scholarly journals Interaction of Hippocampal Ripples and Cortical Slow Waves Leads to Coordinated Large-Scale Sleep Rhythm

2019 ◽  
Author(s):  
Pavel Sanda ◽  
Paola Malerba ◽  
Xi Jiang ◽  
Giri P. Krishnan ◽  
Sydney Cash ◽  
...  
Keyword(s):  
Slow Wave ◽  
Large Scale ◽  
State Transition ◽  
Slow Wave Sleep ◽  
Slow Waves ◽  
Spatiotemporal Pattern ◽  
Spatiotemporal Structure ◽  
Cortical Input ◽  
Up States ◽  
Intracranial Recordings

AbstractThe dialogue between cortex and hippocampus is known to be crucial for sleep dependent consolidation of long lasting memories. During slow wave sleep memory replay depends on slow oscillation (SO) and spindles in the (neo)cortex and sharp wave-ripple complexes (SWR) in the hippocampus, however, the mechanisms underlying interaction of these rhythms are poorly understood. Here, we examined the interaction between cortical SOs and hippocampal SWRs in a computational model of the hippocampo-cortico-thalamic network and compared the results with human intracranial recordings during sleep. We observed that ripple occurrence peaked following the onset of SO (Down-to-Up-state transition) and that cortical input to hippocampus was crucial to maintain this relationship. Ripples influenced the spatiotemporal structure of cortical SO and duration of the Up/Down-states. In particular, ripples were capable of synchronizing Up-to-Down state transition events across the cortical network. Slow waves had a tendency to initiate at cortical locations receiving hippocampal ripples, and these “initiators” were able to influence sequential reactivation within cortical Up states. We concluded that during slow wave sleep, hippocampus and neocortex maintain a complex interaction, where SOs bias the onset of ripples, while ripples influence the spatiotemporal pattern of SOs.

scholarly journals Bidirectional Interaction of Hippocampal Ripples and Cortical Slow Waves Leads to Coordinated Spiking Activity During NREM Sleep

2020 ◽  
Vol 31 (1) ◽  
pp. 324-340
Author(s):  
Pavel Sanda ◽  
Paola Malerba ◽  
Xi Jiang ◽  
Giri P Krishnan ◽  
Jorge Gonzalez-Martinez ◽  
...  
Keyword(s):  
Slow Wave ◽  
Memory Consolidation ◽  
Slow Wave Sleep ◽  
Nrem Sleep ◽  
Slow Waves ◽  
Slow Oscillation ◽  
Cortical Input ◽  
Sharp Wave ◽  
Up States ◽  
Intracranial Recordings

Abstract The dialogue between cortex and hippocampus is known to be crucial for sleep-dependent memory consolidation. During slow wave sleep, memory replay depends on slow oscillation (SO) and spindles in the (neo)cortex and sharp wave-ripples (SWRs) in the hippocampus. The mechanisms underlying interaction of these rhythms are poorly understood. We examined the interaction between cortical SO and hippocampal SWRs in a model of the hippocampo–cortico–thalamic network and compared the results with human intracranial recordings during sleep. We observed that ripple occurrence peaked following the onset of an Up-state of SO and that cortical input to hippocampus was crucial to maintain this relationship. A small fraction of ripples occurred during the Down-state and controlled initiation of the next Up-state. We observed that the effect of ripple depends on its precise timing, which supports the idea that ripples occurring at different phases of SO might serve different functions, particularly in the context of encoding the new and reactivation of the old memories during memory consolidation. The study revealed complex bidirectional interaction of SWRs and SO in which early hippocampal ripples influence transitions to Up-state, while cortical Up-states control occurrence of the later ripples, which in turn influence transition to Down-state.

scholarly journals 0352 A Large-Scale EEG Study at Home to Objectivise Effects of Ageing on Slow Wave Sleep and Process S

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A133-A134
Author(s):  
K El Kanbi ◽  
V Thorey ◽  
L Artemis ◽  
A Chouraki ◽  
T Trichet ◽  
...  
Keyword(s):  
Slow Wave ◽  
Large Scale ◽  
Slow Wave Sleep ◽  
Large Population ◽  
Nrem Sleep ◽  
Slow Waves ◽  
High Signal ◽  
Eeg Signals ◽  
Sleep Complaints ◽  
Loop Procedure

