I have a few questions on how I'm setting up a multi-session ICA.
I have 5 scan sessions from the same individual. In each scan session,
the task was slightly different (e.g. different stimuli/response) and
the timing was also slightly different.
What I would like to do is identify components that are:
i) common across all sessions
ii) unique to specific sessions and correlate with stimulus timing for
that session
iii) unique to certain combinations of sessions and correlate with
stimulus timing for those sessions
iv) for ii) and iii), it would be even better if I could show the
uniqueness of the components to those sessions in some way (i.e. who
that they exist for those sessions but not for the others)
So far, I've done a pretty stock standard multi-session Tensor ICA,
but two questions arise:
a. how do incorporate a different time series model for each session
(in the post-stats part)?
b. what's the best way of getting at ii and iii above - when I set up
a session-subjects model with multiple higher-level contrasts, I don't
seem to get any output regarding those contrasts?
Would I be better off doing separate ICAs on each session and then
performing higher level GLM on the resulting components? If I do this
though, how do I match up components across sessions? It seems to me
that multi-session tensor ICA should in principle allow me to answer
these questions - just need a bit of guidance perhaps.
Many thanks,
Tom
--
School of Psychology and CLS
University of Reading
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