Freesurfer October 2013

freesurfer@nmr.mgh.harvard.edu

136 participants
244 discussions

Re: [Freesurfer] Tracula
by ebelleau＠uwm.edu 22 Oct '13

22 Oct '13

----- Original Message ----- From: ebelleau(a)uwm.edu To: freesurfer(a)nmr.mgh.harvard.edu Sent: Thursday, October 10, 2013 3:14:06 PM Subject: Tracula Hello, I had a question regarding batch processing in trac all. When you batch process and put in multiple subjects in your subject list in your configuration file, are the subjects run parallel or serial? Also, I am having a hard time finding the linux version of tracula that is compatible with Freesurfer version 5.0 (assuming it exists)? Thank you so much and I appreciate your guidance as I am brand new to freesurfer and tracula. Emily

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ill conditioned matrix with covariates that are unequal between groups
by Laura M. Tully 22 Oct '13

22 Oct '13

Hello freesurfers, I'm trying to run mri_glmfit between groups analysis (patients vs. controls) with gender, age, medication (continuous), and duration of illness as covariates. The problem is, only the patients have medication and duration of illness values - by virtue of being control participants, the control group have values of zero for medication and duration of illness. This causes issues with the matrix and I'm trying to figure out how to get around it. Suggestions would be most appreciated. See output and details below, plus attached files. Thanks! Laura. *Command line: * mri_glmfit --y lh_grpDiffs_thickness10.mgh --fsgd grpdiffs_thickness_lh_Med_Dur.fsgd --fsgd-rescale --C Contrast1A_grp_Diffs_HCvsSZ_Med_Dur.mtx --surf fsaverage lh --cortex --glmdir lh_grpdiffs_lh_thickness_Med_Dur.glmdir * * *Output:* gdfReadHeader: reading grpdiffs_thickness_lh_Med_Dur.fsgd INFO: demeaning continuous variables Continuous Variable Means (all subjects) 0 Age 36.0909 11.4927 1 LH_Mean_Thick 2.34931 0.118499 2 medication 204.545 330.217 3 duration_illness 7.74545 11.4005 Class Means of each Continuous Variable 1 HCmale 34.3000 2.3799 0.0000 0.0000 2 HCfemale 32.5556 2.3723 0.0000 0.0000 3 SZmale 36.5000 2.3124 391.2500 14.6875 4 SZfemale 42.2000 2.3265 499.0000 19.1000 INFO: gd2mtx_method is dods Reading source surface /ncf/snp/04/SCORE/freesurfer_analysis/fsaverage/surf/lh.white Number of vertices 163842 Number of faces 327680 Total area 65416.648438 AvgVtxArea 0.399267 AvgVtxDist 0.721953 StdVtxDist 0.195470 $Id: mri_glmfit.c,v 1.196.2.6 2011/05/05 20:54:25 greve Exp $ cwd /ncf/snp/04/SCORE/freesurfer_analysis/glm_analysis cmdline mri_glmfit --y lh_grpDiffs_thickness10.mgh --fsgd grpdiffs_thickness_lh_Med_Dur.fsgd --fsgd-rescale --C Contrast1A_grp_Diffs_HCvsSZ_Med_Dur.mtx --surf fsaverage lh --cortex --glmdir lh_grpdiffs_lh_thickness_Med_Dur.glmdir sysname Linux hostname ncfws14.rc.fas.harvard.edu machine x86_64 user ltully FixVertexAreaFlag = 1 UseMaskWithSmoothing 1 OneSampleGroupMean 0 y /ncf/snp/04/SCORE/freesurfer_analysis/glm_analysis/lh_grpDiffs_thickness10.mgh logyflag 0 usedti 0 FSGD grpdiffs_thickness_lh_Med_Dur.fsgd labelmask /ncf/snp/04/SCORE/freesurfer_analysis/fsaverage/label/lh.cortex.label maskinv 0 glmdir lh_grpdiffs_lh_thickness_Med_Dur.glmdir