If you include 2 sessions in a single model, how would you deal with the baseline? Would you
implicitly model it (ie only model the activity periods), or would you explicitly model a
separate baseline regressor for each session? For the comparisons I'm interested in I contrast
an activity of interest condition to a control activity condition, but would like to get out main
effect contrast estimates.
thanks,
Amit
>> 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.
\
|