Dear Gaia,
Regarding the conflict adaptation effects you mention in question 1, here's
a different thought than the one you suggest. What if you model 4
event-related trial types?
(1) compatible preceded by compatible,
(2) compatible preceded by incompatible,
(3) incompatible preceded by compatible, and
(4) incompatible preceded by incompatible.
Then, for each of these four trial types, what if you include a parametric
regressor, giving a total of four parametric regressors?
Each parametric regressor could code for the number of trials preceding the
current trial that belong to the same trial type as the current trial. For
example, if the current trial is "compatible preceded by compatible" and was
preceded by two compatible trials, then the value of the parametric
regressor for the current trial would be two.
By including parametric regressors, I think you would be able to investigate
whether the response to any of your four main trial types varies linearly
with the number of number of trials preceding the current trial that belong
to the same trial type as the current trial.
Just a thought,
Daniel
Daniel Weissman, PhD
Center for Cognitive Neuroscience
Box 90999
Duke University
Durham, NC 27705
-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On
Behalf Of Gaia Scerif
Sent: Thursday, May 18, 2006 1:29 PM
To: [log in to unmask]
Subject: [SPM] Modelling non-linear regressors and spiral sequences
Dear SPMers,
I am a new user of SPM(2) and I have two (probably very
naive) unrelated questions. I did not have much success
when I looked for answers in previous mailings - I
apologise in advance if they have already been discussed.
1) Together with a colleague who is using AFNI, I am
trying to model data from an event-related experiment in
which trials of interest (stimulus-response compatible
trials and incompatible ones) are intermixed randomly. We
are hoping to model interactions between the
characteristics of trials of interest and the context set
up by the trials that precede them (e.g., an incompatible
trial preceded by a compatible one, etc.).
From our understanding of the literature, previous
imaging papers (e.g., Jon Cohen's studies, etc.) have
focused on context as it is set up by the trial
immediately preceding the trial of interest (n-1).
However, this does not fully take into account the
cumulative effects of potential repetitions or changes
preceding the n-1 trial. For example, we have up to three
series of trials of an identical type preceding the trial
of interest. How should we model these cumulative effects?
Has anybody dealt with this problem before, or do you know
of where I could read about it? We thought of adding an
additional non-linear regressor (e.g., an exponential
function, but which one?) to modulate the HRF. How does
one do this in SPM2?
2) Our data were acquired using a spiral sequence, rather
than EPI, and when I load images using the EPI template in
SPM I get some bad distortions - what can I do about this?
There were some previous messages on this topic on the
list, but ... I did not understand the solution.
Many thanks for any suggestion,
Gaia
~~~~~~~~
Gaia Scerif, PhD
School of Psychology
University of Nottingham
Nottingham NG7 2RD
United Kingdom
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