1
|
Nalci A, Luo W, Liu TT. Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression. Neuroimage 2019; 202:116005. [PMID: 31336189 DOI: 10.1016/j.neuroimage.2019.07.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 06/06/2019] [Accepted: 07/08/2019] [Indexed: 12/13/2022] Open
Abstract
In resting-state functional MRI, the correlation between blood-oxygenation-level-dependent (BOLD) signals across brain regions is used to estimate the functional connectivity (FC) of the brain. FC estimates are prone to the influence of nuisance factors including scanner-related artifacts and physiological modulations of the BOLD signal. Nuisance regression is widely performed to reduce the effect of nuisance factors on FC estimates on a per-scan basis. However, a dedicated analysis of nuisance effects on the variability of FC metrics across a collection of scans has been lacking. This work investigates the effects of nuisance factors on the variability of FC estimates across a collection of scans both before and after nuisance regression. Inter-scan variations in FC estimates are shown to be significantly correlated with the geometric norms of various nuisance terms, including head motion measurements, signals derived from white-matter and cerebrospinal regions, and the whole-brain global signal (GS) both before and after nuisance regression. In addition, it is shown that GS regression (GSR) can introduce GS norm-related fluctuations that are negatively correlated with inter-scan FC estimates. The empirical results are shown to be largely consistent with the predictions of a theoretical framework previously developed for the characterization of dynamic FC measures. This work shows that caution must be exercised when interpreting inter-scan FC measures across scans both before and after nuisance regression.
Collapse
Affiliation(s)
- Alican Nalci
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
| | - Wenjing Luo
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA
| | - Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Departments of Radiology, Psychiatry, and Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
| |
Collapse
|
2
|
Nalci A, Rao BD, Liu TT. Nuisance effects and the limitations of nuisance regression in dynamic functional connectivity fMRI. Neuroimage 2018; 184:1005-1031. [PMID: 30223062 DOI: 10.1016/j.neuroimage.2018.09.024] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 09/04/2018] [Accepted: 09/08/2018] [Indexed: 11/16/2022] Open
Abstract
In resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain's intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from different brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the effects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between seed-specific sliding window correlation-based DFC estimates and nuisance factors. We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors. Strong correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not necessarily eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed between the DFC estimates and nuisance norms even after nuisance regression. We present theoretical bounds on the difference between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the efficacy of nuisance regression with regards to DFC estimates.
Collapse
Affiliation(s)
- Alican Nalci
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
| | - Bhaskar D Rao
- Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Departments of Radiology, Psychiatry and Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
| |
Collapse
|
3
|
Bright MG, Tench CR, Murphy K. Potential pitfalls when denoising resting state fMRI data using nuisance regression. Neuroimage 2016; 154:159-168. [PMID: 28025128 PMCID: PMC5489212 DOI: 10.1016/j.neuroimage.2016.12.027] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 12/07/2016] [Accepted: 12/10/2016] [Indexed: 12/15/2022] Open
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.
Collapse
Affiliation(s)
- Molly G Bright
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Division of Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
| | - Christopher R Tench
- Division of Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| |
Collapse
|
4
|
Muschelli J, Nebel MB, Caffo BS, Barber AD, Pekar JJ, Mostofsky SH. Reduction of motion-related artifacts in resting state fMRI using aCompCor. Neuroimage 2014; 96:22-35. [PMID: 24657780 PMCID: PMC4043948 DOI: 10.1016/j.neuroimage.2014.03.028] [Citation(s) in RCA: 267] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2013] [Revised: 02/28/2014] [Accepted: 03/10/2014] [Indexed: 11/17/2022] Open
Abstract
Recent studies have illustrated that motion-related artifacts remain in resting-state fMRI (rs-fMRI) data even after common corrective processing procedures have been applied, but the extent to which head motion distorts the data may be modulated by the corrective approach taken. We compare two different methods for estimating nuisance signals from tissues not expected to exhibit BOLD fMRI signals of neuronal origin: 1) the more commonly used mean signal method and 2) the principal components analysis approach (aCompCor: Behzadi et al., 2007). Further, we investigate the added benefit of "scrubbing" (Power et al., 2012) following both methods. We demonstrate that the use of aCompCor removes motion artifacts more effectively than tissue-mean signal regression. In addition, inclusion of more components from anatomically defined regions of no interest better mitigates motion-related artifacts and improves the specificity of functional connectivity estimates. While scrubbing further attenuates motion-related artifacts when mean signals are used, scrubbing provides no additional benefit in terms of motion artifact reduction or connectivity specificity when using aCompCor.
Collapse
Affiliation(s)
- John Muschelli
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street Baltimore, MD 21205, USA
| | - Mary Beth Nebel
- Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA.
| | - Brian S Caffo
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street Baltimore, MD 21205, USA
| | - Anita D Barber
- Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA
| | - James J Pekar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street Baltimore, MD 21287, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 North Broadway Baltimore, MD 21205, USA
| | - Stewart H Mostofsky
- Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA; Department of Psychiatry, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA
| |
Collapse
|
5
|
Hallquist MN, Hwang K, Luna B. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 2013; 82:208-25. [PMID: 23747457 DOI: 10.1016/j.neuroimage.2013.05.116] [Citation(s) in RCA: 418] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2013] [Revised: 05/03/2013] [Accepted: 05/23/2013] [Indexed: 12/24/2022] Open
Abstract
Recent resting-state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates despite bandpass filtering and nuisance regression, which are intended to reduce such nuisance variability. We provide evidence that the effects of head motion and other nuisance signals are poorly controlled when the fMRI time series are bandpass-filtered but the regressors are unfiltered, resulting in the inadvertent reintroduction of nuisance-related variation into frequencies previously suppressed by the bandpass filter, as well as suboptimal correction for noise signals in the frequencies of interest. This is important because many RS-fcMRI studies, including some focusing on motion-related artifacts, have applied this approach. In two cohorts of individuals (n=117 and 22) who completed resting-state fMRI scans, we found that the bandpass-regress approach consistently overestimated functional connectivity across the brain, typically on the order of r=.10-.35, relative to a simultaneous bandpass filtering and nuisance regression approach. Inflated correlations under the bandpass-regress approach were associated with head motion and cardiac artifacts. Furthermore, distance-related differences in the association of head motion and connectivity estimates were much weaker for the simultaneous filtering approach. We recommend that future RS-fcMRI studies ensure that the frequencies of nuisance regressors and fMRI data match prior to nuisance regression, and we advocate a simultaneous bandpass filtering and nuisance regression strategy that better controls nuisance-related variability.
Collapse
|