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
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