Hi all,
I have a quick question about how 3dDeconvolve computes the T statistic and the F statistic of a regressor and the R² of the model.
In particular, is there any difference between having a set of regressor specified as a baseline model (i.e. with -ortvec or -stim_base) versus specifying all the regressors as stimuli (i.e. with -stim_file but without -stim_base)?

As an example, I have a stimulus of interest in a file "A.1D" and then six motion regressors (in a file "M.par").
I could set up 3dDeconvolve specifying a baseline model as:

3dDeconvolve -input data.nii.gz  -num_stimts 1 \
-ortvec M.par motion_params \
-stim_file 1 A.1D -stim_label 1 stim_A \
[...]

Or without a baseline model, as:

3dDeconvolve -input data.nii.gz  -num_stimts 7 \
-stim_file 1 A.1D -stim_label 1 stim_A \
-stim_file 2 M.par[1] -stim_label 2 motion_params_1 \
-stim_file 3 M.par[2] -stim_label 3 motion_params_2 \
-stim_file 4 M.par[3] -stim_label 4 motion_params_3 \
-stim_file 5 M.par[4] -stim_label 5 motion_params_4 \
-stim_file 6 M.par[5] -stim_label 6 motion_params_5 \
-stim_file 7 M.par[6] -stim_label 7 motion_params_6 \
[...]

Would there be a difference in R² between these two calls, as well as in the F and T statistics of the first regressor ("stim_A")?
I suspect that there would not be a difference in R², but there would be a difference in F and T - but I would like to double check.

Of note, I know that the recommended way is to specify motion parameters in the baseline model. However, I'm asking about this difference because in case of high collinearity between motion parameters and signal of interest it wouldn't be unreasonable to evaluate a model fit having all the regressors treated in the same way, as it would be tricky to estimate what part of the variance could be assigned to which regressor.

Thank you,
Stefano Moia



Edited 1 time(s). Last edit at 04/02/2020 07:16PM by smoia.