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Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Marsh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Newly acquired memory traces are spontaneously reactivated during slow-wave sleep (SWS), leading to the consolidation of recent memories. Empirical studies found that sensory stimulation during SWS can selectively enhance memory consolidation with the effect depending on the phase of stimulation. In this new study, we aimed to understand the mechanisms behind the role of sensory stimulation on memory consolidation using computational models implementing effects of neuromodulators to simulate transitions between awake and SWS sleep, and synaptic plasticity to allow the change of synaptic connections due to the training in awake or replay during sleep. We found that when closed-loop stimulation was applied during the Down states of sleep slow oscillation, particularly right before the transition from Down to Up state, it significantly affected the spatiotemporal pattern of the slow waves and maximized memory replay. In contrast, when the stimulation was presented during the Up states, it did not have a significant impact on the slow waves or memory performance after sleep. For multiple memories trained in awake, presenting stimulation cues associated with specific memory trace could selectively augment replay and enhance consolidation of that memory and interfere with consolidation of the others (particularly weak) memories. Our study proposes a synaptic-level mechanism of how memory consolidation is affected by sensory stimulation during sleep. SIGNIFICANCE STATEMENT Stimulation, such as training-associated cues or auditory stimulation, during sleep can augment consolidation of the newly encoded memories. In this study, we used a computational model of the thalamocortical system to describe the mechanisms behind the role of stimulation in memory consolidation during slow-wave sleep. Our study suggests that stimulation preferentially strengthens memory traces when delivered at a specific phase of the slow oscillation, just before the Down to Up state transition when it makes the largest impact on the spatiotemporal pattern of sleep slow waves. In the presence of multiple memories, presenting sensory cues during sleep could selectively strengthen selected memories. Our study proposes a synaptic-level mechanism of how memory consolidation is affected by sensory stimulation during sleep., (Copyright © 2020 the authors.)

Sleep spindles are brief oscillatory events during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Here, using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the contrasting properties of the core and matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, which projects to middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. This property was sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Furthermore, consistent with human recordings, in the model, spindles occurred independently in the core system but the matrix system spindles commonly co-occurred with core spindles. We also found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of intra- and inter-cortical connectivity can potentially be a mechanism for increased spindle density as has been observed during learning., Competing Interests: The authors have declared that no competing interests exist.

The link between the combined action of neuromodulators in the brain and global brain states remains a mystery. In this study, using biophysically realistic models of the thalamocortical network, we identified the critical intrinsic and synaptic mechanisms, associated with the putative action of acetylcholine (ACh), GABA and monoamines, which lead to transitions between primary brain vigilance states (waking, non-rapid eye movement sleep [NREM] and REM sleep) within an ultradian cycle. Using ECoG recordings from humans and LFP recordings from cats and mice, we found that during NREM sleep the power of spindle and delta oscillations is negatively correlated in humans and positively correlated in animal recordings. We explained this discrepancy by the differences in the relative level of ACh. Overall, our study revealed the critical intrinsic and synaptic mechanisms through which different neuromodulators acting in combination result in characteristic brain EEG rhythms and transitions between sleep stages., Competing Interests: The authors declare that no competing interests exist.

Sleep, specifically non-rapid eye movement (NREM) sleep, is thought to play a critical role in the consolidation of recent memories. Two main oscillatory activities observed during NREM, cortical slow oscillations (SO, 0.5-1.0 Hz) and thalamic spindles (12-15 Hz), have been shown to independently correlate with memory improvement. Yet, it is not known how these thalamocortical events interact, or the significance of this interaction, during the consolidation process. Here, we found that systemic administration of the GABAergic drug (zolpidem) increased both the phase-amplitude coupling between SO and spindles, and verbal memory improvement in humans. These results suggest that thalamic spindles that occur during transitions to the cortical SO Up state are optimal for memory consolidation. Our study predicts that the timely interactions between cortical and thalamic events during consolidation, contribute to memory improvement and is mediated by the level of inhibitory neurotransmission.

