randomise doesn't currently contain outlier modelling, although we are working on including it - but it's not available yet. If it looks like you have bad outliers in the TBSS-preprocessed data it would probably be worth tracking down the source of this - and if necessary excluding problematic subjects.

On 27 Jan 2011, at 17:19, Miguel Burgaleta wrote:

Hi FSLers,

I am performing a cross-sectional study with TBSS (FA against cognitive performance) and noticed that, for some of my peak vertices (local maxima in clusters after FWE correction), I have outliers in radial and axial diffusivity that account for the significance at that point. I was wondering if there is any outlier detection algorithm implemented in FSL (if not, in any other format) that I can use?

Thanks
Miguel

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