I agree that it could be added as a feature (if ordinal and properly
scaled). But that would let the classifier use the behavioral trait as
well as the voxel values, which I think is opposite of what you want: to
do the analysis while minimizing the effect of the behavioral trait.
Options 2 and 3 from Cameron strike me as promising: try to take the
trait out of the features before classification.
Jo
On 7/12/2011 1:50 PM, Cameron Craddock wrote:
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
[email protected]
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