My data have 60 orientations at b=1100, 10 orientations at b=300, and 10 b0 volumes (total 80 volumes). This looks like some multishell but not with a true second shell.
Well, we tend to refer to even a solitary b=0 volume as a ‘shell’, so I wouldn’t be too harsh I’ve heard of a number of people acquiring a low-density shell with a very low b-value. 10 volumes is maybe not ideal though, even at b=300 an lmax=4 fit would probably be preferable.
If I assume these are multishell data and I follow the HCP tutorial, response estimation should be obtained with multishell multi-tissue options using 5tt. My data may be multishell, but my patients may not have a reliable 5tt segmentation, a darkened white matter around the lesion may be considered GM. How should I estimate the response on these data?
Just to post an update. I managed to run
dwi2response dhollander followed
If you don’t trust your 5TT segmentations, then
dwi2response dhollander is the best bet. Though out of curiosity, just how big is the ‘darkened white matter around the lesion’? If it’s literally just a ring of partial volume between WM and the lesion hyperintensity, then in an ideal world even an intensity-based tissue segmentation would still correctly label those voxels as WM & CSF; furthermore,
dwi2response msmt_5tt will only use voxels in tissue response function estimation if they are labelled as comprising at least 95% of that tissue, so small ‘noisy’ segmentations shouldn’t influence the response estimation too much. But if the ‘darkened white matter’ is more extensive than a couple of voxels, and the tissue segmentation only has a T1 image to work with, then yes, such voxels could be labelled as GM since that’s what it looks like; labelling it otherwise would require more image data, or more anatomical prior information.
For tractography, I know that -act followed by SIFT would be the best option, but, again, my patients may not have a plausible WM/GM border to rely on -act. So I think I should run tckgen the old way, with seeds from everywhere, …
For stroke I’d expect the WM/GM border to be OK in non-affected regions; it’s a question of whether the tissue segmentation in affected regions will result in the incorrect anatomical priors being applied during seeding / tractography. This intrinsically depends on having a sense of what the correct anatomical priors actually are. There’s a few options:
Correct the tissue segmentations manually using the
5ttedit command, based on your knowledge / expectation of the condition.
Using either manual or automated methods, obtain lesion segmentations, and modify those regions to the pathological tissue type in the 5TT image (again using
5ttedit). This is basically equivalent to saying ‘I don’t know what tissue is underlying these regions, so I’m not going to apply any anatomical priors to streamlines whilst they are traversing these regions, and instead rely purely on diffusion image data’’.
Disabling ACT altogether may ‘remove the confound’, and may not require any user intervention, but it means that you won’t get the benefits of ACT in those regions where its application would be perfectly reasonable.
Without ACT you may still get decent tracking if MSMT is able to reduce the influence of non-WM tissue on the WM FODs. But it will still pose some problems for connectome construction later (see below).
… and run SIFT later. Does this seem reasonable?
You can run SIFT in the absence of ACT, but it can be prone to biases. For instance: if all voxels contribute equally to the model fit, then streamlines that project through the cortex and into CSF are more likely to be retained than those that terminate at or near the GM-WM interface, since they assist in reconstructing the non-zero FODs in those non-WM voxels. As with ACT, there’s scope for improving this behaviour a little if your multi-shell DWI-based tissue segmentations are good enough; but I honestly don’t know what the algorithm’s performance will be like for your acquisition scheme.
Finally, I will use the connectome for graph theory estimations.
There will always be difficulties in connectome construction if ACT is not used: the termination points are too ill-posed. If you use a volume-based pacellation like AAL it won’t be as difficult to get streamlines terminating near the cortex to be assigned correctly, but it will also mean that streamlines terminating in lesions will be more likely to be assigned to the closest parcel. You could adjust the maximal distance in the endpoint radial search to somewhat balance between these.
However, patients may have big lesions. This means that the same (i.e., 1 million) streamlines will be distributed only in one part of the brain in patients. Could there be a systematic bias in the pattern of connections when getting the same number of streamlines from an intact brain and from a lesioned brain? In other words, if I have the same identical brain and get two tractograms, one from everywhere, and one from the right hemisphere only, are the tracts within the right hemisphere following the same pattern, or is there some bias simply because the number of streamlines is twice as much?
Depends entirely on the type of analysis you’re performing on the connectomes themselves. It’s pretty obvious that for a brain where one hemisphere is more-or-less knocked out, the overall topology of the network is going to be vastly different, and this will be reflected in a wide range of network measures that can be calculated from the connectome.
It sounds like you’re (theoretically) comparing a stroke patient with one hemisphere basically non-existent with a healthy patient where one hemisphere is ‘masked’, and referring purely to the difference in streamline count rather than any residual topological differences. Again, this depends on the particular analysis being performed - as I’m sure I’ve mentioned a number of times, if your so-called ‘network connectivity measure’ is not invariant to a global scaling of the connectome matrix values (which is approximately what scaling the number of streamlines will do), the meaning / interpretation / usefulness of that measure needs to come under scrutiny. But if you’re referring to differences in the fundamental connectome edge values (streamline counts) between these two cases, then what you have is a connection density normalisation problem; tracking one hemisphere with 1m streamlines, and tracking both hemispheres with 1m streamlines then masking out one hemisphere, will give something resembling a factor of 2 difference in ‘connection strength’. This simply highlights that one streamline in one subject is not quantitatively equivalent to 1 streamline in another subject; this is something I’ve been threatening to publish on for years now…
Is deterministic tractography more appropriate in this context?
The selection of a deterministic vs. probabilistic tractography algorithm doesn’t really have an influence in the issue discussed above (unless that selection were to have ramifications in how other steps of processing / analysis were to be performed).
Preprocessing also needed a fix because FSL 5.0.5 was outdated and wasn’t rotating the vectors. The 5.0.9 FSL patch for eddy refused to work because the data looked like DSI, I had to hack the dwipreproc script to force --data_is_shelled into the eddy call. It might be a good idea to add this option in the dwipreproc script.
Yes, I read something somewhere about
eddy now testing for ‘shelled-ness’ of the gradient scheme. However I can’t simply add that option to the script, since that would cause the script to fail if run with an older version of
eddy: I need to find a robust way of determining within the script whether or not such an option is available. Script maintenance is becoming a bit of an overhead for me… I also don’t quite understand why
eddy refuses to run on DSI data given that the whole framework was supposedly designed around Gaussian Processes which isn’t explicitly dependent on shelled data, can anybody clarify this for me?
After a quick look, the newer eddy_openmp followed by dhollander and csd_msmt produced less noisy tracts than the old eddy followed by tournier and csd.
This will primarily be the effect of multi-tissue CSD, hopefully your results mimic what was shown in that paper. It’s difficult to know how much of an effect the change in
eddy version would have, and would require an explicit test where just that stage alone was varied to know for sure.