On July 25, 2014 5:35:49 AM Jingwen Jin <[email protected]> wrote:
1), When the searchlight sphere get close to the brain edges, it will
include surely fewer number of voxels. Based on my limited readings, I have
not seen how people address this issue. Would somebody help explain how
this should be solved ?
This is one of those details that's not usually mentioned in papers, but
should be!
With volumetric searchlights the usual practice is to set a minimum number
of voxels that must be in each searchlight, and not calculate searchlights
that are smaller. I often use around 10% as a threshold; if a "full"
searchlight is 30 voxels, I don't allow searchlights smaller than 3 voxels.
With surface searchlights it's more typical to set searchlight size by
number of voxels, then adjust radius as needed to obtain the number of
voxels (e.g. see some of Nick Oosterhof's work).
I don't really like either strategy, but that's what we have now. We know
there is an interaction between number of voxels and classifier
(correlation, whatever) behavior, so it makes sense to fix number of
voxels, but having radically different size (radius-wise) searchlights in
different parts of the brain is also troublesome for interpretation.
2), Most of the MVPA analyses using searchlight approach are using SVM.
Because of the nature of my hypothesis, I am comparing two correlations:
e.g., if A-B correlation is higher than A-C. Theoretically, I think it
makes sense to run correlation based searchlight. But just want to check is
there a reason for not using correlation based searchlight?
Not that I can think of. You can use any metric in a searchlight that is
appropriate for your analysis. Many of the issues (sensitivity to
searchlight radius, coping with spatial variability between people) are the
same for all metrics.
good luck,
Jo
Any answer will be appreciated !!
Best,
Frances
--
Jingwen Frances Jin
Department of Psychology
Stony Brook University
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