Use tckedit. But from what I understand the -include option in this command would allow to compute the streamlines that connect a pair of ROIs only. In my case, I should do this an enormous number of times, as I want to compute the number of streamlines connecting all the 24 regions between them.
This is the way that many people had constructed connectomes in the past. But it’s far from ideal, and not just because of the computational expense of running tckedit multiple times. For instance: If a track passes through more than two ROIs, then it will contribute to more than one edge in the connectome. Additionally, if a streamline terminates just prior to entering a parcel, tckedit will fail to assign the streamline to that parcel.
Use connectome2tck. But from my interpretation this command allow to select streamlines connecting seeds within the freesurfer parcellation, which is not my case.
The goal of the labelconfig / tck2connectome design is that it should be possible to use parcellations from any source to estimate pairwise connectivity in a robust way. Indeed we demonstrated this year at ISMRM how the combination of ACT with the (relatively simple) mechanism used in tck2connectome for assigning streamlines to grey matter parcels is robust against differences in parcellation schemes.
@rsmith, do you have any convenience commands to help out with the process of generating a nodes image from a set of ROIs…?
I don’t; depending on the nature of those mask ROIs, it may be a relatively simple operation to perform, but it’s difficult to provide something that will perform as expected in all cases (and different users have different expectations of what it will do in certain circumstances).
@Marica_Padula: The main determining factor is whether or not the individual mask images contain any overlap, either in subject or template space:
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If there’s no overlap between them in template space, then probably the best approach is to convert those individual mask images into a parcellation label image in template space, that can then be warped to each individual subject in a single step (as opposed to individually warping 24 masks for each subject).
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If there is overlap between them, then generally my recommendation is actually to null any voxels containing more than one parcel in subject space. This allows the tck2connectome mechanism to make the decision regarding which parcel to assign a particular streamline to, rather than relying on heuristic mechanisms for resolving those voxel labelling ambiguities. But this is a decision you need to make yourself, and implement explicitly.
For the fundamental operation (combining 24 non-overlapping mask images into a single label image), you could try something like this (not guaranteed to work out of the box, just laying the idea down). Imagine you have files named as 1.mif … 24.mif.
for i in $(seq 2 24); do
STRING=${STRING}" ${i}.mif ${i} -mult -add"
done
mrcalc 1.mif ${STRING} -datatype uint32 labels.mif
That produces a long call to mrcalc that, for each of the 24 images, multiplies by the integer corresponding to that label, and adds it to the label image. But as mentioned before, this breaks down if there’s any overlap between the input images.
Cheers
Rob