[pymvpa] question about cross-subject analysis

[J.A. Etzel]

My instinct in that situation is not to look for MVPA-specific solutions 
but rather general fMRI ones: how to best align your specific people to 
a common space. For example, if you are interested in a particular part 
of the brain or have a patient population, use methods tweaked for those 
situations; there's been quite a bit of work for techniques (though no 
perfect solution).

This assumes that you want to do a "standard" MVPA (e.g. linear svm) 
using voxels from many people (something like a leave-one-subject-out 
cross-validation); methods like Raj's similarity comparisons are quite 
different.

Jo


On 1/18/2012 11:10 AM, Yaroslav Halchenko wrote:
>> This sometimes works surprisingly well and is fairly straightforward.
>
> yeap -- but I thought that the main question behind was on how could we
> account for areas where nice (linear) alignment is difficult so
> that union (pretty much your scenario) of voxels across subjects would
> not be ideal either.
>
> On Wed, 18 Jan 2012, J.A. Etzel wrote:
>
>> To run multiple-subjects tests I've usually converted everyone's
>> functional images to a standard space first (MNI or whatever), then
>> subsetted to only have voxels with non-zero variance in all
>> subjects.
>
>> This sometimes works surprisingly well and is fairly straightforward.
>
>> Jo