Resting-state “Physiological Networks”

Chen, Jingyuan E.; Lewis, Laurie; Chang, Catie; Fultz, Nina E; Ohringer, Ned A.; Rosen, Bruce R.; Polimeni, Jon̈athan R.

doi:10.1101/660787

Cited by 11 publications

(19 citation statements)

References 100 publications

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“…2) imply that removal of widely shared variance (e.g., with global signal regression), although useful for enhancing spatial specificity (96), alters rather than eliminates the manifestation of global waves in BOLD time series. The available evidence supports this interpretation (22,51,59). Our results pose a challenge to the commonly held view that BOLD associations with physiological variables, even if related to neuronal activity, are purely a "nuisance" in investigations not explicitly concerned with fluctuating arousal.…”

Section: Implications For Bold Imagingcontrasting

confidence: 44%

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Global waves synchronize the brain’s functional systems with fluctuating arousal

et al. 2021

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We propose and empirically support a parsimonious account of intrinsic, brain-wide spatiotemporal organization arising from traveling waves linked to arousal. We hypothesize that these waves are the predominant physiological process reflected in spontaneous functional magnetic resonance imaging (fMRI) signal fluctuations. The correlation structure (“functional connectivity”) of these fluctuations recapitulates the large-scale functional organization of the brain. However, a unifying physiological account of this structure has so far been lacking. Here, using fMRI in humans, we show that ongoing arousal fluctuations are associated with global waves of activity that slowly propagate in parallel throughout the neocortex, thalamus, striatum, and cerebellum. We show that these waves can parsimoniously account for many features of spontaneous fMRI signal fluctuations, including topographically organized functional connectivity. Last, we demonstrate similar, cortex-wide propagation of neural activity measured with electrocorticography in macaques. These findings suggest that traveling waves spatiotemporally pattern brain-wide excitability in relation to arousal.

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Section: Implications For Bold Imagingcontrasting

confidence: 44%

“…[e.g., (39,(49)(50)(51); see Discussion]. However, our model posits that propagating BOLD signal fluctuations observed with fMRI are physiologically coupled to electrophysiological waves reflecting neuronal activity.…”

Section: Global Waves In Macaque Ecogmentioning

confidence: 98%

Global waves synchronize the brain’s functional systems with fluctuating arousal

et al. 2021

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“…Large amplitude peaks or transients in the BOLD, however, in addition to neural events may also reflect motion (Power et al, 2012) and physiological noise, such as spontaneous fluctuations in arterial CO2 (Golestani et al, 2016;Prokopiou et al, , 2016, cardiac pulsatility (Glover et al, 2000), respiration and heart rate variability (Birn et al, 2008;Chang et al, , 2009Kassinopoulos and Mitsis, 2019). These non-neuronal sources of BOLD signal variability have been shown to elicit networks of coherent BOLD activity, which resemble previously reported resting-state networks derived from fMRI data (Chen et al, 2019;Nalci et al, 2019;Nikolaou et al, 2016;Shokri-Kojori et al, 2018). In addition, covariation of the BOLD signal in different brain regions that is sufficient to give rise to spatial patterns of resting-state activity can be also observed at the timings of lower amplitude peaks, or even at regularly or randomly selected timepoints along the time course of the signal.…”

Section: Introductionsupporting

confidence: 73%

“…Moreover, recent studies in the fMRI literature investigated a large number of fMRI preprocessing pipelines and pointed out that no preprocessing pipeline offers a perfect noise free signal (Parkes et al, 2018). Also, other studies showed that the network structure elicited by non-neural sources of BOLD signal variability is conformable to the structure of previously reported resting-state networks (Bright and Murphy, 2015;Chen et al, 2019;Nalci et al, 2019). On account of these considerations, we believe that the contribution proportion of neural versus non-neural sources in the high amplitude peaks of the BOLD cannot be easily elucidated.…”

Section: Lower-amplitude Co-fluctuations In Bold-fmri Contribute To Rmentioning

confidence: 82%

Modeling the hemodynamic response function using simultaneous EEG-fMRI data and convolutional sparse coding analysis with rank-1 constraints

Prokopiou

Kassinopoulos

Xifra‐Porxas

et al. 2020

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Over the last few years, an increasing body of evidence points to the hemodynamic response function as an important confound of resting-state functional connectivity. Several studies in the literature proposed using blind deconvolution of resting-state fMRI data to retrieve the HRF, which can be subsequently used for hemodynamic deblurring. A basic hypothesis in these studies is that relevant information of the resting-state brain dynamics is condensed in discrete events resulting in large amplitude peaks in the BOLD signal. In this work, we showed that important information of resting-state activity, in addition to the larger amplitude peaks, is also concentrated in lower amplitude peaks. Moreover, due to the strong effect of physiological noise and head motion on the BOLD signal, which in many cases may not be completely removed after preprocessing, the neurophysiological origin of the large amplitude BOLD signal peaks is questionable. Hence, focusing on the large amplitude BOLD signal peaks may yield biased HRF estimates. To define discrete events of neuronal origins, we proposed using simultaneous EEG-fMRI along with convolutional sparse coding analysis. Our results suggested that events detected in the EEG are able to describe the slow oscillations of the BOLD signal and to obtain consistent HRF shapes across subjects under both task-based and resting-state conditions.

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“…Modelling of respiratory influences on BOLD signal fluctuations is nontrivial, as respiratory response functions often fail to capture the impact of pauses and subtle variations in rate and depth of breathing [42,78]. Recently, it has also been shown that resting-state BOLD signal fluctuations that track respiratory dynamics exhibit spatially heterogeneous time courses (lags and shape) throughout the brain [82]. Thus, we cannot fully rule out respiratory mechanisms underpinning our observed time of day effects on BOLD signal fluctuation.…”

Section: Respiratory Variation Also Shows Time Of Day Modulation But mentioning

confidence: 89%

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

et al. 2020

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The brain exhibits substantial diurnal variation in physiology and function, but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state functional MRI (fMRI) in around 900 individuals scanned between 8 AM and 10 PM on two different days. Multiple studies across animals and humans have demonstrated that the brain's global signal (GS) amplitude (henceforth referred to as "fluctuation") increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that GS fluctuation would be lowest in the morning, increase in the midafternoon, and dip in the early evening. Instead, we observed a cumulative decrease in GS fluctuation as the day progressed. Although respiratory variation also decreased with time of day, control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting-state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal time of day effects on global brain activity that are not easily explained by expected arousal state or physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies.

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Resting-state “Physiological Networks”

Cited by 11 publications

References 100 publications

Global waves synchronize the brain’s functional systems with fluctuating arousal

Global waves synchronize the brain’s functional systems with fluctuating arousal

Modeling the hemodynamic response function using simultaneous EEG-fMRI data and convolutional sparse coding analysis with rank-1 constraints

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

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