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

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.