Hi, Sam-

Re. #1: "What are the minimum D.O.F. acceptable for resting state data?"
That's a million dollar question! (Or at least an R21 one....) Given that most people don't report this valuable/necessary bit of information in their studies, I don't believe there is consensus in the field (well, is there consensus about *anything* in the field??). I think many people have ignored the degree of freedom loss with bandpassing, and it is really a problem in interpreting results-- people say "I had 200 time points and censored only 5 due to motion", but they leave out that they probably removed 60% or more degrees of freedom from bandpassing alone (and since each censored time point is one degree of freedom, it would be like censoring an additional 120 time points!!!). Sooooo, I don't know, precisely, unfortunately.

Re. #2: typically, people do bandpassing for the purported reason of removing breathing+cardiac frequencies from the data-- these tend to be >0.1 Hz (however, some can get aliased back into the lower range, depending on the TR...). So, the point of that statement in the help file is: if you want to get rid of breathing/heart rate effects, perhaps measuring them more directly would be better, because then you include a couple regressors in your model (i.e., reduce your DFs by a couple), rather than the much blunter bandpassing (reducing DFs by 60% or more, typically). If you are doing surgery, use a scalpel, not a chainsaw (if you can help it!).

Re. #2b: do I recommend bandpassing: welllllllllllllllllllllll, it's hard to say. There are a fair number of studies that have shown that higher frequency information does contain useful signal (it probably has to, but these have shown there it is a sizeable amount, useful for things like seedbased correlation, ICA, etc.); in particular, see Gohel & Biswal 2015, and other work by them; Chen & Glover have also written a couple useful papers on the topic.
Off the record (no one reads the internet, right?), I would thing that not bandpassing might be a better way to go: you reduce your information content SO much with bandpassing; people say "bandpassing makes my data more consistent/reproducible", but one should note that multiplying all time points by 0 would also make results quite reproducible... OK, that might be going too far, but hopefully the trade-offs are a bit more apparent.

AFNI: complicating analyses with pesky (but important) technical considerations since 1994!

--pt