Hi, all. I'm currently analyzing data from a training paradigm and
could use some advice, please.
In the "Between subjects designs: an alternative approach" section of
Jill's generally excellent PPI page, here:
http://www2.fmrib.ox.ac.uk/Members/joreilly/what-is-ppi
, she talks about a style of "PPI" where the "Psychological" regressor
is a constant "group A" or "group B" indicator, which makes the
physiological regressor and the interaction regressor entirely
redundant. The upshot is that you end up with simply a physiological
regressor in your model, and you can directly compare statistical maps
from that regressor to the other groups'. She then suggests that you
can also use this approach to compare connectivity before and after
training/TMS/whatever.
To do that analysis correctly, Jill suggests a regressor that includes
the timecourse only during the blocks of interest, and to de-mean that
timecourse in order to partial out the main effect of task (which we
assume is similar from pre- to post-training). I'd like to do that,
but have some clarification questions to make sure I'm not doing
something absurd.
So, here are my two questions:
1) What does "during the blocks of interest" actually mean? Presumably
I don't want to include the portion of the time course that is rising
from baseline activity, since this dramatic shift in activation will
dwarf the variability observed during the block and create
artificially high "connectivities" with all of the other
task-responding regions? Is simply ignoring the first 6s of the block
activity safe, or is there a better approach?
2) What else goes into the model? I have 4 conditions, and am
interested in the connectivity changes from pre- to post- on only one
of them. Presumably I should clean up the unexplained variance in the
model with a normal convolved regressor for the other three conditions
and nuisance regressors? Does that sound right? Just a model with one
seed timecourse that is 0's everywhere except for the blocks of
interest and has the demeaned signal in the blocks of interest, then 3
convolved condition regressors, then nuisance regressors for motion
and WM/CSF signal?
Thanks so much, in advance, and if there's a paper that goes into more
detail on this topic I'd love to read it!
Todd
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Todd Thompson
Doctoral Candidate, Dept. of Brain and Cognitive Sciences
Massachusetts Institute of Technology, 46-4037C
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