I have three questions relating to a TBSS project, and if the answer to the first is “not appropriate” then I will need to apply feedback regarding the remaining two questions elsewhere.

I am tasked with comparing FA and ADC symmetry using tbss_sym between 2 equal sized groups (n=10 controls, n=10 athletes).

(1)
First, I would like to compare FA and ADC values . Unfortunately, differing diffusion acquisition parameters were used for each group, and I assume that this is not the recommended circumstance.
The bvals = 1000 in both groups. However, as shown below in the fslinfo output of one (pre eddy correction) file from each group, the resolution, mm per voxel, and number of direction gradients differ
between the groups. Would it be inappropriate to compare these two groups using tbss_sym? Assume preprocessing is completed in fsl (eddy correction, DTIFIT)

Controls
charlies-mbp:dti$ fslinfo s1_dti.nii.gz
data_type INT16
dim1 128
dim2 128
dim3 56
dim4 65
datatype 4
pixdim1 1.5000000000
pixdim2 1.5000000000
pixdim3 1.9999998808
pixdim4 6.9000000954
cal_max 0.0000
cal_min 0.0000
file_type NIFTI-1+

Athletes
charlies-mbp:EP2D_DIFF_MDDW_30_P2_0007$ fslinfo 57_KS.nii.gz
data_type INT16
dim1 122
dim2 122
dim3 60
dim4 31
datatype 4
pixdim1 1.9672131538
pixdim2 1.9672131538
pixdim3 2.0000000000
pixdim4 8.3000001907
cal_max 0.0000
cal_min 0.0000
file_type NIFTI-1+


(2)

After completing TBSS steps 1-4, and prior to running randomize I know I can use fslmeants on the all_FA_skeletonized.nii.gz 4D file
 (thank you Kirstie) to extract information for histograms. But I would like to make white matter masks for a few tracts including the CC,
uncinate and longitudinal faciculi, coronal radiate and cingulum. I can make white matter masks from the JHU white matter tractography atlas
and perhaps the JHU ICBM DTI white matter labels (though I am uncertain if a mask can be made from a label?). Is there any method other than simple
visual inspection and comparison for using the Mori et al., (2008) white matter atlas to make MNI space masks?

(3)

I would like to add some additional supportive statistics to the randomize statistical analysis using R statistics software. Any recommendations?
Perhaps setting some of the randomize output options (I don’t know the exact meaning of permuted vs unpermuted in the context
of randomise options, e.g. glm_output unpermuted case only). I have only a basic understanding of FSL and a basic-to-intermediate understanding of R.


As always, any feedback or suggestions would be very much appreciated.
  
Charlie