Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep

Stevner, Angus; Vidaurre, Diego; Cabral, Joana; Rapuano, Kristina M.; Nielsen, Signe Tellerup; Tagliazucchi, Enzo; Laufs, Helmut; Vuust, Peter; Deco, Gustavo; Woolrich, Mark W.; Someren, Eus J.W. Van; Kringelbach, Morten L.

doi:10.1038/s41467-019-08934-3

Cited by 209 publications

(203 citation statements)

References 67 publications

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“…This calls for a brain-state-informed analysis of functional connectivity in task-free "resting state" studies, both in humans as well as in animal models. Human subjects may transition between these brain states when they fall asleep 84 . Even more so, animals react differently to the same anaesthetic regimen 83 .…”

Section: Implications On Resting State Studiesmentioning

confidence: 99%

Brain states govern the spatio-temporal dynamics of resting state functional connectivity

Aedo-Jury

Schwalm

Hamzehpour

et al. 2019

Preprint

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Previously, using simultaneous task-free fMRI and optic-fiber-based neuronal calcium recordings in the anesthetized rat, we identified BOLD responses directly related to slow calcium waves, revealing a cortex-wide and spatially organized BOLD correlate (Schwalm et al. 2017). Here, with these bimodal recordings, we reveal two distinct brain states: persistent state, in which compartmentalized network activity was observed, including defined subsets such as the default mode network; and slow wave state, dominated by a cortex-wide component, suggesting a strong functional coupling of brain activity. In slow wave state we find a correlation between slow wave events and the strength of functional connectivity. These findings suggest that indeed down-up transitions of neuronal excitability drive cortex-wide functional connectivity. This study provides strong evidence that previously reported changes in functional connectivity are highly dependent on the brain's current state and directly linked with cortical excitability and slow waves generation.

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Section: Implications On Resting State Studiesmentioning

confidence: 99%

Brain states govern the spatio-temporal dynamics of resting state functional connectivity

Aedo-Jury

Schwalm

Hamzehpour

et al. 2019

Preprint

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“…2E, Fig. S1) is archetypical of reduced in arousal associated with early sleep-like behaviour in both humans and macaques (Chang C et al 2016;Liu X et al 2018;Stevner ABA et al 2019). Hence, we postulated that by monitoring intrusions of the fMRI activity map associated with EEGtheta activity, we may be able to infer sleep onsets in the fMRI data from the study 2 without the need for simultaneous EEG recordings.…”

Section: A Methods For Detecting Sleep Onsets In the Awake Human Brainmentioning

confidence: 92%

Sleeping While Awake: The Intrusion of Neural Activity Associated with Sleep Onset in the Awake Human Brain

Hawes

Innes

Parsons

et al. 2020

Preprint

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Sleep can intrude into the awake human brain when sleep deprived or fatigued, even while performing cognitive tasks. However, how the brain activity associated with sleep onset can co-exist with the activity associated with cognition in the awake humans remains unexplored. Here, we used simultaneous fMRI and EEG to generate fMRI activity maps associated with EEG theta (4-7 Hz) activity associated with sleep onset. We implemented a method to track these fMRI activity maps in individuals performing a cognitive task after well-rested and sleep-deprived nights. We found frequent intrusions of the fMRI maps associated with sleep-onset in the task-related fMRI data. These sleep events elicited a pattern of transient fMRI activity, which was spatially distinct from the task-related activity in the frontal and parietal areas of the brain. They were concomitant with reduced arousal as indicated by decreased pupil size and increased response time. Graph theoretical modelling showed that the activity associated with sleep onset emerges from the basal forebrain and spreads anterior-posteriorly via the brain's structural connectome. We replicated the key findings in an independent dataset, which suggests that the approach can be reliably used in understanding the neuro-behavioural consequences of sleep and circadian disturbances in humans. 3 Brief sleep onsets can intrude into wakefulness when homeostatic sleep drive is elevated due to sleep loss, fatigue, or extended monitoring tasks. These transient sleep intrusions can be local and restricted to the momentary silencing of a few neurons (Krueger JM and G Tononi 2011; Vyazovskiy VV et al. 2011; Nir Y et al. 2017; Quercia A et al. 2018) or on a global scale; characterised by the slowing of neural activity in widespread cortical and sub-cortical regions (Boyle LN et al. 2008; Ong JL et al. 2015; Jonmohamadi Y et al. 2016; Toppi J et al. 2016; Wang C et al. 2016; Poudel GR et al. 2018). While some extreme sleep episodes are associated with behavioural signs of falling asleep such as attentional lapses (Drummond SP et al. 2005; Chee MWL et al. 2008) and slow closing of the eyelids (Poudel GR et al. 2014; Poudel GR et al. 2018), sleep onset can also occur without overt behavioural signs. Sleep onsets are particularly frequent in individuals who are exposed to sleep and circadian disturbances, and can acutely reduce cognitive function (Nir Y et al. 2017). Hence, monitoring sleep onsets while awake has major implications for understanding human behaviour in shiftwork, safety-critical operations including motor vehicle accidents, as well as circadian and sleep disorders.Electroencephalography (EEG), pupil sizes, and response behaviour can be used to monitor overt signs of reduced arousal in humans. Any brief (3-15 s) intrusions of theta waves (4-7 Hz theta) that replace higher-frequency alpha waves (>8 Hz) on EEG recordings are considered to be microsleeps (Boyle LN et al. 2008). In drowsy individuals, response lapses are associated with these microsleeps (Poudel GR et al. 2014;Poud...