IllCondOK 0 ReScaleX 1 DoFFx 0 Creating output directory lh_grpdiffs_lh_thickness_Med_Dur.glmdir Loading y from /ncf/snp/04/SCORE/freesurfer_analysis/glm_analysis/lh_grpDiffs_thickness10.mgh INFO: gd2mtx_method is dods Saving design matrix to lh_grpdiffs_lh_thickness_Med_Dur.glmdir/Xg.dat Normalized matrix condition is 1e+08 Design matrix ------------------ 0.000 0.000 1.000 0.000 0.000 0.000 0.740 0.000 0.000 0.000 -1.700 0.000 0.000 0.000 0.329 0.000 0.000 0.000 0.817 0.000; 0.000 0.000 1.000 0.000 0.000 0.000 -0.392 0.000 0.000 0.000 0.233 0.000 0.000 0.000 0.632 0.000 0.000 0.000 -0.148 0.000; 0.000 0.000 0.000 1.000 0.000 0.000 0.000 -0.191 0.000 0.000 0.000 0.013 0.000 0.000 0.000 -1.511 0.000 0.000 0.000 -0.272; 0.000 0.000 1.000 0.000 0.000 0.000 0.479 0.000 0.000 0.000 0.427 0.000 0.000 0.000 0.212 0.000 0.000 0.000 0.729 0.000; 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.940 0.000 0.000 0.000 -0.907 0.000 0.000 0.000 0.912 0.000 0.000 0.000 -0.009; 0.000 0.000 1.000 0.000 0.000 0.000 1.436 0.000 0.000 0.000 -0.029 0.000 0.000 0.000 -0.276 0.000 0.000 0.000 1.431 0.000; 1.000 0.000 0.000 0.000 0.757 0.000 0.000 0.000 -0.235 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 1.018 0.000 0.000 0.000 -0.775 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 1.627 0.000 0.000 0.000 -0.032 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 1.953 0.000 0.000 0.000 -0.070 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 1.105 0.000 0.000 0.000 0.854 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 1.518 0.000 0.000 0.000 -0.864 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 -0.713 0.000 0.000 0.000 0.359 0.000 0.000 0.000 -0.784 0.000 0.000 0.000 -0.886; 0.000 1.000 0.000 0.000 0.000 1.083 0.000 0.000 0.000 0.428 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 -1.088 0.000 0.000 0.000 0.190 0.000 0.000 0.000 0.632 0.000 0.000 0.000 -0.937 0.000; 1.000 0.000 0.000 0.000 -0.548 0.000 0.000 0.000 -0.454 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 1.000 0.000 0.000 0.000 1.375 0.000 0.000 0.000 0.612 0.000 0.000 0.000 1.517 0.000 0.000 0.000 1.395; 1.000 0.000 0.000 0.000 0.496 0.000 0.000 0.000 0.010 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 0.131 0.000 0.000 0.000 0.435 0.000 0.000 0.000 -1.185 0.000 0.000 0.000 0.378 0.000; 0.000 0.000 1.000 0.000 0.000 0.000 -1.001 0.000 0.000 0.000 -0.864 0.000 0.000 0.000 0.212 0.000 0.000 0.000 -1.201 0.000; 0.000 1.000 0.000 0.000 0.000 -0.222 0.000 0.000 0.000 -2.138 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 0.931 0.000 0.000 0.000 -1.214 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 1.088 0.000 0.000 0.000 -0.856 0.000 0.000 0.000 -0.943 0.000 0.000 0.000 0.992 0.000; 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.070 0.000 0.000 0.000 0.401 0.000 0.000 0.000 -1.208 0.000 0.000 0.000 0.342; 0.000 0.000 0.000 1.000 0.000 0.000 0.000 1.114 0.000 0.000 0.000 -1.051 0.000 0.000 0.000 1.214 0.000 0.000 0.000 2.009; 0.000 0.000 1.000 0.000 0.000 0.000 -1.088 0.000 0.000 0.000 -0.147 0.000 0.000 0.000 0.212 0.000 0.000 0.000 -1.113 0.000; 0.000 0.000 0.000 1.000 0.000 0.000 0.000 -0.017 0.000 0.000 0.000 -1.219 0.000 0.000 0.000 0.306 