Extract pathway after Sift2

[ThomasHMAC]

Thank you for your thorough answer, it actually helped me lots and I am sorry for the confusion. I will try my best to clarify both methods that I have done.

@rsmith

needs to be stated if your principal interest is part b?

First of all, my original purpose was conducting a “ targeted tracking” from ROI_1 to ROI_2 and estimate the structural connectivity. The ROIs masks were created from GM regions of Shen parcellation atlas that already co-registered to individual diffusion space, and these ROIs represent the left and right rostral middle frontal.The reason I chose these ROIs in mini “targeted tracking experiment” is to replicate the similar findings of previous study. Hence, I used the steps described in method_b to perform my mini “ targeted tracking” experiment.

b)

1.tckgen FOD.mif pathway.tck -algorithm iFOD2 –act 5TT.mif -seed_image ROI_1 -include ROI_2 -seeds 0 -seed_unidirectional -select 50000 -stop -backtrack

2.tckedit WB.tck pathway.tck combined.tck -nthreads 0

3.ticksift2 combined.tck FOD.mif combined_probweight.txt

4.tckedit combinded.tck pathway_postsift2.tck -include ROI_1 -include ROI_2 -tck_weights_in combined_pro_weight -tck_weights_out pathway_prob_weight -ends_only -nthreads 0

5.Structural connectivity of pathway is the sum of all streamlines weight

@rsmith

For method a, the mechanism of selection of streamlines corresponding to the pathway of interest is restricted to the ROIs at the streamlines endpoints. In method b, use of tckedit means that conditions other than the endpoints of the streamlines can be used in determining which streamlines are attributed to the pathway of interest and which are not.

If i used the option -ends_only in step 4 above, I believe this would only test the ends of each streamline against my included ROIs? If so how does it different compared to radial_search in tck2connectome in method_a because my ROIs’ mask correspond to node 146 and node 11 of the Shen atlas?

@rsmith

This all leads to “the structural connectivity represent track counts from both direction”. It would be accurate to say that structural connectivity is calculated based on streamlines that were generated in both directions.

At this point, I will try to experiment tckgen without the option unidirectional.

@rsmith

I certainly hope I’ve never said that; please provide a link if I’ve given a description somewhere that might give this impression.

This post was dated back to 2017 and your answer was “ If you want the track counts to be biologically meaningful, approach b) is required. ”. I may have interpreted your explanation in a very wrong way, please correct me if I am wrong.

@rsmith

The main benefit is for small bundles where selection of streamlines from a whole-brain reconstruction yields a streamline count that is too small to be trustworthy. If your pathway of interest is reasonably large, I probably wouldn’t bother with the added complexity.

Since tck2connectome and connectome2tck was recommeded here as well as in the BATMAN tutorial to select connections of interest. I have follow similar steps described in method_a below

a)

1.tckgen -algorithm iFOD2 -samples 4 -act 5TT.mif -seed_gmwmi gmwmi.mif FOD.mif WB.tck -select 5M

2.tcksift2 WB.tck FOD.mif WB_probweight.txt

3.tck2connectome WB.tck shen_atlas conmat_shen.csv -assignment_radial_search 2 -scale_invnodevol -tck_weights_in WB_probweight.txt -zero_diagonal -symmetric -out_assignments

4.connectome2tck -nodes node146, node11 -exclusive WB.tck assignments.csv pathway.tck -tck_weights_in -prefix_tck_weights_out

With the generation of connectivity matrix, I can manually extract structural connectivity betwee node 146 and node 11. I further used connectome2tck in step 4 to extract streamlines between node 146 and node 11. It turned out like you have mentioned in here, there is variability in total number of streamlines between my healthy subjects ranging from 38 to 661. Although I have no prior ground knowledge of this frontal connection, I believe the number of streamlines were too small in this case.

Again, thank you for your efforts to explain such complicated matter

Thomas