Freesurfer

freesurfer@nmr.mgh.harvard.edu

19 participants
30088 discussions

mris_preproc not running
by Sims, Sara A (Campus) 02 Apr '19

02 Apr '19

External Email - Use Caution Hello! I have 786 subjects that I am trying to do a paired difference analysis using mri_glm. I am getting stuck at the mris_preproc stage. I have tried running it on a handful of subjects and it works wonderfully. However, when I run it on all the subjects it outputs an empty file. I think part of my problem is that I am having to do 1572 flags to enter all the subjects files (which I made a script to create, I did not hand type them). Is there a better way to do this? Do I just have too many subjects? This is what I have that isn’t working: mris_preproc --target fsaverage --hemi lh --isp $in/${network}/100206.${run1}/lhsurf.mgh --isp $in/${network}/100206.${run2}/lhsurf.mgh --isp $in/${network}/100307.${run1}/lhsurf.mgh --isp $in/${network}/100307.${run2}/lhsurf.mgh ……. --out $out/preproc_${network}_${pair}/lh.paired-diff.${network}_${pair}.mgh --f $sublist --paired-diff --no-cortex-only Thanks for the help! Sara Sims

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dt_recon ERROR: DTIloadBValues
by Daniel Callow 02 Apr '19

02 Apr '19

External Email - Use Caution Hello, I am running dt_Recon with the following specifications and am running into this issue? I wasn't running into this issue previously. Two changes I made are I added the --eres-save flag and I am running the code on the dev version instead of the freesurfer6.0 version. Any idea what could be contributing to this issue? I am running the following mri_convert image000001.dcm /Volumes/DANIEL/dti_freesurf/diffusion_recons/${subj}.${cond}/dwi.nii.gz dt_recon --sd /Volumes/DANIEL/dti_freesurf/diffusion_recons --i /Volumes/DANIEL/dti_freesurf/diffusion_recons/${subj}.${cond}/dwi.nii.gz --s ${subj}.${cond} --b dwi.bvals dwi.voxel_space.bvecs --eres-save --o /Volumes/DANIEL/dti_freesurf/dtrecon/${cond}.${subj} and get an error at this point #@#------------------------------- Fitting Tensors Mon Apr 1 15:16:29 EDT 2019 cd /Volumes/DANIEL/dti_freesurf/diffusion_recons/AES114.Rest mri_glmfit --y /Volumes/DANIEL/dti_freesurf/dtrecon/Rest.AES114/dwi-ec.nii.gz --glmdir /Volumes/DANIEL/dti_freesurf/dtrecon/Rest.AES114 --nii.gz --dti dwi.bvals dwi.voxel_space.bvecs --eres-save $Id: mri_glmfit.c,v 1.246 2017/02/15 21:04:18 greve Exp $ cwd /Volumes/DANIEL/dti_freesurf/diffusion_recons/AES114.Rest cmdline mri_glmfit --y /Volumes/DANIEL/dti_freesurf/dtrecon/Rest.AES114/dwi-ec.nii.gz --glmdir /Volumes/DANIEL/dti_freesurf/dtrecon/Rest.AES114 --nii.gz --dti dwi.bvals dwi.voxel_space.bvecs --eres-save sysname Darwin hostname 255a-280.umd.edu machine x86_64 user amos FixVertexAreaFlag = 1 UseMaskWithSmoothing 1 OneSampleGroupMean 0 y /Volumes/DANIEL/dti_freesurf/dtrecon/Rest.AES114/dwi-ec.nii.gz logyflag 1 usedti 1 glmdir /Volumes/DANIEL/dti_freesurf/dtrecon/Rest.AES114 IllCondOK 0 ReScaleX 1 DoFFx 0 Creating output directory /Volumes/DANIEL/dti_freesurf/dtrecon/Rest.AES114 Loading y from /Volumes/DANIEL/dti_freesurf/dtrecon/Rest.AES114/dwi-ec.nii.gz ... done reading. Using DTI Loading BValues from dwi.bvals ERROR: DTIloadBValues(dwi.bvals): could not scan value(s) Found 65 bvalues ERROR: DTIloadBValues(dwi.bvals): could not scan value(s) Loading BVectors from dwi.voxel_space.bvecs ERROR: DTIloadBValues(dwi.voxel_space.bvecs): could not scan value(s) Found 0 bvectorss ERROR: DTIloadBValues: no bvectors found in dwi.voxel_space.bvecs 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