0.000 0.000 0.000 0.868; 0.000 0.000 1.000 0.000 0.000 0.000 0.218 0.000 0.000 0.000 -0.721 0.000 0.000 0.000 -0.983 0.000 0.000 0.000 -0.323 0.000; 0.000 0.000 0.000 1.000 0.000 0.000 0.000 -1.149 0.000 0.000 0.000 2.139 0.000 0.000 0.000 0.154 0.000 0.000 0.000 -1.325; 0.000 0.000 0.000 1.000 0.000 0.000 0.000 -0.452 0.000 0.000 0.000 -0.325 0.000 0.000 0.000 -0.300 0.000 0.000 0.000 -1.061; 0.000 0.000 1.000 0.000 0.000 0.000 -0.044 0.000 0.000 0.000 0.486 0.000 0.000 0.000 -1.064 0.000 0.000 0.000 -0.060 0.000; 0.000 0.000 1.000 0.000 0.000 0.000 0.653 0.000 0.000 0.000 -0.392 0.000 0.000 0.000 -0.912 0.000 0.000 0.000 1.080 0.000; 0.000 0.000 0.000 1.000 0.000 0.000 0.000 -0.975 0.000 0.000 0.000 -0.021 0.000 0.000 0.000 -0.300 0.000 0.000 0.000 -1.061; 0.000 0.000 1.000 0.000 0.000 0.000 -1.349 0.000 0.000 0.000 0.579 0.000 0.000 0.000 0.026 0.000 0.000 0.000 -1.113 0.000; 0.000 0.000 1.000 0.000 0.000 0.000 -0.566 0.000 0.000 0.000 1.895 0.000 0.000 0.000 0.632 0.000 0.000 0.000 -0.850 0.000; 1.000 0.000 0.000 0.000 0.670 0.000 0.000 0.000 -0.809 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 -0.722 0.000 0.000 0.000 -0.716 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 -0.831 0.000 0.000 0.000 1.398 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 1.453 0.000 0.000 0.000 -0.978 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 0.670 0.000 0.000 0.000 -2.429 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 -0.744 0.000 0.000 0.000 -0.121 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 -0.657 0.000 0.000 0.000 0.082 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 1.018 0.000 0.000 0.000 1.318 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 -1.244 0.000 0.000 0.000 1.208 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 -1.179 0.000 0.000 0.000 0.444 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 -1.244 0.000 0.000 0.000 0.364 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 -0.809 0.000 0.000 0.000 1.790 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 -0.983 0.000 0.000 0.000 0.558 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 -1.157 0.000 0.000 0.000 0.254 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 -1.157 0.000 0.000 0.000 0.752 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 -0.918 0.000 0.000 0.000 0.841 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 1.088 0.000 0.000 0.000 -1.354 0.000 0.000 0.000 3.660 0.000 0.000 0.000 1.343 0.000; 0.000 0.000 1.000 0.000 0.000 0.000 -0.305 0.000 0.000 0.000 1.819 0.000 0.000 0.000 -1.185 0.000 0.000 0.000 -1.025 0.000; 1.000 0.000 0.000 0.000 -0.461 0.000 0.000 0.000 0.870 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 -1.418 0.000 0.000 0.000 -0.336 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000; -------------------------------- ERROR: matrix is ill-conditioned or badly scaled, condno = 1e+08 -- -- Laura M. Tully, PhD Post-Doctoral Fellow in Psychiatry UC Davis Imaging Research Center 4701 X Street, Sacramento, CA 95817 Phone: (916) 734-7927 Fax: (916) 734-8750 Alumnus of Social Neuroscience & Psychopathology Lab, Harvard University ltully(a)fas.harvard.edu Follow me on twitter: @tully_laura --

