Freesurfer March 2019

freesurfer@nmr.mgh.harvard.edu

114 participants
172 discussions

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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回复： How to extract cortical features of a ROI
by 310913949 05 Mar '19

05 Mar '19

External Email - Use Caution Hi Bruce， The ‘mris_anatomical_stats‘’ and 'mri_segstats’ command line just output mean thickness of ROI, but I want to know cortical thickness for each vertex in the ROI. So I think the key is to find the coordination of each vertex in the ROI,but I dont know how. I found that the RAS coordinates in the lh.cortex.label do not correspond to the coordinates in the lh.thickness. ------------------ 原始邮件 ------------------ 发件人: "Bruce Fischl"<fischl(a)nmr.mgh.harvard.edu>; 发送时间: 2019年3月5日(星期二) 晚上11:35 收件人: "Freesurfer support list"<freesurfer(a)nmr.mgh.harvard.edu>; 主题: Re: [Freesurfer] How to extract cortical features of a ROI Hi Kan you can give the surface-based label to mris_anatomical_stats and it will constrain it's calculations to the label (where it makes sense): mris_anatomical_stats -l <path to your label> ... you can probably do it with mri_segstats too - Doug will know cheers Bruce On Tue, 5 Mar 2019, 310913949 wrote: > > External Email - Use Caution > > Hi bruce?? > I still have some questions about extracting cortical features of a ROI. I used the > mri_label2label to sample the volumetric label to the surface of the subject. But I dont know how to > do next, as i found that the RAS coordinates do not correspond to the coordinates in the > lh.thickness, and why? > Besides, I found the web of how to extract cortical thickness values for a region-of-interest > (ROI) defined in volume > space(https://surfer.nmr.mgh.harvard.edu/fswiki/VolumeRoiCorticalThickness)… it doesn't report > the coordinates information. How can I get the ROI's coordinates information if i use this method? > Thanks for your help! > Kan Deng > > > > ------------------ Original ------------------ > From: "Bruce Fischl"<fischl(a)nmr.mgh.harvard.edu>; > Date: Fri, Jan 25, 2019 10:58 PM > To: "Freesurfer support list"<freesurfer(a)nmr.mgh.harvard.edu>; > Subject: Re: [Freesurfer] How to extract cortical features of a ROI > > Hi Kan > > if you draw a volumetric label you first need to sample it to the surface > of that subject (mri_label2label --sample). If all you want are stats from > the subject there is no need to map it to fsaverage - that is only > necessary if you want to use a group coordinate system. > > cheers > Bruce > > > > On Fri, 25 Jan 2019, > 310913949 wrote: > > > > > External Email - Use Caution > > > > > > Dear Freesurfer Experts, > > I want to extract cortical features of a ROI that I drawn manually on the rawavg.mgz in > Freesurfer, but i > > don't know how to do that. I think maybe first the lesion mask should be registered to the > standard templates > > fsaverage, but I don't know what the command line is , could you tell me? And secondly, I should > use > > mri_label2label to map the label to individual subject and extract the cortical features of the > ROI, am I right? > > I am looking forward to your reply!Thanks. > > > > Best wishes! > > Kan Deng > > > > > _______________________________________________ > Freesurfer mailing list > Freesurfer(a)nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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Postdoc positions at Washington University
by Aristeidis Sotiras 05 Mar '19

