Impact of global signal regression on characterizing dynamic functional connectivity and brain states

Abstract

Recently, resting-state functional magnetic resonance imaging (fMRI) studies have been extended to explore fluctuations in correlations over shorter timescales, referred to as dynamic functional connectivity (dFC). However, the impact of global signal regression (GSR) on dFC is not well established, despite the intensive investigations of the influence of GSR on static functional connectivity (sFC). This study aimed to examine the effect of GSR on the performance of the sliding-window correlation, a commonly used method for capturing functional connectivity (FC) dynamics based on resting-state fMRI and simultaneous electroencephalograph (EEG)-fMRI data. The results revealed that the impact of GSR on dFC was spatially heterogeneous, with some susceptible regions including the occipital cortex, sensorimotor area, precuneus, posterior insula and superior temporal gyrus, and that the impact was temporally modulated by the mean global signal (GS) magnitude across windows. Furthermore, GSR substantially changed the connectivity structures of the FC states responding to a high GS magnitude, as well as their temporal features, and even led to the emergence of new FC states. Conversely, those FC states marked by obvious anti-correlation structures associated with the default model network (DMN) were largely unaffected by GSR. Finally, we reported an association between the fluctuations in the windowed magnitude of GS and the time-varying EEG power within subjects, which implied changes in mental states underlying GS dynamics. Overall, this study suggested a potential neuropsychological basis, in addition to nuisance sources, for GS dynamics and highlighted the need for caution in applying GSR to sliding-window correlation analyses. At a minimum, the mental fluctuations of an individual subject, possibly related to ongoing vigilance, should be evaluated during the entire scan when the dynamics of FC is estimated.

Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network

“…The following sequence of steps was completed Turner, 1996) and signals from white matter and ventricles were regressed from the BOLD time courses for each gray matter voxel, to correct for head motion and physiological noise. Global signal regression was not applied, given that this procedure has been suggested to alter the covariance structure of the data (Murphy, Birn, Handwerker, Jones, & Bandettini, 2009), particularly when assessing dynamics (Xu et al, 2018). After smoothing, the voxel time courses were band-pass filtered (0.01-0.1 Hz) to alleviate low-frequency drifts and high-frequency physiological noise (Cordes et al, 2001).…”

“…After smoothing, the voxel time courses were band-pass filtered (0.01-0.1 Hz) to alleviate low-frequency drifts and high-frequency physiological noise (Cordes et al, 2001). Global signal regression was not applied, given that this procedure has been suggested to alter the covariance structure of the data (Murphy, Birn, Handwerker, Jones, & Bandettini, 2009), particularly when assessing dynamics (Xu et al, 2018).…”

Nuisance Effects and the Limitations of Nuisance Regression in Dynamic Functional Connectivity fMRI

“…In support of this view, Nikolaou et al (2016) found that nuisance regression diminished but did not completely remove the relationship between network degree and measures of cardiac and respiratory activity. On the other hand, Xu et al (2018) reported that global signal regression (GSR) had a spatially heterogeneous impact 40 on DFC estimates, but did not assess whether GSR removed GS contributions from the DFC estimates.…”

Predicting the fMRI signal fluctuation with echo-state neural networks trained on vascular network dynamics

“…The global rs-fMRI signal is a critical confound of correlation analysis with many contributing factors from both physiological and non-physiological sources. In particular, whether the global mean fMRI signal should be removed before the analysis, which can create spurious correlation features, has been debated [36][37][38][39][40][41][42]44 . Also, the global rs-fMRI signal can over-shadow specific intrinsic RSN features, e.g.…”

“…Because of the high variability in different dynamic states, physiological and non-physiological confounding factors also contribute to the rs-fMRI lowfrequency oscillation [33][34][35] . In particular, global fMRI signal fluctuations are one of the most controversial oscillatory features to be linked to dynamic brain signals [36][37][38][39][40][41][42][43][44] . For example, the rs-fMRI signal from the white-matter tract has been used as a nuisance regressor to remove the global noise contribution 45,46 .…”