A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics (original) (raw)
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Modeling Resting-State Functional Networks When the Cortex Falls Asleep: Local and Global Changes
Cerebral Cortex, 2013
The transition from wakefulness to sleep represents the most conspicuous change in behavior and the level of consciousness occurring in the healthy brain. It is accompanied by similarly conspicuous changes in neural dynamics, traditionally exemplified by the change from "desynchronized" electroencephalogram activity in wake to globally synchronized slow wave activity of early sleep. However, unit and local field recordings indicate that the transition is more gradual than it might appear: On one hand, local slow waves already appear during wake; on the other hand, slow sleep waves are only rarely global. Studies with functional magnetic resonance imaging also reveal changes in resting-state functional connectivity (FC) between wake and slow wave sleep. However, it remains unclear how resting-state networks may change during this transition period. Here, we employ large-scale modeling of the human cortico-cortical anatomical connectivity to evaluate changes in resting-state FC when the model "falls asleep" due to the progressive decrease in arousal-promoting neuromodulation. When cholinergic neuromodulation is parametrically decreased, local slow waves appear, while the overall organization of resting-state networks does not change. Furthermore, we show that these local slow waves are structured macroscopically in networks that resemble the resting-state networks. In contrast, when the neuromodulator decrease further to very low levels, slow waves become global and resting-state networks merge into a single undifferentiated, broadly synchronized network.
Modeling Resting-State Functional Networks When the Cortex Falls Sleep: Local and Global Changes
Cerebral Cortex, 2013
The transition from wakefulness to sleep represents the most conspicuous change in behavior and the level of consciousness occurring in the healthy brain. It is accompanied by similarly conspicuous changes in neural dynamics, traditionally exemplified by the change from "desynchronized" electroencephalogram activity in wake to globally synchronized slow wave activity of early sleep. However, unit and local field recordings indicate that the transition is more gradual than it might appear: On one hand, local slow waves already appear during wake; on the other hand, slow sleep waves are only rarely global. Studies with functional magnetic resonance imaging also reveal changes in resting-state functional connectivity (FC) between wake and slow wave sleep. However, it remains unclear how resting-state networks may change during this transition period. Here, we employ large-scale modeling of the human cortico-cortical anatomical connectivity to evaluate changes in resting-state FC when the model "falls asleep" due to the progressive decrease in arousal-promoting neuromodulation. When cholinergic neuromodulation is parametrically decreased, local slow waves appear, while the overall organization of resting-state networks does not change. Furthermore, we show that these local slow waves are structured macroscopically in networks that resemble the resting-state networks. In contrast, when the neuromodulator decrease further to very low levels, slow waves become global and resting-state networks merge into a single undifferentiated, broadly synchronized network.
Modeling Sleep and Wakefulness in the Thalamocortical System
Journal of Neurophysiology, 2004
When the brain goes from wakefulness to sleep, cortical neurons begin to undergo slow oscillations in their membrane potential that are synchronized by thalamocortical circuits and reflected in EEG slow waves. In order to provide a self-consistent account of the transition from wakefulness to sleep and of the generation of sleep slow waves, we have constructed a largescale computer model that encompasses portions of two visual areas and associated thalamic and reticular thalamic nuclei. Thousands of model neurons, incorporating several intrinsic currents, are interconnected with millions of thalamocortical, corticothalamic, intra-and inter-areal corticocortical connections. In the waking mode, the model exhibits irregular spontaneous firing and selective responses to visual stimuli. In the sleep mode, neuromodulatory changes lead to slow oscillations that closely resemble those observed in vivo and in vitro. A systematic exploration of the effects of intrinsic currents and network parameters on the initiation, maintenance and termination of slow oscillations shows the following: 1. An increase in potassium leak conductances is sufficient to trigger the transition from wakefulness to sleep. 2. The activation of persistent sodium currents is sufficient to initiate the up-state of the slow oscillation. 3. A combination of intrinsic and synaptic currents is sufficient to maintain the upstate. 4. Depolarization-activated potassium currents and synaptic depression terminate the upstate. 5. Corticocortical connections synchronize the slow oscillation. The model is the first to integrate intrinsic neuronal properties with detailed thalamocortical anatomy and reproduce neural activity patterns in both wakefulness and sleep, thereby providing a powerful tool to investigate the role of sleep in information transmission and plasticity.
Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep
Communications Biology, 2021
Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this man...
Excitable dynamics of NREM sleep: a unifying model for neocortex and hippocampus
2018
ABSTRACTDuring non-rapid eye movement (NREM) sleep, the neocortex and hippocampus alternate between periods of neuronal spiking and inactivity. By directly comparing experimental observations with a mean field model of an adapting, recurrent neuronal population, we find that the neocortical alternations reflect a dynamical regime in which a stable active state is interrupted by transient inactive states (slow waves) while the hippocampal alternations reflect a stable inactive state interrupted by transient active states (sharp waves). We propose that during NREM sleep, hippocampal and neocortical populations are excitable: each in a stable state from which internal fluctuations or external perturbation can evoke the stereotyped population events that mediate NREM functions.
Scaling a slow-wave sleep cortical network model using NEOSIM
We describe a case study transforming a simulation model coded in sequential C++ to run in parallel under Neosim, to enable much larger compartmental network models to be run. For some network models cut down scale is su cient; however, there are cases where network behaviour cannot be reproduced on a smaller model (e.g. Neurocomputing 32-33 . The example we present is a model of slow-wave sleep oscillations. In an earlier paper (Neurocomputing 38 we outlined the design of the Neosim framework for scaling models, focussing on networks of compartmental neuron models built using existing simulation tools Neuron and Genesis. Here, we explain how a Hodgkin-Huxley network model coded in C++ for a cortical network was adapted for Neosim, and describe the experiments planned. This case study should be of interest to others considering how best to scale up existing models and interface their own coded models with other simulators.
Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
Nature Communications, 2019
The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting th...
2019
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 c...
A neurobiological model of the human sleep/wake cycle
BMC Neuroscience, 2008
Welcome to CNS*2008! The international Computational Neuroscience meeting (CNS) has been a premier forum for presenting experimental and theoretical results exploring the biology of computation in the nervous system for the last 17 years. The meeting is organized by the Organization for Computational Neurosciences (OCNS), a non-profit organization governed by an international executive committee and board of directors. A separate program committee is responsible for the scientific program of the meeting. Participants at the meeting are from academia and industry. The meeting not only provides a venue for research presentation and discussion by senior scientists but actively offers a forum for promoting and supporting young scientists and students from around the world.
Maximum Entropy models can be inferred from large data-sets to uncover how local interactions generate collective dynamics. Here, we employ such models to investigate the characteristics of neurons recorded by multielectrode arrays in the cortex of human and monkey throughout states of wakefulness and sleep. Taking advantage of the separation of excitatory and inhibitory types, we construct a model including this distinction. By comparing the performances of Maximum Entropy models at predicting neural activity in wakefulness and deep sleep, we identify the dominant interactions between neurons in each brain state. We find that during wakefulness, dominant functional interactions are pairwise while during sleep, interactions are population-wide. In particular, inhibitory neurons are shown to be strongly tuned to the inhibitory population. This shows that Maximum Entropy models can be useful to analyze data-sets with excitatory and inhibitory neurons, and can reveal the role of inhibi...