2 2

Colourful Brain
by May, Markus 02 Apr '19

02 Apr '19

Hi, My university has a new FDM 3D printer which is able to print colours. They want to print a brain in colour or maybe in multiple colours. Is there anybody who knows how I could get it? Best, Markus ———————————— With best regards, M.Eng. Markus May Ph.D. Student at Technische Hochschule Mittelhessen - University of Applied Sciences Wiesenstr. 14, 35390 Gießen<x-apple-data-detectors://27/0>, Germany Gebäude D10, Raum 0.16 Telefon: 0049 641 309 2646<tel:0049%20641%20309%202646> Mail: markus.may(a)lse.thm.de<mailto:markus.may@lse.thm.de> Web: www.thm.de/imps<http://www.thm.de/imps><http://www.thm.de/imps>

3 3

(no subject)
by Giuliana Klencklen 02 Apr '19

02 Apr '19

External Email - Use Caution Hi FS experts, I use tksurfer (e.g., tksurfer fsaverage/ lh inflated -gray) to open the overlays (i.e., something.sig.cluster.mgh) I generated with the permutation simulation correction. It goes perfectly well for most of them expect for some, where the overlay does not show at all the significant clusters summarized in the something.sig.cluster.summary. I run the mri_glmfit-sim command again but the problem persists. Does anyone have a clue about what is going wrong? Many thanks, Giuliana Klencklen -- Giuliana Klencklen, Ph.D. Helen Wills Neuroscience Institute University of California, Berkeley 118 Barker Hall Berkeley, CA 94720-3190 510-395-0040 giuliana.klencklen(a)berkeley.edu

