Hi-
Re 1: Sure, resampling that way makes sense. Note that if there are tiny ROIs in the Glasser atlas, those might disappear.
Re. 2: if your time series has zero mean (e.g., which might occur if it is residuals from resting state processing, say), then there shouldn't be a difference between extra zeros or not.
Note that you can also input a "weight" vector with "-weight_ts ..", which could be 1 for non-censored time points and 0 for censored time points-- such a beast would be created by afni_proc.py, and likely called censor_${subj}_combined_2.1D in the *.results/ directory.
... and you can compare the results of using the weight vector and not doing so to verify that there is no difference (again, *if* your times series all have zero mean).
Note that in assessing the statistical significance of the Pearson r, you would want to use the degrees of freedom of the time series, which is even different than the number of time points, often.
--pt
