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Published Online: 6 November 2015

Using Edge Voxel Information to Improve Motion Regression for rs-fMRI Connectivity Studies

Publication: Brain Connectivity
Volume 5, Issue Number 9


Recent fMRI studies have outlined the critical impact of in-scanner head motion, particularly on estimates of functional connectivity. Common strategies to reduce the influence of motion include realignment as well as the inclusion of nuisance regressors, such as the 6 realignment parameters, their first derivatives, time-shifted versions of the realignment parameters, and the squared parameters. However, these regressors have limited success at noise reduction. We hypothesized that using nuisance regressors consisting of the principal components (PCs) of edge voxel time series would be better able to capture slice-specific and nonlinear signal changes, thus explaining more variance, improving data quality (i.e., lower DVARS and temporal SNR), and reducing the effect of motion on default-mode network connectivity. Functional MRI data from 22 healthy adult subjects were preprocessed using typical motion regression approaches as well as nuisance regression derived from edge voxel time courses. Results were evaluated in the presence and absence of both global signal regression and motion censoring. Nuisance regressors derived from signal intensity time courses at the edge of the brain significantly improved motion correction compared to using only the realignment parameters and their derivatives. Of the models tested, only the edge voxel regression models were able to eliminate significant differences in default-mode network connectivity between high- and low-motion subjects regardless of the use of global signal regression or censoring.

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Published In

cover image Brain Connectivity
Brain Connectivity
Volume 5Issue Number 9November 2015
Pages: 582 - 595
PubMed: 26107049


Published online: 6 November 2015
Published in print: November 2015
Published ahead of print: 28 September 2015
Published ahead of production: 24 June 2015


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Rémi Patriat
Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin.
Erin K. Molloy
Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin.
Department of Computer Science, University of Illinois-Urbana-Champaign, Urbana, Illinois.
Rasmus M. Birn
Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin.
Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin.


Address correspondence to:Rémi PatriatDepartment of Medical PhysicsUniversity of Wisconsin-Madison1111 Highland Avenue, Room 1005Madison, WI 53705-2275E-mail: [email protected]

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The authors have nothing to disclose.

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