> After modelling both 1st and 2nd order modulations, I assume that a plot of
> parametric responses shows a linear combination of both estimates. Is there
> any way to tease the contribution of the two estimates apart and plot them
> separately?

Their contributions can be teased apart by separate contrasts, of course, but
I don't think SPM offers a way to plot them separately.


>> It depends on 1) what F-test you did in the model with both A and B
>> modulations, and 2) what contrasts you used to create the con*.img files
>> that comprise the data for the model.
>
> Sorry, I didn't mention the contrasts as I did not want my previous message to
> be too long. I am actually using a FIR model, so the contrasts are almost
> intelligible. But let say I only model the canonical hrf with 1st and 2nd
> order parametric estimates. The t-contrasts I entered in the RFX analysis
> were:
> [0 1 0 0 0 0] and [0 0 1 0 0 0] for condition A
> [0 0 0 0 1 0] and [0 0 0 0 0 1] for condition B
> At the 2nd level I entered an F-contrast [-1 0 1 0; 0 -1 0 1] that yielded a
> significant effect in area X, and the sign of this effect was negative.
> So, If I have understood you correctly, there may be several alternative
> interpretions, and the correct one may be inferred by having a look at data
> plots:
> 1) both show U-shaped modulations (just larger for A)
> 2) both show inverted-U-shaped modulations (just smaller for A)
> 3) A shows a U-shape and B an inverted U-shape.
> The average parametric plot actually presents yet a different situation with A
> showing an inverse U-shaped response and B a U-shaped response.
> But maybe I am looking at data plots in the wrong way... Does it make any
> sense to extract individual parametric plots and then look at the average
> response? The variability across subjects/sessions is quite high: is there
> any way to gain a measure of variability from the SPM parametric plot
> facility?

There is little point averaging the plots per se: just calculate the average
parameter estimates across subjects for each component/basis function,
and then create the plot your self by multiplying each average by the
corresponding modulation function / basis function (in matlab).

R