Hi Nick,
Thank you very much for your quick reply!
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.
Cool.
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.
No, it was R A R B R B R A ...
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)
I assume "block" refers to the "A B" blocks you mention above? I see
your point, but as there was a rest phase between every single block,
this shouldn't be an issue, right?
For now, I arbitrarily assigned 4 blocks to each chunk (so, 3 chunks
overall) and detrended and z-scored. PyMVPA told me that this would be
"discouraged" given the small number of samples, so I wasn't sure what
the best thing to do would be.
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.
Thank you for these suggestions!
Cheers,
Jan
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