Hello, I have lesion data, and I am trying to test whether particular patterns of lesions distinguish two classes of patients. I have two questions:
1) What is the best way to scale the lesion data? Traditionally, these data are represented with 1s (lesion) and 0s (no lesion). I've played around with different scalings, and I've gotten different (but replicable) results using the SMLR classifier in PyMVPA 0.4. See below: first column is the leave-one-out CV; second column the value for the spared voxels; third column is the value for the damaged voxels. CV NoLesion Lesion 83.571 000 001 75.000 001 002 77.143 002 004 81.429 100 200 81.429 200 400 2.) What is the best way to control for a nuisance factor? I know there is an additional variable (i.e., lesion volume) that can distinguish between my two patient groups, so I would like the resulting CV and heavily weighted voxels to be uncontaminated by this nuisance factor. Ideally, I would like to know how much additional predictive power is gained over and above this nuisance factor. Thanks, David _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
