Subject motion is a common problem in all forms of BOLD MRI and it is accepted that conventional motion correction does not remove all of this variance from the data. Hypothesis-driven approaches use the shifts from these corrections as unwanted-effects regressors, whereas data-driven analysis techniques often cannot, and so are more susceptible to this form of artefact. Modified temporal cluster analysis (MTCA) is a data-driven approach to the examination of fMRI and phMRI data. It is, however, susceptible to motion artefact, which we use here as an advantage and introduce the dual MTCA method as a means to discover when and where in the brain is being affected by motion which is uncorrected for, so that those slices may be suppressed before further data-driven analysis. This is particularly useful in experiments with long repetition times since only a subset of slices will require further correction. We apply this to placebo data from a phMRI study and conclude that it will be a useful step in the preprocessing and further analysis of such experiments.