  Head motion during functional Magnetic Resonance Imaging acquisition can
significantly contaminate the neural signal and introduce spurious,
distance-dependent changes in signal correlations. This can heavily confound
studies of development, aging, and disease. Previous approaches to suppress
head motion artifacts have involved sequential regression of nuisance
covariates, but this has been shown to reintroduce artifacts. We propose a new
motion correction pipeline using an omnibus regression model that avoids this
problem by simultaneously regressing out multiple artifacts using the best
performing algorithms to estimate each artifact. We quantitatively evaluate its
motion artifact suppression performance against sequential regression pipelines
using a large heterogeneous dataset (n=151) which includes high-motion subjects
and multiple disease phenotypes. The proposed concatenated regression pipeline
significantly reduces the association between head motion and functional
connectivity while significantly outperforming the traditional sequential
regression pipelines in eliminating distance-dependent head motion artifacts.