Abstract Introduction Several studies have shown slow wave sleep (SWS) is altered with ageing. However, most of these studies have been conducted in-lab and usually over a single night. In this study, we assessed the evolution of process S with ageing by analysing the dynamics of endogenous and auditory-evoked slow waves in a large population. Methods 300 participants (200 M, 20 - 70 y.o.) were selected from volunteers users wearing a sleep headband for at least 3 nights, meeting the criteria of high signal quality and having no subjective sleep complaints nor being shift-workers. The Dreem headband is a connected device able to monitor EEG signals as well as pulse and movement and performs sleep staging in real-time automatically. Slow waves were detected as large negative deflections on the filtered EEG signals during NREM sleep. The auditory evoked slow waves were done using a previously validated closed-loop procedure. Results In our study, age was strongly correlated with N3 sleep duration (r=-0.34, p<0.0001), slow wave amplitude (r=-0.25, p<0.0001), and slow wave density (r=-0.40, p<0.0001). The slope of the slow wave activity, representing the process S here, was significantly decreased (r=-0.32, p<0.0001). This effect was mainly due to changes in the density of slow waves in the first 2 hours of sleep (r=-0.41, p<0.0001). Finally, our results show a decrease in the probability of auditory evoked slow waves (r=-0.43, p<0.0001). Conclusion These results confirmed the in-lab studies showing a heterogeneous alteration of homoeostatic process S with age, as well as a general decrease of slow wave occurrences, that is observed in parallel of a decrease of the probability of evoking slow waves, suggesting a global change in the system responsible for slow wave generation. Support This study was supported by Dreem sas and ANR, FLAG ERA 2015, HPB SLOW-Dyn

scholarly journals Brain-scale emergence of slow-wave synchrony and highly responsive asynchronous states based on biologically realistic population models simulated in The Virtual Brain

2020 ◽  
Author(s):  
Jennifer S. Goldman ◽  
Lionel Kusch ◽  
Bahar Hazal Yalcinkaya ◽  
Damien Depannemaecker ◽  
Trang-Anh E. Nghiem ◽  
...  
Keyword(s):  
Slow Wave ◽  
Large Scale ◽  
Slow Wave Sleep ◽  
Mean Field ◽  
Population Models ◽  
Slow Waves ◽  
Brain Dynamics ◽  
Promising Tool ◽  
Activity State ◽  
The Many

ABSTRACTUnderstanding the many facets of the organization of brain dynamics at large scales remains largely unexplored. Here, we construct a brain-wide model based on recent progress in biologically-realistic population models obtained using mean-field techniques. We use The Virtual Brain (TVB) as a simulation platform and incorporate mean-field models of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons. Such models can capture the main intrinsic firing properties of central neurons, such as adaptation, and also include the typical kinetics of postsynaptic conductances. We hypothesize that such features are important to a biologically realistic simulation of brain dynamics. The resulting “TVB-AdEx” model is shown here to generate two fundamental dynamical states, asynchronous-irregular (AI) and Up-Down states, which correspond to the asynchronous and synchronized dynamics of wakefulness and slow-wave sleep, respectively. The synchrony of slow waves appear as an emergent property at large scales, and reproduce the very different patterns of functional connectivity found in slow-waves compared to asynchronous states. Next, we simulated experiments with transcranial magnetic stimulation (TMS) during asynchronous and slow-wave states, and show that, like in experimental data, the effect of the stimulation greatly depends on the activity state. During slow waves, the response is strong but remains local, in contrast with asynchronous states, where the response is weaker but propagates across brain areas. To compare more quantitatively with wake and slow-wave sleep states, we compute the perturbational complexity index and show that it matches the value estimated from TMS experiments. We conclude that the TVB-AdEx model replicates some of the properties of synchrony and responsiveness seen in the human brain, and is a promising tool to study spontaneous and evoked large-scale dynamics in the normal, anesthetized or pathological brain.

scholarly journals The Aging Slow Wave: A Shifting Amalgam of Distinct Slow Wave and Spindle Coupling Subtypes Define Slow Wave Sleep Across the Human Lifespan

SLEEP ◽  
2021 ◽  
Author(s):  
Brice V McConnell ◽  
Eugene Kronberg ◽  
Peter D Teale ◽  
Stefan H Sillau ◽  
Grace M Fishback ◽  
...  
Keyword(s):  
Slow Wave ◽  
Neurological Disease ◽  
Mixed Model ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Slow Waves ◽  
Cross Sectional ◽  
Relative Contribution ◽  
Human Lifespan ◽  
Development And Aging

