Potential pitfalls when denoising resting state fMRI data using nuisance regression
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...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/41479/ |
| _version_ | 1848796283688452096 |
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| author | Bright, Molly G. Tench, Christopher R. Murphy, Kevin |
| author_facet | Bright, Molly G. Tench, Christopher R. Murphy, Kevin |
| author_sort | Bright, Molly G. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:45:31Z |
| format | Article |
| id | nottingham-41479 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:45:31Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-414792020-05-04T18:24:30Z https://eprints.nottingham.ac.uk/41479/ Potential pitfalls when denoising resting state fMRI data using nuisance regression Bright, Molly G. Tench, Christopher R. Murphy, Kevin 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. Elsevier 2016-12-23 Article PeerReviewed Bright, Molly G., Tench, Christopher R. and Murphy, Kevin (2016) Potential pitfalls when denoising resting state fMRI data using nuisance regression. NeuroImage . ISSN 1095-9572 (In Press) Resting state; fMRI; Noise correction; Nuisance regression; Connectivity https://doi.org/10.1016/j.neuroimage.2016.12.027 doi:10.1016/j.neuroimage.2016.12.027 doi:10.1016/j.neuroimage.2016.12.027 |
| spellingShingle | Resting state; fMRI; Noise correction; Nuisance regression; Connectivity Bright, Molly G. Tench, Christopher R. Murphy, Kevin Potential pitfalls when denoising resting state fMRI data using nuisance regression |
| title | Potential pitfalls when denoising resting state fMRI data using nuisance regression |
| title_full | Potential pitfalls when denoising resting state fMRI data using nuisance regression |
| title_fullStr | Potential pitfalls when denoising resting state fMRI data using nuisance regression |
| title_full_unstemmed | Potential pitfalls when denoising resting state fMRI data using nuisance regression |
| title_short | Potential pitfalls when denoising resting state fMRI data using nuisance regression |
| title_sort | potential pitfalls when denoising resting state fmri data using nuisance regression |
| topic | Resting state; fMRI; Noise correction; Nuisance regression; Connectivity |
| url | https://eprints.nottingham.ac.uk/41479/ https://eprints.nottingham.ac.uk/41479/ https://eprints.nottingham.ac.uk/41479/ |