2 2

implausible values of BrainSegVolNotVent
by Antonin Skoch 01 Apr '19

01 Apr '19

External Email - Use Caution Dear experts, I noticed suspicious values of segmentation volume in aseg.stats in subjects processed with version 6, with recon-all modified for HCP data. There is striking difference between BrainSegVolNotVent and BrainSegVolNotVentSurf. The values of BrainSegVolNotVent seem implasibly high. See example values of one of my subjects: # Title Segmentation Statistics # # generating_program mri_segstats # cvs_version $Id: mri_segstats.c,v 1.121 2016/05/31 17:27:11 greve Exp $ # cmdline mri_segstats --seg mri/aseg.mgz --sum stats/aseg.stats --pv mri/norm.mgz --empty --brainmask mri/brainmask.mgz --brain-vol-from-seg --excludeid 0 --excl-ctxgmwm --supratent --subcortgray --in mri/norm.mgz --in-intensity-name norm --in-intensity-units MR --etiv --surf-wm-vol --surf-ctx-vol --totalgray --euler --ctab /my_freesurfer_home/ASegStatsLUT.txt --subject mysubj # sysname Linux # hostname myhost # machine x86_64 # user skoch # anatomy_type volume # # SUBJECTS_DIR /my_subjects # subjectname mysubj # Measure BrainSeg, BrainSegVol, Brain Segmentation Volume, 3474569.000000, mm^3 # Measure BrainSegNotVent, BrainSegVolNotVent, Brain Segmentation Volume Without Ventricles, 3432059.000000, mm^3 # Measure BrainSegNotVentSurf, BrainSegVolNotVentSurf, Brain Segmentation Volume Without Ventricles from Surf, 1444546.289023, mm^3 # Measure VentricleChoroidVol, VentricleChoroidVol, Volume of ventricles and choroid plexus, 33665.000000, mm^3 # Measure lhCortex, lhCortexVol, Left hemisphere cortical gray matter volume, 268625.129703, mm^3 # Measure rhCortex, rhCortexVol, Right hemisphere cortical gray matter volume, 268347.204256, mm^3 # Measure Cortex, CortexVol, Total cortical gray matter volume, 536972.333959, mm^3 # Measure lhCerebralWhiteMatter, lhCerebralWhiteMatterVol, Left hemisphere cerebral white matter volume, 158213.556983, mm^3 # Measure rhCerebralWhiteMatter, rhCerebralWhiteMatterVol, Right hemisphere cerebral white matter volume, 156867.398082, mm^3 # Measure CerebralWhiteMatter, CerebralWhiteMatterVol, Total cerebral white matter volume, 315080.955064, mm^3 # Measure SubCortGray, SubCortGrayVol, Subcortical gray matter volume, 159729.000000, mm^3 # Measure TotalGray, TotalGrayVol, Total gray matter volume, 1034461.333959, mm^3 # Measure SupraTentorial, SupraTentorialVol, Supratentorial volume, 1036297.289023, mm^3 # Measure SupraTentorialNotVent, SupraTentorialVolNotVent, Supratentorial volume, 1002632.289023, mm^3 # Measure SupraTentorialNotVentVox, SupraTentorialVolNotVentVox, Supratentorial volume voxel count, 2998814.000000, mm^3 # Measure Mask, MaskVol, Mask Volume, 5060060.000000, mm^3 # Measure BrainSegVol-to-eTIV, BrainSegVol-to-eTIV, Ratio of BrainSegVol to eTIV, 2.172140, unitless # Measure MaskVol-to-eTIV, MaskVol-to-eTIV, Ratio of MaskVol to eTIV, 3.163315, unitless # Measure lhSurfaceHoles, lhSurfaceHoles, Number of defect holes in lh surfaces prior to fixing, 62, unitless # Measure rhSurfaceHoles, rhSurfaceHoles, Number of defect holes in rh surfaces prior to fixing, 48, unitless # Measure SurfaceHoles, SurfaceHoles, Total number of defect holes in surfaces prior to fixing, 110, unitless # Measure EstimatedTotalIntraCranialVol, eTIV, Estimated Total Intracranial Volume, 1599606.583237, mm^3 # SegVolFile mri/aseg.mgz # SegVolFileTimeStamp 2018/05/09 15:43:12 # ColorTable /my_freesurfer_home/ASegStatsLUT.txt # ColorTableTimeStamp 2017/01/18 23:00:02 # InVolFile mri/norm.mgz # InVolFileTimeStamp 2018/05/07 13:29:02 # InVolFrame 0 # PVVolFile mri/norm.mgz # PVVolFileTimeStamp 2018/05/07 13:29:02 # Excluding Cortical Gray and White Matter # ExcludeSegId 0 2 3 41 42 # VoxelVolume_mm3 0.343 # TableCol 1 ColHeader Index # TableCol 1 FieldName Index # TableCol 1 Units NA # TableCol 2 ColHeader SegId # TableCol 2 FieldName Segmentation Id # TableCol 2 Units NA # TableCol 3 ColHeader NVoxels # TableCol 3 FieldName Number of Voxels # TableCol 3 Units unitless # TableCol 4 ColHeader Volume_mm3 # TableCol 4 FieldName Volume # TableCol 4 Units mm^3 # TableCol 5 