[pymvpa] classification based on individual parameter estimates from FSL

[David Soto]

Thanks for the response, I have not managed to extract the whole-brain
classification map...following the 1st example code below, the output from
the crossvalidation is
Dataset(array([[ 0.35526316],
       [ 0.35855263]]),
sa=SampleAttributesCollection(items=[ArrayCollectable(name='cvfolds',
doc=None, value=array([0, 1]), length=2)]),
fa=FeatureAttributesCollection(items=[]),
a=DatasetAttributesCollection(items=[]))

How can i extract the whole brain classification map? Using niftires does
not work either
niftires = map2nifti(res)


niftires.to_filename('/home/dsoto/Documents/fmri/wholebrainsearchlight_results.nii.gz')


Cheers

ds




On Fri, Aug 1, 2014 at 9:41 AM, Nick Oosterhof <nikolaas.oosterhof at unitn.it>
wrote:

>
> On Jul 31, 2014, at 10:49 PM, David Soto <d.soto.b at gmail.com> wrote:
>
> > Hi, I keep plugging away with this pretty basic classification
> > [...]
> > I get a whole-brain classification accuracy of around 68%
> > (though did not assess significance)
> > Then I run a searchlight analyses and looking at the classification
> accuracy maps it appears like a chance distribution with mean 50% and the
> max classification accuracy
> > around 56%- I wonder how it be that none of the searchlights reaches the
> level of wholebrain classification ? and if this is the case then can it be
> the wholebrain classification meaningful at all?
>
> That is quite possible because the whole-brain classification uses many
> more features than each searchlight.
>
> Assuming there is sufficient signal in the data (which there seems to be
> in your case) which is not limited to a small subset of features (voxels),
> generally one sees better classification with more features. This was
> already reported by Cox et al 2003, and later by e.g. [disclaimer:
> shameless self promotion] Oosterhof et al 2011. (there are some cases where
> this might not be true)
>
> There's often tradeoff between spatial selectivity and classification
> accuracy. In one extreme you use all features for a single classification
> analysis (i.e. your whole-brain classification), in the other extreme you
> use one feature at a time (i.e., univariate analysis). A searchlight
> analysis is somewhere in between, finding a compromise between getting high
> classification accuracy and good spatial selectivity. But also for a
> searchlight it holds that neighborhood (sphere or disc) size can affect
> both classification accuracy and spatial selectivity.
>
>
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-- 
http://www1.imperial.ac.uk/medicine/people/d.soto/
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