Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.

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Single or Multi-Frequency generators in on-going brain activity: a mechanistic whole-brain model of empirical MEG data

10.1101/084103 ◽

2016 ◽

Author(s):

Gustavo Deco ◽

Joana Cabral ◽

Mark W. Woolrich ◽

Angus B.A Stevner ◽

Tim J. van Hartevelt ◽

...

Keyword(s):

Large Scale ◽

Brain Activity ◽

Brain Area ◽

Temporal Structure ◽

Single Frequency ◽

Multiple Frequency ◽

Whole Brain ◽

Frequency Bands ◽

Band Limited ◽

The Brain

AbstractDuring rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies.Considering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.

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Selection of stimulus parameters for enhancing slow wave sleep events with a Neural-field theory thalamocortical computational model

10.1101/2021.02.03.429528 ◽

2021 ◽

Author(s):

Felipe A. Torres ◽

Patricio Orio ◽

María-José Escobar

Keyword(s):

Computational Model ◽

Slow Wave ◽

Memory Consolidation ◽

Closed Loop ◽

Slow Wave Sleep ◽

Brain Activity ◽

Sensory Stimulation ◽

Sleep Spindles ◽

Slow Oscillations ◽

Closed Loop Stimulation

AbstractSlow-wave sleep cortical brain activity, conformed by slow-oscillations and sleep spindles, plays a key role in memory consolidation. The increase of the power of the slow-wave events, obtained by auditory sensory stimulation, positively correlates to memory consolidation performance. However, little is known about the experimental protocol maximizing this effect, which could be induced by the power of slow-oscillation, the number of sleep spindles, or the timing of both events’ co-occurrence. Using a mean-field model of thalamocortical activity, we studied the effect of several stimulation protocols, varying the pulse shape, duration, amplitude, and frequency, as well as a target-phase using a closed-loop approach. We evaluated the effect of these parameters on slow-oscillations (SO) and sleep-spindles (SP), considering: (i) the power at the frequency bands of interest, (ii) the number of SO and SP, (iii) co-occurrences between SO and SP, and (iv) synchronization of SP with the up-peak of the SO. The first three targets are maximized using a decreasing ramp pulse with a pulse duration of 50 ms. Also, we observed a reduction in the number of SO when increasing the stimulus energy by rising its amplitude. To assess the target-phase parameter, we applied closed-loop stimulation at 0º, 45º, and 90º of the phase of the narrow-band filtered ongoing activity, at 0.85 Hz as central frequency. The 0º stimulation produces better results in the power and number of SO and SP than the rhythmic or aleatory stimulation. On the other hand, stimulating at 45º or 90º change the timing distribution of spindles centers but with fewer co-occurrences than rhythmic and 0º phase. Finally, we propose the application of closed-loop stimulation at the rising zero-cross point using pulses with a decreasing ramp shape and 50 ms of duration for future experimental work.Author summaryDuring the non-REM (NREM) phase of sleep, events that are known as slow oscillations (SO) and spindles (SP) can be detected by EEG. These events have been associated with the consolidation of declarative memories and learning. Thus, there is an ongoing interest in promoting them during sleep by non-invasive manipulations such as sensory stimulation. In this paper, we used a computational model of brain activity that generates SO and SP, to investigate which type of sensory stimulus –shape, amplitude, duration, periodicity– would be optimal for increasing the events’ frequency and their co-occurrence. We found that a decreasing ramp of 50 ms duration is the most effective. The effectiveness increases when the stimulus pulse is delivered in a closed-loop configuration triggering the pulse at a target phase of the ongoing SO activity. A desirable secondary effect is to promote SPs at the rising phase of the SO oscillation.

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Exploring the dynamic mechanisms of steady-state visual evoked potentials at the whole-brain level: evidence from a computational study

10.1101/2021.02.05.429877 ◽

2021 ◽

Author(s):

Ge Zhang ◽

Yan Cui ◽

Yangsong Zhang ◽

Hefei Cao ◽

Guanyu Zhou ◽

...

