I would like to produce structural connectivity (SC) matrices from some DWI data (5 b0, 100 volumes acquired with b=3000) with MRtrix. The nodes of the SC matrix that I would like to produce are both cortical GM and subcortical GM regions (e.g. Desikan atlas parcels).
I was using grey matter-white matter interface as seed for whole-brain tract generation. However, BATMAN tutorial reports:
“One drawback of starting the streamlines at the gray-matter/white-matter-boundary is that streamlines between subcortical regions are hardly reconstructed. Therefore, if you are interested between such connections, you should create an additional streamlines file where you seed from a (binary)subcortical mask only, and combine both results.”
As I am interested in including subcortical GM regions in my SC matrices, I changed my pipeline as follows:
tckgen -act 5tissue_pm_coreg.mif -backtrack -seed_gmwmi gmwmSeed_coreg.mif -select 10000000 wmfod_norm.mif tracks_10mio.tck
tckgen -act 5tissue_pm_coreg.mif -backtrack -seed_image subcort_gm_mask.mif -select 10000000 wmfod_norm.mif tracks_sub10mio.tck
tckedit tracks_10mio.tck tracks_sub10mio.tck merged.tck
tcksift2 -act 5tissue_pm_coreg.mif merged.tck wmfod_norm.mif sift2_weigths.csv
tck2connectome -symmetric -zero_diagonal -scale_invnodevol -tck_weights_in sift2_weigths.csv merged.tck aparc_parcels_coreg.mif aparc.csv -out_assignment assignments_aparc.csv
Here are my questions:
- Do you think that generating a whole brain SC matrix from merged.tck, in this specific case, is more appropriate than generating it from tracks_10mio.tck (i.e. the .tck file generated just with seed_gmwmi gmwmSeed_coreg.mif)?
- When using the subcortical gray matter binary mask as -seed_image for tckgen, has -act option to be included or not? In other words, for generating tracks_sub10mio.tck, which is the best option bertween the following?
a) tckgen -act 5tissue_pm_coreg.mif -backtrack -seed_image subcort_gm_mask.mif -select 10000000 wmfod_norm.mif tracks_sub10mio.tck
b) tckgen -backtrack -seed_image subcort_gm_mask.mif -select 10000000 wmfod_norm.mif tracks_sub10mio.tck
Can you please help me with solving these doubts, in this specific framework? I would like to avoid introducing biases in SC matrices due to metodological choices.