How to improve the FBA results?

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I like how you think that we have an existing stash of tricks for making FBA better that we don’t already tell people to use :laughing: :grinning_face_with_smiling_eyes:

  1. Make sure that you are using version 3.0.0 or newer; that includes this change, which can make a substantial difference especially in smaller cohorts.

  2. Partly as a consequence of 1.: Take a look at the generated image “null_contributions.mif”. What this image should ideally look like is a random homogeneous scattering of fixels with very low values, e.g. 1-5. If instead you can find a small number of fixels with very large values, e.g. over 10-15, this is a problem.
    The way to address this is to constrain your statistical inference to a fixel mask containing fixels for which there is adequate streamlines-based connectivity. Currently this can be done just based on streamline count (tck2fixel | mrthreshold); in the future I’ll make it possible to do this based on the extent of fixel-fixel connectivity, which would be better.

  3. Take a look at the various fixel data files output by the fixelcfestats command. E.g. even if there is no statistically significant effect reported, you can still quantify the standard effect size (/ Cohen’s d), and see its spatial distribution.

Why does the first diagram show so few significant streamlines? Is that normal?

It’s important to keep in mind that statistical inference like FBA does not tell you “where the differences are”; it tells you “if there are differences of sufficient robustness; and if so, where they are”. If every single FBA performed yielded extensive significant differences, I’d actually be slightly concerned. That’s not to say that there isn’t any effect present in your data, it’s just that the intrinsic variance in your data combined with the stringent nature of assignment of statistical inference means that it can’t be reported at the pre-specified inference threshold.

CFE refers to the overall framework for correction for multiple comparisons using permutation testing, with statistical enhancement along white matter pathways based on estimates of connectivity derived using tractography.

There can be a little bit of ambiguity here. Personally when I use the phrase CFE it tends to be specifically in reference to the enhancement of statistics produced by the GLM according to fixel-fixel connectivity, not the entire GLM / statistical enhancement / permutation testing block of FBA, particularly since for the latter the code is shared wholesale across connectome / voxel / fixel stats. But maybe that’s just because I’m working away within the guts of it at a finer granularity than most… :nerd_face:

Rob