[pymvpa] Visualization of the sensitivity map

[Nick Oosterhof]

> On 21 Jan 2016, at 15:18, Maria Hakonen <maria.hakonen at gmail.com> wrote:
> 
> I am working on my first fMRI data and would like to try MVPA analysis. I have two classes that I have classified with linear SVM. I would like to determine which voxels contribute most to the clasifier’s successful discrimination of the classes. As far as understand, the absolute value of the SVM weights directly reflect the importance of a feature (voxel) in discriminating the two classes.

Interpretation of SVM weights is quite tricky, see for example Haufe et al 2015 Neuroimage, doi:10.1016/j.neuroimage.2013.10.067.

If you want to make inferences about the spatial location of multivariate discrimination, you may want to consider using a searchlight analysis instead.

> I would like to average the SVM weights across all 18 cross-validation folds for each voxel and wrap the resulting map into the standard space in order to display a map of the resulting overlap.  

Even if one would be confident that SVM weights were interpretable, why take the absolute value? It would seem that this makes it much more difficult to do any stats or interpret the results. In particular, lack of signal but difference in variance of weights across regions may then yield differences in average absolute values.