[pymvpa] Hyperalignment and Sensitivity Maps
swaroopgj at gmail.com
Thu Dec 6 18:29:41 UTC 2012
Yes, I think.
First, you run your favourite classification analysis on the hyperaligned
data and get the sensitivity scores for features in the common space. Then
you project these sensitivities back into orginal voxel space to look at
their location in cortical space etc. This back projection can be achieved
by using .reverse() function of the hyperalignment parameters (mappers) you
computed & used to hyperalign the data.
This is similar to what we did to project back face-shoe vector back into
the brain in the paper.
On Thu, Dec 6, 2012 at 12:43 PM, Kimberly Zhou <kyqzhou at gmail.com> wrote:
> Hi All,
> My apologies in advance for the noob questions.
> I've been looking at hyperalignment in PyMVPA and was wondering if anyone
> else has been using this and is clear on how it works. Is there a way to
> get a sensitivity map after hyperalignment and more importantly, would that
> be a valid thing to do?
> Thanks much!-
> Pkg-ExpPsy-PyMVPA mailing list
> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
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