The global signal in fMRI: Nuisance or Information?
Introduction
The development of methods to mitigate the effects of noise has played a key role in the development of functional magnetic resonance imaging (fMRI). One approach that has gained widespread adoption is the removal of a global signal component, either as a preprocessing step or through its inclusion as a nuisance regressor in general linear model analyses. This approach is commonly referred to as global signal regression (GSR). However, the use of GSR has sparked a great deal of controversy, especially in the analysis of resting-state fMRI studies where some investigators routinely remove the effects of the global signal while others argue strongly against its use (Fox et al., 2009, Saad et al., 2012, Burgess et al., 2016, Murphy and Fox, 2016).
At first glance, it is rather surprising that such a simple signal should spark so much debate. After all the computation of the global signal is straightforward – it is simply the mean of the voxel time-series within the brain. What could be controversial about projecting out the effects of this global signal? We believe that there are several factors that have led to the continuing controversy. First, because the global signal is a “catch-all” signal that reflects the contributions of a variety of noise components, it is not always clear what exactly GSR is removing. Second, while GSR is a fairly straightforward mathematical operation, it still involves the regression of a mean time course (with hundreds of time points or more) from each voxel time series in the brain (ranging from tens to hundreds of thousands of voxels), where the exact numbers of time points and voxels depends on the duration and the temporal and spatial resolutions of the acquisition. Due to the large size of the signal space, it can be difficult to understand the effects of GSR not only on the signals themselves but also on the relation (e.g. correlation) between signals from different regions. Furthermore, it has not been clear how well the arguments made with relatively low-dimensional simulations apply to the high-dimensional datasets obtained in experiments. Finally, with the growing evidence supporting a link between neural activity and the global signal, there has been some concern that GSR may also be removing information that is of interest.
Our goal in this paper is to provide a deeper understanding of the characteristics of the global signal and its role in the analysis of fMRI studies. We will begin by examining the components of the global signal, focusing primarily on the contributions due to low-frequency drifts, motion, physiological activity, and neural fluctuations. We will then review the use of the global signal in the analysis of both task-related and resting-state fMRI studies. Although our focus will be on GSR, we will also examine related methods such as global signal subtraction and global signal normalization. We will conclude with a look at emerging methods for understanding both the global signal and GSR.
Section snippets
What is the global signal?
In this section, we review the basic properties of the global signal. Although the basic definition of the global signal is rather straightforward, we will see that there is considerable variability in the implementation of this definition. We will also build up an intuitive picture of the global signal as a time-varying measure of spatial homogeneity.
Nuisance components in the global signal
In this section we describe the unwanted nuisance components that can contribute to the global signal. These are termed “nuisance” components because they are generally thought to reflect fluctuations unrelated to the underlying neural activity that is of interest. However, as we will discuss below in Section 4.6, the line between nuisance and information can sometimes be difficult to delineate. Table 1, Table 2 provide a summary of the primary signal sources that are discussed both in this
Information in the global signal
In the section above we reviewed a number of the“nuisance-like” components that can contribute to the global signal. Because these components comprise such a large fraction of the variance of the global signal, approaches that use the global signal as a “catch-all” regressor are highly effective for minimizing the contributions of these nuisance components. However, there is also growing evidence for the existence of a significant neural component in the global signal, suggesting that there may
The role of the global signal in fMRI analysis
We now turn our attention to the role of the global signal in the analysis of fMRI studies. We take a largely historical approach and begin in Section 5.1 with a review of the initial application of the global signal in the context of task-related fMRI experiments. Although the global signal continues to receive some attention in this context, it has found much broader usage in the analysis of resting-state fMRI studies, most notably in the form of a widely used pre-processing approach known as
Conclusion
The global signal currently plays an integral part in the analysis of fMRI studies. This is especially true for resting-state fMRI studies in which the efficacy of GSR has led to its widespread adoption for both standard resting-state analyses and emerging approaches, such as methods for characterizing dynamic functional connectivity. Yet despite its widespread use there is still a great deal of confusion and controversy regarding the use of the global signal in fMRI analyses. In some cases,
Acknowledgements
We would like to thank Dr. Hongjian He for preparing the initial version of Fig. 3. This work was partially supported by NIH grant R21MH112155 and a UC San Diego Frontiers of Innovation Scholars Program (FISP) Project Fellowship.
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