Improved motion correction for functional MRI using an omnibus regression model

Vyom Raval, Kevin P. Nguyen, Cooper Mellema, Albert Montillo

Research output: Contribution to journalArticlepeer-review


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 highmotion 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.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Nov 22 2019


  • - fMRI
  • Concatenated regression
  • Head motion
  • Noise suppression
  • Parkinson's Disease

ASJC Scopus subject areas

  • General

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