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Would someone happen to know how many neighboring volumes are used to interpolate the censored volume and what would happen in the case outlined above (i.e., if two or more consequent volumes in a time series are outliers)?

The two closest neighboring volumes (in time) are use -- one before and one after, and the interpolation used is linear. For example, if time indexes 1 and 5 are kept, and time indexes 2,3,4 are censored, then

  s(2) = 0.75*s(1)+0.25*s(5)
  s(3) = 0.50*s(1)+0.50*s(5)
  s(4) = 0.25*s(1)+0.75*s(5)

where s(t) is the signal at time index t.

If s(5) is still an outlier (that is, wasn't censored but "should have been"), then the interpolated values will be outlier-ish themselves. However, you have to judge the likelihood of this happening enough to contaminate your results significantly. No signal processing method (even "machine learning") can perfectly suppress noise and outliers.

(As an aside, when I did resting state analysis [in my pre-retirement life], I usually used a fairly strict motion threshold for part of the censoring decision.)

The NTRP option was put there at the request of a particular Spanish researcher. Personally, I don't like it, as it gives the impression that you have a full set of "real" data, whereas some of your data times series is now fictional -- that is, you don't actually have the number of degrees of freedom that it seems like from the time series length. However, if you are going to do inter-subject time series correlations, then you have to do something to make that possible.