[pymvpa] null classification performance in the presence of strong univariate signal??

David Soto d.soto.b at gmail.com
Thu Sep 18 13:36:34 UTC 2014


Hi Nick,

I have now carried individual whole-brain searchlight with SVM and
crossvalidation (using the 8 betas in condition A and B from each of the 8
runs as input, no Zscoring as each chunk only has 2 samples, 1 for A and 1
for B).

I reiterate I did this separately for each of the 19 subjects.

 I then aimed to carry out a group analyses using the individual accuracy
maps. to do this I merged the 19 nifti accuracy maps into a 4D file and run
a one-sample t-test in FSL using randomise -i searchpred -o
searchpredOneSampT -1 -v 5 -T.

Weirdly the output gives a group map with all brain voxels over p<0.001 !?
which cannot be right...


Is there any advise please on performing the group level analyses on
individual searchlight accuracy maps?

cheers
david

On Fri, Sep 12, 2014 at 12:10 PM, Nick Oosterhof <
nikolaas.oosterhof at unitn.it> wrote:

>
> On Sep 11, 2014, at 9:34 PM, David Soto <d.soto.b at gmail.com> wrote:
>
> > now my question is - would not zscoring solve the issue you mentioned?
>
> Quite possibly so, but the only way to know is to try.
>
> >
> > I realise is not possible to zscore by chunks because these are the
> subjects
> > and only 2 samples....but I tried other zscoring schemes (i.e. by task
> condition
> > A vs. B or globally across the 38 samples) and still get null MVPA
> performance
>
> That's not impossible, but still a bit curious.
>
> >
> > would there be any advantage in trying the tstats or zstats instead of
> the betas
> > as the former are already zscored?
>
> It could, with t-stats there is usually less of an issue with outlier
> values.
>
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-- 
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