Fixel-based analysis - assign significant fixels to tracks


#1

Dear experts,

after obtaining significant fixels in fixel-based analysis (FBA), is it possible to select and identify tracks to which the significant fixels belong? I think this could help much in interpretation of results of FBA but I did not find any information of this possibility.

Regards,

Antonin Skoch


#2

Hi Antonin,
Not sure if you have seen this thread. This will allow you to crop the template tractogram to show only streamline segments associated with significant fixels. This can be useful for visualising results in 3d.

If you wanted to see the entire streamlines that extend from the significant fixels, we don’t currently have an easy way to do this. @rsmith it might good to modify tckedit in the future to allow streamline selection with fixel data.

Cheers,
Dave


#3

Hi Dave,

yes, I know about this visualization option discussed in the thread you mentioned. It seems very helpful.

Actually I meant this option. I was not too much interested to visualize the streamlines corresponding to particular fixel, but to assign specific gray matter connection to it. This could enable to identify connectivity of which specific parcels is altered at the group level and would add great potential for interpretation.

As I think about this, I wonder how would be the actual accuracy and precision of such potential fixel-track assignment, given the tractography is performed in template space, only once per whole group. In this context, several questions raise in my mind:

What is amount of smoothness in common FOD template? Is the template-based FOD and subsequently tractogram sufficiently “sharp” to identify also more subtle connections?

What is the precision of subject-template registration? Given complex white matter structure, could we expect to assure correspondence between subjects and template for individual subtle tracks or could this assignment be valid only for main white-matter fascicles?

What do you think?

Antonin


#4

Hi Antonin,
Both tough questions!

Factors such as your image resolution and number of subjects will influence the spatial smoothness of the template. In general, larger structures in the core of white matter should be reasonably well aligned regardless, but structures that are either small or near the periphery of the brain (where there is more anatomical variance across individuals) may not be as well aligned, and therefore not well defined in the template.

When considering template-based FOD tractography there is also additional angular smoothness. Variance in the FOD peak orientations (e.g. from noise, natural anatomical variance, or errors in spatial alignment) cause the (averaged) FODs in the template to be smoother (i.e. peaks broader). This can cause additional ‘spread’ in probabilistic tractography compared to tracking on an individual.

I would say mainly valid for major white matter fascicles. However, smaller structures such as the fornix, anterior commissure tend to align well too. You may want to load up several subjects in mrview then flick between them if you have a hypothesis about a particular structure. You could also try decreasing the spatial regularisation (smoothing parameters) in the registration to see if it improves things.

Either way, I think the alignment should be better than FA-based registration, and/or FA-based skeleton projection.

We have been discussing the option of supplying a MNI-space FOD template. This would be useful for assigning tracts to MNI-space GM parcellations or fMRI results.

While we don’t currently have software for easily extracting streamlines that pass through significant fixels, you could take a more manual approach of:

  1. obtaining a mask of significant fixels using fixelthreshold
  2. converting this to a voxel-wise mask using fixel2voxel
  3. extract streamlines that pass through voxels in the mask with tckedit -include. This will work well in voxels with single fixels. However, if your significant fixels are in voxels with multiple fixels, you could then manually remove any streamlines that are extracted and associated with non-significant fixels (in the same voxel) using manually drawn ROIs and tckedit -exclude.

You could always use tcknormalise to warp the resulting streamlines back to the space if an individual if you want to compare with GM parcellations. Alternatively you could also warp the significant voxel mask back to the space of the individual and perform tracking (and editing) there. However, GM assignment from doing this on just one subject may not generalise to all individuals.

Hope this helps,
Dave


#5

Actually I meant this option. I was not too much interested to visualize the streamlines corresponding to particular fixel, but to assign specific gray matter connection to it. This could enable to identify connectivity of which specific parcels is altered at the group level and would add great potential for interpretation.

This can certainly be done, though I would advise caution. The statistical inference is performed at the template fixel level, and that is therefore where the interpretation should ideally be applied, particularly when only a subset of the relevant pathway is identified as significant. E.g. Imagine a single fixel being identified as significant, and then performing probabilistic tractography from that fixel with a high degree of orientation dispersion: A large number of grey matter - grey matter connections could conceivably be generated, but labelling all of those pathways as ‘altered’ would likely be erroneous.

Such interpretation is better reserved for analyses where the inference is in fact performed on the endpoint-endpoint connectivity directly