Significant thalamocortical coherence of sleep spindle,theta, delta,and slow oscillations in NREM sleep: Recordings from the human thalamus

The electrophysiological studies of thalamocortical oscillations were mostly done in animal models. Placement of stimulation electrodes at the anterior nucleus of the thalamus (ANT) for seizure reduction enables the study of the thalamocortical interplay in human subjects. Nocturnal sleep electroencephalograms (EEGs) and local field potentials (LFPs) of the left and right thalamus (LT, RT) were recorded in three subjects receiving ANT stimulation. Sleep stages were scored according to American Academy of Sleep Medicine criteria. The whole-night time–frequency coherence maps between EEG (C3, C4) and LFP (LT, RT) showed specific coherence patterns during non-rapid eye movement (NREM) sleep. Pooled coherence in the NREM stage was significant in slow, delta, theta and spindle frequency ranges. The spindle oscillations had the highest coherence (0.17–0.58) in the homolateral hemisphere. Together, these observations indicate that the oscillations were related to thalamocortical circuitry.     

Circuit properties generating gamma oscillations in a network model of the olfactory bulb

The study of the neural basis of olfaction is important both for understanding the sense of smell and for understanding the mechanisms of neural computation. In the olfactory bulb (OB), the spatial patterning of both sensory inputs and synaptic interactions is crucial for processing odor information, although this patterning alone is not sufficient. Recent studies have suggested that representations of odor may already be distributed and dynamic in the first olfactory relay. The growing evidence demonstrating a functional role for the temporal structure of bulbar neuronal activity supports this assumption. However, the detailed mechanisms underlying this temporal structure have never been thoroughly studied. Our study focused on gamma (40-100 Hz) network oscillations in the mammalian OB, which is a form of temporal patterning in bulbar activity elicited by olfactory stimuli. We used computational modeling combined with electrophysiological recordings to investigate the basic synaptic organization necessary and sufficient to generate sustained gamma rhythms. We found that features of gamma oscillations obtained in vitro were identical to those of a model based on lateral inhibition as the coupling modality (i.e., low irregular firing rate and high oscillation stability). In contrast, they differed substantially from those of a model based on lateral excitatory coupling (i.e., high regular firing rate and instable oscillations). Therefore we could precisely tune the oscillation frequency by changing the kinetics of inhibitory events supporting the lateral inhibition. Moreover, gradually decreasing GABAergic synaptic transmission decreased the degree of relay neuron synchronization in response to sensory inputs, both theoretically and experimentally. Thus we have shown that lateral inhibition provides a mechanism by which the dynamic processing of odor information might be finely tuned within the OB circuit.     

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold

Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, and amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300 ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.     

Learning-dependent changes in sleep spindles and Stage 2 sleep

It has become increasingly clear that sleep is necessary for efficient memory consolidation. Recently, it has been found that Stage 2 sleep disruption impairs procedural memory performance, and that memory performance is correlated with the duration of Stage 2 sleep; but the mechanisms involved in synaptic plasticity for procedural memory during sleep have not been identified. The present study examined the learning-dependent changes in sleep, including Stage 2 sleep spindles. Following an intense period of simple motor procedural learning, the duration of Stage 2 sleep and spindle density increased. There were no changes observed in the duration of any other stage of sleep or in the density of rapid eye movements. These findings support the hypothesis that sleep spindles are involved in the off-line reprocessing of simple motor procedural memory during Stage 2 sleep.     

Electroencephalographic sleep macrostructure and sleep spindles in early infancy

Study ObjectivesSleep features in infancy are potential biomarkers for brain maturation but poorly characterized. We describe normative values for sleep macrostructure and sleep spindles at 4–5 months of age.MethodsHealthy term infants were recruited at birth and had daytime sleep electroencephalograms (EEGs) at 4–5 months. Sleep staging was performed and five features were analyzed. Sleep spindles were annotated and seven quantitative features were extracted. Features were analyzed across sex, recording time (am/pm), infant age, and from first to second sleep cycles.ResultsWe analyzed sleep recordings from 91 infants, 41% females. Median (interquartile range [IQR]) macrostructure results: sleep duration 49.0 (37.8–72.0) min (n = 77); first sleep cycle duration 42.8 (37.0–51.4) min; rapid eye movement (REM) percentage 17.4 (9.5–27.7)% (n = 68); latency to REM 36.0 (30.5–41.1) min (n = 66). First cycle median (IQR) values for spindle features: number 241.0 (193.0–286.5), density 6.6 (5.7–8.0) spindles/min (n = 77); mean frequency 13.0 (12.8–13.3) Hz, mean duration 2.9 (2.6–3.6) s, spectral power 7.8 (4.7–11.4) µV², brain symmetry index 0.20 (0.16–0.29), synchrony 59.5 (53.2–63.8)% (n = 91). In males, spindle spectral power (µV²) was 24.5% lower (p = .032) and brain symmetry index 24.2% higher than females (p = .011) when controlling for gestational and postnatal age and timing of the nap. We found no other significant associations between studied sleep features and sex, recording time (am/pm), or age. Spectral power decreased (p < .001) on the second cycle.ConclusionThis normative data may be useful for comparison with future studies of sleep dysfunction and atypical neurodevelopment in infancy. Clinical Trial Registration: BABY SMART (Study of Massage Therapy, Sleep And neurodevelopMenT) (BabySMART)URL: https://clinicaltrials.gov/ct2/show/results/NCT03381027?view=results.ClinicalTrials.gov Identifier: NCT03381027     

