Hello,
I am currently assisting a colleague in analyzing a rather problematic
study using SPM5. Although there are problems with the design of which we
are only too aware, at this point the data has been collected and we must
simply make the best of it.
The task block consists of a 3 second instruction, a 21.6 second task,
and a 5 second feedback. Task type alternates, with four different flavors
of the task, and 10 blocks per run. There is no break between the blocks,
although there is fixation before and after the 10 blocks.
I began by modeling this as a block design study, with 12 regressors -
one each for instruction, task, and feedback for each of the four flavors.
The results were not good. I began to inspect the time series, where I
discovered that response to the task was not sustained over the entire 21.6
second interval, which was not well fit by the block model.
So, I've turned to a FIR model - but here's where I've run into snags.
Ideally, I think I would like to model the instruction and feedback as short
blocks, and only model the task itself with the FIR regressors. However,
this does not seem possible within the SPM5 interface - is there a way to do
this outside the interface? Alternatively, is it possible to use a
different number of FIR regressors for different stimuli? It would be
unreasonable, I think, to model the 2s instruction block with the same
number of FIR regressors as the 21.6 second task. Our final option would be
to combine the instruction and feedback and task into one giant block.
In any case, I would appreciate any help with enhancing the flexibility
of the FIR models, as well as any input on how to model a problematic task.
Thanks!
Allison
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