ColHeader StructName # TableCol 5 FieldName Structure Name # TableCol 5 Units NA # TableCol 6 ColHeader normMean # TableCol 6 FieldName Intensity normMean # TableCol 6 Units MR # TableCol 7 ColHeader normStdDev # TableCol 7 FieldName Itensity normStdDev # TableCol 7 Units MR # TableCol 8 ColHeader normMin # TableCol 8 FieldName Intensity normMin # TableCol 8 Units MR # TableCol 9 ColHeader normMax # TableCol 9 FieldName Intensity normMax # TableCol 9 Units MR # TableCol 10 ColHeader normRange # TableCol 10 FieldName Intensity normRange # TableCol 10 Units MR # NRows 45 # NTableCols 10 # ColHeaders Index SegId NVoxels Volume_mm3 StructName normMean normStdDev normMin normMax normRange 1 4 14344 5109.0 Left-Lateral-Ventricle 33.1230 13.9291 5.0000 93.0000 88.0000 2 5 1383 503.7 Left-Inf-Lat-Vent 40.0911 14.4387 10.0000 86.0000 76.0000 3 7 38137 13247.3 Left-Cerebellum-White-Matter 80.9953 6.2349 21.0000 97.0000 76.0000 4 8 165378 56702.4 Left-Cerebellum-Cortex 63.3988 8.4866 13.0000 93.0000 80.0000 5 10 21580 7298.1 Left-Thalamus-Proper 86.6751 10.0317 34.0000 112.0000 78.0000 6 11 11020 3773.5 Left-Caudate 75.3689 7.7926 39.0000 107.0000 68.0000 7 12 12372 4236.2 Left-Putamen 81.2602 6.8960 41.0000 108.0000 67.0000 8 13 5644 1867.1 Left-Pallidum 90.9569 6.9118 39.0000 109.0000 70.0000 9 14 1863 663.4 3rd-Ventricle 38.3065 13.2033 11.0000 73.0000 62.0000 10 15 4110 1458.1 4th-Ventricle 31.4457 11.5906 11.0000 73.0000 62.0000 11 16 57498 19626.8 Brain-Stem 80.1195 8.8419 17.0000 111.0000 94.0000 12 17 11009 3687.8 Left-Hippocampus 64.4524 7.7197 21.0000 96.0000 75.0000 13 18 3836 1299.8 Left-Amygdala 62.8180 6.4462 33.0000 83.0000 50.0000 14 24 2872 958.5 CSF 44.5933 13.6240 15.0000 97.0000 82.0000 15 26 1621 547.5 Left-Accumbens-area 70.0956 6.6855 21.0000 87.0000 66.0000 16 28 12429 4141.1 Left-VentralDC 85.4310 10.3448 19.0000 108.0000 89.0000 17 30 49 25.9 Left-vessel 56.8163 7.2935 32.0000 69.0000 37.0000 18 31 1120 372.4 Left-choroid-plexus 53.3348 11.7100 18.0000 86.0000 68.0000 19 43 14869 5206.5 Right-Lateral-Ventricle 33.5337 14.0357 7.0000 91.0000 84.0000 20 44 657 266.5 Right-Inf-Lat-Vent 45.2953 12.1767 17.0000 88.0000 71.0000 21 46 56785 19552.6 Right-Cerebellum-White-Matter 77.5366 9.1843 19.0000 100.0000 81.0000 22 47 163433 56188.2 Right-Cerebellum-Cortex 63.3668 8.8083 16.0000 95.0000 79.0000 23 49 20893 6985.6 Right-Thalamus-Proper 86.0407 10.3032 27.0000 111.0000 84.0000 24 50 11145 3814.2 Right-Caudate 74.8492 7.5833 45.0000 103.0000 58.0000 25 51 14128 4888.6 Right-Putamen 80.7402 6.5321 44.0000 104.0000 60.0000 26 52 5341 1736.3 Right-Pallidum 93.4239 6.1032 46.0000 109.0000 63.0000 27 53 11399 3861.1 Right-Hippocampus 65.9275 7.3061 34.0000 102.0000 68.0000 28 54 4239 1412.2 Right-Amygdala 63.2640 6.9283 30.0000 102.0000 72.0000 29 58 1510 510.4 Right-Accumbens-area 71.0742 5.3612 48.0000 93.0000 45.0000 30 60 11723 3884.1 Right-VentralDC 86.7650 10.9309 23.0000 111.0000 88.0000 31 62 33 17.0 Right-vessel 56.2727 8.0478 39.0000 73.0000 34.0000 32 63 1310 439.3 Right-choroid-plexus 53.4656 12.4803 23.0000 85.0000 62.0000 33 72 0 0.0 5th-Ventricle 0.0000 0.0000 0.0000 0.0000 0.0000 34 77 2290 713.0 WM-hypointensities 71.8258 11.6413 36.0000 105.0000 69.0000 35 78 0 0.0 Left-WM-hypointensities 0.0000 0.0000 0.0000 0.0000 0.0000 36 79 0 0.0 Right-WM-hypointensities 0.0000 0.0000 0.0000 0.0000 0.0000 37 80 0 0.0 non-WM-hypointensities 0.0000 0.0000 0.0000 0.0000 0.0000 38 81 0 0.0 Left-non-WM-hypointensities 0.0000 0.0000 0.0000 0.0000 0.0000 39 82 0 0.0 Right-non-WM-hypointensities 0.0000 0.0000 0.0000 0.0000 0.0000 40 85 576 208.2 Optic-Chiasm 73.1979 15.5846 28.0000 100.0000 72.0000 41 251 2189 711.4 CC_Posterior 103.1558 13.1432 40.0000 120.0000 80.0000 42 252 1288 409.4 CC_Mid_Posterior 97.6079 16.6021 26.0000 120.0000 94.0000 43 253 1703 556.6 CC_Central 89.3676 13.4535 38.0000 111.0000 73.0000 44 254 1529 492.8 CC_Mid_Anterior 94.3931 14.5866 28.0000 119.0000 91.0000 45 255 2421 802.6 CC_Anterior 98.2759 13.0648 29.0000 118.0000 89.0000 It is in all my processed subjects. Could you please comment on? Thank you in advance, Antonin Skoch

