The Nuisance of Nuisance Regression: Spectral Misspecification in a Common Approach to Resting-State fMRI Preprocessing Reintroduces Noise and Obscures Functional Connectivity

By Michael N. Hallquist, Kai Hwang, Beatriz Luna. NeuroImage, 2013

715 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6d7a4439fac301f4df97f2a32a0f5b338116d0e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, neutral): “Consequently, there has been significant research on rs-fMRI denoising (Chai et al., 2012; Hallquist et al., 2013; Jo et al., 2013; Patel et al., 2014; Bright et al., 2017), but there is still no consensus approach for rs-fMRI preprocessing (Murphy et al.”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “Structural images were acquired using a magnetization-prepared, rapid-acquisition gradient-echo (MPRAGE) T1-weighted sequence (T = 1810 msR ; T = 3.51 msE ; FoV = 180 × 240 mm; resolution 1 mm isotropic).”
Reduction of motion-related artifacts in resting state fMRI using aCompCor (2014, methodology): “To date, all of the research examining the effects of motion on seedbased rs-FC has included mean signals from WM and CSF as nuisance regressors (Hallquist et al., 2013; Jo et al., 2013; Power et al., 2012; Satterthwaite et al., 2012, 2013; Yan et al., 2013); in some cases, the first derivative of e”
The Development of Human Amygdala Functional Connectivity at Rest from 4 to 23 Years: a cross-sectional study (2014, neutral): “…several recent reports have demonstrated that resting-state functional connectivity analyses are especially sensitive to motion artifacts (e.g. Hallquist et al., 2013; Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012), thus several further steps were taken in the…”
Methods for cleaning the BOLD fMRI signal (2016, neutral): “Considering the average signals of white matter (WM) and ventricular cerebrospinal fluid (CSF) tissues as nuisance regressors has become fairly common in resting state and task-based fMRI approaches (Anderson et al., 2011; Hallquist et al., 2013; Jo et al., 2010, 2013; Power et al., 2012; Weissenbac”
Recent progress and outstanding issues in motion correction in resting state fMRI (2014, neutral): “This approach can be combined with nuisance regression in a single regression model (Hallquist et al., 2013; Jo et al., 2013).”
Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data (2018, methodology): “In this study, all regressors, including motion parameters and confound time courses, were band-pass filtered to the same frequency range as the time series data to prevent frequencydependent mismatch during confound regression (Hallquist et al., 2013).”
Precision functional mapping of individual human brains (2017, neutral): “…of the values at censored frames (Power et al., 2014) so that continuous data can be passed through (iv) a band-pass filter (0.009 Hz < f < 0.08 Hz) without re-introducing nuisance signals (Hallquist et al., 2013) or contaminating frames near high motion frames (Carp, 2013; Power et al., 2012).”
Image processing and analysis methods for the Adolescent Brain Cognitive Development Study (2018, methodology): “Analyses of cortical and subcortical changes in volume with even greater sensitivity is available through Quantitative Analysis of Regional Change (QUARC) (Holland et al., 2009; Holland and Dale, 2011; Holland et al., 2012; Thompson and Holland, 2011).”
The Global Signal in fMRI: Nuisance or Information? (2017, neutral): “It should be noted that Hallquist et al. (2013) found that a mismatch between the frequency contents of the data and the regressors used in preprocessing could also give rise to motion-related global signal changes, but this mismatch has been largely addressed by changes to the preprocessing…”