[pymvpa] Analysing data from a 1-run block design

[Nick Oosterhof]

On 31 Mar 2015, at 11:56, Jan Derrfuss <derrfuss at gmail.com> wrote:

> I was wondering if there's a recommended way to analyse fMRI data from a block design where all data were acquired in one run. I've modelled each block individually and now have one parameter estimate for each block. There were two conditions (A and B) with 6 blocks each. Conditions alternated (A - B - A - ... - B) and were separated by 15-s rest phases. Half of the participants started with A, the other half with B.
> 
> In particular, I was wondering
> a) how much of a problem the single run is,

Not ideal, but also not necessarily a game-breaker.

> b) how chunks should be assigned,

You mention 15-s rest phases. Does that mean the design was:

R A B R A B R A B R A B R A B R A B R

If that is the case, then each “A B” piece (block) would have a unique chunk value. Given the duration of 15 s between blocks, this should be enough to assume independence between blocks.

> c) what type of preprocessing should be applied

Was the order randomised for each block or not?
If it was not randomised, then detrending becomes seriously important. But in any case I would suggest to apply detrending. z-scoring may also be a good idea, in particular if you have not normalised the data otherwise (such as dividing each voxel’s time course by the mean value over that voxel’s time course)

> d) if there's a classifier that would be expected to work well under these conditions.

I may be wrong, but I don’t expect very significant differences between the typical classifiers; I would suggest to try SVM or regularised LDA. 

However, there are not too many samples, only 12, so the classification accuracy can only have 13 possible values (i/12 for i from 0 to 12 inclusive). 

Alternatively you could do a split-half correlation analysis, which may give a more continuous measure of pattern descriminability.