Precisely timed action potentials related to stimuli and behavior have been observed in the cerebral cortex. However, information carried by the precise spike timing has to propagate through many cortical areas, and noise could disrupt millisecond precision during the transmission. Previous studies have demonstrated that only strong stimuli that evoke a large number of spikes with small dispersion of spike times can propagate through multilayer networks without degrading the temporal precision. Here we show that feedback projections can increase the number of spikes in spike volleys without degrading their temporal precision. Feedback also increased the range of spike volleys that can propagate through multilayer networks. Our work suggests that feedback projections could be responsible for the reliable propagation of information encoded in spike times through cortex, and thus could serve as an attentional mechanism to regulate the flow of information in the cortex. Feedback projections may also participate in generating spike synchronization that is engaged in cognitive behaviors by the same mechanisms described here for spike propagation.

Sleep spindles and K-complexes (KCs) define stage 2 NREM sleep (N2) in humans. We recently showed that KCs are isolated downstates characterized by widespread cortical silence. We demonstrate here that KCs can be quasi-synchronous across scalp EEG and across much of the cortex using electrocorticography (ECOG) and localized transcortical recordings (bipolar SEEG). We examine the mechanism of synchronous KC production by creating the first conductance based thalamocortical network model of N2 sleep to generate both spontaneous spindles and KCs. Spontaneous KCs are only observed when the model includes diffuse projections from restricted prefrontal areas to the thalamic reticular nucleus (RE), consistent with recent anatomical findings in rhesus monkeys. Modeled KCs begin with a spontaneous focal depolarization of the prefrontal neurons, followed by depolarization of the RE. Surprisingly, the RE depolarization leads to decreased firing due to disrupted spindling, which in turn is due to depolarization-induced inactivation of the low-threshold Ca2+ current (IT). Further, although the RE inhibits thalamocortical (TC) neurons, decreased RE firing causes decreased TC cell firing, again because of disrupted spindling. The resulting abrupt removal of excitatory input to cortical pyramidal neurons then leads to the downstate. Empirically, KCs may also be evoked by sensory stimuli while maintaining sleep. We reproduce this phenomenon in the model by depolarization of either the RE or the widely-projecting prefrontal neurons. Again, disruption of thalamic spindling plays a key role. Higher levels of RE stimulation also cause downstates, but by directly inhibiting the TC neurons. SEEG recordings from the thalamus and cortex in a single patient demonstrated the model prediction that thalamic spindling significantly decreases before KC onset. In conclusion, we show empirically that KCs can be widespread quasi-synchronous cortical downstates, and demonstrate with the first model of stage 2 NREM sleep a possible mechanism whereby this widespread synchrony may arise.

Sleep spindles are bursts of 11-15 Hz that occur during non-rapid eye movement sleep. Spindles are highly synchronous across the scalp in the electroencephalogram (EEG) but have low spatial coherence and exhibit low correlation with the EEG when simultaneously measured in the magnetoencephalogram (MEG). We developed a computational model to explore the hypothesis that the spatial coherence spindles in the EEG is a consequence of diffuse matrix projections of the thalamus to layer 1 compared with the focal projections of the core pathway to layer 4 recorded in the MEG. Increasing the fanout of thalamocortical connectivity in the matrix pathway while keeping the core pathway fixed led to increased synchrony of the spindle activity in the superficial cortical layers in the model. In agreement with cortical recordings, the latency for spindles to spread from the core to the matrix was independent of the thalamocortical fanout but highly dependent on the probability of connections between cortical areas.

