Resting-state global brain activity induces bias in fMRI motion estimates

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Abstract
Head motion is a significant source of artifacts in resting-state fMRI (rsfMRI) studies and has been shown to affect resting-state functional connectivity (rsFC) measurements. In many rsfMRI studies, motion parameters estimated from volume registration are used to characterize head motion and to mitigate motion artifacts in rsfMRI data. While prior task-based fMRI studies have shown that task-evoked brain activations may induce temporally correlated bias in the motion estimates, resulting in artificial activations after registration, relatively little is known about neural-related bias in rsfMRI motion parameter. In this study, we demonstrate that neural-related bias exists in rsfMRI motion estimates and characterize the potential effects of the bias on rsFC estimates. Using a public multi-echo rsfMRI dataset, we use the differences between motion estimates from the first echo and second echo data as a measure of neural-induced bias. We show that the resting-state global activity of the brain, as characterized with the global signal (GS), induces bias in the motion estimates in the y- and z-translational axes. Furthermore, we demonstrate that the GS-related bias reflects superior-inferior and anterior-posterior asymmetries in the GS beta coefficient map. Finally, we demonstrate that regression with biased motion estimates can negatively bias rsFC estimates and also reduce rsFC differences between young and old subjects.
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