1.ACT seem to benefit maximally by the anatomical shape of the brain. But the IIT FOD atlas is an average of 72 people, the shape of the brain is also averaged (with the T1 being also blurry). Is ACT still useful in this context? I would think so but I’d appreciate your opinion.
Ah OK, I completely missed that you said you were going to perform tracking on the HARDI atlas. In our own work we haven’t used ACT when tracking on population template images, but we’ve been focused on fixel measures rather than connectome properties in this context. It may still be advantageous in a number of ways, particularly in ensuring that streamlines terminate in appropriate locations, but the magnitude of that effect may be less than that observed in single-subject data. The tracks themselves may however appear very ‘cropped’ and ugly-ish compared to non-ACT streamlines as they don’t project into the cortex at all. One trick you can try is taking the cortical GM image, adding it to the sub-cortical GM image, then zeroing the GM image: This will make ACT apply the sub-cortical GM priors in the cortex, allowing tracks to project into the cortex (but not allowing them to re-enter WM).
2.To make sure, the act file (i.e. 5tt.mif) contains probability images, not binarized segmentations, right?
Yep, it can make use of partial volume images. It will terminate streamlines once they cross the sub-voxel location where the tri-linear-interpolated value changes from ‘more-WM-than-GM’ to ‘more-GM-than’WM’. That way, you don’t get funny voxel-shaped jagged edges where the streamlines terminate.
3.Is there a structurally based parcellation I could use? I could not find one from a quick look of the literature. And since I am using the Shen parcellation for some functional data, I though’t might be good to match them. The Desikan/Destrieux atlases that come with IIT atlas have large parcels including the entire temporal lobe gyri, clearly not well suited to distinuish specific tracts ending in perisylvian areas.
This comes back to my misunderstanding at 1. When volume-based parcellations are registered to per-subject images, subject variation can have a significant influence on the overlap between the parcels and the subject’s cortex; this is why e.g. in the AAL atlas the parcels are massive. Whereas a surface-based such as FreeSurfer provides a parcellation image that is tailored to that subject’s cortical shape. Here, I would try using your parcellation as-is on the group template, but use the
connectome2tck command to take a look at those streamlines where at least one endpoint wasn’t assigned to a parcel; that should highlight any problem areas.
4.I plan to bring the Shen atlas in IIT atlas space, and I don’t expect it to match perfectly the gray matter of the IIT atlas. To resolve the issue, I am thinking perhaps to expand the parcels in ANTsR until all gray matter of IIT is covered. Then remove voxels that do not fall in gray matter, to allow a good ACT run. But you mentioned the “-assignment_radial_search” which may be useful in this context. Do you have a feeling if one strategy might be more accurate than the other?
Personally I prefer the radial search mechanism to the parcel dilation approach. With the latter, there will inevitably be ambiguity about how to label voxels where two parcels intersect, and this uncertainty will propagate with subsequent iterations. With the radial search, the closest parcel to the sub-voxel streamline termination point will be chosen - up to the maximal search radius. So to me it’s more predictable.
Also, you wouldn’t strictly need to worry about removing non-grey-matter voxels: recall that ACT and the streamline-to-node assignment are two separate mechanisms that operate independently. The only benefit of such an approach would be if you then subsequently used a small-radius search, and the erosion of non-GM voxels would prevent streamlines terminating in WM from being assigned to the nearest parcel; but given the whole approach was predicated on using ACT that shouldn’t be present in your data.
5.I tried to find a solution by lesioning the connectome matrix, or by using lesioning tract probability maps of IIT, but I can’t come to a good solution. In principle a lesion can disrupt A-to-B connection at 80% and leave the remaining streamlines intact (I have real patient lesions). This information cannot be computed easily from a matrix or from voxelwise probability maps, unless I really check which streamlines does the lesion “cut”. I am happy to hear ideas on how to do this without going through tck* commands though. Unfortunately the .tck files are not available with the IIT atlas.
If you’re looking to perform image-based lesion simulation, I don’t think you have a choice other than to perform some form of tractography in order to assess the influence of lesions on the tracking, and concomitantly the connectome. Even if you were to have tract probability maps for every connectome edge, then you still couldn’t really assess the influence of a lesion ROI on the connection density: it’s the influence of the lesion on the tract cross-sectional area that determines the disruption of connectivity, ie. the lesion doesn’t necessarily have to infiltrate the entire tract volume.