Amit Etkin wrote:
> All,
>
> I am running an experiment with two related tasks in a group of normals. My question is this:
> is there any reason why I should make two separate 1st level models for each subject (one per
> subject), rather than one model with two sessions (one for each task)? Will running one bigger
> model change the way effects and variances are estimated? or influence my ability to compare
> the magnitude of activation in a given brain region between the two taks.
>
Re. the effect estimation and variance estimation issues - both will be estimated differently
in the two model versus one model approach. Which approach is better depends on how closely
matched the two sessions are. The one model approach has more data to explain but has more
parameters to play with. Whether or not the error variance (assumed constant over both sessions) is
higher or lower is an empirical issue.
However, from a flexibility perspective, I think running one big model (ie. modelling both sessions
in the one model) is the better option here.
This is because it allows you to assess differences between sessions. You'd certainly
want to do this if an experimental effect of interest was expressed over sessions eg.
session 1: before drug, session 2: after drug.
Then, you could use the contrast manager to estimate the drug effect
in each subject.
Without doing this there is no easy way in SPM to assess between session (within-subject)
effects.
Best,
Will.
> thanks!
>
> Amit
>
>
>
--
William D. Penny
Wellcome Department of Imaging Neuroscience
University College London
12 Queen Square
London WC1N 3BG
Tel: 020 7833 7475
FAX: 020 7813 1420
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URL: http://www.fil.ion.ucl.ac.uk/~wpenny/
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