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.

Becoming Interested In Experiments: American Igneous Petrologists and the Geophysical Laboratory, 1905-1965

1995 ◽  
Vol 14 (1) ◽  
pp. 47-61 ◽  
Author(s):  
Carl-Henry Geschwind
Keyword(s):  
Experimental Data ◽  
World War Ii ◽  
Interwar Period ◽  
Large Scale ◽  
Experimental Approach ◽  
Laboratory Data ◽  
Large Body ◽  
Experimental Science ◽  
World War ◽  
Igneous Petrology

The establishment of the Carnegie Institution of Washington's Geophysical Laboratory in 1905 and the pathbreaking work conducted there by Norman L. Bowen in the 1910s and 1920s are commonly considered to have rendered igneous petrology an experimental science. A closer examination of the work of American igneous petrologists outside the Laboratory reveals, however, that consideration of experimental data did not become an integral part of petrological practice until after World War II. To be sure, igneous petrologists celebrated the Geophysical Laboratory and its experimental approach in speeches and historical reviews throughout the interwar period. In their actual research, though, most igneous petrologists ignored the large body of experimental results gathered by the Geophysical Laboratory and treated Bowen's theory of differentiation as merely another speculative petrogenetic hypothesis to be tested against field data. These petrologists, many of whom were engaged in mapping for state and federal surveys, regarded laboratory data on simple anhydrous systems as simply inapplicable to real rocks. Not until after World War II, when the Geophysical Laboratory began large-scale experimentation on hydrous systems and natural rocks, did field petrologists generally accept the relevance of experimental data. At the same time, the institutional framework of igneous petrology changed in the late 1940s and 1950s as a number of experimental petrologists took advantage both of support from new government agencies, and of the rapidly increasing prestige of geochemistry within the discipline, to establish numerous new experimental laboratories. This change in the structure of the discipline, coupled with the greater reliance on hydrous systems and natural rocks in experimental work, finally led to the general incorporation of experimentation into petrological practice.

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.

scholarly journals Breakdown of Effective Connectivity During Slow Wave Sleep: Investigating the Mechanism Underlying a Cortical Gate Using Large-Scale Modeling

2009 ◽  
Vol 102 (4) ◽  
pp. 2096-2111 ◽  
Author(s):  
Steve K. Esser ◽  
Sean Hill ◽  
Giulio Tononi
Keyword(s):  
Slow Wave ◽  
Large Scale ◽  
Slow Wave Sleep ◽  
Effective Connectivity ◽  
Signal Transmission ◽  
Sensory Stimulation ◽  
Scale Model ◽  
Synaptic Excitation ◽  
Cortical Areas

Effective connectivity between cortical areas decreases during slow wave sleep. This decline can be observed in the reduced interareal propagation of activity evoked either directly in cortex by transcranial magnetic stimulation (TMS) or by sensory stimulation. We present here a large-scale model of the thalamocortical system that is capable of reproducing these experimental observations. This model was constructed according to a large number of physiological and anatomical constraints and includes over 30,000 spiking neurons interconnected by more than 5 million synaptic connections and organized into three cortical areas. By simulating the different effects of arousal promoting neuromodulators, the model can produce a waking or a slow wave sleep-like mode. In this work, we also seek to explain why intercortical signal transmission decreases in slow wave sleep. The traditional explanation for reduced brain responses during this state, a thalamic gate, cannot account for the reduced propagation between cortical areas. Therefore we propose that a cortical gate is responsible for this diminished intercortical propagation. We used our model to test three candidate mechanisms that might produce a cortical gate during slow wave sleep: a propensity to enter a local down state following perturbation, which blocks the propagation of activity to other areas, increases in potassium channel conductance that reduce neuronal responsiveness, and a shift in the balance of synaptic excitation and inhibition toward inhibition, which decreases network responses to perturbation. Of these mechanisms, we find that only a shift in the balance of synaptic excitation and inhibition can account for the observed in vivo response to direct cortical perturbation as well as many features of spontaneous sleep.

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&lt;0.0001), slow wave amplitude (r=-0.25, p&lt;0.0001), and slow wave density (r=-0.40, p&lt;0.0001). The slope of the slow wave activity, representing the process S here, was significantly decreased (r=-0.32, p&lt;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&lt;0.0001). Finally, our results show a decrease in the probability of auditory evoked slow waves (r=-0.43, p&lt;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 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 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.

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