Dear SPMers,
I'm trying to decide on the best way to model an event-related response
and thought I'd seek your input. In my experiment, there are 2 basic types
of randomized events. In Event Type 1, a cue stimulus is presented for 200
ms and then followed by a target stimulus 1500 ms later. In Event Type 2,
the same cue stimulus is presented for 200 ms, but no target is ever
presented. Each type of event lasts 3 seconds and my TR is 1.5.
I'd be interested to know what people think about the two options below
for modelling these events.
(A) Model event types I and II with different regressors
(B) Model all cue stimuli with one regressor and target stimuli with a
second regressor.
Option A seems very straightforward to me. One issue here is whether to
address the fact that event type I has 2 stimuli by (1) including a
dispersion derivative or (2) specifying two onsets for event type 1 - one
for the cue, one for the target. In practice, specifying two onsets seems
to better capture target-related activity than does using a dispersion
derivative. But, is specifying two onsets the best approach? It seems to
assume that cue and target stimuli will produce identical responses.
Option B seems tenable as well since the same cue stimulus is presented
in Event types I and II. I worry, though, that there might be a
multicollinearity problem because all target stimuli are always preceded by
the same cue stimulus. Still, not all cue stimuli are followed by a target
(66% of cues are followed by a target, 33% are not). In this situation, can
SPM come up with independent estimates of the responses to cue and target
stimuli? In practice, this approach seems to do a better job of capturing
cue-related activity than Option A, although target-related activity is
slightly weaker, which made me think of the multicollinearity issue.
Any advice would be much appreciated!
Thanks,
:> Daniel
Daniel Weissman, PhD
Center for Cognitive Neuroscience
Duke University
Durham, NC 27705
phone: (919)-681-1029
fax: (919)-681-0815
e-mail: [log in to unmask]
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