[pymvpa] Dataset with multidimensional feature vector per voxel

Ulrike Kuhl kuhl at cbs.mpg.de
Wed Nov 4 15:26:28 UTC 2015

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,

Max Planck Institute for Human Cognitive and Brain Sciences 
Department of Neuropsychology (A219) 
Stephanstraße 1a 
04103 Leipzig 

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

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