Heartbeat‐related EEG amplitude and phase modulations from wakefulness to deep sleep: Interactions with sleep spindles and slow oscillations

Based on physiological models of neurovisceral integration, different studies have shown how cognitive processes modulate heart rate and how the heartbeat, on the other hand, modulates brain activity. We tried to further determine interactions between cardiac and electrical brain activity by means of EEG. We investigated how the heartbeat modulates EEG in 23 healthy controls from wakefulness to deep sleep and showed that frontocentral heartbeat evoked EEG amplitude and phase locking (as measured by intertrial phase locking), at about 300‐400 ms after the R peak, decreased with increasing sleep depth with a renewed increase during REM sleep, which underpins the assumption that the heartbeat evoked positivity constitutes an active frontocortical response to the heartbeat. Additionally, we found that individual heart rate was correlated with the frequency of the EEG's spectral peak (i.e., alpha peak frequency during wakefulness). This correlation was strongest during wakefulness and declined linearly with increasing sleep depth. Furthermore, we show that the QRS complex modulates spindle phase possibly related to the correspondence between the frequency of the QRS complex and the spindle frequency of about 12–15 Hz. Finally, during deep sleep stages, a loose temporal coupling between heartbeats and slow oscillation (0.8 Hz) could be observed. These findings indicate that cardiac activity such as heart rate or individual heartbeats can modulate or be modulated by ongoing oscillatory brain activity.     

Long-range synchronization of gamma and beta oscillations and the plasticity of excitatory and inhibitory synapses: a network model

The ability of oscillating networks to synchronize despite significant separation in space, and thus time, is of biological significance, given that human gamma activity can synchronize over distances of several millimeters to centimeters during perceptual and learning tasks. We use computer simulations of networks consisting of excitatory pyramidal cells (e-cells) and inhibitory interneurons (i-cells), modeling two tonically driven assemblies separated by large (>or=8 ms) conduction delays. The results are as follows. 1) Two assemblies separated by large conduction delays can fire synchronously at beta frequency (with i-cells firing at gamma frequency) under two timing conditions: e-cells of (say) assembly 2 are still inhibited "delay + spike generation milliseconds" after the e-cell beat of assembly 1; this means that the e-cell inhibitory postsynaptic potential (IPSP) cannot be significantly shorter than the delay (2-site effect). This implies for a given decay time constant that the interneuron --> pyramidal cell conductances must be large enough. The e-cell IPSP must last longer than the i-cell IPSP, i.e., the interneuron --> pyramidal cell conductance must be sufficiently large and the interneuron --> interneuron conductance sufficiently small (local effect). 2) We define a "long-interval doublet" as a pair of interneuron action potentials-separated by approximately "delay milliseconds"-in which a) the first spike is induced by tonic inputs and/or excitation from nearby e-cells, while b) the second spike is induced by (delayed) excitation from distant e-cells. "Long-interval population doublets" (long-interval doublets of the i-cell population) are necessary for synchronized firing in our networks. Failure to produce them leads to almost anti-phase activity at gamma frequency. 3) An (almost) anti-phase oscillation is the most stable oscillation pattern of two assemblies that are separated by axonal conduction delays of approximately one-half a gamma period (delays from 8 to 17 ms in our simulations) and that are firing at gamma frequency. 4) Two assemblies separated by large conduction delays can synchronize their activity with the help of interneuron plasticity. They can also synchronize without pyramidal cell --> pyramidal cell connections being present. The presence of pyramidal cell --> pyramidal cell connections allows, however, for synchronization if other parameters are at inappropriate values for synchronization to occur. 5) Synchronization of two assemblies separated by large conduction delays with the help of interneuron plasticity is not simply due to slowing down of the oscillation frequency. It is reached with the help of a "synchronizing-weak-beat," which induces sudden changes in the oscillation period length of the two assemblies.     

Modeling sleep and wakefulness in the thalamocortical system

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. 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 large-scale 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, and both intra- and interareal 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 up-state. 4) Depolarization-activated potassium currents and synaptic depression terminate the up-state. 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.     