05 Mar '19

External Email - Use Caution ** Apologies for cross-postings ** The research group of Dr. Sotiras (www.asotiras.com) a part of the Electronic Radiology Lab at Washington University in St. Louis ( https://wustl.edu) is currently seeking a highly motivated postdoctoral research associate with a strong research record in computational neuroscience, computational image analysis, computer vision, or machine learning, who is interested in solving important clinical problems. The lab is a diverse and interdisciplinary team whose research focuses on advancing imaging informatics technology and developing advanced computational techniques for quantitative image analysis. The successful candidate will lead or participate in projects developing, implementing and applying machine learning algorithms for the analysis and interpretation of medical images and related clinical data. More specifically, the successful candidate will develop and apply computational and analytical techniques to quantify morphology and function from multi-modal imaging data, analyze the relationship between brain imaging markers and cognition or clinical status, and integrate this information into predictive tools for patient-specific diagnosis and prognosis with the goal of improving our understanding of brain structure in health and disease. Applications of interest include multi-modal neuroimaging studies in neurology and psychiatry. The successful candidate will have a unique opportunity to work closely with clinical investigators and will have ample opportunities to present research methods and scientific findings at national and international conferences, and to publish results in academic journals. The Mallinckrodt Institute of Radiology (https://www.mir.wustl.edu/) encompasses a multi-disciplinary research center, home to more than 200 research faculty, postdocs, and graduate students, with a vibrant culture of collegiality (e.g., journal groups, weekly seminars, etc). Researchers study the human brain using a number of biomedical imaging modalities, such as MRI, PET, optical imaging and microscopy and collaborate with academic institutions and industry across the world. MIR is considered a premier biomedical imaging center in the world and is a great place to grow as a scientist, and build a highly visible career at the intersection of neuroscience, medicine, and engineering. For more information, please follow the link https://tinyurl.com/SotirasPostDocAd, or contact Dr. Sotiras ( aristeidis.sotiras(a)wustl.edu) Interested applicants should send 1. a one-page cover letter summarizing their interests, how they can contribute to the lab, as well as the earliest date that they can start the appointment, 2. curriculum vitae, and 3. contact information for two references to Dr. Aristeidis Sotiras, aristeidis.sotiras(a)wustl.edu. Only applicants considered for employment will be contacted. Applications are reviewed on a rolling basis.

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HCP Course 2019: Get ready for more data, and a new way of getting it
by Jennifer Elam 05 Mar '19

05 Mar '19

External Email - Use Caution *More HCP data are coming....and will be released in a new way.....will you be ready? * This spring, the first data from the Lifespan Human Connectome Projects - (HCP) Development (healthy subjects ages 5-21) and HCP Aging (ages 36-100+) will be publicly released. Yay! Also, for the first time, we will be releasing these data on the cloud, via the NIMH Data Archive (NDA). This will enable computing on the cloud to analyze HCP and Lifespan data. Come to the 2019HCP Course: "Exploring the Human Connectome" <https://store.humanconnectome.org/courses/2019/exploring-the-human-connecto…> <https://store.humanconnectome.org/courses/2019/exploring-the-human-connecto…> , July 8 – 12, 2019 in Portland, Oregon, USA to learn all about the new data, how to access it on NDA, and using HCP methods, tools, and pipelines on your own neuroimaging data. This 5-day intensive course will provide training in acquisition, processing, analysis and visualization of whole brain imaging and behavioral data of humans and non-human primates using methods and tools developed by the WU-Minn-Oxford HCP consortium. Both new and current users of HCP data, methods, and tools are welcome and the course will cover both basic and advanced topics. For more info and to register visit the HCPCourse 2019 website <https://store.humanconnectome.org/courses/2019/exploring-the-human-connecto…> . If you have any questions, please contact us at: hcpcourse(a)humanconnectome.org We look forward to seeing you in Portland! Best, 2019 HCP Course Organizers -- Jennifer Elam, Ph.D. Scientific Outreach, Human Connectome Project Washington University School of Medicine Department of Neuroscience, Box 8108 660 South Euclid Avenue St. Louis, MO 63110 314-362-9387 elam(a)wustl.edu www.humanconnectome.org

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Computing LGI using Octave instead of Matlab
by Natasha Sharma 05 Mar '19

05 Mar '19

External Email - Use Caution Hello all, Is there a way to run LGI commands listed below using Octave instead of Matlab? I've read somewhere on the forums of being able to get Freesurfer to use Octave instead of Matlab in FSFAST but I'm not sure if it will work with the commands below. Could I please get instructions on how to do this? mris_compute_lgi --i lh.pial mris_compute_lgi --i rh.pial Any help is appreciated! Thank you.

