It matters indeed less if you’re not after quantitative analysis. However, if you’re still working with “a group of subjects”, in the sense that the goal is to compare or even just “do” something across them, and as long as they’ve of course been acquired using the exact same protocol, I’d still recommend just using one single unique (set of, in case multi-tissue) response function. In my experience, the response functions vary very little across subjects in shape/contrast (not size, see below!); and if they do, it’s also due to data quality, amount of certain tissues present, and in the end, performance of the response function selection algorithm, which is not per se something that is uniquely valuable to a single subject. Also, the kinds of variations across subjects I’ve seen (which are very little indeed) seems to barely affect the CSD outcome in a substantial manner at all. So there’s no real worries, I’d say.
It sure does, since all our FOD-based tractography algorithms have an amplitude threshold to cut off streamlines ("
-cutoff"). However, using a single (set of, in case of multi-tissue) response function is only half of the requirements to make sure that this doesn’t affect any consistency across subjects. The other half is
mtnormalise, which accounts for the intensity differences that directly affect the size (amplitude) of the FODs.
That said, there is in a strange way something to be said for indeed using the response function(s) of the subject itself in a scenario where you’re really only after single-subject tractography; since the size of the response functions will also scale with the data; so performing a CSD technique will actually normalise the data to it’s own scale up to a certain extent. So let’s say, e.g., in a clinical scenario where you perform tractography on individual subjects for their own sake (e.g. delineating a bundle for surgical workup), you can probably stick to just
dwi2response on the individual subject and CSD using it’s own response function(s), and then directly tractography with “known” good values for
-cutoff that suit your scenario (e.g. a specific bundle of interest). But, really strictly speaking, if you want to establish a well controlled processing protocol for a given fixed acquisition protocol, I’d argue to compute (“average”) response function(s) once, based on (a group of) healthy subjects, and always use these for similar subjects (e.g. responses of healthy adult humans, to be used for adult humans in general, both healthy and with a condition / pathology), but also always follow up with
mtnormalise, so your
-cutoff thresholds can generalise in a consistent manner.
So well, in conclusion, if you’re going to do stuff “across subjects”, or even wanting to set up a well-controlled “standardised” processing pipeline to be used consistently, I’d recommend a fixed set of responses +
mtnormalise. However, in practice, in some tractography scenarios, you could be fine with
dwi2response per subject and CSD using their own response(s); and then the need for
mtnormalise is in practice much less. The latter strategy is in another way also potentially a bit “safer”, since it’s “robust against unexpected things changing to the acquisition protocol”. But of course, if you’re doing anything across a group of subjects, that should never, ever (ever, ever) happen. In a clinical scenario though, I’ve seen cases of that happening, due to diverse reasons…