Andreas -
> I would like to covary out reaction time in one of my experiments (using
> SPM99). The experiment contained two conditions (and not explicitly
> modelled null events as a baseline condition). I entered the reaction
> times as a parametric modulation of each trial.
>
> Intuitively, I thought that e.g. the contrast [1 0 -1 0] identifies
> brain regions differentially activated by the two tasks (having covaried
> out common RT effects), and that e.g. the contrast [0 1 0 1] identifies
> the main effect of reaction time.
>
> In one of your recent e-mails I found the following:
>
> "(...) The slight complication comes if you have more than one
> trial-type. You could enter a separate parametric modulation of each
> trial-type by the RTs for that trial-type, but that will only covary out
> trial-specific RT effects. You probably want to covary out common RT
> effects. The way to do this is to collapse all your trials into one
> trial-type, then enter two parametric modulations. One modulation would
> be RTs, as above. The other would be a "categorical" modulation that
> indicates whether each trial is of type1 or type2. A contrast of [1] on
> the column for this "categorical" modulation will identify regions that
> show a difference between your trial-types, having covaried out common
> RT effects (...)"
>
> Is there something wrong with the way I have built my design matrix?
Your design matrix will "covary out" RT differences across trials
WITHIN an condition. However, it will not covary out RT differences
BETWEEN conditions. This is because the parametric modulations (RTs
in your case) are always mean-corrected (so the resulting regressor is
orthogonal to that for the condition effect itself). Thus any difference
in mean RT between your two conditions will not be "covaried out", and
could possibly* still contribute to any BOLD differences you find
between your two conditions (in your [1 0 -1 0] contrast). If you want
to covary out RTs that differ across conditions, you need to create the
alternative design matrix that I described above.
*Or alternatively, you may want to ask: "why am I covarying out RTs
if they are simply another consequence of the same underlying cause
that generates my BOLD differences?", ie are they really a "confound"
or simply another correlated dependent variable?...
Rik
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DR RICHARD HENSON
Institute of Cognitive Neuroscience
& Wellcome Department of Imaging Neuroscience
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