Real-time fMRI is especially vulnerable to task-correlated movement artifacts because statistical methods normally available in conventional analyses to remove such signals cannot be used in the context of real-time fMRI. Multi-voxel classifier-based methods, although advantageous in many respects, are particularly sensitive. Here we systematically studied various movements of the head and face to determine to what extent these can “masquerade” as signal in multi-voxel classifiers.METHODS
Ten subjects were instructed to move systematically (twelve instructed movements) throughout fMRI exams and data from a previously published real-time study was also analyzed to determine the extent to which non-neural signals contributed to the high reported accuracy in classifier output.RESULTS
Of potential concern, whole-brain classifiers based solely on movements exhibited false positives in all cases (P < .05). Artifacts were also observed in the spatial activation maps for two of the twelve movement tasks. In the retrospective analysis, it was determined that the relatively high reported classification accuracies were (fortunately) mostly explainable by neural activity, but that in some cases performance was likely dominated by movements.CONCLUSION
Movement tasks of many types (including movements of the eyes, face, and body) can lead to false positives in classifier-based real-time fMRI paradigms.