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Subject processing difference between computers
by West, John D. 22 Oct '13

22 Oct '13

Hello. We are moving our processing from an older super computer to a new one here at Indiana University. Preliminary testing is showing a significant difference between final summary stats results between subjects processed on the old system versus the new. The version of freesurfer on both systems is identical. Comparing the logs of the same subject run on both systems, I've found that the differences seem to start with the mri_watershed function specifically the main basin size. For the same subject we find: Old computer: main basin size=22667431830 voxels, voxel volume =1.000 = 22667431830 mmm3 = 22667431.936 cm3 New computer: main basin size=22208239350 voxels, voxel volume =1.000 = 22208239350 mmm3 = 22208239.616 cm3 As you can see they are very different. My theory is this is propagating through the rest of the process and giving us different summary stats at the end. I should note that both aseg and aparc stats are affected. I am also enclosing the logs for both runs as well as the differences found through the diff command (this is a lot parse through as even time stamp differences are picked up). Essentially what I am trying to find out is if these differences we are noticing are typical when changing computers and if not what could be causing these differences that I may not be picking up on. Thanks for the help. -John

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result is L-R fliped in native space
by wen.zhang55 22 Oct '13

22 Oct '13

Dear Freesurfer： I am now working in converting cortical labels from freesurfer to volumetric masks. First i gained the registration matrix by bbregister, then applied those matrix to tranform ROI labels into native space using label2vol. However, when i got the final .NII results, both of them turn out to be L-R fliped. (left ROI on the right hemisphere). From my understanding, freesurfer will automaticly recognize the head direction of the original T1 images and my original T1 data contains the flip information due to the way we scaned. So why this happen? By the way, the recon-all results did good. Thank you very much! best, Cowen 2013-10-22 wen.zhang55

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QDEC
by Franklin, Crystal G 22 Oct '13

22 Oct '13

Freesurfer, I want to make sure I am setting up my group analysis correctly. I have 2 groups, one a control population and the other a patient population , that I am wanting to see where there are differences controlling for age. I set the group as the fixed factor and I demeaned the ages and used them as the nuisance factor. Is it necessary to demean the ages before running QDEC? Thanks, Crystal

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Fwd: 2nd Whistler Scientific Workshop on Brain Function, March 9-12, 2014
by Thomas Yeo 22 Oct '13