Simultaneous activation of gamma and theta network oscillations in rat hippocampal slice cultures

Hippocampal activity in vivo is characterized by concurrent oscillations at theta (4–15 Hz) and gamma (20–80 Hz) frequencies. Here we show that cholinergic receptor activation (methacholine 10–20 nm) in hippocampal slice cultures induces an oscillatory mode of activity, in which the intrinsic network oscillator (located in the CA3 area) expresses simultaneous theta and gamma network oscillations. Pyramidal cells display synaptic theta oscillations, characterized by cycles consisting of population EPSP-IPSP sequences that are dominated by population IPSPs. These rhythmic IPSPs most probably result from theta-modulated spiking activity of several interneurons. At the same time, the majority of interneurons consistently display synaptic gamma oscillations. These oscillatory cycles consist of fast depolarizing rhythmic events that are likely to reflect excitatory input from CA3 pyramidal cells. Interneurons comprising this functional group were identified morphologically. They include four known types of interneurons (basket, O-LM, bistratified and str. lucidum-specific cells) and one new type of CA3 interneuron (multi-subfield cell). The oscillatory activity of these interneurons is only weakly correlated between neighbouring cells, and in about half of these (44 %) is modulated by depolarizing theta rhythmicity. The overall characteristics of acetylcholine-induced oscillations in slice cultures closely resemble the rhythmicity observed in hippocampal field and single cell recordings in vivo . Both rhythmicities depend on intrinsic synaptic interactions, and are expressed by different cell types. The fact that these oscillations persist in a network lacking extra-hippocampal connections emphasizes the importance of intrinsic mechanisms in determining this form of hippocampal activity.     

Modulation of gamma and spindle-range power by slow oscillations in scalp sleep EEG of children

Deep sleep is characterized by slow waves of electrical activity in the cerebral cortex. They represent alternating down states and up states of, respectively, hyperpolarization with accompanying neuronal silence and depolarization during which neuronal firing resumes. The up states give rise to faster oscillations, notably spindles and gamma activity which appear to be of major importance to the role of sleep in brain function and cognition. Unfortunately, while spindles are easily detectable, gamma oscillations are of very small amplitude. No previous sleep study has succeeded in demonstrating modulations of gamma power along the time course of slow waves in human scalp EEG. As a consequence, progress in our understanding of the functional role of gamma modulation during sleep has been limited to animal studies and exceptional human studies, notably those of intracranial recordings in epileptic patients.     

Sound‐induced perturbations of the brain network in non‐REM sleep,and network oscillations in wake

During sleep, the brain network processes sensory stimuli without awareness. Stimulation must affect differently brain networks in sleep versus wake, but these differences have yet to be quantified. We recorded cortical activity in stage 2 (SII) sleep and wake using EEG while a tone was intermittently played. Zero‐lag correlation measured input to pairs of sensors in the network; cross‐correlation and phase‐lag index measured pairwise corticocortical connectivity. Our analysis revealed that under baseline conditions, the cortical network, in particular the central regions of the frontoparietal cortex, interact at a characteristic latency of 50 ms, but only during wake, not sleep. Nonsalient auditory stimulation causes far greater perturbation of connectivity from baseline in sleep than wake, both in the response to common input and corticocortical connectivity. The findings have key implications for sensory processing.     

Motor sequence learning increases sleep spindles and fast frequencies in post-training sleep

STUDY OBJECTIVES: To investigate polysomnographic (PSG) sleep and NREM sleep characteristics, including sleep spindles and spectral activity involved in offline consolidation of a motor sequence learning task. DESIGN: Counterbalanced within-subject design. SETTING: Three weekly visits to the sleep laboratory. PARTICIPANTS: Fourteen healthy participants aged between 20 and 30 years (8 women). INTERVENTIONS: Motor sequence learning (MSL) task or motor control (CTRL) task before sleep. MEASUREMENTS AND RESULTS: Subjects were trained on either the MSL or CTRL task in the evening and retested 12 hours later the following morning on the same task after a night of PSG sleep recording. Total number and duration of sleep spindles and spectral power between 0.5 and 24 Hz were quantified during NREM sleep. After performing the MSL task, subjects exhibited a large increase in number and duration of sleep spindles compared to after the CTRL task. Higher sigma (sigma; 13 Hz) and beta (beta; 18-20 Hz) spectral power during the post-training night's sleep were also observed after the MSL task. CONCLUSIONS: These results provide evidence that sleep spindles are involved in the offline consolidation of a new sequence of finger movements known to be sleep dependent. Moreover, they expand on prior findings by showing that changes in NREM sleep following motor learning are specific to consolidation (and learning), and not to nonspecific motor activity. Finally, these data demonstrate, for the first time, higher fast rhythms (beta frequencies) during sleep after motor learning.     

