[pymvpa] classification based on individual parameter estimates from FSL

[David Soto]

Hi,

I m really new to machine learning and have just collected some fMRI data
for analysis with PyMVPA

The design is simple, basically I have 2 tasks, S and I and each task has 2
conditions: a and b

Each task occurs on a separate fMRI run  and the conditions a  & b are
blocked such as 'ababbaabbaabbaba' (each block is 4 trials each

Data has been preprocessed in FSL (as part of univariate-based analyses),
including a 5 mm smoothing. I have derived parameter estimates for each
task condition a & b....so have 8 betas per subject per condition.

​Basically I would like to train a SVM classifier to discriminate
conditions a & b in task S​ and then test it on the independent dataset
from the different task I.

For this I thought to normalise to MNI and concatenate all the arameter
estimates for a & b for task S across all subjects and in principle use
whole-brain classification, with the intention of trying searchligh
analyses later on...

Does this make sense? or would it be better to do it differently? Any
advise or pointers would be much appreciated!

cheers,

david


-- 
http://www1.imperial.ac.uk/medicine/people/d.soto/
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