22 Oct '13

---------- Forwarded message ---------- From: Todd Constable <todd.constable(a)yale.edu> Date: Thu, Oct 17, 2013 at 9:25 AM Subject: 2nd Whistler Scientific Workshop on Brain Function, March 9-12, 2014 To: Todd Constable <todd.constable(a)yale.edu> Cc: Todd Constable <todd.constable(a)yale.edu> The 2nd Whistler Scientific Workshop on Brain Functional Organization, Connectivity and Behavior Whistler, BC, Canada March 9-12, 2014 There has been an explosion of activity recently in the area of functional MRI and connectivity mapping and this small workshop in the beautiful town of Whistler BC is aimed at exploring the latest breakthroughs in analysis approaches, models, and applications. Emphasis will be on methods, the relationship of network measures to behavior, and the implications of network mapping in terms of broadening our understanding of functional neuroanatomy and basic neuroscience. The workshop will be kept small (less than 100 attendees) in order to facilitate discussion amongst all participants so please register early to ensure a spot. Abstracts: One page Abstracts for proffered talks or posters can be submitted to:lesley.nadeau@yale.edu. Topics: 1. Functional connectivity methods - network measures 2. Connectivity and Behavior 3. Integrated structural and functional connectivity – methods & stationarity and spectral components 4. Clinical applications of functional connectivity or multimodal measures of connectivity 5. Neuroanatomy and brain mapping For more information and to register please visit: fmriworkshop.yale.edu Target Audience: Experts in functional brain imaging and connectivity mapping, neuroscientists, psychologists, clinicians, and trainees (pre- and postdoctoral fellows, residents and clinical fellows), with an interest in brain function, functional organization, and neuroimaging. Organizing Committee: R. Todd Constable, Committee Chair, Yale University, New Haven, CT, USA Karla Miller, Oxford Centre for Functional MRI of the Brain, Oxford University, UK Marvin Chun, Yale University, Department of Psychology, New Haven, CT USA Bruce Pike, Division of Image Science, University of Calgary, Alberta, Canada Xiaoping Hu, Coulter Dept. of BME at Georgia Tech and Emory, Atlanta, GA USA Invited Speakers: Michael Breakspear, Systems Neuroscience, Queensland Inst. of Med. Research, AU Mark Chiew, Oxford University Centre for Functional MRI of the Brain, Oxford, UK Jessica Damoiseaux, Institute of Gerontology, Wayne State University, Detroit, MI Manus Donahue, Vanderbilt University Institute of Imaging Science, Nashville, TN Lila Davachi, Center for Neural Science & Center for Brain Imaging, NYU, NY, NY Maxime Guye, Centre de Resonance Magnetique Biologique et Medicale, France Xiaoping Hu, Coulter Dept. of BME at Georgia Tech and Emory, Atlanta, GA Angela Laird, Department of Physics, Florida International University, Miami, Fl Tom Liu, USD Center for Functional MRI, La Jolla, CA Zhongming Liu, Biomedical Engineering, Purdue University, West Lafayette, Indiana Joseph Mandeville, Martinos Center for Biomedical Imaging, Harvard, Boston, MA Galit Pelled, Kennedy Krieger Institute, Baltimore, MD Amir Shmuel, Montreal Neurological Institute, McGill University, Montreal, Canada Nick Turk-Browne, Department of Psychology, Princeton University, Princeton, NJ Martijn van den Heuvel, Dept. of Psychiatry, Rudolf Magnus Inst, Utrecht, Netherlands Gael Varoquaux, NeuroSpin, CEA Saclay, Gif-sur-Yvette, France Thomas Yeo, Dept. of Electrical and Computer Engineering, Natl. Univ. of Singapore Program: Sunday, March 9 Check-in: noon – 6pm 6:00 – 8:00pm Opening cocktail party Monday, March 10 7:30-8:30am Breakfast Session 1 Session Chair: 8:30 – 9:00am Gael Varoquaux: “What can we learn of brain connectivity by mining resting-state?” 9:00 – 9:30am Thomas Yeo: “Organization of the Human Brain Estimated by Intrinsic Functional Connectivity” 9:30 – 10:00am Tom Liu: “What can we learn from multimodal Imaging (fMRI, EEG, MEG) of resting state brain activity?” 10:00 – 10:30am Coffee 10:30-12 noon Proffered talks 10 – 15min each depending upon how many 12 noon Box Lunch 12:00 – 4:30pm Break 4:30 – 5:00pm Coffee Session 2 Session Chair: 5:00 – 5:30pm Galit Pelled: “Mapping and manipulating neuroplasticity” 5:30 – 6:00pm Lila Davachi: “Dynamic alterations in low-frequency BOLD connectivity related to memory encoding, retrieval and consolidation” 6:00 – 6:30pm Nick Turk-Browne: “Attention and modulation of connectivity between high and low level visual areas (or) category-specific connectivity in the visual cortex” 6:30 – 7:15pm Proffered talks Tuesday, March 11, 2014 7:30-8:30am Breakfast Session 3 Session Chair: Noam Harel 8:30 – 9:00am Martijn van den Heuvel: “The Rich Club: an anatomical infrastructure for communication and integration among functional brain networks” 9:00 – 9:30am Michael Breakspear: “Dwelling quietly in the rich club: Network determinants of mood and interoception” 9:30 – 10:00am 2-3 proffered talks 10:00 – 10:30am Coffee Break 10:30 – 11:00am Amir Shmuel: “Neurophysiological mechanisms underlying spontaneous fluctuations in hemodynamic signals” 11:00 – 11:30am Manus Donahue: “Hemodynamic and neurochemical constructs of functional connectivity” 11:30 – 12:00 Joe Mandeville: “Interpreting fMRI through PET correlations with occupancy within and beyond the classical model” 12 noon Box Lunch 12:00 – 4:30pm Break 4:30 – 5:00pm Coffee Session 4 Session Chair: 5:00 – 5:30pm 2-3 Proffered talks 5:30 – 6:00pm Zhongming Liu: “Multimodal Hyperspectral Imaging of Brain Activity and Connectivity” 6:00 – 6:30pm Mark Chiew: “Accelerating resting state FMRI acquisition with rank-constrained image reconstruction” 6:30 – 7:15pm 3-4 Proffered talks 7:30 – 9:30 Dinner/Reception Wednesday, March 12, 2014 7:30-8:30am Breakfast Session 5 Session Chair: 8:30 – 9:00am Jessica Damoiseaux: “Early detection of abnormal functional connectivity changes in older adults” 9:00 – 9:30am Maxime Guye: “Functional connectivity in epilepsy” 9:30 – 10:00am 2-3 Proffered talks 10:00 – 10:30am Coffee Break 10:30 – 11:00am Angela Laird: “Understanding Intrinsic Connectivity Networks: A Meta-Analytic Approach” 11:00 – 11:30am Xiaoping Hu: “Dealing with Individual Variability in Resting state Functional Connectivity” 11:30 – 12:00 2-3 Proffered talks 12:00 – 12:15pm Wrap up 12:15pm Box Lunch – meeting adjourned For additional information please go to: http://mrrc.yale.edu/home/seminars/workshop/index.aspx R. Todd Constable, Ph.D. Professor Diagnostic Radiology, Biomedical Engineering, and Neurosurgery Director MRI Research co-Director Yale MRRC Yale University School of Medicine The Anlyan Center 300 Cedar Street PO Box 208043 New Haven, CT 06520-8043 Tel: 203-785-5296 Fax: 203-785-6534 Website: http://mri.med.yale.edu Whistler Workshop on Brain Function, March 9-12, 2014 http://mrrc.yale.edu/home/seminars/workshop/index.aspx

