Motion correction and the use of motion covariates in multiple-subject fMRI analysis

The impact of using motion estimates as covariates of no interest was examined in general linear modeling (GLM) of both block design and rapid event-related functional magnetic resonance imaging (fMRI) data. The purpose of motion correction is to identify and eliminate artifacts caused by task-correlated motion while maximizing sensitivity to true activations. To optimize this process, a combination of motion correction approaches was applied to data from 33 subjects performing both a block-design and an event-related fMRI experiment, including analysis: (1) without motion correction; (2) with motion correction alone; (3) with motion-corrected data and motion covariates included in the GLM; and (4) with non-motion-corrected data and motion covariates included in the GLM. Inclusion of covariates was found to be generally useful for increasing the sensitivity of GLM results in the analysis of event-related data. When motion parameters were included in the GLM for event-related data, it made little difference if motion correction was actually applied to the data. For the block design, inclusion of motion covariates had a deleterious impact on GLM sensitivity when even moderate correlation existed between motion and the experimental design. Based on these results, we present a general strategy for block designs, event-related designs, and hybrid designs to identify and eliminate probable motion artifacts while maximizing sensitivity to true activations.