Dear Claus, List,
I'd say simply model it as a normal condition (with a set of onsets,
duration(s), basis function(s), hrf if you prefer), and disregard it in your
contrast vector (a 0 for that regressor). Basically, a regressor of no
interest is a regressor you don't have any interest in, which is largely
independent of how SPM models things but rather in what you include in your
contrast.
Good luck,
Bas
Op woensdag 7 september 2005 14:14, schreef Claus Lamm:
> Dear all
>
> Though I have been searching the archive up and down I am still not
> 100% clear about the following question.
>
> I have performed an efMRI study (2x2 factorial, but this is not so relevant
> here) where we had to present instruction screens of about 10 s duration.
>
> I see three ways to deal with this fact
>
> 1) model the instruction as a condition, effectively reducing my dfs, but
> also increasing the model fit (at least I hope so)
> 2) model the instruction as a regressor of no interest
> 3) ignore the instruction in my model
>
> Apart from the fact that I am not sure which way is the best (I guess this
> also has should be determined empirically, no?), I have two questions:
>
> 1) how would I enter my regressor of no interest? SPM asks for
> a 100 scan vector (1 session=100 scan). Would this be something like
> 1 1 1 1 1 0 0 0 0 ... 0, assuming that the first 5 scans represent the
> instruction? thus 1 specifiyng regressor is 'on', 0=off
> 2) If this approach is correct: how would the model deal with the fact then
> that due to the sluggishness of the hrf I do not only have influences of
> the instruction screen on my data during the time the instruction screen is
> one, but also later on; wouldn't this fact speak for modeling the
> instruction as a condition since then it would be convolved with the hrf?
>
> any help and comments are gratefully appreciated
>
> claus
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