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Potential pitfalls when denoising resting state fMRI data using nuisance regression

Bright, Molly G.; Tench, Christopher R.; Murphy, Kevin

Authors

Molly G. Bright

Christopher R. Tench

Kevin Murphy



Abstract

In resting state fMRI, it is necessary to remove signal variance associated with noise sources, leaving cleaned fMRI time-series that more accurately reflect the underlying intrinsic brain fluctuations of interest. This is commonly achieved through nuisance regression, in which the fit is calculated of a noise model of head motion and physiological processes to the fMRI data in a General Linear Model, and the “cleaned” residuals of this fit are used in further analysis. We examine the statistical assumptions and requirements of the General Linear Model, and whether these are met during nuisance regression of resting state fMRI data. Using toy examples and real data we show how pre-whitening, temporal filtering and temporal shifting of regressors impact model fit. Based on our own observations, existing literature, and statistical theory, we make the following recommendations when employing nuisance regression: pre-whitening should be applied to achieve valid statistical inference of the noise model fit parameters; temporal filtering should be incorporated into the noise model to best account for changes in degrees of freedom; temporal shifting of regressors, although merited, should be achieved via optimisation and validation of a single temporal shift. We encourage all readers to make simple, practical changes to their fMRI denoising pipeline, and to regularly assess the appropriateness of the noise model used. By negotiating the potential pitfalls described in this paper, and by clearly reporting the details of nuisance regression in future manuscripts, we hope that the field will achieve more accurate and precise noise models for cleaning the resting state fMRI time-series.

Citation

Bright, M. G., Tench, C. R., & Murphy, K. (2017). Potential pitfalls when denoising resting state fMRI data using nuisance regression. NeuroImage, 154, 159-168. https://doi.org/10.1016/j.neuroimage.2016.12.027

Journal Article Type Article
Acceptance Date Dec 10, 2016
Online Publication Date Dec 23, 2016
Publication Date Jul 1, 2017
Deposit Date Mar 23, 2017
Publicly Available Date Mar 23, 2017
Journal NeuroImage
Print ISSN 1053-8119
Electronic ISSN 1095-9572
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 154
Pages 159-168
DOI https://doi.org/10.1016/j.neuroimage.2016.12.027
Keywords Resting state; fMRI; Noise correction; Nuisance regression; Connectivity
Public URL https://nottingham-repository.worktribe.com/output/832342
Publisher URL https://www.sciencedirect.com/science/article/pii/S1053811916307480

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0





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