Keyword(s):

Steady State ◽

Evoked Potentials ◽

Visual Evoked Potentials ◽

Large Scale ◽

Whole Brain ◽

Frequency Sensitivity ◽

Dynamic Mechanisms ◽

Brain Level ◽

The Brain ◽

Visual Evoked

AbstractPeriodic visual stimulation can induce stable steady-state visual evoked potentials (SSVEPs) distributed in multiple brain regions and has potential applications in both neural engineering and cognitive neuroscience. However, the underlying dynamic mechanisms of SSVEPs at the whole-brain level are still not completely understood. Here, we addressed this issue by simulating the rich dynamics of SSVEPs with a large-scale brain model designed with constraints of neuroimaging data acquired from the human brain. By eliciting activity of the occipital areas using an external periodic stimulus, our model was capable of replicating both the spatial distributions and response features of SSVEPs that were observed in experiments. In particular, we confirmed that alpha-band (8-12 Hz) stimulation could evoke stronger SSVEP responses; this frequency sensitivity was due to nonlinear resonance and could be modulated by endogenous factors in the brain. Interestingly, the stimulus-evoked brain networks also exhibited significant superiority in topological properties near this frequency-sensitivity range, and stronger SSVEP responses were demonstrated to be supported by more efficient functional connectivity at the neural activity level. These findings not only provide insights into the mechanistic understanding of SSVEPs at the whole-brain level but also indicate a bright future for large-scale brain modeling in characterizing the complicated dynamics and functions of the brain.

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Increased Alpha (8–12 Hz) Activity during Slow Wave Sleep as a Marker for the Transition from Implicit Knowledge to Explicit Insight

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00097 ◽

2012 ◽

Vol 24 (1) ◽

pp. 119-132 ◽

Cited By ~ 27

Author(s):

Juliana Yordanova ◽

Vasil Kolev ◽

Ullrich Wagner ◽

Jan Born ◽

Rolf Verleger

Keyword(s):

Slow Wave ◽

Explicit Knowledge ◽

Slow Wave Sleep ◽

Sleep Stage ◽

Implicit Knowledge ◽

Specific Power ◽

Specific Marker ◽

Knowledge Representations ◽

Beta Power ◽

The Brain

The number reduction task (NRT) allows us to study the transition from implicit knowledge of hidden task regularities to explicit insight into these regularities. To identify sleep-associated neurophysiological indicators of this restructuring of knowledge representations, we measured frequency-specific power of EEG while participants slept during the night between two sessions of the NRT. Alpha (8–12 Hz) EEG power during slow wave sleep (SWS) emerged as a specific marker of the transformation of presleep implicit knowledge to postsleep explicit knowledge (ExK). Beta power during SWS was increased whenever ExK was attained after sleep, irrespective of presleep knowledge. No such EEG predictors of insight were found during Sleep Stage 2 and rapid eye movement sleep. These results support the view that it is neuronal memory reprocessing during sleep, in particular during SWS, that lays the foundations for restructuring those task-related representations in the brain that are necessary for promoting the gain of ExK.

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Interactions Between Membrane Conductances Underlying Thalamocortical Slow-Wave Oscillations

Physiological Reviews ◽

10.1152/physrev.00012.2003 ◽

2003 ◽

Vol 83 (4) ◽

pp. 1401-1453 ◽

Cited By ~ 151

Author(s):

A. DESTEXHE ◽

T. J. SEJNOWSKI

Keyword(s):

Experimental Data ◽

Single Cell ◽

Slow Wave ◽

Large Scale ◽

Slow Wave Sleep ◽

Large Body ◽

Electrophysiological Properties ◽

Membrane Conductances ◽

Synaptic Receptors ◽

Slow Wave Oscillations

Destexhe, A., and T. J. Sejnowski. Interactions Between Membrane Conductances Underlying Thalamocortical Slow-Wave Oscillations. Physiol Rev 83: 1401-1453, 2003; 10.1152/physrev.00012.2003.—Neurons of the central nervous system display a broad spectrum of intrinsic electrophysiological properties that are absent in the traditional “integrate-and-fire” model. A network of neurons with these properties interacting through synaptic receptors with many time scales can produce complex patterns of activity that cannot be intuitively predicted. Computational methods, tightly linked to experimental data, provide insights into the dynamics of neural networks. We review this approach for the case of bursting neurons of the thalamus, with a focus on thalamic and thalamocortical slow-wave oscillations. At the single-cell level, intrinsic bursting or oscillations can be explained by interactions between calcium- and voltage-dependent channels. At the network level, the genesis of oscillations, their initiation, propagation, termination, and large-scale synchrony can be explained by interactions between neurons with a variety of intrinsic cellular properties through different types of synaptic receptors. These interactions can be altered by neuromodulators, which can dramatically shift the large-scale behavior of the network, and can also be disrupted in many ways, resulting in pathological patterns of activity, such as seizures. We suggest a coherent framework that accounts for a large body of experimental data at the ion-channel, single-cell, and network levels. This framework suggests physiological roles for the highly synchronized oscillations of slow-wave sleep.

