What you’ve got there is an exceptionally open-ended question!
Firstly, I’m going to commence by taking your text literally, in that you are interested in one specific grey matter structure. If your interest were instead in a specific white matter bundle, then my answer would be quite different.
In a similar vein, the other thing to consider is the dimensionality of such “numerical data”. I.e. Are you specifically looking for attributes that are scalar in nature, so that you can plot the value of that attribute as a function of tracking angular threshold? Or are other “forms” of data useful, as long as they somehow satisfy the criterion of being “quantitative”?
One way of conceptualising this task is to think about construction of a whole-brain structural connectome, based on a parcellation within which the insula is one node.
(This would actually be interrogating the influence of tracking angular threshold on the subset of whole-brain connectivity that connects the insula as opposed to its influence on targeted tractography, which depending on the measures involved, may differ slightly; but it’s nevertheless an option to consider)
This would give you the ability to:
Extract the subset of streamlines that involve connections to the insula, and do “something” with those;
In this case, one thing you could do is generate a spatial map of streamlines density, and look at the differences in these maps for different angles. This is obviously a voxel-wise measure, but it’s still “quantitative” per se.
(If you want to do this using e.g. SIFT2 rather than raw streamline count there’s a couple of additional tricks I can provide for it to be done “correctly”)
If, of all of the connections between the insula and other parcels, there are specific connections that are of more interest than others, then this becomes a question about quantifying bundles of interest, which as I stated at the outset is its whole own thing.
Extract the row / column of the connectome matrix that corresponds to the insula, and do “something” with that.
E.g. You could quantify the similarity of those connectivity vectors for different angles, and look for peaks as a function of angle, suggesting that there is some form of “transition” where the “profile” of connectivity of the insula to the rest of the brain changes as a result of changing the tracking angle threshold.
There’s a huge scope of possibilities here, though many I’m foreseeing would require development of tailored commands, so I’ll try not to jump down the rabbit hole straight away.
What might help to make the question a bit more concrete would be if you could produce screenshots of results at two different tracking angle thresholds, and point out the “feature” that is different that you want to quantify in some way; that would help both yourself and others consider what solutions might be possible.