2 1

Changing colour of clusters generated after MonteCarlo Correction
by Arsenije Subotic 01 Apr '19

01 Apr '19

External Email - Use Caution Dear experts, Is it possible to change the colour of the clusters generated after using Monte Carlo Corrections? I would like to have it either blue or red to represent thinning or thickening respectively, since there are many different cluster colours that may confuse readers at first glance. Thank you, Arsenije --- Arsenije Subotic | MSc Student Department of Clinical Neurosciences Hotchkiss Brain Institute, University of Calgary | T2N 4N1, Canada Tel: (403) 918-6970 arsenije.subotic1(a)ucalgary.ca

2 1

segmentHA_T1.sh
by Daniel Callow 01 Apr '19

01 Apr '19

External Email - Use Caution I thought a solution like this might work but there seems to be an issue reading in the file. I tried using mri_segstats on the .mgz file like this to create the stats file as this is what I do for my dt_recon results however it doesn't seem to work when I just want the volume stats? mri_segstats --seg /Volumes/DANIEL/freesurf/${cond}.${subj}.long.base_${subj}/mri/lh.hippoAmygLabels-T1.long.v21.HBT.FSvoxelSpace.mgz --ctab /Applications/freesurfer_dev/FreeSurferColorLUT.txt --id 226 --id 231 --id 232 --sum ${SUBJECTS_DIR}/${cond}.${subj}/stats/lhipp_volume.stats Any ideas on how to make this work or a potential other work around. 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

2 2

trouble converting between .mgz and .nii.gz
by JD Knotts 01 Apr '19

01 Apr '19

External Email - Use Caution Hello experts, As a sanity check in a pipeline I'm running I tried converting an atlas file containing visual ROIs from .mgz to .nii.gz and then immediately converting the resulting .nii.gz file back to .mgz: mri_convert lh.atlas.mgz lh.atlas.nii.gz --reslice_like subject_t1.nii.gz --resample_type nearest --out_data_type int --no_scale 1 mri_convert lh.atlas.nii.gz lh.atlas_reconverted.mgz --reslice_like subject_t1.nii.gz --resample_type nearest --out_data_type int --no_scale 1 Something is getting lost in this conversion and reconversion, because when I run the following two lines (to generate a label for V1) on the two different .mgz files, the original file correctly results in the identification of 1591 label voxels, while the converted/reconverted file incorrectly results in the identification of 111 label voxels, none of which overlap with the 1591 correct voxels: mri_vol2surf --src mri/lh.atlas.mgz \ --src_type FLOAT \ --srcreg mri/register.dat \ --hemi lh \ --surf white \ --o mri/lh.atlas.mgh mri_cor2label --c mri/lh.atlas.mgh \ --id 1 \ --surf SubID lh \ --l label/lh.V1_ventral.label Any feedback on where I'm going wrong here would be greatly appreciated! Thanks for your time, JD Knotts 1) FreeSurfer version: freesurfer-Darwin-lion-stable-pub-v5.3.0 2) Platform: Mac OSX 10.11.6 3) Darwin Jeffreys-MacBook-Pro.local 15.6.0 Darwin Kernel Version 15.6.0: Thu Jun 23 18:25:34 PDT 2016; root:xnu-3248.60.10~1/RELEASE_X86_64 x86_64 -- Doctoral Candidate, Cognitive Neuroscience Department of Psychology University of California, Los Angeles

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segmentHA_T1.sh
by Daniel Callow 01 Apr '19

01 Apr '19

External Email - Use Caution Hello, I have finished running segmentHA_T1.sh on my data and have gotten all the .mgz and .txt files in my mri folder. I would like to collect all the hippocampal volumes and get them into a table to run stats on. When reading the documentation it says that It will also produce files in the stats folder called hipposubfields.[lr]h.T1.v2x.stats and amygdalar-nuclei.[lr]h.T1.v2x.stats. These can be compiled across subject with, eg, asegstats2table --statsfile=hipposubfields.lh.T1.v21.stats --tablefile=hipposubfields.lh.T1.v21.dat ... However, the stats folders for my subjects do not have these files in the stats folder. Just the usual aseg.stats lh.aparc.a2009s.stats lh.aparc.DKTatlas.stats lh.aparc.pial.stats lh.aparc.stats lh.BA_exvivo.stats lh.BA_exvivo.thresh.stats lh.curv.stats lh.w-g.pct.stats rh.aparc.a2009s.stats rh.aparc.DKTatlas.stats rh.aparc.pial.stats rh.aparc.stats rh.BA_exvivo.stats rh.BA_exvivo.thresh.stats rh.curv.stats rh.w-g.pct.stats wmparc.stats Is there a way to get the correct stats files in my stats folder so I can use the suggested command? Or is there a way to extract the information from the .txt files? Any help you can provide would be greatly appreciated! *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

1 0

Custom Colormap
by Emanuel Alvaredo 01 Apr '19

01 Apr '19

External Email - Use Caution Hello! I would like to ask you about customs colormaps. I have created my own FreeSurferColorLUT.txt and I would like configure the color map using command line. Is it possible? Thanks in advance. Emanuel

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