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Increased Stability and Breakdown of Brain Effective Connectivity During Slow-Wave Sleep: Mechanistic Insights from Whole-Brain Computational Modelling

Scientific Reports ◽

10.1038/s41598-017-04522-x ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 38

Author(s):

Beatrice M. Jobst ◽

Rikkert Hindriks ◽

Helmut Laufs ◽

Enzo Tagliazucchi ◽

Gerald Hahn ◽

...

Keyword(s):

Slow Wave ◽

Slow Wave Sleep ◽

Effective Connectivity ◽

Computational Modelling ◽

Whole Brain

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Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication

10.1101/2021.12.07.471633 ◽

2021 ◽

Author(s):

Cassie J Hilditch ◽

Kanika Bansal ◽

Ravi Chachad ◽

Lily R Wong ◽

Nicholas G Bathurst ◽

...

Keyword(s):

Long Range ◽

Slow Wave ◽

Path Length ◽

Slow Wave Sleep ◽

Mechanistic Explanation ◽

Clustering Coefficient ◽

Sleep Inertia ◽

The Awakening ◽

Beta Power ◽

The Brain

Sleep inertia is the brief period of impaired alertness and performance experienced immediately after waking. While the neurobehavioral symptoms of sleep inertia are well-described, less is known about the neural mechanisms underlying this phenomenon. A better understanding of the neural processes during sleep inertia may offer insight into the cognitive impairments observed and the awakening process generally. We observed brain activity following abrupt awakening from slow wave sleep during the biological night. Using electroencephalography (EEG) and a network science approach, we evaluated power, clustering coefficient, and path length across frequency bands under both a control condition and a blue-enriched light intervention condition in a within-subject design. We found that under control conditions, the awakening brain is typified by an immediate reduction in global theta, alpha, and beta power. Simultaneously, we observed a decrease in the clustering coefficient and an increase in path length within the delta band. Exposure to blue-enriched light immediately after awakening ameliorated these changes, but only for clustering. Our results suggest that long-range network communication within the brain is crucial to the waking process and that the brain may prioritize these long-range connections during this transitional state. Our study highlights a novel neurophysiological signature of the awakening brain and provides a potential mechanistic explanation for the effect of light in improving performance after waking.

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Effect of Interaction Between Slow Wave Sleep and Obstructive Sleep Apnea on Insulin Resistance: A Large-scale Study

10.21203/rs.3.rs-135755/v1 ◽

2020 ◽

Author(s):

Weijun Huang ◽

Xiaoting Wang ◽

Yuenan Liu ◽

Xinyi Li ◽

Yupu Liu ◽

...

Keyword(s):

Insulin Resistance ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Slow Wave ◽

Large Scale ◽

Slow Wave Sleep ◽

Harmful Effect ◽

Joint Effect ◽

Obstructive Sleep ◽

The Status

Abstract Objectives: Slow wave sleep (SWS) and obstructive sleep apnea (OSA) have attracted more and more attention. Their joint effect on insulin resistance (IR) remains to be further studied. This study explored whether less SWS influences the relationship between OSA and IR.Methods: We enrolled potential participants in our sleep center from 2007 to 2019. We collected demographic and clinical characteristics and gauged the IR status. SWS was derived from polysomnography data. Logistic regression analyses were used to reveal the associations between SWS and IR.Results: In all, 6966 participants (5709 OSA and 1257 primary snoring [PS] subjects) were enrolled. Less SWS increases the risk of IR in OSA patients but not in PS patients. OSA patients with SWS < 6.5% were more likely to have IR than those with SWS > 21.3%. OSA was an independent risk factor for IR after adjusting for all potential confounding factors. In stratified analyses according to the percentage of SWS, patients with OSA with SWS < 6.5% had 38.2% higher odds of IR than the PS group after adjusting for all potential confounders. Conclusions: Less SWS is associated with higher odds for IR in OSA patients but not in PS patients. OSA is independently correlated with IR. In addition, OSA combined with an extreme lack of SWS has a more harmful effect on the status of IR than OSA itself.

