[pymvpa] classification based on individual parameter estimates from FSL

[Michael Hanke]

Hey,

sorry for the delay... Aren't you watching the world cup? ;-)

On Thu, Jun 26, 2014 at 02:11:00PM +0100, David Soto wrote:
> 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.

I don't fully understand how two conditions time two tasks make 8
betas...

> ​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!

The general approach is sane. However, I don't know if that SVM can be
trained properly with 8 training samples. Doing it in a searchlight
brings the number of features closer to the number of samples. You could
also consider a simple k-nearest-neighbor approach (prediction
determined by the closest (eucl./corr-distane) training dataset sample).
However, the latter is not really applicable in the full-brain case, as
the distance measure will be dominated/contaminated by thousands of
noise voxels...


HTH,

Michael


-- 
J.-Prof. Dr. Michael Hanke
Psychoinformatik Labor,    Institut für  Psychologie II
Otto-von-Guericke-Universität Magdeburg,  Universitätsplatz 2, Geb.24
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