The principal reason for advising against use of the FSL bias field correction algorithm is that it fails to extrapolate the estimated bias field beyond the provided mask. This is conflated by the fact that estimated brain masks are often not very good prior to bias field estimation. So e.g. if a voxel at the edge of the brain is erroneously omitted from the mask, and the estimated bias field in an adjacent voxels that is included in the mask is say 30%, then following bias field correction there will be a 30% difference in image intensity between those two adjacent voxels. This would clearly be artefactual, and could have downstream effects e.g. in registration & transformation of FOD data to template space. I should perhaps at some point generate an example image with which I can demonstrate this behaviour.
If you have only one subject with a clear artifact and no such behaviour for any other subjects, I would be tempted to remove it.
However I would also be very curious to know what it is about that subject’s data that is causing N4 to misbehave; maybe it could shed some light on how we could modify the default bias field estimation parameters to prevent such from occurring (this cerebellar hyperintensity has been a persistent issue since the introduction of this script). Any chance of sharing that one subject, along with a few from the same cohort where the bias field estimation succeeded, for a closer look?