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Your contrasts are correct. It shows the effect of time; however, I'm not
sure the you eliminate the issues that occur in the task event contrasts.

For example, what if the task-default switch or visual activation changes
over time, then the time effect is not the pure effect of the task.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
Office: (773) 406-2464
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On Tue, Dec 3, 2013 at 3:32 PM, Colin Hawco <[log in to unmask]> wrote:

> Hi all,
>
> I am working on an analysis for a study design on 'effort'. We are
> hypothesizing that neural activity related to effort will increase over
> time for our 'effort' events.
>
> I am attempting to model this using parametric modulators, which I believe
> is the correct approach. But I have no direct experience using parametric
> modulators and thus would prefer to check my work with the list before
> showing results to co-authors.
>
> We have 6 sessions per person. I am not interested in session x effort
> interactions at this time (due to the nature of the experiment), but wish
> to identify voxels where the neural activity to our effort events increases
> over time. So, I added a 1-st order time modulation. We actually have 2
> event types, and I added a modulator for each, and ran the GLM using an HRF
> only model for now.
>
> So my design matrix has 4 columns per run [type1, type1*time, type2,
> type2*time].
>
> if I want to find voxels where activity increases over time for event
> type2, can I just run a contrast of 0 0 0 1? I think this is explicitly
> effect of time after removing task effects, and thus doesn't have the sort
> of issues (task-default switch, visual activation, etc.) with a main
> effects contrast of a task event (like 1 0 0 0).
>
> Any advice and/or confirmation that I am on the right track would be
> appreciated.
>
> Thanks.
> Colin
>