Epileptic activity often occurs in the cortex after a latent period after head trauma; this delay has been attributed to the destabilizing influence of homeostatic synaptic scaling and changes in intrinsic properties. However, the impact of the spatial organization of cortical trauma on epileptogenesis is poorly understood. We addressed this question by analyzing the dynamics of a large-scale biophysically realistic cortical network model subjected to different patterns of trauma. Our results suggest that the spatial pattern of trauma can greatly affect the propensity for developing posttraumatic epileptic activity. For the same fraction of lesioned neurons, spatially compact trauma resulted in stronger posttraumatic elevation of paroxysmal activity than spatially diffuse trauma. In the case of very severe trauma, diffuse distribution of a small number of surviving intact neurons alleviated posttraumatic epileptogenesis. We suggest that clinical evaluation of the severity of brain trauma should take into account the spatial pattern of the injured cortex.

Spindle oscillations are commonly observed during stage 2 of non-rapid eye movement sleep. During sleep spindles, the cerebral cortex and thalamus interact through feedback connections. Both initiation and termination of spindle oscillations are thought to originate in the thalamus based on thalamic recordings and computational models, although some in vivo results suggest otherwise. Here, we have used computer modeling and in vivo multisite recordings from the cortex and the thalamus in cats to examine the involvement of the cortex in spindle oscillations. We found that although the propagation of spindles depended on synaptic interaction within the thalamus, the initiation and termination of spindle sequences critically involved corticothalamic influences.

The role of changes in the extracellular potassium concentration [K(+)](o) in epilepsy has remained unclear. Historically, it was hypothesized that [K(+)]( o) is the causal factor for epileptic seizures. This so-called potassium accumulation hypothesis led to substantial debate but subsequently failed to find wide acceptance. However, recent studies on the pathophysiology of tissue from epileptic human patients and animal epilepsy models revealed aberrations in [K(+)](o) regulation. Computational models of cortical circuits that include ion concentration dynamics have catalyzed a renewed interest in the role of [K(+)](o) in epilepsy. The authors here connect classical and more recent insights on [K(+)]( o) dynamics in the cortex with the goal of providing starting points for a next generation of [K(+)](o) research. Such research may ultimately lead to an entirely new class of antiepileptic drugs that act on the [K(+)](o) regulation system.

Slow periodic EEG discharges are common in CNS disorders. The pathophysiology of this aberrant rhythmic activity is poorly understood. We used a computational model of a neocortical network with a dynamic homeostatic scaling rule to show that loss of input (partial deafferentation) can trigger network reorganization that results in pathological periodic discharges. The decrease in average firing rate in the network by deafferentation was compensated by homeostatic synaptic scaling of recurrent excitation among pyramidal cells. Synaptic scaling succeeded in recovering the network target firing rate for all degrees of deafferentation (fraction of deafferented cells), but there was a critical degree of deafferentation for pathological network reorganization. For deafferentation degrees below this value, homeostatic upregulation of recurrent excitation had minimal effect on the macroscopic network dynamics. For deafferentation above this threshold, however, a slow periodic oscillation appeared, patterns of activity were less sparse, and bursting occurred in individual neurons. Also, comparison of spike-triggered afferent and recurrent excitatory conductances revealed that information transmission was strongly impaired. These results suggest that homeostatic plasticity can lead to secondary functional impairment in case of cortical disorders associated with cell loss.

We study the reliability of cortical neuron responses to periodically modulated synaptic stimuli. Simple map-based models of two different types of cortical neurons are constructed to replicate the intrinsic resonances of reliability found in experimental data and to explore the effects of those resonance properties on collective behavior in a cortical network model containing excitatory and inhibitory cells. We show that network interactions can enhance the frequency range of reliable responses and that the latter can be controlled by the strength of synaptic connections. The underlying dynamical mechanisms of reliability enhancement are discussed.

