# How to improve the FBA results?

**URL:** <https://community.mrtrix.org/t/how-to-improve-the-fba-results/4453>\
**Category:** Uncategorized\
**Created:** [January 12, 2021, 9:30am UTC](https://community.mrtrix.org/t/how-to-improve-the-fba-results/4453 "2021-01-12T09:30:53Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![jdtournier](https://community.mrtrix.org/user_avatar/community.mrtrix.org/jdtournier/32/2594_2.png) [@jdtournier](https://community.mrtrix.org/u/jdtournier)\
**Post date:** [January 13, 2021, 10:19am UTC](https://community.mrtrix.org/t/how-to-improve-the-fba-results/4453/2 "2021-01-13T10:19:18Z")

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> [@silver\_mark](#):
>
> Most (1-p) values are not significant after correction.Is this normal?

Yes, in general, any correction for multiple comparisons (which this is) will increase your _p_-values (i.e. decrease significance). There’s not much we can do about this, it’s a general statistical result (see e.g. [here for details](https://en.wikipedia.org/wiki/Multiple_comparisons_problem)).

> [@silver\_mark](#):
>
> 1、Why does the first diagram show so few significant streamlines? Is that normal?Is there a way to make the results better?Or is it just that?

As above: yes, this is normal, and without acquiring more data, there isn’t anything simple that can be done to improve these results (assuming all the processing steps have been performed as well as they can). The statistical correction procedures used in CFE are already pretty much as good as we can make them. There are a few things that @rsmith is working on, but I don’t expect they will make an enormous difference to your results, and they certainly won’t make your corrected results as extensive as the uncorrected ones.

> [@silver\_mark](#):
>
> 2、The third diagram shows whole-brain tracking streamlines (20 million SIFT to 2 million),Does it look right?

It looks plausible, but it’s impossible to tell from a simple snapshot like this – there are far too many streamlines to get a sense of streamline density. A better way to verify is to generate the corresponding TDI (using `tckmap`) and compare with the WM FOD that was used to generate the streamlines in the first place (e.g. figure 9 in the [SIFT paper](https://pubmed.ncbi.nlm.nih.gov/23238430/))

> [@silver\_mark](#):
>
> Lastly,How to understand the “Connectivity Based Fixel Enhancement and Non-parametric Permutation Testing” in FixelCfestats command? I read this article——“Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres” but I still don’t know what CFE is.Is CFE is a statistical analysis method like"Paired T test"? and, which testing method is used in " non-parametric permutation testing" ?

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. The framework in general is relatively agnostic to the exact test performed for each fixel, but the current implementation will typically perform a _t_-test for each fixel ([the ability to perform an _F_-test has also recently been introduced](https://community.mrtrix.org/t/mrtrix-3-0-rc3-3-0-0-changelog/3552/3)). I’m not sure this answers your question, but I can’t think of a way to explain this simply without referring you to the CFE article – it’s quite an involved framework!

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