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30132 discussions

Average myelination within ROI
by SHAHIN NASR 06 Jul '17

06 Jul '17

Hi Surfers, I want to measure the average amount of myelinations within my ROI(s), I have already generated my myelination maps on surfaces (e.g. lh.myelin_ProjFrac0p5.nii ). Then I used mri_segstats as below: mri_segstats --i lh.myelin_ProjFrac0p5.nii --slabel SBJ_001 lh lh.V1 --sum V1_Stats.txt --accumulate but the numbers don't make any sense. For instance: # ColHeaders Index SegId NVertices Area_mm2 StructName Mean StdDev Min Max Range 1 0 125261 82573.4 Seg0000 110280.4453 0.2922 -5.5000 51.0000 56.5000 2 1 1138 764.8 Seg0001 941.0988 0.1572 0.2675 1.4203 1.1527 How can mean=941.0988 when the max=1.4203 ??? Regards -- Shahin Nasr PhD in Cognitive Neuroscience Martinos Imaging Center, MGH Harvard Medical School

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Re: [Freesurfer] Still confused by group analysis..
by Douglas Greve 06 Jul '17

06 Jul '17

On 6/12/13 7:03 AM, Tudor Popescu wrote: > > Sorry Doug, wasn't sure whether you'd seen my last reply to this.. > > On 6 Jun 2013 21:37, "Tudor Popescu" <tudor3(a)gmail.com > <mailto:tudor3@gmail.com>> wrote: > > Thanks Doug, > > > > So if I want to use the study-specific "average" subject > surface with mri_surf2surf, rather than the generic > "fsaverage" one, how can this be done given that the > required ls.white file only exists in the latter but not > in the former? Can I just rename (or duplicate) ls.white > into ls.white_avg? > > You can copy it or create a symbolic link. > > So it is OK to copy the required lh.white file from the generic > average subject "fsaverage" and apply mri_surf2surf upon the > study-specific average subject ("average")? It still seems to me > that "average" should have its own (different) lh.white... > Yes it should. Did make_average_subject not create it? > > > > Shouldn't the input to mri_glmfit be the output of > mri_surf2surf? In the group tutorial, the former is > "lh.gender_age.thickness.10.mgh" while the latter is > "lh.gender_age.thickness.10B.mgh". Or should it just be > the unsmoothed output of mris_preproc? > > Yes, I can't remember why I put the "B" there, maybe to keep > people from overwriting lh.gender_age.thickness.10.mgh during > the tutorial and having to wait the 10min or so for it to get > recreated. > > But if all these commands transform the same file (output of > mris_preproc becomes input of mri_surf2surf, which then feeds into > mri_glmfit), then why is it still necessary to keep intact the > original lh.gender_age.thickness.10.mgh, when all you need for the > group analysis is the mri_surf2surf'd version? > Sorry, I'm lost here. What are the names of the files? > > > > Assuming the label files (corresponding to my ROI, and > extracted from the Destrieux atlas) are different for each > subject, and given mri_glmfit is called just once, for all > subjects, which label file (corresponding to what subject) > should I point it to? > > You need to create the label in the average subject. There is > a Destrieux atlas there. > > Again, by "average subject" do you mean here the generic one > (fsaverage) or the study-specific one (average)? Should the former > be used at any point of a group analysis, if the latter exists? > If you are doing your group analysis on a custom average, then do not use fsaverage at any point. doug

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content of "subjects_dir"
by Schumman Resonance 05 Jul '17

05 Jul '17

Dear free surfer community, Silly question here. I note the default "subjects_dir" in freesurfer contains a bunch of directories and files. If I set the environment so that my "subjects_dir" is not the default location, do I need to move any of the files in the original location to the new one? I hope this makes sense. Thanks, Kev

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Question mri_glmfit fwhm smoothing
by Van Der Deijl, Rosanne 05 Jul '17

