[pymvpa] classification based on individual parameter estimates from FSL

[cfygj]

>1) make a sensitivity map: http://www.pymvpa.org/examples/sensanas.html
>2) run a searchlight.
Dear every one! Though I know something about these analytic methods, I can not disentangle between them. Can you tell me  what's the difference between them. Thank you!







At 2014-08-01 08:31:06, "Nick Oosterhof" <nikolaas.oosterhof at unitn.it> wrote:
>On Aug 1, 2014, at 11:29 AM, David Soto <d.soto.b at gmail.com> wrote:
>
>> 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=[]))
>
>To be clear, you seem to have two accuracies (or whatever value you computed here), corresponding to two folds. Both folds have used data from all features.
>
>> 
>> How can i extract the whole brain classification map?
>
>There is no whole-brain classification map. Your two accuracies indicate how well the conditions can be distinguished using all features. These values do not tell you /where/ in the brain conditions can be distinguished. In other words, you've lost all spatial selectivity (see my earlier message).
>
>To get a brain map (one value for each voxel), you have at least two options:
>
>1) make a sensitivity map: http://www.pymvpa.org/examples/sensanas.html
>2) run a searchlight.
>
>Personally I prefer option (2) because option (1) may obscure some informative regions if there are other, 'more' informative, regions as well.
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