The Sleep Cycle Modelled as a Cortical Phase Transition

We present a mean-field model of the cortex that attempts to describe the gross changes in brain electrical activity for the cycles of natural sleep. We incorporate within the model two major sleep modulatory effects: slow changes in both synaptic efficiency and in neuron resting voltage caused by the ∼90-min cycling in acetylcholine, together with even slower changes in resting voltage caused by gradual elimination during sleep of somnogens (fatigue agents) such as adenosine. We argue that the change from slow-wave sleep (SWS) to rapid-eye-movement (REM) sleep can be understood as a first-order phase transition from a low-firing, coherent state to a high-firing, desychronized cortical state. We show that the model predictions for changes in EEG power, spectral distribution, and correlation time at the SWS-to-REM transition are consistent not only with those observed in clinical recordings of a sleeping human subject, but also with the on-cortex EEG patterns recently reported by Destexhe et al. [J. Neurosci. 19(11), (1999) 4595–4608] for the sleeping cat.     

Muscle tone regulation during REM sleep: neural circuitry and clinical significance

Rapid eye movement (REM) sleep is a distinct behavioral state characterized by an activated cortical and hippocampal electroencephalogram (EEG) and concurrent muscle atonia. Research conducted over the past 50 years has revealed the neuronal circuits responsible for the generation and maintenance of REM sleep, as well as the pathways involved in generating the cardinal signs of REM sleep such as cortical activation and muscle atonia. The generation and maintenance of REM sleep appear to involve a widespread network in the pons and medulla. The caudal laterodorsal tegmental nucleus (cLDT) and sublaterodorsal nucleus (SLD) within the dorsolateral pons contain REM-on neurons, and the ventrolateral periaqueductal grey (vlPAG) contains REM-off neurons. The interaction between these structures is proposed to regulate REM sleep amounts. The cLDT-SLD neurons project to the basal forebrain via the parabrachial-precoeruleus (PB-PC) complex, and this pathway may be critical for the EEG activation seen during REM sleep. Descending SLD glutamatergic projections activate the ventromedial medulla, and spinal cord interneurons mediate muscle atonia and suppress phasic muscle twitches in spinal musculature. In contrast, phasic muscle twitches in the masseter muscles may be driven by glutamatergic neurons in the rostral parvicellular reticular nucleus (PCRt); however, the brain region responsible for generating phasic twitches in the other cranial muscles including facial muscles and tongue are not clear.     

Explicit-duration hidden Markov model inference of UP-DOWN states from continuous signals

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics.     

Physiology-based modeling of cortical auditory evoked potentials

Evoked potentials are the transient electrical responses caused by changes in the brain following stimuli. This work uses a physiology-based continuum model of neuronal activity in the human brain to calculate theoretical cortical auditory evoked potentials (CAEPs) from the model’s linearized response. These are fitted to experimental data, allowing the fitted parameters to be related to brain physiology. This approach yields excellent fits to CAEP data, which can then be compared to fits of EEG spectra. It is shown that the differences between resting eyes-open EEG and standard CAEPs can be explained by changes in the physiology of populations of neurons in corticothalamic pathways, with notable similarities to certain aspects of slow-wave sleep. This pilot study demonstrates the ability of our model-based fitting method to provide information on the underlying physiology of the brain that is not available using standard methods.     

Probabilistic pathway representation of cognitive information

We present for mental processes the program of mathematical mapping which has been successfully realized for physical processes. We emphasize that our project is not about mathematical simulation of the brain's functioning as a complex physical system, i.e., mapping of physical and chemical processes in the brain on mathematical spaces. The project is about mapping of purely mental processes on mathematical spaces. We present various arguments--philosophic, mathematical, information, and neurophysiological--in favor of the p-adic model of mental space. p-adic spaces have structures of hierarchic trees and in our model such a tree hierarchy is considered as an image of neuronal hierarchy. Hierarchic neural pathways are considered as fundamental units of information processing. As neural pathways can go through the whole body, the mental space is produced by the whole neural system. Finally, we develop the probabilistic neural pathway model in that mental states are represented by probability distributions on mental space.     

