Abstract
Task-correlated motion artifacts that occur during functional magnetic resonance imaging can be mistaken for brain activity. In this work, a new selective detrending method for reduction of artifacts associated with task-correlated motion (TCM) during speech in event-related functional magnetic resonance imaging is introduced and demonstrated in an overt word generation paradigm. The performance of this new method is compared with that of three existing methods for reducing artifacts because of TCM: (1) motion parameter regression, (2) ignoring images during speech, and (3) detrending time course datasets of signal components related to TCM (deduced from artifact corrupted voxels). The selective detrending method outperforms the other three methods in reducing TCM artifacts and in retaining blood oxygenation level dependent signal.
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