Plastic changes in the synaptic responsiveness of neocortical neurones, which occur after rhythmic stimuli within the frequency range of sleep spindles (10 Hz), were investigated in isolated neocortical slabs and intact cortex of anaesthetized cats by means of single, dual and triple simultaneous intracellular recordings in conjunction with recordings of local field potential responses. In isolated cortical slabs (10 mm long, 6 mm wide and 4-5 mm deep), augmenting responses to pulse-trains at 10 Hz (responses with growing amplitudes from the second stimulus in a train) were elicited only by relatively high-intensity stimuli. At low intensities, responses were decremental. The largest augmenting responses were evoked in neurones located close to the stimulation site. Quantitative analyses of the number of action potentials and the amplitude and area of depolarization during augmenting responses in a population of neurones recorded from slabs showed that the most dramatic increases in the number of spikes with successive stimuli, and the greatest increase in depolarization amplitude, were found in conventional fast-spiking (FS) neurones. The largest increase in the area of depolarization was found in regular-spiking (RS) neurones. Dual intracellular recordings from a pair of FS and RS neurones in the slab revealed more action potentials in the FS neurone during augmenting responses and a significant increase in the depolarization area of the RS neurone that was dependent on the firing of the FS neurone. Self-sustained seizures could occur in the slab after rhythmic stimuli at 10 Hz. In the intact cortex, repeated sequences of stimuli generating augmenting responses or spontaneous spindles could induce an increased synaptic responsiveness to single stimuli, which lasted for several minutes. A similar time course of increased responsiveness was obtained with induction of cellular plasticity. These data suggest that augmenting responses elicited by stimulation, as well as spontaneously occurring spindles, may induce short- and medium-term plasticity of neuronal responses.

The link between the combined action of neuromodulators in the brain and global brain states remains a mystery. In this study, using biophysically realistic models of the thalamocortical network, we identified the critical intrinsic and synaptic mechanisms, associated with the putative action of acetylcholine (ACh), GABA and monoamines, which lead to transitions between primary brain vigilance states (waking, non-rapid eye movement sleep [NREM] and REM sleep) within an ultradian cycle. Using ECoG recordings from humans and LFP recordings from cats and mice, we found that during NREM sleep the power of spindle and delta oscillations is negatively correlated in humans and positively correlated in animal recordings. We explained this discrepancy by the differences in the relative level of ACh. Overall, our study revealed the critical intrinsic and synaptic mechanisms through which different neuromodulators acting in combination result in characteristic brain EEG rhythms and transitions between sleep stages.

Sleep is critical for regulation of synaptic efficacy, memories, and learning. However, the underlying mechanisms of how sleep rhythms contribute to consolidating memories acquired during wakefulness remain unclear. Here we studied the role of slow oscillations, 0.2-1 Hz rhythmic transitions between Up and Down states during stage 3/4 sleep, on dynamics of synaptic connectivity in the thalamocortical network model implementing spike-timing-dependent synaptic plasticity. We found that the spatiotemporal pattern of Up-state propagation determines the changes of synaptic strengths between neurons. Furthermore, an external input, mimicking hippocampal ripples, delivered to the cortical network results in input-specific changes of synaptic weights, which persisted after stimulation was removed. These synaptic changes promoted replay of specific firing sequences of the cortical neurons. Our study proposes a neuronal mechanism on how an interaction between hippocampal input, such as mediated by sharp wave-ripple events, cortical slow oscillations, and synaptic plasticity, may lead to consolidation of memories through preferential replay of cortical cell spike sequences during slow-wave sleep. [ABSTRACT FROM AUTHOR]

Oscillations of neural activity are ubiquitous in the brain and are critical for normal cognitive function. In the visual system, repetitive presentation of a stimulus results in the reduction of power elicited in the gamma frequency band. However, this reduction does not result in degradation of perception; on the contrary, perception is improved by prior experience with the stimulus. To explain how reduction of gamma frequency oscillations, observed in priming experiments, can lead to improvement in behavior, we assume that visual processing takes place in two distinct stages: representation sharpening in the early visual areas and competitive interaction among representations in the higher visual areas and the prefrontal cortex. Here, we present a network model of spiking neurons that demonstrates how stimulus repetition leads to a decrease in power of the local field potential oscillations in the gamma frequency range in the early layer and also improves network response by reducing the latency to reach a decision in the higher area. [ABSTRACT FROM AUTHOR]