MicroRNA 132 alters sleep and varies with time in brain

MicroRNA (miRNA) levels in brain are altered by sleep deprivation; however, the direct effects of any miRNA on sleep have not heretofore been described. We report herein that intracerebroventricular application of a miRNA-132 mimetic (preMIR-132) decreased duration of non-rapid-eye-movement sleep (NREMS) while simultaneously increasing duration of rapid eye movement sleep (REMS) during the light phase. Further, preMIR-132 decreased electroencephalographic (EEG) slow-wave activity (SWA) during NREMS, an index of sleep intensity. In separate experiments unilateral supracortical application of preMIR-132 ipsilaterally decreased EEG SWA during NREMS but did not alter global sleep duration. In addition, after ventricular or supracortical injections of preMIR-132, the mimetic-induced effects were state specific, occurring only during NREMS. After local supracortical injections of the mimetic, cortical miRNA-132 levels were higher at the time sleep-related EEG effects were manifest. We also report that spontaneous cortical levels of miRNA-132 were lower at the end of the sleep-dominant light period compared with at the end of the dark period in rats. Results suggest that miRNAs play a regulatory role in sleep and provide a new tool for investigating sleep regulation.     

Modeling the effect of sleep regulation on a neural mass model

In mammals, sleep is categorized by two main sleep stages, rapid eye movement (REM) and non-REM (NREM) sleep that are known to fulfill different functional roles, the most notable being the consolidation of memory. While REM sleep is characterized by brain activity similar to wakefulness, the EEG activity changes drastically with the emergence of K-complexes, sleep spindles and slow oscillations during NREM sleep. These changes are regulated by circadian and ultradian rhythms, which emerge from an intricate interplay between multiple neuronal populations in the brainstem, forebrain and hypothalamus and the resulting varying levels of neuromodulators. Recently, there has been progress in the understanding of those rhythms both from a physiological as well as theoretical perspective. However, how these neuromodulators affect the generation of the different EEG patterns and their temporal dynamics is poorly understood. Here, we build upon previous work on a neural mass model of the sleeping cortex and investigate the effect of those neuromodulators on the dynamics of the cortex and the corresponding transition between wakefulness and the different sleep stages. We show that our simplified model is sufficient to generate the essential features of human EEG over a full day. This approach builds a bridge between sleep regulatory networks and EEG generating neural mass models and provides a valuable tool for model validation.     

Large-scale cortical dynamics of sleep slow waves

Slow waves constitute the main signature of sleep in the electroencephalogram (EEG). They reflect alternating periods of neuronal hyperpolarization and depolarization in cortical networks. While recent findings have demonstrated their functional role in shaping and strengthening neuronal networks, a large-scale characterization of these two processes remains elusive in the human brain. In this study, by using simultaneous scalp EEG and intracranial recordings in 10 epileptic subjects, we examined the dynamics of hyperpolarization and depolarization waves over a large extent of the human cortex. We report that both hyperpolarization and depolarization processes can occur with two different characteristic time durations which are consistent across all subjects. For both hyperpolarization and depolarization waves, their average speed over the cortex was estimated to be approximately 1 m/s. Finally, we characterized their propagation pathways by studying the preferential trajectories between most involved intracranial contacts. For both waves, although single events could begin in almost all investigated sites across the entire cortex, we found that the majority of the preferential starting locations were located in frontal regions of the brain while they had a tendency to end in posterior and temporal regions.     

