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Hi David,
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<div>When you use map2nifti to get an image of your searchlight, if requires 2 inputs.</div>
<div>img=map2nifti(dataset=ds, data=sl_res))</div>
<div>img.to_filename(‘foo.nii.gz’)</div>
<div>So once you run your searchlight like you have it set up (though I’m not sure that what you’re doing with those center_ids is necessary), just pass your original dataset to the map2nifti function along with your res dataset to the ‘data=' argument.</div>
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<div>Only the original dataset contains the mapper info that allows you to recapitulate the 3D image, while the second argument, data, will take a result from a searchlight (or any vector/matrix of the right shape).</div>
<div>Hopefully that’ll help you get your searchlight into a form you can visualize. </div>
<div>- Gavin</div>
<div><br>
</div>
<div>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~</div>
<div>Gavin Hanson, B.S.<br>
Research Assistant<br>
Department of Psychology<br>
University of Kansas<br>
1415 Jayhawk Blvd., 534 Fraser Hall <br>
Lawrence, KS 66045</div>
<div><br>
<div>
<div>On Aug 1, 2014, at 4:29 AM, David Soto <<a href="mailto:d.soto.b@gmail.com">d.soto.b@gmail.com</a>> wrote:</div>
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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<br>
Dataset(array([[ 0.35526316],<br>
[ 0.35855263]]), sa=SampleAttributesCollection(items=[ArrayCollectable(name='cvfolds', doc=None, value=array([0, 1]), length=2)]), fa=FeatureAttributesCollection(items=[]), a=DatasetAttributesCollection(items=[]))<br>
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<div class="gmail_default" style="font-family: georgia, serif; font-size: large;">
How can i extract the whole brain classification map? Using niftires does not work either
<br>
niftires = map2nifti(res)
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<div style="margin: 0px; text-indent: 0px;">niftires.to_filename('/home/dsoto/Documents/fmri/wholebrainsearchlight_results.nii.gz')</div>
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<div style="margin: 0px; text-indent: 0px;">Cheers</div>
<div style="margin: 0px; text-indent: 0px;">ds<br>
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<div class="gmail_extra"><br>
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<div class="gmail_quote">On Fri, Aug 1, 2014 at 9:41 AM, Nick Oosterhof <span dir="ltr">
<<a href="mailto:nikolaas.oosterhof@unitn.it" target="_blank">nikolaas.oosterhof@unitn.it</a>></span> wrote:<br>
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On Jul 31, 2014, at 10:49 PM, David Soto <<a href="mailto:d.soto.b@gmail.com">d.soto.b@gmail.com</a>> wrote:<br>
<br>
> Hi, I keep plugging away with this pretty basic classification<br>
</div>
> [...]<br>
<div class="">> I get a whole-brain classification accuracy of around 68%<br>
> (though did not assess significance)<br>
> 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<br>
> 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?<br>
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That is quite possible because the whole-brain classification uses many more features than each searchlight.<br>
<br>
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)<br>
<br>
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.<br>
<div class="HOEnZb">
<div class="h5"><br>
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