Outlier scores in the ‘Parzen_outlier_mvtroi’ table are p-values associated with the test ‘the observation is normal’. That means that one has a probability p of rejecting a normal subject when discarding an observation with associated p-value p. Thus, one will discard subjects with associated p-value < 0.01 if he tolerates 1% error in the outlier removal process. Yet, one should be aware of the multiple comparison problem and the different strategies to alleviate this (see )
The p-values computation strongly rely on precise assumptions about the data structure. In practice, these assumptions can not be checked and one should be careful when discarding outliers at a given level. For this reason, a common practice within the neuroimaging community is to fix a proportion of observations that one is ready to discard, and then remove that proportion of observations according to a ranking index. The p-values from the 'Parzen_outlier_mvtroi' table can be used as a ranking index (observations with smallest p-value to be discarded first).
Former outlier scores are deprecated.
 Brent R. Logan, Daniel B. Rowe, An evaluation of thresholding techniques in fMRI analysis, NeuroImage Volume 22, Issue 1, May 2004, Pages 95–108