Growth hormone-releasing hormone: cerebral cortical sleep-related EEG actions and expression

Growth hormone-releasing hormone (GHRH), its receptor (GHRHR), and other members of the somatotropic axis are involved in non-rapid eye movement sleep (NREMS) regulation. Previously, studies established the involvement of hypothalamic GHRHergic mechanisms in NREMS regulation, but cerebral cortical GHRH mechanisms in sleep regulation remained uninvestigated. Here, we show that unilateral application of low doses of GHRH to the surface of the rat somatosensory cortex ipsilaterally decreased EEG delta wave power, while higher doses enhanced delta power. These actions of GHRH on EEG delta wave power occurred during NREMS but not during rapid eye movement sleep. Further, the cortical forms of GHRH and GHRHR were identical to those found in the hypothalamus and pituitary, respectively. Cortical GHRHR mRNA and protein levels did not vary across the day-night cycle, whereas cortical GHRH mRNA increased with sleep deprivation. These results suggest that cortical GHRH and GHRHR have a role in the regulation of localized EEG delta power that is state dependent, as well as in their more classic hypothalamic role in NREMS regulation.     

Sleep-Stage Correlates of Hippocampal Electroencephalogram in Primates

It has been demonstrated in the rodent hippocampus that rhythmic slow activity (theta) predominantly occurs during rapid eye movement (REM) sleep, while sharp waves and associated ripples occur mainly during non-REM sleep. However, evidence is lacking for correlates of sleep stages with electroencephalogram (EEG) in the hippocampus of monkeys. In the present study, we recorded hippocampal EEG from the dentate gyrus in monkeys overnight under conditions of polysomnographical monitoring. As result, the hippocampal EEG changed in a manner similar to that of the surface EEG: during wakefulness, the hippocampal EEG showed fast, desynchronized waves, which were partly replaced with slower waves of intermediate amplitudes during the shallow stages of non-REM sleep. During the deep stages of non-REM sleep, continuous, slower oscillations (0.5–8 Hz) with high amplitudes were predominant. During REM sleep, the hippocampal EEG again showed fast, desynchronized waves similar to those found during wakefulness. These results indicate that in the monkey, hippocampal rhythmic slow activity rarely occurs during REM sleep, which is in clear contrast to that of rodents. In addition, the increase in the slower oscillations of hippocampal EEG during non-REM sleep, which resembled that of the surface EEG, may at least partly reflect cortical inputs to the dentate gyrus during this behavioral state.     

Dipole Source Localization of Mouse Electroencephalogram Using the Fieldtrip Toolbox

The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust in vivo brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG) with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE), scanning with multiple signal classification (MUSIC), and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC) analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.     

Estimation of neurophysiological parameters from the waking EEG using a biophysical model of brain dynamics

This paper presents the results from using electroencephalographic (EEG) data to estimate the values of key neurophysiological parameters using a detailed biophysical model of brain activity. The model incorporates spatial and temporal aspects of cortical function including axonal transmission delays, synapto-dendritic rates, range-dependent connectivities, excitatory and inhibitory neural populations, and intrathalamic, intracortical, corticocortical and corticothalamic pathways. Parameter estimates were obtained by fitting the model's theoretical spectrum to EEG spectra from each of 100 healthy human subjects. Statistical analysis was used to infer significant parameter variations occurring between eyes-closed and eyes-open states, and a correlation matrix was used to investigate links between the parameter variations and traditional measures of quantitative EEG (qEEG). Accurate fits to all experimental spectra were observed, and both inter-subject and between-state variability were accounted for by the variance in the fitted biophysical parameters, which were in turn consistent with known independent experimental and theoretical estimates. These values thus provide physiological information regarding the state. transitions (eyes-closed vs. eyes-open) and phenomena including cortical idling and alpha desynchronization. The parameters are also consistent with traditional qEEG, but are more informative, since they provide links to underlying physiological processes. To our knowledge, this is the first study where a detailed biophysical model of the brain is used to estimate neurophysiological parameters underlying the transitions in a broad range (0.25-50 Hz) of EEG spectra obtained from a large set of human data.     