05 Jul '17

Hi, I have a question about mri_glmfit. As an input, you give e.g. the stacked thickness data from all subjects, smoothed with a certain fwhm. But then as an output file, it gives an fwhm.dat file, which, at least in my case, contains another smoothing level. I noticed this because I ran mri_glmfit-sim forclusterwise multiple comparison correction, which gives me the error: ERROR: cannot find /usr/local/freesurfer/stable6/average/mult-comp-cor/fsaverage/rh/cortex/fwhm33/abs/th13/mc-z.csd As input I gave data smoothed to 25fwhm, and not fwhm33, as it seems to think. But indeed, in the fwhm.dat file it gives fwhm = 32.940499. I read in the tutorial that the fwhm.dat file contains the average FWHM of the residual. But still my questions are: - what this means - why mri_glmfit-sim would need this number, and whether/how I can run the mult. comparison correction on the 25fwhm-smoothed data. Thank you in advance! - Rose Commands: mri_glmfit: mri_glmfit --fsgd $CALMGREY/CALM_FSGD.txt dods --C CALMlong_contrast1.mat --C CALMlong_contrast2.mat --C CALMlong_contrast3.mat --C CALMlong_contrast4.mat --glmdir rh.CALMlong.thickness-spc.fwhm25 --y rh.CALMlong.thickness-spc.stack.fwhm25.mgh --cortex --surf fsaverage rh mri_glmfit-sim: mri_glmfit-sim --glmdir rh.CALMlong.thickness-spc.fwhm0 --cache 1.3 neg --cwp --2spaces

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mri_glmfit error (mri_reshape number of elements)
by Mcnorgan, Christopher 05 Jul '17

05 Jul '17

Hi, I’m running a group-level analysis and have encountered an error that results in a core dump. The first-level analyses were run using the fsaverage surfaces, rather than self surfaces. I’ve already inspected the individual contrasts, loading the fsaverage surface in tksurfer and rendering each subject’s contrast as an overlay, and would now like to compute the group-level results. I first ran isxconcat-sess, and saw no errors indicated in the program output. mri_glmfit was then called as followed, and produced the following output (same result for the rh): $ mri_glmfit --y ces.nii.gz --wls cesvar.nii.gz --osgm --surf fsaverage lh --glmdir glm.wls --nii.gz Reading source surface /home/chris/LDT/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 1.196.2.8 2012/11/01 18:51:41 greve Exp $ cwd /home/chris/LDT/RFX/LDT_fsaverage.sm4.rh/hi-v-lo cmdline mri_glmfit --y ces.nii.gz --wls cesvar.nii.gz --osgm --surf fsaverage lh --glmdir glm.wls --nii.gz sysname Linux hostname brocasarea machine x86_64 user chris FixVertexAreaFlag = 1 UseMaskWithSmoothing 1 OneSampleGroupMean 1 y /home/chris/LDT/RFX/LDT_fsaverage.sm4.rh/hi-v-lo/ces.nii.gz logyflag 0 usedti 0 labelmask /home/chris/LDT/fsaverage/label/lh.cortex.label maskinv 0 glmdir glm.wls IllCondOK 0 ReScaleX 1 DoFFx 0 wFile cesvar.nii.gz weightinv 1 weightsqrt 1 Creating output directory glm.wls Loading y from /home/chris/LDT/RFX/LDT_fsaverage.sm4.rh/hi-v-lo/ces.nii.gz Saving design matrix to glm.wls/Xg.dat Normalized matrix condition is 1 Matrix condition is 1 Found 149955 points in label. ERROR: mri_reshape: number of elements cannot change nv1 = 163842, nv1 = 537168 Pruning voxels by thr: 0.000000 Segmentation fault (core dumped) This seems to have its root in a mismatch between the vertices in the fsaverage lh.cortex label and those in the contrast data. However, I carried out subject-level analyses in the fsaverage surface space specifically to facilitate a group-level analysis in fsaverage surface space, so I can’t see where I went wrong. The only other post I could find that was similar was related to an attempt to carry out a group analysis in native surface space, which is not the case here. Thanks for your time

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Building your own fsaverage
by Silas 05 Jul '17

05 Jul '17

Dear FreeSurfer team, I'm making a surface based group analysis of 40 multiple sclerosis subjects using FreeSurfer tools. What are the advantages of building your own fsaverage (make_average_subject) instead of using the premade fsaverage? Would you in general recommend this? What is the pro's and con's of building your own fsaverage? All the best, Silas I searched the mailing stream and found this answer: 'make_average_subject script, but as Doug implies you probably don't need to. Our warp is high-enough dimensional that you rarely need to build average subjects from your own population'

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freesurfer@nmr.mgh.harvard.edu
by 糜超 05 Jul '17

