[pymvpa] search-light vs. ROI analysis: significance puzzle
axel.vadim at gmail.com
Tue Feb 26 11:12:11 UTC 2013
I am not sure what exactly Malin Björnsdotter' is doing and how it relates
to what I am doing - in the paper that I found used Monte-Carlo method. In
your method, the central voxel of the light is assigned a hitrate of the
light, so the prediction rate of each voxel reflects prediction of a
unique, specific environment. In my method, the hitrate is assigned to all
the voxels in the light and given that each voxel participates in many
lights, the hitrates are averaged. So, using my method a voxel hitrate
reflects many possible environments. I try to compare the results of both
and so far it seems that with your method the results are more patchy.
Though, I personally smooth predictions afterwards anyway.
On Mon, Feb 25, 2013 at 2:12 PM, J.A. Etzel <jetzel at artsci.wustl.edu> wrote:
> On 2/25/2013 2:17 PM, Vadim Axel wrote:
>> BTW, why one should use a search-light which is substantially
>> smaller than ROI? I deliberately make it of size of my ROI to make
>> results more comparable between two analyses.
> That isn't the suggestion I meant to make! I was just listing the
> assumptions behind my advice: often, searchlights are quite a bit
> smaller than ROIs. Using searchlights similar in size to your ROI is
> quite sensible for comparison purposes, and avoids that issue of different
> numbers of voxels in the ROI and searchlights.
> I move my search-light each time one voxel in one of the directions,
>> so each voxel participates in dozens of lights. At the end, I
>> average all the predictions in each voxel.
> Do you mean you're not assigning each searchlight's accuracy to its center
> voxel but rather averaging, sort of like Malin Björnsdotter's work? That
> will change things; I'd assumed you were doing a more "typical" analysis.
> I would appreciate if you notify me once your paper is in press.
> I'll let everyone know. :)
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