Abstract
Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, “dynamic global signal regression” (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional “static” global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.
Highlights
Over the past two decades, interpreting the spatiotemporal patterns and strengths of correlations in low frequency spontaneous hemodynamic fluctuations (∼0.01–0.1 Hz) of blood oxygen level dependent (BOLD) signals has become the focus of many functional magnetic resonance imaging studies, especially during resting state (Fox and Raichle, 2007)
To compare the performance of dynamic global signal regression” (dGSR) and the conventional static global signal regression (sGSR) methods in terms of functional network detection, we focused on two systems that are suggested to be intrinsically organized into anticorrelated networks: the task negative network (TNN), or in other words default mode network (DMN), which consists of brain regions that exhibit coherent spontaneous fluctuations in the absence of a task and deactivation during cognitive tasks (Shulman et al, 1997; Raichle et al, 2001); and the task positive network (TPN) which has been shown to exhibit coherent hemodynamic fluctuations during cognitive and attentional tasks (Fox et al, 2005; Fransson, 2005)
Improved correlation between global signal from the brain and the systemic low frequency oscillations (sLFOs) signal measured from the periphery after applying an optimal time delay is consistent with the idea that sLFOs propagate throughout the body, including the brain, with the blood with varying time delays, which has been extensively discussed in our previous work (Tong and Frederick, 2010, 2012; Tong et al, 2013, 2016)
Summary
Over the past two decades, interpreting the spatiotemporal patterns and strengths of correlations in low frequency spontaneous hemodynamic fluctuations (∼0.01–0.1 Hz) of blood oxygen level dependent (BOLD) signals has become the focus of many functional magnetic resonance imaging (fMRI) studies, especially during resting state (Fox and Raichle, 2007). SLFOs are widely intermixed with spontaneous neuronal oscillations in the resting state BOLD signals, resulting in inflated, or in other words, spurious positive correlations between brain regions and an increase in apparent functional connectivity strengths (Murphy et al, 2013) which in turn, reduces the specificity for detecting brain regions with neuronal activity related coherent hemodynamic fluctuations in fcMRI analyses
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