Hey Gaia,

I guess you have to decide whether to stick with 1st order sequence
effects (i.e. modelling trials as CC, CI, IC, II) or whether to go
into 2nd order or even higher order effects. The problem with the
latter is that your number of conditions/regressors goes up
exponentially, and thus the number of events of each type will
decrease...you'd need an awful lot of events to reliably model even
all possible 2nd order sequences.

The problem with the parametric modulation approach suggested by
Daniel is that it would only account for the same type of trials (i.e.
CCCC) but not for other higher order sequences (e.g. IIIC, etc).

Good luck,

Tobias.

On 5/18/06, Gaia Scerif <[log in to unmask]> wrote:
> 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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--
Tobias Egner, PhD
fMRI Research Center
Columbia University
Neurological Institute, Box 108
710 West 168th Street
New York, NY 10032
Tel: (+1) 212 342 0121
Fax: (+1) 212 342 0855
Email: [log in to unmask]