Changes in effective connectivity models in the presence of task-correlated motion: an fMRI study

We investigated the effects of motion-correction strategy and time course selection method when structural equation modeling is applied to fMRI data in the presence of task-correlated motion. Three motion-correction methods were employed for a group of 12 subjects performing an orthographic lexical retrieval task: (1) a rigid body realignment as implemented in SPM99, (2) a rigid body realignment combined with the inclusion of motion parameters in the statistical model, and (3) the FLIRT motion correction followed by an ICA analysis aiming to identify and remove the motion-related components and the ghosting artifacts. For each motion correction, the time courses of the activated regions were selected in three ways: (1) using the voxels with the highest Z scores, (2) using the average across all the statistically significant voxels in the region of interest, and (3) using a within-region, across-subjects, singular value decomposition. The resulting models of effective connectivity were markedly different, although the activation pattern was not substantially altered by the motion-correction method. Higher values for the path coefficients were obtained for the models fitted to the covariance matrices based on the average time courses than for the covariance matrices based on a single voxel time course. Our results suggest caution with the interpretation of task-induced changes in effective connectivity since, for higher-order cognitive brain functions, multiple models can be fitted to a given data set and these models cannot be rejected on an anatomical or cognitive basis. Hum. Brain Mapping 21:49-63, 2004.