Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

Krishnan, Giri P.; González, Oscar C.; Bazhenov, Maxim

doi:10.1101/186098

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…These effects can build up across more complex networks: for example, when rat whiskers were stimulated by white noise on top of which sinusoidal modulation with 0.3 -0.03 Hz frequency was added, barrel cortex neurons preceded the sinusoidal stimulus envelope by ~0.8 rad on average, while thalamic neurons were leading by less than half as much (Lundstrom et al, 2010). An alternative, recently proposed possibility is that infraslow firing rate fluctuations are driven by slow changes in ion concentrations (Krishnan et al, 2018), whereas our data suggests that brain-wide neuromodulatory inputs have a major role in this phenomenon. While the contribution of each of these mechanisms remains to be elucidated, it is likely that their effect on cortical dynamics is particularly complex on intermediate timescales (~1 Hz) where they interact with fast-timescale local synaptic activity.…”

Section: Discussioncontrasting

confidence: 77%

Distinct structure of cortical population activity on fast and infraslow timescales

Okun

Steinmetz

Lak

et al. 2018

Preprint

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Cortical activity is organised across multiple spatial and temporal scales. Most research on the dynamics of neuronal spiking is concerned with timescales of 1 ms ̶ 1 s, and little is known about spiking dynamics on timescales of tens of seconds and minutes. Here, we used frequency domain analyses to study the structure of individual neurons' spiking activity and its coupling to local population rate and to arousal level across frequencies ranging from 0.01 to 100 Hz. In mouse medial prefrontal cortex (mPFC), the spiking dynamics of individual neurons could be quantitatively captured by a combination of interspike interval and firing rate power spectrum distributions. The relative strength of coherence with local population often differed across timescales: a neuron strongly coupled to population rate on fast timescales could be weakly coupled on slow timescales, and vice versa. On slow but not fast timescales, a substantial proportion of neurons showed firing anti-correlated with the population. Infraslow firing rate changes were largely determined by arousal rather than by local factors, which could explain the timescale dependence of population coupling strength of individual neurons. These observations demonstrate how individual neurons simultaneously partake in fast local dynamics, and slow brain-wide dynamics, extending our understanding of infraslow cortical activity beyond the mesoscale resolution of fMRI studies.

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Section: Discussioncontrasting

confidence: 77%

Distinct structure of cortical population activity on fast and infraslow timescales

Okun

Steinmetz

Lak

et al. 2018

Preprint

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“…The relevant biology is wide-ranging and likely includes many metabolic and nonneuronal process that slowly modulate neuronal excitability [see (8,28) and references therein]. Although reference limits preclude proper treatment of this literature, we note likely essential roles for redox metabolism (90,91), ion fluxes (3,92), and glial physiology [including upstream of the neurovascular cascade (4,93)]. These interrelated factors are each influenced by neuromodulators that track behavioral state over infra-slow time scales (4,37,94).…”

Section: Implications For Bold Imagingmentioning

confidence: 99%

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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Decoding task-specific cognitive states with slow, directed functional networks in the human brain

Sridharan

Ajmera

Jain

et al. 2019

Preprint

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2 16 Abstract 17 Flexible functional interactions among brain regions mediate critical cognitive functions. Such interactions 18 can be measured from functional magnetic resonance imaging (fMRI) data with either instantaneous (zero-19 lag) or lag-based (time-lagged) functional connectivity; only the latter approach permits inferring directed 20 functional interactions. Yet, the fMRI hemodynamic response is slow, and sampled at a 21 timescale (seconds) several orders of magnitude slower than the underlying neural dynamics 22 (milliseconds). It is, therefore, widely held that lag-based fMRI functional connectivity, measured with 23 approaches like as Granger-Geweke causality (GC), provides spurious and unreliable estimates 24 of underlying neural interactions. Experimental verification of this claim has proven challenging because 25 neural ground truth connectivity is often unavailable concurrently with fMRI recordings. We address this 26 challenge by combining machine learning with GC functional connectivity estimation. We estimated 27 instantaneous and lag-based GC functional connectivity networks using fMRI data from 1000 participants, 28 drawn from the Human Connectome Project database. A linear classifier, trained on either instantaneous or 29 lag-based GC, reliably discriminated among seven different task and resting brain states, with over 80% 30 cross-validation accuracy. With network simulations, we demonstrate that instantaneous and lag-based 31 GC exploited interactions at fast and slow timescales, respectively, to achieve robust classification. With 32 human fMRI data, instantaneous and lag-based GC identified distinct, cognitive core networks. Finally, 33 variations in GC connectivity explained inter-individual variations in a variety of cognitive scores. Our 34 findings show that instantaneous and lag-based methods reveal complementary aspects of functional 35 connectivity in the brain, and suggest that slow, directed functional interactions, estimated with fMRI, 36 provide robust markers of behaviorally relevant cognitive states.3 38 Author Summary 39 Functional MRI (fMRI) is a leading, non-invasive technique for mapping networks in the human brain. Yet, 40 fMRI signals are noisy and sluggish, and fMRI scans are acquired at a timescale of seconds, considerably 41 slower than the timescale of neural spiking (milliseconds). Can fMRI, then, be used to infer dynamic 42 processes in the brain such as the direction of information flow among brain networks? We sought to 43 answer this question by applying machine learning to fMRI scans acquired from 1000 participants in the 44 Human Connectome Project (HCP) database. We show that directed brain networks, estimated with a 45 technique known as Granger-Geweke Causality (GC), accurately predicts individual subjects' task-specific 46 cognitive states inside the scanner, and also explains variations in a variety of behavioral scores across 47 individuals. We propose that directed functional connectivity, as estimated with fMRI-GC, is relevant 48 for understandin...

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Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

Cited by 3 publications

References 54 publications

Distinct structure of cortical population activity on fast and infraslow timescales

Distinct structure of cortical population activity on fast and infraslow timescales

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

Decoding task-specific cognitive states with slow, directed functional networks in the human brain

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