05 Jul '17

Hi, I am preprocessing my fMRI data acquired with Philips. when I using the command 'dcmunpack -src . -targ sess01 -run 101 bold nii f.nii .....' The error occurred likes below: Found 31 total files. Found 12 unique series: 0 101 301 401 501 601 701 801 901 1001 1101 1201 Subject xxx Date xxx Time 110223 Institution xxxxx. 0 Survey unknown unknown unknown unknown unknown unknown ./PS_0002 101 Survey unknown unknown unknown unknown unknown 140 ./XX_0003 301 T1W_3D_TFE_ref unknown unknown unknown unknown unknown 190 ./IM_0004 401 FE_EPI unknown unknown unknown unknown unknown 2835 ./XX_0007 501 FE_EPI_EX1 unknown unknown unknown unknown unknown 2835 ./IM_0008 601 FE_EPI_EX2 unknown unknown unknown unknown unknown 2835 ./IM_0011 701 FE_EPI_EX3 unknown unknown unknown unknown unknown 2835 ./IM_0013 801 FE_EPI_EX4 unknown unknown unknown unknown unknown 2835 ./XX_0016 901 FE_EPI_EX5 unknown unknown unknown unknown unknown 2835 ./XX_0018 1001 FE_EPI_EX6 unknown unknown unknown unknown unknown 2835 ./IM_0019 1101 FE_EPI_EX7 unknown unknown unknown unknown unknown 2835 ./XX_0022 1201 FE_EPI_EX8 unknown unknown unknown unknown unknown 2835 ./XX_0024 mri_convert ./XX_0003 ./bold/101/f.nii --nskip 0 --ndrop 0 nskip = 0 ndrop = 0 $Id: mri_convert.c,v 1.179.2.7 2012/09/05 21:55:16 mreuter Exp $ reading from ./XX_0003... Starting DICOMRead2() dcmfile = ./XX_0003 dcmdir = . WARNING: tag image orientation not found in ./XX_0003 Ref Series No = 101 ERROR: bits = 0 not supported. My os is Linux 16.04 and the Freesurfer's version is 5.3.

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GLM fitting for the cortical volume
by Ali Radaideh 05 Jul '17

05 Jul '17

Dear FS experts, I have managed to run the *glm_fit* on the cortical thickness and area on a group of MS patients. However, i am not sure how to do this on the cortical volume. which file represents the cortical volume in subject's directory? Many thanks, -- *Ali M. Al-Radaideh. PhDHead of Department of Medical ImagingVice Dean of the Faculty of Allied Health SciencesThe Hashemite University, Zarqa, JordanW.phone +962 5 3903333 ext.5422, 5355, 5364Email: ali.radaideh(a)hu.edu.jo <ali.radaideh(a)hu.edu.jo>* *webpage: http://staff.hu.edu.jo/Default.aspx?id=ABZUPDzYeqM= <http://staff.hu.edu.jo/Default.aspx?id=ABZUPDzYeqM=>*

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Strange Discrepancy Between Activation Location on Surface vs. Volume
by Taylor, Johnmark 05 Jul '17

05 Jul '17

Hello, I am running into an odd issue. In particular, I have run a GLM analysis in *volume* space, and I am visualizing the results both on the surface using tksurfer, and on the volume using tkmedit. In both cases, I am using the register.dof6.dat file to do the registration. Oddly, when I visualize the results on the *surface*, the activation patch falls on the medial surface of the hemisphere, in the *precuneus*, whereas when I visualize the results on the *volume*, the patch is in the *intraparietal sulcus* (which is where we expect to see this particular activation). I have made a label out of the activation in both cases and viewed it in the other space just to make sure that it is in fact the same activation, and it is. Any idea what might be going on? It seems to me that it can't be an issue with the BOLD volume --> structural volume registration, since the activations look normal on tkmedit. It seems to me, then, that it must have to do with the relationship between the anatomical volume and the anatomical surface--could this be where the problem lies? Maybe an imperfect surface reconstruction? I've attached pictures of what I mean. The activation in question is in the right hemisphere. On the surface it can be seen on the medial part of the hemisphere , and in the volume it's where the red cursor is (as I mentioned, I verified by making labels that these are in fact the same activation). Thank you very much, JohnMark

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Using multimodal slab input for hippocampus subfield segmentation in FS6
by Frauke Beyer 05 Jul '17

05 Jul '17

Dear experts, I am trying to use T2-weighted images (0.5x0.5x1mm) as an additional scan to a T1-weighted image (0.7x0.7x0.7mm) for hippocampal subfield segmentation in Freesurfer 6. The T2-weighted data is a slab which includes the whole hippocampus and its y-axis is parallel to the hippocampal long axis (image dimensions [384,384,50]). When I try to use recon-all -s <subject_name> -hippocampal-subfields-T2 <file name of additional scan> <analysisID>I always get a faulty alignment (through visual inspection by overlaying T2.FSspace.mgz on T1.mgz in freeview or by looking at the quality control image T1_to_T2.v10.QC.gif), even when I manually align the images beforehand. Do you have any idea or hint how to improve the registration and thus start the segmentation correctly? I also tried to find out which registration tool is used for this (mri_robust_register?) in order to play around with it and would also be thankful for any hint on this. Thank you for your advice and time, Best, Frauke

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