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“…This result is compatible with the integrated information theory of consciousness [3][4][5][6][7] , which holds that different levels of consciousness must correspond to the capacity of the brain to integrate information. Many other studies have shown that integration is impaired during non-wakefulness 10,85,86 .…”

Section: /19mentioning

confidence: 93%

Dynamical informational structures characterize the different human brain states of wakefulness and deep sleep

Galadí

Pereira

Perl

et al. 2019

Preprint

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The dynamical activity of the human brain describes an extremely complex energy landscape changing over time and its characterisation is central unsolved problem in neuroscience. We propose a novel mathematical formalism for characterizing how the landscape of attractors sustained by a dynamical system evolves in time. This mathematical formalism is used to distinguish quantitatively and rigorously between the different human brain states of wakefulness and deep sleep. In particular, by using a whole-brain dynamical ansatz integrating the underlying anatomical structure with the local node dynamics based on a Lotka-Volterra description, we compute analytically the global attractors of this cooperative system and their associated directed graphs, here called the informational structures. The informational structure of the global attractor of a dynamical system describes precisely the past and future behaviour in terms of a directed graph composed of invariant sets (nodes) and their corresponding connections (links). We characterize a brain state by the time variability of these informational structures. This theoretical framework is potentially highly relevant for developing reliable biomarkers of patients with e.g. neuropsychiatric disorders or different levels of coma. of the phase space described by a set of selected invariant global solutions of the associated dynamical system, such as stationary points (equilibria), connecting orbits among them, periodic solutions and limit cycles. The informational structure of the GA is defined as a directed graph composed of nodes associated with those invariants and links establishing their connections (see Methods and Supplementary Information for a formal rigorous definition). Informational Structure has been used to show the dependence between the topology, the value of the parameters and the state with respect to its level of integration 14 , as such pointing for the small world configuration of the brain 16 (although see recent controversies 17 ).Previous research has applied Informational Structure to population dynamics in complex networks 18,19 . Equally used for modelling mutualistic systems in Theoretical Ecology and Economy 20, 21 , Informational Structure can relate the topology of complex networks and their dynamics. In mutualistic systems when achieving robustness and life abundance -the so-called architecture of biodiversity 22, 23 -the dynamics is determined not only by the topology 24 but also by modularity 25 and the strength of the parameters 26 .These kinds of relationships are also at the heart of many important open questions in neuroscience 27,28 . Informational Structures and their continuous time-dependence on the strength of connections can allow to understand the dynamics of the system as a coherent process, whose information is structured, and potentially providing new insights into sudden bifurcations 29 .Here, we were interested in characterising human sleep, which is traditionally subdivided into different stages that alternate in the course...

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Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep

Cited by 209 publications

References 67 publications

Brain states govern the spatio-temporal dynamics of resting state functional connectivity

Brain states govern the spatio-temporal dynamics of resting state functional connectivity

Sleeping While Awake: The Intrusion of Neural Activity Associated with Sleep Onset in the Awake Human Brain

Dynamical informational structures characterize the different human brain states of wakefulness and deep sleep

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