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D-C potential changes in rabbit brain during slow-wave and paradoxical sleep

American Journal of Physiology-Legacy Content ◽

10.1152/ajplegacy.1964.207.6.1379 ◽

1964 ◽

Vol 207 (6) ◽

pp. 1379-1386 ◽

Cited By ~ 33

Author(s):

H. Kawamura ◽

C. H. Sawyer

Keyword(s):

Reticular Formation ◽

Slow Wave ◽

Slow Wave Sleep ◽

Paradoxical Sleep ◽

Reference Electrode ◽

Reference Points ◽

Rabbit Brain ◽

Positive Side ◽

The Brain ◽

Stimulation Of

A study has been made of d-c potential changes in the brain during various states of sleep and wakefulness in the unrestrained unanesthetized rabbit with chronically implanted electroencephalogram and d-c electrodes. In preliminary acute experiments with an occipital bone reference electrode, the direction of the d-c potential change induced by stimulation of the midbrain reticular formation was the same in cortical and subcortical sites. With frontal and occipital bones as reference points the cortical d-c potential changes were similar to one another, but arousal stimuli readily elicited positive d-c shifts with the frontal reference electrode. In the chronic rabbit with an occipital reference electrode, cortical and hypothalamic d-c potentials shifted strongly to the positive side during slow-wave sleep and after injection of pentobarbital; from these conditions stimulation of the reticular formation induced a marked negative shift. During paradoxical sleep both cortical and subcortical d-c electrodes showed negative shifts similar to those seen during an arousal reaction. Grooming and eating elicited strong positive shifts in both cortical and hypothalamic d-c electrodes.

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Hippocampal Sharp Wave-Ripples Linked to Slow Oscillations in Rat Slow-Wave Sleep

Journal of Neurophysiology ◽

10.1152/jn.00014.2006 ◽

2006 ◽

Vol 96 (1) ◽

pp. 62-70 ◽

Cited By ~ 201

Author(s):

Matthias Mölle ◽

Oxana Yeshenko ◽

Lisa Marshall ◽

Susan J. Sara ◽

Jan Born

Keyword(s):

Slow Wave ◽

Slow Wave Sleep ◽

Temporal Dynamics ◽

Slow Oscillation ◽

Network Activity ◽

Event Correlation ◽

Slow Oscillations ◽

Sharp Wave ◽

Neuronal Network Activity ◽

Up And Down States

Slow oscillations originating in the prefrontal neocortex during slow-wave sleep (SWS) group neuronal network activity and thereby presumably support the consolidation of memories. Here, we investigated whether the grouping influence of slow oscillations extends to hippocampal sharp wave-ripple (SPW) activity thought to underlie memory replay processes during SWS. The prefrontal surface EEG and multiunit activity (MUA), along with hippocampal local field potentials (LFP) from CA1, were recorded in rats during sleep. Average spindle and ripple activity and event correlation histograms of SPWs were calculated, time-locked to half-waves of slow oscillations. Results confirm decreased prefrontal MUA and spindle activity during EEG slow oscillation negativity and increases in this activity during subsequent positivity. A remarkably close temporal link was revealed between slow oscillations and hippocampal activity, with ripple activity and SPWs being also distinctly decreased during negative half-waves and increased during slow oscillation positivity. Fine-grained analyses of temporal dynamics revealed for the slow oscillation a phase delay of approximately 90 ms with reference to up and down states of prefrontal MUA, and of only approximately 60 ms with reference to changes in SPWs, indicating that up and down states in prefrontal MUA precede corresponding changes in hippocampal SPWs by approximately 30 ms. Results support the notion that the depolarizing surface-positive phase of the slow oscillation and the associated up state of prefrontal excitation promotes hippocampal SPWs via efferent pathways. The preceding disfacilitation of hippocampal events temporally coupled to the negative slow oscillation half-wave appears to serve a synchronizing role in this neocorticohippocampal interplay.

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