SPM Users,
I would like some opinions regarding the optimal way to analyse a
dataset. The study is a 2x2 event-related design, crossing memory type
(semantic, episodic) with spatial content (spatial, nonspatial). Each
trial includes a memory question that the subject reads and then responds
to, lasting a total of 8 seconds. Subjects respond to the question with a
button press, usually 3-4 secs after the onset of the trial. The time
period after the button press, based on their rt for each trial, is
specified as a separate "wait" condition and is not of interest. There is
a separate control condition (reading a string of nonwords) that also
requires a button press after about 3-4 secs.
I'm currently analysing the data in spm99 as event-related, with 0 duration
and no global scaling, and got some small but reliable regions of
activation when comparing the various conditions to the control
condition. I then re-analysed the data, also using 0 duration and no
global scaling, but adding time and dispersion derivatives. Now when I do
a t-test on the canonical HRF component, I get a very different pattern of
activations that are much "messier".
I'd appreciate hearing a) how people would deal with long trials (3-4
secs). Would you specify 0 duration or use the rt as the specified duration for
each trial? And b) Why does including the time and dispersion derivatives
change the t-test results for the canonical HRF so dramatically? Does anyone
have suggestions on the best way to analyse this dataset?
Any guidance or advice on the matter would be greatly appreciated.
Thanks,
Siobhan Hoscheidt
Lee Ryan
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