I wouldn’t say bandpassing is default in AFNI. We have very few defaults, leaving those choices to The Researchers.
Not bandpassing shouldn’t cause problems with ReHo calculations-- that parameter is a measure of similarity of time series in a neighborhood, and it is agnostic to the presence of bandpassing.
The “standard” range of LFFs (low frequency fluctuations) is something like 0.01-0.1 Hz. The lower bound is chosen to be a bit above zero (and away from the low/baseline drifts, which are probably more scanner related than physiological). The upper bound is probably more historical, as well as driven by considerations of leaving out typical breathing+heart rate frequencies (though those might be aliased back into the LFF range, depending on TR). For the sake of comparison with literature, I would leave the LFF range for the ALFF definition to be around the standard range.
Re. censoring-- it is typically about movement, yes (I guess scanner artifacts could occur in a single volume, but probably >99% of censoring is about subject motion).
Re. degrees of freedom (DFs): you start with 1 for every time point you have (so, for N time points, you have N DFs). Every censored time point removes 1 DF (motion events lead to censoring of 2 time points, so 2DFs). Every single frequency that is bandpassed (and they are discrete frequencies, because we have finite time series) leads to 2 DFs being removed; for a TR=2s time series, the Nyquist frequency is f_N = 1/(2*2) = 0.25 Hz, and so bandpassing 0.01-0.1 Hz would lead to a loss of 60% of DFs just from that alone (and the rate of loss is higher for shorter TRs). Every regressor added to the model (e.g., polynomial or motion regressor) removes 1 DF. All this is to say-- gather your data and choose your processing wisely, for the sake of your final statistics!
1D file to input into 3dLombScargle to point out where censoring has occurred? If so, I don’t think you need to worry with afni_proc.py output. As per this help comment on that file:
-censor_1D C1D :single row or column of 1s (keep) and 0s (censored)
describing which volumes of FILE are kept in the
sampling and which are censored out, respectively. The
length of the list of numbers must be of the
same length as the number of volumes in FILE.
If not entered, then the program will look for subbricks
of all-zeros and assume those are censored out.
… if no censor list is input, then the program will look for fully empty volumes and assume that those have been censored. afni_proc.py will provide you those (you can verify that the text output from there points out the censored volumes).