Hi Doug,

Yes. Dr. Yeo Thomas helped me figuring out ways to extract ROI values from Buckner 2011 7 Cerebellar network. Please see below for the steps I performed to extract time series from Buckner 2011 7 Cerebellar networks. If you could give your thoughts on the steps/process that'd be great. 

1. Run MNI152 1mm template through recon-all
    recon-all -i FSL_MNI152_FreeSurferConformed_1mm.nii -subjid Yeo2011_MNI152_FS
    recon-all -all Yeo2011_MNI152_FS

2. Warp the Buckner_atlas1mm.nii.gz (use the LooseMask) to freesurfe nonlinear volumetric space
    mri_vol2vol --mov $FREESURFER_HOME/average/Buckner_JNeurophysiol11_MNI152/Buckner2011_17Networks_MNI152_FreeSurferConformed1mm_LooseMask.nii.gz \
--s Yeo2011_MNI152_FS --targ $FREESURFER_HOME/average/mni305.cor.mgz --m3z talairach.m3z --o BucknerAtlas1mm_FSL.nii.gz --nearest

3. warp the BucknerAtlas1mm_FSI.nii.gz from freesurfer nonlinear
volumetric space to each subject:
    mri_vol2vol --mov $SUBJECTS_DIR/FSSub/mri/norm.mgz --s FSSub --targ BucknerAtlas1mm_FSL.nii.gz --m3z talairach.m3z --o Buckner2011_atlas_FSSub.nii.gz --nearest --inv-morph


4. Create a cerebellum gray matter mask in the native subject's space by applying mri_binarize to aparc2009s+aseg.mgz of the subject

    mri_binarize --i FSSub/mri/aparc.a2009s+aseg.mgz --match 8  --o cerebellum_mask_FSSub_m8.nii.gz     #  For left
    mri_binarize --i FSSub/mri/aparc.a2009s+aseg.mgz --match 47 --o cerebellum_mask_FSSub_m47.nii.gz     #  FOr right

5. Using this mask to mask the Buckner cerebellum parcellations

    fslmaths Buckner2011_atlas_FSSub.nii.gz -mas cerebellum_mask_FSSub_m8.nii.gz Buckner_atlas_FSSub_m8.nii.gz     #  For left
    fslmaths Buckner2011_atlas_FSSub.nii.gz -mas cerebellum_mask_FSSub_m47.nii.gz Buckner_atlas_FSSub_m47.nii.gz     #  For right

6. Functional data to anatomical space

    mri_vol2vol --mov fmcpr.nii.gz --fstarg --reg register.dof6.dat --o fmcpr.anat.nii.gz     #  functional into anatomical space

7. Extract L/R Cerebellar ROIs time series

     mri_segstats --seg {analysisrootdir}/analysis/Buckner_atlas_FSSub_m8.nii.gz   --ctab $FREESURFER_HOME/average/Yeo_JNeurophysiol11_MNI152/Yeo2011_7Networks_ColorLUT.txt --excludeid 0 --i fmcpr.anat.nii.gz --avgwf Cerebellum_networks_L.txt     #  extract Left Cerebellum time series 
mri_segstats --seg {analysisrootdir}/analysis/Buckner_atlas_FSSub_m47.nii.gz  --ctab $FREESURFER_HOME/average/Yeo_JNeurophysiol11_MNI152/Yeo2011_7Networks_ColorLUT.txt --excludeid 0 --i fmcpr.anat.nii.gz --avgwf Cerebellum_networks_R.txt     #  extract right Cerebellum time series

Cheers,
Sabin Khadka

On Tue, Jul 5, 2016 at 3:48 PM, Douglas N Greve <greve@nmr.mgh.harvard.edu> wrote:
has this question been answered?

On 06/17/2016 09:42 AM, curry_sam77@yahoo.com wrote:
> Hi all,
>
> I am trying to extract mean time series BOLD data using FSFAST from
> Cortical ROIs + sub cortical ROIs and cerebellum.
>
> preproc-sess -s rest210 -fsd bold -surface fsaverage lhrh -mni305
> -fwhm 6 -per-run -stc odd    mri_segstats --annot fsaverage lh aparc
> --i rest210/bold/001/fmcpr.odd.sm6.fsaverage.lh.nii.gz --excludeid
> 1000 --excludeid 1004 --avgwf CorticalTimeSeries_lh.txt
> mri_segstats --annot fsaverage rh aparc --i
> rest210/bold/001/fmcpr.odd.sm6.fsaverage.rh.nii.gz --excludeid 2000
> --excludeid 2004 --avgwf CorticalTimeSeries_rh.txt
>
> and for cortical time series
>
> mri_segstats --seg fsaverage/mri.2mm/aseg.mgz --ctab
> /opt/freesurfer/FreeSurferColorLUT.txt --id 10 --id 11 --id 12 --id 13
> --id 17 --id 18 --id 26 --id 49 --id 50 --id 51 --id 52 --id 53 --id
> 54 --id 58 --i rest210/bold/001/fmcpr.odd.sm6.mni305.2mm.nii.gz
> --avgwf subCorticalTimeSeries.txt
>
> But I am not sure on how exactly to extract time series from
> cerebellar regions. So if I want to extract time series from regions
> as described in Buckner et.al 2011. Could I just use mri_segstats for
> the labels 601-628 in FreeSurferCololLUT.txt? If not could you please
> direct me to processes that I can use.
>
>
>
> Sent from Yahoo Mail. Get the ap <https://yho.com/148vdq>
>
>
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MGH-NMR Center
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