I want to generate an average TDI image for subjects in two groups (from single shell data) and compare them either qualitatively or, better, quantitatively (as in a voxel-based analysis as suggested is possible in Calamente et al. 2010)
For qualitative comparison, I was thinking to simply:
1- perform preprocessing: denoising and unringing; motion and distortion correction; bias field correction; and global intensity normalisation across subjects
2- estimate response function; perform CSD; perform tractography (ACT); perform SIFT1
3- to normalise the tracts, use tcktransform (as detailed in this post), after generating (e.g., in SPM), and correcting (with warpcorrect) the necessary warps.
4- perform tckmap on the resulting normalised tracts (as specified in this post).
5- Average across participants within group and compare output
Couple of questions: if using tcksift2, is there a way to normalise the weights or apply them to images in standard space? Similarly, could/should mu (from sift1 in step 2 above) be applied to normalised TDIs?
For quantitative comparison, I think perhaps a modified version of the single shell FBA pipeline could work:
- perform preprocessing: denoising and unringing; motion and distortion correction; bias field
correction; and global intensity normalisation across subjects
- compute an (average) white matter response function
- upsample DW images
- compute upsampled brain mask images
- Fibre Orientation Distribution estimation (spherical deconvolution)
- generate a study-specific unbiased FOD template
- register all subject FOD images to the FOD template
- compute the template mask (intersection of all subject masks in template space)
- warp FOD images to template space (with reorientation)
- perform whole-brain fibre tractography on all subject FOD images in template space
using template mask as seed
- perform SIFT2 on subject tractograms in template space
- generate tract density images (TDI) for each subject (and multiply by the respective mu value?)
- average TDIs across individuals per group; perform VBA
Your thoughts on the above two approaches is very much appreciated.