Hola Stefano,

The statistics computed in 3dDeconvolve are between a "signal" model and the "non signal" model (S and N in what I write below).
What regressors are considered to be in S and what are considered to be in N depends on the statistic being computed.
The statistic bricks measure how much adding S to N improved the least squares fit.

For individual regressors NOT marked as "baseline" (via -stim_base or -ortvec), then their individual t (and F and R^2) statistics are computed with S = that regressor and N = all other regressors. So these bricks are what is sometimes called a "marginal" statistic, showing how much this one regressor improved the model when it was added in after all the other regressors.

So your two runs should have the same statistical result for stim_A.

On the other hand, the Full F statistic is a collective statistic, where S = all regressors not in the baseline model, and N = all regressors in the baseline model. That is, the Full F measures the improvement of model fit (in the least squares sense) when all non-baseline regressors are added to the baseline fit.

So your two runs should have different results for the Full F brick, since in the first run, the only S regressor is stim_A while in the second run the motion regressors are also in S (and will each get their own t brick output -- assuming you use the -tout option).

You should OF COURSE run the program both ways to be sure that what I'm saying is true. Empirical knowledge wins over trans-Atlantic philosophy.

** bob cox