Transient depression of excitatory synapses on interneurons contributes to epileptiform bursts during gamma oscillations in the mouse hippocampal slice

Persistent gamma frequency (30-70 Hz) network oscillations occur in hippocampal slices under conditions of metabotropic glutamate receptor (mGluR) activation. Excessive mGluR activation generated a bistable pattern of network activity during which epochs of gamma oscillations of increasing amplitude were terminated by synchronized bursts and very fast oscillations (>70 Hz). We provide experimental evidence that, during this behavior, pyramidal cell-to-interneuron synaptic depression takes place, occurring spontaneously during the gamma rhythm and associated with the onset of epileptiform bursts. We further provide evidence that excitatory postsynaptic potentials (EPSPs) in pyramidal cells are potentiated during the interburst gamma oscillation. When these two types of synaptic plasticity are incorporated, phenomenologically, into a network model previously shown to account for many features of persistent gamma oscillations, we find that epochs of gamma do indeed alternate with epochs of very fast oscillations and epileptiform bursts. Thus the same neuronal network can generate either gamma oscillations or epileptiform bursts, in a manner depending on the degree of network drive and network-induced fluctuations in synaptic efficacies.     

Pain perception, obstructive imagery and phase-ordered gamma oscillations.

The neural mechanisms underlying pain perception and anti-nociceptive effects of mental imagery are not well understood. Using a measure of phase-ordered beta and gamma EEG oscillations in response to painful electric stimulation, we recently found that somatosensory event-related phase-ordered gamma oscillations (38-42 Hz), elicited by the onset of painful stimuli over Cz scalp site, were linearly related to pain perception. In the present study, 38 subjects were engaged in a painful stimulus detection task using an oddball paradigm. This task was performed under a condition in which subjects were required simply to count the number of target stimuli (pain condition) and under another condition in which subjects were required to produce an obstructive mental imagery of painful stimulus perception (obstructive imagery). Only EEG responses to standard stimuli were analyzed in this study. Correlation analysis of sweeps for each individual revealed brief intervals of phase ordering of EEG patterns in the beta and gamma bands. The frequencies of interest were the beta1 (26-30 Hz), beta2 (30-34 Hz), gamma1 (34-38 Hz), gamma2 (38-42 Hz) and gamma3 (42-46 Hz) bands. Obstructive imagery treatment, compared to pain condition, significantly reduced pain perception. This reduction was paralleled by significant decreases of evoked phase-ordered gamma2 and gamma3 patterns over Cz scalp site. Phase-ordered oscillations at Cz scalp site, for both gamma2 and gamma3 bands, significantly predicted pain ratings during pain condition. Phase-ordered oscillation scores, obtained for these gamma bands over parietal and frontal scalp sites, resulted the best predictor of pain ratings during obstructive imagery. This study provides evidence for the role of gamma oscillations in the subjective experience of pain. Further, it has provided support for the view that pain reduction during obstructive mental imagery is the product of an inhibitory process involving frontal and parietal cortical regions.     

Selective optical drive of thalamic reticular nucleus generates thalamic bursts and cortical spindles

The thalamic reticular nucleus (TRN) is hypothesized to regulate neocortical rhythms and behavioral states. Using optogenetics and multi-electrode recording in behaving mice, we found that brief selective drive of TRN switched the thalamocortical firing mode from tonic to bursting and generated state-dependent neocortical spindles. These findings provide causal support for the involvement of the TRN in state regulation in vivo and introduce a new model for addressing the role of this structure in behavior.     

Pacemaker-like and other types of spontaneous membrane potential oscillations of thalamocortical cells

During EEG-synchronized sleep, thalamic activity is characterized by rhythmic oscillations that till recently have been suggested to require the contribution of intra- and extra-thalamic inputs. The present experiments show that thalamocortical (TC) cells, mechanically and pharmacologically isolated from their intra-thalamic, cortical and brainstem inputs, are capable of different types of spontaneous membrane potential oscillations some of which resemble those observed in TC cells of the living animal during EEG-synchronization.     

Source models of sleep spindles using MEG and EEG measurements

Summary MEG measurements can detect brain sources that are difficult to detect with EEG measurements. The purpose of this study was to investigate models of sleep spindles using both MEG and EEG activity that had been recorded simultaneously. The components of magnetic fields perpendicular to the surface of the head were measured using a DC-SQUID with a first-derivative gradiometer. We propose three models for sleep spindles. In the first model, the source slides into the superficial region of the head so as to be perpendicular to it's surface, and with this model, the power spectrum of the MEG is decreased. In the second model, the source slides into the deeper structures, so that it is perpendicular to the surface. Here, the power spectra of both the MEG and the EEG are decreased. The third model has the source perpendicular to the surface, leaning and sliding into the deeper structures. Here, the power spectrum of the EEG is decreased but that of the MEG is not.