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Processing MEMPRAGE
by Rusche, Johann 05 Mar '19

05 Mar '19

Dear Freesurfers, Reading through the mailing list I understood how to process our nmr MEPRAGEs and I would like to double-check we are using unpacksdcmdir correctly. Unpacking with unpacksdcmdir gives me two nifti files, a 4-echo nifti file (92MB) and a 1-echo nifti file (23MB). Am I correct to understand that the smaller 1-echo nifti file is the RMS created automatically? Can you confirm that this is the file that I want to send to recon-all vis-a-vis VBM processing? >From the unpacklog: 7 MEMPRAGE_tfl_mgh_multiecho_1mm_iso ok 256 256 176 4 MR.1.3.12.2.1107.5.2.19.45407.2014103021271096561867095 8 MEMPRAGE_tfl_mgh_multiecho_1mm_iso ok 256 256 176 1 MR.1.3.12.2.1107.5.2.19.45407.2014103021271165037167199 Thanks for your time! Johann __________________ Johann Rusche Graduate Research Student, Lazar Lab Department of Psychiatry – Neurosciences Massachusetts General Hospital

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Should I edit or reject?
by Ryan Wales 05 Mar '19

05 Mar '19

External Email - Use Caution Hi, Although there are many ways to correct for pial, WM, and segmentation defects, it seems common in the literature to forego any edits and simply either accept or reject a subject's FreeSurfer output in order to avoid subjectivity and potential over-editing. Another group rejected subjects if their defects spanned 6 or more slices, for example. Are there any guide lines on how much editing is too much editing? Should it just be edited enough so it looks tolerable by eye? Maybe by a few different raters? The defects vary in their severity, so it's difficult to know if a subject should be considered passable or requiring edits based on a minor defect. My current plan is to conduct my analysis once with excluding subjects with poor segmentation and then again, including those subjects' edited outputs. Do you agree with this logic? Thanks for your advice, Ryan -- Ryan Wales Graduate Student Cognition and Motor Control Neuroscience Laboratory Integrative Neuroscience Psychology Department Stony Brook University E-mail: ryan.wales(a)stonybrook.edu <ryanwales(a)stonybrook.edu>

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Re: [Freesurfer] Surface to Volume Conversion
by Bunting, Emma 05 Mar '19