Abstract Study Objectives Slow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan. Methods Coupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6-88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles. Results Two different subtypes of spindles were identified during the upstates of (distinct) slow waves: an “early-fast” spindle, more common in stage N2 sleep, and a “late-fast” spindle, more common in stage N3. We further found stages N2 and N3 sleep contain a mixture of discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails with increasing age. Conclusions Distinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep. Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease.

scholarly journals Interactions Between Membrane Conductances Underlying Thalamocortical Slow-Wave Oscillations

2003 ◽  
Vol 83 (4) ◽  
pp. 1401-1453 ◽  
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.

scholarly journals The Aging Slow Wave: A Shifting Amalgam of Distinct Slow Wave and Spindle Coupling Subtypes Define Slow Wave Sleep Across the Human Lifespan

2020 ◽  
Author(s):  
Brice V. McConnell ◽  
Eugene Kronberg ◽  
Peter D. Teale ◽  
Grace M. Fishback ◽  
Rini I. Kaplan ◽  
...  
Keyword(s):  
Slow Wave ◽  
Cognitive Aging ◽  
Neurological Disease ◽  
Mixed Model ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Slow Waves ◽  
Cross Sectional ◽  
Relative Contribution ◽  
Human Lifespan

AbstractStudy ObjectivesSlow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan.MethodsCoupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6-88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles.ResultsTwo different subtypes of spindles were identified during the upstates of (distinct) slow waves: an “early-fast” spindle, more common in stage N2 sleep, and a “late-fast” spindle, more common in stage N3. We further found stages N2 and N3 sleep are composed of two discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails during old age.ConclusionsDistinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep. Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease.Statement of SignificanceSlow waves of nonrapid eye movement sleep couple with sleep spindles in a process hypothesized to support memory functions. This coupling has recently gained interest as a possible biomarker of cognitive aging and onset of Alzheimer’s disease. Most studies have been limited by an assumption that all slow waves (and coupled spindles) are fundamentally the same physiological events. Here we demonstrate that distinct subtypes of slow waves and their coupled spindles can be identified in human sleep. A mixture of different slow wave and spindle subtypes shifts in composition during lighter versus deeper sleep, and aging favors the deep sleep subtypes. These data should inform any future attempts to use slow wave sleep as a biomarker or clinical interventional target.

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

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.

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

2022 ◽  
Vol 15 ◽  
Author(s):  
Caglar Cakan ◽  
Cristiana Dimulescu ◽  
Liliia Khakimova ◽  
Daniela Obst ◽  
Agnes Flöel ◽  
...  
Keyword(s):  
Slow Wave ◽  
Traveling Waves ◽  
Large Scale ◽  
Slow Wave Sleep ◽  
Brain Area ◽  
Optimization Approach ◽  
Whole Brain ◽  
Slow Oscillations ◽  
Lower Amplitude ◽  
The Brain

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.

scholarly journals Replay of large-scale spatio-temporal patterns from waking during subsequent slow-wave sleep in human cortex

2017 ◽  
Author(s):  
Xi Jiang ◽  
Isaac Shamie ◽  
Werner Doyle ◽  
Daniel Friedman ◽  
Patricia Dugan ◽  
...  
Keyword(s):  
Slow Wave ◽  
Memory Consolidation ◽  
Animal Studies ◽  
Large Scale ◽  
Slow Wave Sleep ◽  
Cognitive Task ◽  
Human Memory ◽  
Sharp Wave ◽  
Direct Demonstration ◽  
Spatio Temporal

AbstractAnimal studies support the hypothesis that in slow-wave sleep, replay of waking neocortical activity under hippocampal guidance leads to memory consolidation. However, no intracranial electrophysiological evidence for replay exists in humans. We identified consistent sequences of population firing peaks across widespread cortical regions during complete waking periods. The occurrence of these Motifs were compared between sleeps preceding the waking period (Sleep-Pre) when the Motifs were identified, and those following (Sleep-Post). In all subjects, the majority of waking Motifs (most of which were novel) had more matches in Sleep-Post than in Sleep-Pre. In rodents, hippocampal replay occurs during local sharp-wave ripples, and the associated neocortical replay tends to occur during local sleep spindles and down-to-up transitions. These waves may facilitate consolidation by sequencing cell-firing and encouraging plasticity. Similarly, we found that Motifs were coupled to neocortical spindles, down-to-up transitions, theta bursts, and hippocampal sharp-wave ripples. While Motifs occurring during cognitive task performance were more likely to have more matches in subsequent sleep, our studies provide no direct demonstration that the replay of Motifs contributes to consolidation. Nonetheless, these results confirm a core prediction of the dominant neurobiological theory of human memory consolidation.

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