Are those maps corrected for multiple comparisons or uncorrected? If corrected, how similar are the uncorrected? When doing a correction, you can get clusters that are just above significance in one analysis and just below sig in the other making it look like there are huge differences. I've looked at a bunch of FDG data comparing spmregister and bbregister and not found a big difference. Having said that, I think that spmregister in general does a better job on smooth images. I wrote my own version (not reliant on matlab or spm) called mri_coreg, which you can get from here
ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/mri_coreg
On 03/31/2016 02:44 PM, Alshikho, Mohamad J. wrote:
Hi Doug,
In order to run surface based analysis on PET maps, _I used the following steps: _
*_1. Co-registering SUVR to T1_*
bbregister --s name --mov suvr.nii --bold --init-fsl --reg reg.dat --o suvr_T1.mgh
*_2. Concatenate SUVR maps_*
mris_preproc --target fsaverage --hemi lh --iv subject1/suvr/suvr_T1.mgh subject1/suvr/reg.dat --iv subject2/suvr/suvr_T1.mgh subject2/suvr/reg.dat --iv … --projfrac 0.5 --out lh.suvr.mgh
*_3. Smoothing(sm)_*
mri_surf2surf --hemi lh --s fsaverage --fwhm 10 --cortex --sval lh.suvr.mgh --tval lh.suvr.sm10.mgh
*_4.Group analysis (GLM)_*
mri_glmfit--y lh.suvr.sm10.mgh --fsgd fsgd.txt --C X.mtx --surf fsaverage lh --cortex --glmdir lh.suvr
*_5. Correction for multiple comparisons ( Cluster –wise)_*
mri_glmfit-sim --glmdir lh.suvr --cache 1.3 neg --cwp 0.05 --2spaces # Cache 1.3 ( P<0.05)
·In a second analysis for the same set of data, I replaced the first step ( co registration) by the following command line:
spmregister --s name --mov suvr.nii --reg reg.dat --o suvr_T1.mgh
I got the results as attached ( figure1 using bbregister and figure 2 using SPM register)
Kindly,
1.Where this difference came from? In other words, should I expect such like difference between “bbregister” and “spmregister” ?
2.For co registration which command do you recommend “bbregister” or “spmregister” ?
Best,
Mohamad
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