05 Mar '19

External Email - Use Caution Hi Bruce, Yes, the brains were registered in fsaverage space. The issue I am facing is my sig.mgh output from GLM will not convert into a .nii file using mri_convert. I am trying to load them both as overlays not view them directly. The sig.mgh file loads fine, it is the .nii file that sees the problem. Similarly, if I do GLM directly with a .nii output, I see the same issues. You asked me to run mri_info on both the .mgh and .nii.gz files, which I gave in my previous email. Please let me know how to proceed from here! Best wishes, Bruce Fischl<https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from…> Mon, 04 Feb 2019 06:31:00 -0800<https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date…> Hi Emma the dimensions (163842 x 1 x 1) mean that these are overlays on the fsaverage surface. Can you remind me what issue you are trying to solve? cheers Bruce External Email - Use Caution Hi Bruce, I ran the mri_info command on the sig.mgh and the sig.lh.nii.gz files for participant 124 and got these outputs... For sig.mgh: Volume information for sig.mgh type: MGH dimensions: 163842 x 1 x 1 voxel sizes: 1.000000, 1.000000, 1.000000 type: FLOAT (3) fov: 163842.000 dof: 0 xstart: -81921.0, xend: 81921.0 ystart: -0.5, yend: 0.5 zstart: -0.5, zend: 0.5 TR: 0.00 msec, TE: 0.00 msec, TI: 0.00 msec, flip angle: 0.00 degrees nframes: 1 PhEncDir: UNKNOWN FieldStrength: 0.000000 ras xform present xform info: x_r = -1.0000, y_r = 0.0000, z_r = 0.0000, c_r = 0.0000 : x_a = 0.0000, y_a = 0.0000, z_a = 1.0000, c_a = 0.0000 : x_s = 0.0000, y_s = -1.0000, z_s = 0.0000, c_s = 0.0000 talairach xfm : Orientation : LIA Primary Slice Direction: coronal voxel to ras transform: -1.0000 0.0000 0.0000 81921.0000 0.0000 0.0000 1.0000 -0.5000 0.0000 -1.0000 0.0000 0.5000 0.0000 0.0000 0.0000 1.0000 voxel-to-ras determinant -1 ras to voxel transform: -1.0000 -0.0000 -0.0000 81921.0000 -0.0000 -0.0000 -1.0000 0.5000 -0.0000 1.0000 -0.0000 0.5000 -0.0000 -0.0000 -0.0000 1.0000 For sig.lh.nii.gz: Volume information for sig.lh.nii.gz type: nii dimensions: 163842 x 1 x 1 voxel sizes: 1.000000, 1.000000, 1.000000 type: FLOAT (3) fov: 163842.000 dof: 0 xstart: -81921.0, xend: 81921.0 ystart: -0.5, yend: 0.5 zstart: -0.5, zend: 0.5 TR: 0.00 msec, TE: 0.00 msec, TI: 0.00 msec, flip angle: 0.00 degrees nframes: 1 PhEncDir: UNKNOWN FieldStrength: 0.000000 ras xform present xform info: x_r = -1.0000, y_r = 0.0000, z_r = 0.0000, c_r = -68267.5000 : x_a = 0.0000, y_a = 0.0000, z_a = 1.0000, c_a = -2.5000 : x_s = 0.0000, y_s = -1.0000, z_s = 0.0000, c_s = 0.0000 Orientation : LIA Primary Slice Direction: coronal voxel to ras transform: -1.0000 0.0000 0.0000 13653.5000 0.0000 0.0000 1.0000 -3.0000 0.0000 -1.0000 0.0000 0.5000 0.0000 0.0000 0.0000 1.0000 voxel-to-ras determinant -1 ras to voxel transform: -1.0000 -0.0000 -0.0000 13653.5000 -0.0000 -0.0000 -1.0000 0.5000 -0.0000 1.0000 -0.0000 3.0000 -0.0000 -0.0000 -0.0000 1.0000 Please let me know if you need any other information in order to help with our problem, Best wishes, Emma Sent from Mail<https://go.microsoft.com/fwlink/?LinkId=550986> for Windows 10 ________________________________ From: freesurfer-bounces(a)nmr.mgh.harvard.edu <freesurfer-bounces(a)nmr.mgh.harvard.edu> on behalf of freesurfer-request(a)nmr.mgh.harvard.edu <freesurfer-request(a)nmr.mgh.harvard.edu> Sent: Monday, February 4, 2019 5:00:09 PM To: freesurfer(a)nmr.mgh.harvard.edu Subject: Freesurfer Digest, Vol 180, Issue 5 Send Freesurfer mailing list submissions to freesurfer(a)nmr.mgh.harvard.edu To subscribe or unsubscribe via the World Wide Web, visit https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer or, via email, send a message with subject or body 'help' to freesurfer-request(a)nmr.mgh.harvard.edu You can reach the person managing the list at freesurfer-owner(a)nmr.mgh.harvard.edu When replying, please edit your Subject line so it is more specific than "Re: Contents of Freesurfer digest..." Today's Topics: 1. gray matter volume of the entorhinal cortex (Laboratorio de Neurociencia Funcional) 2. Re: problem using recon-all (Shachar Gal) 3. Re: problem using recon-all (Glasser, Matthew) 4. Re: Surface to Volume conversion (Bunting, Emma) 5. Re: gray matter volume of the entorhinal cortex (Bruce Fischl) 6. Re: Surface to Volume conversion (Bruce Fischl) 7. Re: gray matter volume of the entorhinal cortex (Laboratorio de Neurociencia Funcional) 8. free surfer install issues (Jordan Fowler) 9. Re: free surfer install issues (Greve, Douglas N.,Ph.D.) 10. (no subject) (Jordan Fowler) ---------------------------------------------------------------------- Message: 1 Date: Mon, 4 Feb 2019 10:08:06 +0100 From: Laboratorio de Neurociencia Funcional <neuroaginglnf(a)gmail.com> Subject: [Freesurfer] gray matter volume of the entorhinal cortex To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> Message-ID: <CABUYonO85t9vP8NBw9HtWuET2n4i-9s-Q_LevCxZ_rK3U4YAew(a)mail.gmail.com> Content-Type: text/plain; charset="utf-8" External Email - Use Caution Dear Freesurfers, gray matter volume of the entorhinal cortex (EC) can be obtained either in the ?h.aparc.stats or ?h.BA_exvivo.stats/?h.BA_exvivo.thresh.stats files. Considering that all our T1 images were manually edited on a slice-by-slice basis to correct pial surface misplacements and erroneous white matter segmentation, which volume measure of the EC would be more precise? I assume that gray matter volume contained in ?h.BA_exvivo.thresh.stats were thresholded at a specific probability value (% of case overlapping). Which probability value was used? Is there a reference paper explaining how probabilistic maps of EC were built based on ex vivo MRI contrast? Thank you in advance. Best, Jose L. Cantero

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Freesurfer March 2019