What was formerly the
dwiintensitynorm script is now provided as
dwinormalise group; so that may be a source of confusion if cross-referencing your own processing with prior discussions on the forum.
For clarity: when you say “non-HARDI”, do you mean that your DWI volumes don’t correspond to equal b-values with different directions, but instead have some more complex organisation in q-space? I think @mblesac may have mistaken “HARDI” with “multi-shell”, but I want to be sure just in cast it was two mistakes cancelling one another out
Personally, I would consider your question by flipping it on its head. Let’s say you were to combine data across all scans into a single
dwinormalise group call, such that a single FA template image is generated and the same WM mask in the space of that template is used to intensity normalise all scans. Now consider the conditions under which this would turn out to have been a bad decision. How different would the diffusion data from a specific subgroup need to be, in order for the FA template image and WM mask to be sufficiently biased due to inclusion of those data, for the downstream results to be substantially different to what they would have been had
dwinormalise group been run on a tailored subgroup for a specific hypothesis?
So as long as you’re not talking about gross differences in age or severe neurological disorders, personally I’d be pooling everything together into a single normalisation step, and keeping just one copy of derivative DWI data for each participant. It makes the scope of possibilities for hypothesis testing down the line far more broad. But that’s just me.
Note however that it still won’t be ideal for post-hoc addition of subjects. Within
dwinormalise group, it is the non-linear transformations estimated in the
population_template step that are utilised to transform the template WM mask to individual subject space. For adding new subjects after the fact, you would need to manually perform an explicit registration between the novel subject’s FA image and the FA template image, and then manually transform the template WM mask to that subject in order to run
dwinormalise individual. This means that the process of deriving the non-linear warp between subject and template FA images would not quite be identical between those subjects used to construct the template, and those added subsequently. A solution to this would be to treat all subjects as post-hoc additions, and perform that explicit registration / transformation /
dwinormalise individual for all participants regardless of whether or not they were used to produce the template.