Freesurfer

freesurfer@nmr.mgh.harvard.edu

11 participants
30080 discussions

extracting cortical parcellation
by Atena Rezaei 06 Mar '19

06 Mar '19

External Email - Use Caution Hello, I am trying to extract the cortical parcellation(atlas) file from freesurfer with all the labels. I found the lh.aparc.annot file in label folder. Should I use mri_annotation2label to create labels? or how can I extract the whole file? Thanks for ur help! BR, Atena

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(no subject)
by Jahan, Bushra 06 Mar '19

06 Mar '19

Hello - I'm wondering how to generate a label in FreeSurfer from peak activation in a cluster? Is there a specific command?

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Whitening/weighting matrix in FSFAST
by Zhi Li 06 Mar '19

06 Mar '19

External Email - Use Caution Hello Freesurfer Experts, May I ask if there is a whitening/weighting matrix used to give weighted least squares estimates (WLS) and whiten the data during the 1st-level model estimation in FSFAST? If so, how could I get it? Many thanks and kind regards, Lizhi

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Group analysis
by Hengameh Marzbani 06 Mar '19

06 Mar '19

External Email - Use Caution Hi Freesurfers, I want to do group analysis on four groups and use the volume values in aseg,stats. 1- Is it correct that each subcortical segmentation is based on a "volumetric segmentation atlas" created by Freesurfer? 2- Are the volume values expressed in the space of this atlas? 3- Can I compare these absolute values between 4 groups ? 4- Should I apply correction to the volume measures by eTIV (also called ICV)? Thanks you in advance! Best regards, Hengameh

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citing freesurfer
by Daniel Callow 06 Mar '19

06 Mar '19

External Email - Use Caution Hello, How should I go about properly citing freesurfer if I am using dt_recon for dti analysis? Best, *Daniel Callow* *PhD Student, Neuroscience and Cognitive Science* Exercise for Brain Health Lab University of Maryland, College Park *ddcc2442(a)gmail.com <ddcc2442(a)gmail.com>* 443-254-6298

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Choroid plexus analysis
by Benjamin Luerweg 06 Mar '19

06 Mar '19

External Email - Use Caution Dear community, anyone out there who can give me some information regarding how valid a volume detection of the choroid plexus is? I used fs 6.0. Kind regards, Ben

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curved reformat image....
by Gonzalo Rojas Costa 06 Mar '19

06 Mar '19

External Email - Use Caution Hi: How can I get in an automatic way curved reformat images (for example, see paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009174/) Sincerely, Gonzalo Rojas Costa Laboratory for Advanced Medical Image Processing Department of Radiology Clínica las Condes Lo Fontecilla 441, Las Condes, Santiago, Chile. Tel: 56-2-2105170 Cel: 56-9-97771785 www.clc.cl http://www.aribrain.info https://scholar.google.com/citations?user=LO7LZ3oAAAAJ https://www.researchgate.net/profile/Gonzalo_Rojas2 https://cl.linkedin.com/in/gonzalorojascosta

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mris_ca_label: assignment of emty surface to wrong label
by Jörg Stadler 06 Mar '19

06 Mar '19

External Email - Use Caution Hi, I transfered the SPM Anatomy Toolbox (http://www.fz-juelich.de/inm/inm-1/DE/Forschung/_docs/SPMAnatomyToolbox/SPM…) from Collin space to fsaverage using Wu J, Ngo GH, Greve DN, Li J, He T, Fischl B, Eickhoff SB, Yeo BTT. *Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems* <http://people.csail.mit.edu/ythomas/publications/2018VolSurfMapping-HBM.pdf>, /Human Brain Mapping/ 39:3793–3808, 2018. mris_seg2annot --seg rh_Anatomy_v22c.nii.gz --ctab-auto Anatomy_v22c --s fsaverage --hemi rh --o rh.anat_v22c mris_seg2annot --seg lh_Anatomy_v22c.nii.gz --ctab-auto Anatomy_v22c --s fsaverage --hemi lh --o lh.anat_v22c This gave me the annot files for fsaverage (annotation.png) and a template for the color lookup table. Training of the classifier: mris_ca_train -t Anatomy_v22c_LUT.txt lh lh.sphere.reg anat_v22c.annot fsaverage lh.Anatomy_v22c.gcs mris_ca_train -t Anatomy_v22c_LUT.txt rh rh.sphere.reg anat_v22c.annot fsaverage rh.Anatomy_v22c.gcs Transfer to individual subject fsaverage: mris_ca_label fs_average lh sphere.reg lh.Anatomy_v22c.gcs Anatomy_v22c.annot mris_ca_label fs_average rh sphere.reg rh.Anatomy_v22c.gcs Anatomy_v22c.annot Now the miracle occurred and the whole surface got labeled. (mris_ca_label.png) How can a keep unassigned regions unlabeled? Joerg

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DPABISurf: A Surface-Based Resting-State fMRI Data Analysis Toolbox
by YAN Chao-Gan 06 Mar '19

06 Mar '19

External Email - Use Caution Dear Colleagues, We are pleased to announce the release of DPABISurf! 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 1.3.0.post3 (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 V4.0 (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 1.3.0.post3 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! 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 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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GLM twin design
by Julian Macoveanu 06 Mar '19

06 Mar '19

External Email - Use Caution Dear FS List, I wanted to check if my GLM design for a surface based analysis is sound. I am new to FS so please don’t skip over details. I have 3 groups of monozygotic twins: low-risk, high-risk, ill. Twin pairs may be present in the same group (e.g both are in the low-risk) or between groups (one of them in high-risk one in affected). Not all twins have their co-twin in the analysis though. In order to account for with-pair variance correlation I was wondering if this FSGD example with 9 subjects might do it or if there might be better options. GroupDescriptorFile 1 Title … Class LR Class HR Class ILL Variables age sex TIV Pair_no1 Pair_no2 Pair_no3 Input subject1 LR 30 M 123456 1 0 0 Input subject2 LR 30 M 123456 1 0 0 Input subject3 LR 23 F 123456 0 0 0 Input subject4 HR 20 F 123456 0 1 0 Input subject5 HR 21 F 123456 0 0 1 Input subject6 HR 22 M 123456 0 0 0 Input subject7 ILL 20 F 123456 0 1 0 Input subject8 ILL 21 F 123456 0 0 1 Input subject9 ILL 35 F 123456 0 0 0 I do have 60+ pairs so it will be a lot of variables. I know that PALM has an options to provide an exchangeability file, but FsPalm seems to perform corrections for multiple comparisons only. Julian

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