Abstract
Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas.
Highlights
In recent years there has been increasing interest in the use of resting state functional magnetic resonance imaging in neuroimaging research
The Results section is structured in three main parts: (1) resting state functional magnetic resonance imaging (rs-fMRI) Human Connectome Project (HCP) results, where we show the results of (a) the effects of applying global signal regression (GSR) on the functional connectivity (FC) of the connectome; (b) the relationship of the global signal (GS) with the vessel BOLD signals (VBS) and the connectome; (c) applying partial information decomposition (PID), mapping the unique and joint information of the GS and VBS on the connectome
(2) Simulation results, where we explain the observed pattern of the PID results in rs-fMRI data in terms of blood arrival time using simulations
Summary
In recent years there has been increasing interest in the use of resting state functional magnetic resonance imaging (rs-fMRI) in neuroimaging research. A popular approach in rs-fMRI is to map the functional architecture of the human brain using patterns in resting-state or intrinsic correlations (Fox and Raichle, 2007). The correlations of low frequency oscillations present in the blood oxygenation level dependent (BOLD) signal reflect the functional connectivity (FC) between different brain regions. Regions that show a high mutual correlation are referred to as a resting-state network (RSN) or intrinsic connectivity network (ICN). The global signal (GS) is obtained by averaging the resting-state time courses over the entire brain (Desjardins et al, 2001). Based on the assumption that processes that are globally spread across the brain cannot be linked to neuronal activation, it could be beneficial to remove them to denoise the data. Fluctuations in the GS have been linked to physiological fluctuations, mainly respiratory effects, head motion, hardware scanner related effects and vascular effects (Murphy and Fox, 2017; Power et al, 2017b)
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