[pymvpa] Cross-participant MVPA and controlling for traits of no interest

Cameron Craddock cameron.craddock at vt.edu
Tue Jul 12 18:50:31 UTC 2011


Hello John,

I have given this alot of thought in the past, and there are a few things
that might be worth trying:

1. When you say that the behavioral trait is not the same "type" as the
features, do you mean that it is not ordinal? If it is ordinal, then I don't
see any problem with adding it as a feature. Indeed this is how a lot of
data fusion algorithms work. You should be careful that the variance of the
behavioral score matches the variance of the other features. I.E. you should
z-score it as well as the features. This will ensure that the resulting
feature weights are comparable.

2. You could regress out the behavior score from your feature space. Fit a
glm to each voxel with the behavioral score as the regressor of interest,
and then perform the MVPA analysis on the residuals.

3. You could perform a MVPA regression to the behavioral score, perform a
feature selection to find the features most predictive of the behavioral
score, and then remove those features from the for the A vs. B
classification.

4. How predictive is the behavioral trait of the group membership? Can you
just threshold the behavioral score to ascertain group membership? If so
then you could perform a MVPA regression to the behavioral score, apply a
threshold to the prediction output, and see if this performs better to
classify group membership than a classifier trained using class membership
as the labels. I think that this is a pretty interesting idea.

Just a few thoughts.

Cheers,
Cameron


Hi Jo -

Thanks for your response. The features are structural data, so yes, they are
voxel values.
It just so happens that a behavioral trait is also predictive of whether or
not participants are in Group A or Group B. Since the behavioral trait is
not of the same "type" as the features, it seems incorrect to simply add it
to the feature space. Still, though, I would like to "control" for the
predictability of that trait in the MVPA.
Does that make more sense?

Cheers,
John
---
R. Cameron Craddock, PhD
Postdoctoral Fellow
Virginia Tech Carilion Research Institute
Roanoke VA

404-625-4973
cameron.craddock at vt.edu


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