We are currently conducting an fMRI study examining whether a novel
pulse sequence will enhance signal in the OFC. In order to demonstrate
the effectiveness of this method, we’d like to graphically compare the
raw time courses of the new vs. conventional sequences, and also compute
and compare event-locked averages in the drop out regions (even though
the signal in these regions will be a flat line). The main problem I’m
encountering with the latter is that during modeling, SPM eliminates
regions where there is no signal, thereby precluding examination of
event-locked averages in the drop out regions. Does anyone know how we
could obtain raw time courses and event-locked averages for these
regions? We are using SPM5 with Matlab 7.1.
Another separate issue is that our images contain portions of the
skull and other external brain tissues. We’ve tried to specify an
explicit mask when creating single subject models to eliminate these
regions (the mask.nii in the apriori folder), but these regions are
still present after model estimation (i.e. SPM doesn’t seem to
acknowledge the mask, or does so very slightly). We’ve also tried
using the grey matter apriori for our explicit mask, which did not work
either. Any suggestions? Thanks so much!
Developmental Cognitive Neurology
Kennedy Krieger Institute
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