Hello,
I am attempting to run probabilistic tractography analysis in a set of macaques. I think what I have done makes sense, but I would really appreciate some feedback on the pipeline. In particular, I have used a few suggestions from other forum posts which may be out of date - for example running msmt_csd on single shell data and creating my own 5TT image for a macaque study. This post is a bit long as I have tried to include the whole pipeline so apologies in advance, hopefully it is simple enough to navigate.
The data
From A macaque connectome for large-scale network simulations in TheVirtualBrain | Scientific Data Shen et al, 2019
9 adult male macaques, 7T Siemens MAGNETOM, b = 1000 s/mm2, 64 directions, 24 slices. 1mm isotropic.
Pre processing
Data released pre-processed having undergone BET, FSL topup and eddy correction. B0 images extracted.
MRTRIX pipeline
echo “mriconvert to .mif”
mrconvert $subj_output_folder/data.nii $subj_output_folder/DWI.mif -fslgrad $subj_output_folder/space-subject_desc-eddy_dwi_updated.bvec $subj_output_folder/space-subject_desc-eddy_dwi_updated.bval -datatype float32 -stride 0,0,0,1 -force -info
echo “dwibiascorrect”
dwibiascorrect ants $subj_output_folder/DWI.mif $subj_output_folder/DWI_bias_ants.mif -bias $subj_output_folder/bias_ants_field.mif -force -info
echo “dwi2response”
dwi2response dhollander $subj_output_folder/DWI_bias_ants.mif $subj_output_folder/response_wm.txt $subj_output_folder/response_gm.txt $subj_output_folder/response_csf.txt -voxels $subj_output_folder/RF_voxels.mif -force -info
echo “dwiextract”
dwiextract $subj_output_folder/DWI_bias_ants.mif - -bzero | mrmath - mean $subj_output_folder/meanb0.mif -axis 3 -force -info
echo “Generating mask”
dwi2mask $subj_output_folder/DWI_bias_ants.mif $subj_output_folder/DWI_mask.mif -force -info
echo “Generate FODs”
Because we only have single shell data, I have used a suggestion posted elsewhere on this forum. Basically we run this in the same way as a typical call to dwi2fod msmt_csd, however we leave out the grey matter parts. Original post here, hopefully I understood properly : FBA Single Shell - High GM FOD values - #2 by bjeurissen. A question I have is just how much will this affect the quality of my results, what kind of drawbacks should I mention, for example, in a limitations section of a paper?
dwi2fod msmt_csd $subj_output_folder/DWI_bias_ants.mif $subj_output_folder/response_wm.txt $subj_output_folder/wmfod.mif $subj_output_folder/response_csf.txt $subj_output_folder/csf.mif -mask $subj_output_folder/DWI_mask.mif -force -info
echo “Normalising data for ${1}”
#mtnormalise wmfod.mif wmfod_norm.mif csf.mif csf_norm.mif -mask DWI_mask.mif -check_norm mtnormalise_norm.mif -check_mask mtnormalise_mask.mif -force -info
Creating custom 5TT image
FSL FAST (via 5TTGEN) can segment the T1w image into gm, wm, and csf but cannot, understandably, locate the human-derived subcortical structures. Instead, I am using the 5TT image provided with the D99 macaque atlas. Here I extract each of the tissue types, warp it to diffusion space (using a transform performed elsewhere in my pipeline) and then put them all together to create an 5TT image suitable for MRTRIX. This was partially based on a forum answer given here: converting FSL FAST segmentation into 5ttgen format
I assume this is an okay approach overall, however the sub-cortical grey matter includes the brainstem and cerebellum, is this okay?
Running tractography
tckgen -act 5tt_output.mif -backtrack -maxlength 250 -nthreads 8 -cutoff 0.06 -select 10000000 -seed_dynamic wmfod_norm.mif wmfod_norm.mif tracks_10M.tck -force
tcksift2 -act 5tt_output.mif -out_mu sift_mu.txt -out_coeffs sift_coeffs.txt -nthreads 8 tracks_10M.tck wmfod.mif sift_1M.txt -force
Many thanks for taking the time to read this, hopefully it might be helpful for anyone performing a similar analysis. Any help or suggestions are really appreciated.
Sam