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Segmentation and Surface Errors
by Jonathan Holt 22 Oct '13

22 Oct '13

Hi Guys, I'm having an issue with a brain involving both inaccurate surfaces and incorrectly segmented voxels. Basically, a sulcus is being skipped over by the main surface where and a bunch of basically empty space is being segmented as white matter. I've edited the segmentation myself by removing it and then I ran recon-all -autorecon2-noaseg -subjid <SUBJID> The problem is still persisting and the surface is still inaccurate. I can attach photos if needed. jon

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Make_average_subject
by Jürgen Hänggi 22 Oct '13

22 Oct '13

Dear FS experts When using make_average_subject the resulting volume files (T1.mgz, orig.mgz, brain.mgz, etc.) are cut off superiorly (see attachment). Any idea? Thanks in advance Regards Jürgen ---------------------------------------------------------------------------- Jürgen Hänggi, Ph.D. Division Neuropsychology Institute of Psychology University of Zurich Binzmuehlestrasse 14, PO Box 25 8050 Zurich, Switzerland 0041 44 635 73 97 (phone office) 0041 76 445 86 84 (phone mobile) 0041 44 635 74 09 (fax office) BIN 4.D.04 (office room number) j.haenggi[at]psychologie.uzh.ch (email) http://www.psychologie.uzh.ch/neuropsy/ (website) http://www.juergenhaenggi.ch (private website) This e-mail (and any attachment/s) contains confidential and/or privileged information. If you are not the intended recipient (or have received this e-mail in error) please notify the sender immediately and destroy this e-mail. Any unauthorised copying, disclosure or distribution of the material in this e-mail is strictly forbidden. ----------------------------------------------------------------------------

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Strange behavior of the dods option
by Jürgen Hänggi 22 Oct '13

