional homogeneity approach to fMRI data analysis. *Neuroimage*, 22(1), 394-400, doi:http://dx.doi.org/10.1016/j.neuroimage.2003.12.030. - Zang, Y.F., He, Y., Zhu, C.Z., Cao, Q.J., Sui, M.Q., Liang, M., Tian, L.X., Jiang, T.Z., Wang, Y.F. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. *Brain Dev*, 29(2), 83-91, doi:10.1016/j.braindev.2006.07.002. - Zou, Q.-H., Zhu, C.-Z., Yang, Y., Zuo, X.-N., Long, X.-Y., Cao, Q.-J., Wang, Y.-F., Zang, Y.-F. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. *Journal of Neuroscience Methods*, 172(1), 137-141, doi: http://dx.doi.org/10.1016/j.jneumeth.2008.04.012. - Zuo, X.-N., Xing, X.-X. (2014). Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective. *Neuroscience & Biobehavioral Reviews*, 45, 100-118, doi:http://dx.doi.org/10.1016/j.neubiorev.2014.05.009. Best, Chao-Gan -- Chao-Gan YAN, Ph.D. Professor, Principal Investigator Director, International Big-Data Center for Depression Research Deputy Director, Magnetic Resonance Imaging Research Center Institute of Psychology, Chinese Academy of Sciences 16 Lincui Road, Chaoyang District, Beijing 100101, China - Initiator <http://rfmri.org/DPARSF>DPABI <http://rfmri.org/DPABI> <http://rfmri.org/DPARSF>, <http://dpabi.org>DPARSF <http://rfmri.org/DPARSF>, PRN <http://rfmri.org/PRN> and The R-fMRI Network <http://rfmri.org> (RFMRI.ORG <http://rfmri.org/>) http://rfmri.org/yan http://scholar.google.com/citations?user=lJQ9B58AAAAJ

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Freesurfer July 2019