Hi,
On 21 Jan 2008, at 13:12, Martin M Monti wrote:
> Dear FSL masters,
>
> I have a design in which 4 tasks are compared 2 x 2 (e.g. TaskA,
> BaselineA; TaskB, BaselineB). Now, I have a significant difference
> in RT between TaskA and TaskB (but not between BaselineA and
> BaselineB), so to prevent the possibility that differential
> activation between the two tasks is just due to extra difficulty or
> activation/timing issues, I'd like to use RT as a covariate.
>
> I have some questions though, on its usage.
>
> - Is the first level analysis the right place for using the
> covariate? I have RTs for every trial, so I could just insert an
> extra EV at every 1st level analysis. There is one thing thought
> that is not-intuitive to me on this point: my EVs of interest are
> defined using RTs (to select the on-off periods) so I somehow have
> the feeling that just everything will be caught in the covariate. I
> suppose this is an incorrect intuition?
There are several ways that such experiments might be modelled. One
thing is that some people separately model the thinking process
leading up to the subject action and the post-action period. Then
indeed some people have one fixed-height EV for the action, and also
add in an action EV where the height is modulated by the task
difficulty etc., or in your case possibly the RT. It's hard to know
what's the best thing here without knowing a little more about the
experiment, but you can play with these possibilities.
> - Would using the Average RT for each task in the within subject
> Second-level analysis (I then do a Second-level analysis aggregating
> all subs with Random Effx) be a more suitable strategy (I'm taking
> this idea from the example on the feat5 page of the FSL website)?
Not necessarily; as you suggest, this is a cruder model as it does not
allow for variable within-subject RTs and modelling. It really depends
on what question you want to answer / model out with your RT data.
> - To Use the covariate, I have to select "Orthogonalize" EVs, correct?
Depends what you want. If you want it just to safely soak up extra
variance and not affect the mean fitting then yes you would do this.
Cheers.
> thanks,
>
> all the best
>
> martin
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Stephen M. Smith, Professor of Biomedical Engineering
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