22 Oct '13

Dear FS experts I compared two groups wrt cortical thickness using total grey matter volume as a covariate. The results obtained without the covariate (first row in the attached figure) and with the covariate modeled across both groups (doss option, second row in the figure) differed only in quantity (clusters are smaller with the covariate). However, when using the covariate with dods, the results changed completely (third row in figure) compared to doss and compared without the covariate. Then I tested (using dods) the covariates itself (using 0 0 1 -1) and get almost identical clusters (although in the opposite direction, forth row in the figure) as found in the group comparison with the covariate (dods, third row). In my opinion, this is puzzling! Any idea? Thanks in advance Regards Jürgen ---------------------------------------------------------------------------- Jürgen Hänggi, Ph.D. Division Neuropsychology Institute of Psychology University of Zurich Binzmuehlestrasse 14, PO Box 25 8050 Zurich, Switzerland 0041 44 635 73 97 (phone office) 0041 76 445 86 84 (phone mobile) 0041 44 635 74 09 (fax office) BIN 4.D.04 (office room number) j.haenggi[at]psychologie.uzh.ch (email) http://www.psychologie.uzh.ch/neuropsy/ (website) http://www.juergenhaenggi.ch (private website) This e-mail (and any attachment/s) contains confidential and/or privileged information. If you are not the intended recipient (or have received this e-mail in error) please notify the sender immediately and destroy this e-mail. Any unauthorised copying, disclosure or distribution of the material in this e-mail is strictly forbidden. ----------------------------------------------------------------------------

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Re: [Freesurfer] medial wall crossing the surface: repost
by silver47＠gmx.com 21 Oct '13

21 Oct '13

Thanks Bruce for your response, I'll fix aseg where it is incorrect, but what about the instances where the aseg is correct and the pial surfaces cross? Other than needing to update aseg.stats, are there any other concerns with the 'hemisphere' approach I described below? Thanks again, Paul > ----- Original Message ----- > From: Bruce Fischl > Sent: 10/22/13 09:46 AM > To: silver47(a)gmx.com > Subject: Re: [Freesurfer] medial wall crossing the surface: repost > > Try fixing the aseg and rerunning > Cheers > Bruce > > > On Oct 21, 2013, at 6:23 PM, "silver47(a)gmx.com" <silver47(a)gmx.com> wrote: > > > > Not always, but yes, in some instances the aseg crosses. > > > > > >> ----- Original Message ----- > >> From: Bruce Fischl > >> Sent: 10/18/13 01:22 PM > >> To: silver47(a)gmx.com > >> Subject: Re: [Freesurfer] medial wall crossing the surface > >> > >> Does the aseg cross as well? > >> > >> > >> > >>> On Oct 17, 2013, at 10:07 PM, "silver47(a)gmx.com" <silver47(a)gmx.com> wrote: > >>> > >>> Dear All, > >>> > >>> I too am seeing the medial pial surfaces crossing hemispheres in some subjects. > >>> > >>> The approach that appears to be working for me involves making copies of brainmask.mgz and aseg.mgz > >>> For the left hemisphere crossing into the right, edit brainmask.mgz and aseg.mgz to delete voxels at the crossing belonging to the right hemisphere. > >>> I then run recon-all -autorecon-pial -hemi lh -subjid subject > >>> followed by returning the brainmask.mgz and aseg.mgz back to their original copy. > >>> > >>> This is similarly repeated for the right hemisphere crossing into the left. > >>> > >>> Are there any issues or concerns with this approach particularly in regard to thickness or the segmentations? > >>> > >>> Thanks, > >>> > >>> Paul > >>> _______________________________________________ > >>> Freesurfer mailing list > >>> Freesurfer(a)nmr.mgh.harvard.edu > >>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > >> > >> > >> The information in this e-mail is intended only for the person to whom it is > >> addressed. If you believe this e-mail was sent to you in error and the e-mail > >> contains patient information, please contact the Partners Compliance HelpLine at > >> http://www.partners.org/complianceline . If the e-mail was sent to you in error > >> but does not contain patient information, please contact the sender and properly > >> dispose of the e-mail. > > > >

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Freesurfer October 2013