[pymvpa] Dataset with multidimensional feature vector per voxel

[Ulrike Kuhl]

Dear PyMVPA experts,

first of all: thank you so much for the great work on providing such a nice and handy toolbox!

I started working with PyMVPA less than a week ago and while the tutorial provides a great overview on how to set up one's fMRI data, I am kind of stuck on how to proceed with my structural data.

What's my story?
I plan to perform an MVPA analysis on structural MRI data. To be exact, I have several maps of parameters derived from diffusion weighted MRI per subject, like fractional anisotropy (FA), mean diffusivity (MD) and the like. Eventually I want to train a classifier on the combination of these parameters per voxel in order to see which voxels (using a searchlight approach) predict best whether the corresponding subject belongs to group A or group B (groups derived based on behavioural measure).

I would like to set up my data in the following way:

* one sample = one participant
* features per sample = 2D-data of the form: numberOfVoxels X numberOfDiffusionDerivedParameters

That is, for each voxel that is encoded in the feature dimension, I want a vector containing the respective diffusion-derived parameters for that voxel.

What's the best way to set up data this way using PyMVPA? 
Is it even possible to do it and then train a classifier on this parameter-vector instead single voxel values?

Thank you so much for your help!

All the best,
Ulrike



-- 
Max Planck Institute for Human Cognitive and Brain Sciences 
Department of Neuropsychology (A219) 
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04103 Leipzig 

Phone: +49 (0) 341 9940 2625 
Mail: kuhl at cbs.mpg.de 
Internet: http://www.cbs.mpg.de/staff/kuhl-12160