27 Jul
2020
27 Jul
'20
4:24 p.m.
You would have to divide them yourself. You can do this by: 1. Creating a label of that network (mri_annotation2label) 2. Creating a binary mask of that network by converting the label into a mask (mri_label2label with --outmask option and --regmethod surface) 3. Divide into individual "clusters" using mri_surfcluster --in mask.mgz --thmin 0.5 --ocn ocn.mgz 4. Get measures for each of the clusters, eg, mri_segstats --excludeid 0 --seg ocn.mgz --i lh.thickness --sum sum.network7.thickness.dat
On 7/27/2020 4:08 PM, Martin Juneja wrote:
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Hi Doug,
By each region I mean cortical measures of *every individual region that is part of a network*. For example, for the network 7 i.e., DMN, I am interested in getting cortical measures of 4 regions shown in the following screenshot in red color: (and similarly I am interested in getting cortical measures for every individual region of all other 6 networks as well)
DMN_Regions.png
On Mon, Jul 27, 2020 at 9:33 AM Douglas N. Greve <dgreve@mgh.harvard.edu mailto:dgreve@mgh.harvard.edu> wrote:
What do you mean "each region"? Do you mean each vertex? On 7/27/2020 2:19 AM, Martin Juneja wrote:External Email - Use Caution Dear Doug, I ran the following command, but it still gives me network-wise cortical measures. But I am actually looking for cortical measures of *each region* within each network: *mris_anatomical_stats -th3 -mgz -cortex 2500a/label/lh.cortex.label -f 2500a/stats/lh.aparc.Yeo7.stats -b -a **Yeo2011_7Networks_N1000.annot -c 2500a/label/aparc.annot.Yeo7.ctab 2500a lh white* INFO: using TH3 volume calc INFO: assuming MGZ format for volumes. INFO: using 2500a/label/lh.cortex.label as mask to calc cortex NumVert, SurfArea and MeanThickness. computing statistics for each annotation in Yeo2011_7Networks_N1000.annot. reading volume /Volumes/HD-DHTR6/01_Project_FreeSurfer/2500a/mri/wm.mgz... reading input surface /Volumes/HD-DHTR6/01_Project_FreeSurfer/2500a/surf/lh.white... Using TH3 vertex volume calc Total face volume 284736 Total vertex volume 281405 (mask=0) reading input pial surface /Volumes/HD-DHTR6/01_Project_FreeSurfer/2500a/surf/lh.pial... reading input white surface /Volumes/HD-DHTR6/01_Project_FreeSurfer/2500a/surf/lh.white... reading colortable from annotation file... colortable with 8 entries read (originally MyColorLUT) Saving annotation colortable 2500a/label/aparc.annot.Yeo7.ctab table columns are: number of vertices total surface area (mm^2) total gray matter volume (mm^3) average cortical thickness +- standard deviation (mm) integrated rectified mean curvature integrated rectified Gaussian curvature folding index intrinsic curvature index structure name atlas_icv (eTIV) = 1393613 mm^3(det: 1.397882 ) lhCtxGM: 279618.799 278859.000diff=759.8pctdiff= 0.272 rhCtxGM: 283065.244 282415.000diff=650.2pctdiff= 0.230 lhCtxWM: 221173.591 221952.500diff= -778.9pctdiff=-0.352 rhCtxWM: 221330.870 222469.500diff=-1138.6pctdiff=-0.514 SubCortGMVol57065.000 SupraTentVol1072714.504 (1069458.000) diff=3256.504 pctdiff=0.304 SupraTentVolNotVent1066020.504 (1062764.000) diff=3256.504 pctdiff=0.305 BrainSegVol1210413.000 (1208649.000) diff=1764.000 pctdiff=0.146 BrainSegVolNotVent1201232.000 (1200612.504) diff=619.496 pctdiff=0.052 BrainSegVolNotVent1201232.000 CerebellumVol 138356.000 VentChorVol6694.000 3rd4th5thCSF 2487.000 CSFVol 723.000, OptChiasmVol 112.000 MaskVol 1616427.000 8855 5731 27910.977 1.446 0.081 0.03512914.2FreeSurfer_Defined_Medial_Wall 2819918195453202.362 0.630 0.131 0.03137434.37Networks_1 2131313976386222.484 0.632 0.119 0.02723922.67Networks_2 1637710929293382.522 0.521 0.116 0.02419215.77Networks_3 12076 8058250132.797 0.650 0.119 0.02814613.27Networks_4 11151 7626290413.067 0.733 0.127 0.03316714.57Networks_5 1620510841329232.607 0.631 0.123 0.02822817.67Networks_6 3475523626783582.825 0.621 0.125 0.02949439.77Networks_7 On Fri, Jul 17, 2020 at 9:35 AM Douglas N. Greve <dgreve@mgh.harvard.edu <mailto:dgreve@mgh.harvard.edu>> wrote: Try something like mris_anatomical_stats -th3 -mgz -cortex ../label/lh.cortex.label -f ../stats/lh.yeo.stats -b -a ../label/lh.yeo.annot -c ../label/yeo.annot.ctab 1040 lh white Assuming that your yeo atlas is in $SUBJECTS_DIR/$subject/label/lh.yeo.annot On 7/15/2020 2:05 PM, Martin Juneja wrote:External Email - Use Caution Dear Doug, Thanks for your response ! Yes, I have Yeo atlas in the individual space, and recon-all.log has the following command: mris_anatomical_stats -th3 -mgz -cortex ../label/lh.cortex.label -f ../stats/lh.aparc.stats -b -a ../label/lh.aparc.annot -c ../label/aparc.annot.ctab 1040 lh white \n computing statistics for each annotation in ../label/lh.aparc.annot. Could you please help me in customizing this because it seems it gives me stats for each annotation e.g. stats for 34 areas (for Desikan atlas) and 7 networks (for Yeo 7 network, I think this is averaged over each network, correct?), but I am looking for stats of the regions which constitute those networks (e.g. stats for the areas which are part of the default mode network i.e., 4 individual stats of 4 individual red colored regions in the following figure). DMN.png On Wed, Jul 15, 2020 at 8:54 AM Douglas N. Greve <dgreve@mgh.harvard.edu <mailto:dgreve@mgh.harvard.edu>> wrote: If you have the Yeo atlas in the individual space, you can use mris_anatomical_stats to compute stats the same as in the Desikan atlas. Look in recon-all.log for the command line and customize it as needed On 7/14/2020 5:00 PM, Martin Juneja wrote:External Email - Use Caution Hi experts, I extracted network-wise cortical measures (i.e., 7 cortical thickness values for 7 networks for Yeo atlas). I was wondering if there is a way to get the cortical thickness of each ROI within each of these networks e.g., cortical thickness values of all the ROIs which constitute default-mode network of Yeo's 7 network parcellation, and then cortical thickness values of all the ROIs which constitute limbic network of Yeo's 7 network parcellation, and so on. I know Desikan atlas can be used to get morphometry measures of 34 ROIs per hemisphere. But the problem is that e.g., default-mode ROIs from Desikan atlas do not completely overlap with the DMN of 7-network parcellation from Yeo atlas. In other words, superior frontal cortex from default-mode network of Yeo 7 network parcellation is a big chunk compared to several small ROIs (some partial and some full) in Desikan atlas, so I do not see any way how to find ROIs which just match with that superior frontal cortex of default-mode of Yeo's 7 network. Any help would be much appreciated ! _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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