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Best Regards, Donald McLaren
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D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School
Office: (773) 406-2464
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On Mon, Mar 21, 2011 at 5:22 AM, Ben Becker <[log in to unmask]> wrote:
Dear SPMers,

We used an event-related spatial working memory fMRI paradigm with 3 levels of working memory load. The task is a delayed-matching-to-sample task. First a sample with 1, 3 or 5 points (in 30 possible locations) is shown. Subsequently  a probe with one point is shown. Participants have to press the left button if the probe point does not match the location of the previously shown 1, 3, or 5 points. If the probe matches the sample they have got to press the right button.
My question concerns the first level design-matrix:

The matrix consists of the following regressors:

1.      Baseline
2.      Correct trials WM Load 1
3.      Correct trials WM Load 2
4.      Correct trials WM Load 3


My questions:

1.      Would it be valid to introduce two further regressors coding the correct and incorrect  trials (for all WM loads together, some participants did not make any mistakes in the WM Load 1 conditions, else it would have been fine to introduce separate regressors for the three WM Load conditions)? My main concern introducing the correct and incorrect regressors is that the regressor coding the correct trials will not be independent from the regressors coding the correct trials for the single WM Load conditions.

Use 3 regressors for incorrect trials. If one type is missing in a particular run, then you just leave it out for that subject/session.
 

2.      What about missed trials? They will not be further analyzed in the imaging data analysis – so do I have to code them in a separate regressor? Or would it be ok to ignore the missed trials?

What are you considering a missed trial? If there is something similar among missed trials, then you can code them a single separate regressor. Otherwise you might want to code each one as separate regressor.

In any analysis, you want to model the expected neural activity in the best way possible.
 

Thanks & Kind regards,

Ben