Dear SPM experts,
we are currently trying to set up a DCM analysis for one single subject with 4
different fMRI sessions. The goal is to test for learning induced changes in
connectivity across sessions.
After reading a lot of messages in the spm mailing list, we have got the idea
that the best current solution would be to do the following:
1. Create a single subject, "single session" fixed effects model, in which the
4 sessions are stitched together.
2. Add a modulatory regressor, modelling for the learning effect (e.g. 1 1
1 . . . 1 1 2 2 2 . . . 2 2 3 3 3 . . . 3 3 4 4 4 . . .4, corresponding to
the 4 different sessions).
3. Add a "transition" regressor, to help soaking up the transition effects
between sessions (e.g. 0 0 0 . . . 0 1 1 0 0 . . . 0 1 1 0 0 . . . 0 1 1 0
0 . . . 0).
We have some doubts about the need to include the transition regressor and
about whether this would be the correct manner to specify it.
Is the transition regressor actually needed?
Is it ok to set 1's just at the end of the nth session and at the beginning of
the of the (n+1)th session, and to set 0's anywhere else?
Does the overall procedure otherwise make sense?
Thanks a lot for your help!
All the best,
Marco
--
Marco Tettamanti, Ph.D.
San Raffaele Scientific Institute
Department of Neuroscience
c/o L.I.T.A. - room 25/5
Via Fratelli Cervi 93
I-20090 Segrate (MI)
Italy
Tel. +39-02-21717552
Fax +39-02-21717558
email: [log in to unmask]
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