Interregional cortical interactions at different stages of natural sleep and the hypnotic state: EEG evidence

The characteristic patterns of EEG spatial organization at different stages of natural sleep and the hypnotic state were studied in 26 volunteers aged 18–22 years. EEGs were recorded using 12 monopolar leads, and EEG cross-correlation coefficient matrices were calculated for consecutive epochs (4 and 8 s). Matrices averaged for each state were treated using factor analysis. The EEG correlation matrices were compared element by element for the states studied and the waking state. Relatively similar changes in the spatial structure of EEG correlations were observed at different stages of natural sleep, with the correlations tending to intensify, especially in the posterior temporal region of the right hemisphere. In the light and deep (somnambular) phases of hypnosis, the interaction between cortical zones that was characteristic of distant relationships of the EEGs of frontal regions, especially the posterior inferior frontal region of the right hemisphere, decreased. The systemic reorganization of the interregional EEG correlations during natural sleep was considerably more pronounced than in the hypnotic state. Notwithstanding, the highly orderly spatial organization of the cortical biopotential field that was typical of the waking state was retained at different stages of natural sleep and hypnosis. Thus, the coordination of the activities of distant nerve centers oriented to providing for a certain function or maintaining a certain functional state occurs against the background of a relatively invariant pattern of interregional integration at the level of the whole brain.Translated from Fiziologiya Cheloveka, Vol. 31, No. 2, 2005, pp. 34–48.Original Russian Text Copyright © 2005 by A. Shepovalnikov, Tsitseroshin, Rozhkov, Galperina, Zaitseva, R. Shepovalnikov.     

Sleep and cortical temperature in the Djungarian hamster under baseline conditions and after sleep deprivation

The Djungarian hamster (Phodopus sungorus) is a markedly photoperiodic rodent which exhibits daily torpor under short photoperiod. Normative data were obtained on vigilance states, electroencephalogram (EEG) power spectra (0.25–25.0 Hz), and cortical temperature (T_CRT) under a 168 h light-dark schedule, in 7 Djungarian hamsters for 2 baseline days, 4 h sleep deprivation (SD) and 20 h recovery.During the baseline days total sleep time amounted to 59% of recording time, 67% in the light period and 43% in the dark period. The 4 h SD induced a small increase in the amount of non-rapid eye movement (NREM) sleep and a marked increase in EEG slow-wave activity (SWA; mean power density 0.75–4.0 Hz) within NREM sleep in the first hours of recovery. T_CRT was lower in the light period than in the dark period. It decreased at transitions from either waking or rapid eye movement (REM) sleep to NREM sleep, and increased at the transition from NREM sleep to waking or REM sleep. After SD, T_CRT was lower in all vigilance states.In conclusion, the sleep-wake pattern, EEG spectrum, and time course of T_CRT in the Djungarian hamster are similar to other nocturnal rodents. Also in the Djungarian hamster the time course of SWA seems to reflect a homeostatically regulated process as was formulated in the two-process model of sleep regulation.Abbreviations EEG electroencephalogram - EMG electromyogram - N NREM sleep - NREM non-rapid eye movement - R REM sleep - REM rapid eye movement - SD sleep deprivation - SWA slow-wave activity - T_CRT cortical temperature - TST total sleep time - VS vigilance state - W waking     

Neuronal Avalanches Differ from Wakefulness to Deep Sleep – Evidence from Intracranial Depth Recordings in Humans

Neuronal activity differs between wakefulness and sleep states. In contrast, an attractor state, called self-organized critical (SOC), was proposed to govern brain dynamics because it allows for optimal information coding. But is the human brain SOC for each vigilance state despite the variations in neuronal dynamics? We characterized neuronal avalanches – spatiotemporal waves of enhanced activity - from dense intracranial depth recordings in humans. We showed that avalanche distributions closely follow a power law – the hallmark feature of SOC - for each vigilance state. However, avalanches clearly differ with vigilance states: slow wave sleep (SWS) shows large avalanches, wakefulness intermediate, and rapid eye movement (REM) sleep small ones. Our SOC model, together with the data, suggested first that the differences are mediated by global but tiny changes in synaptic strength, and second, that the changes with vigilance states reflect small deviations from criticality to the subcritical regime, implying that the human brain does not operate at criticality proper but close to SOC. Independent of criticality, the analysis confirms that SWS shows increased correlations between cortical areas, and reveals that REM sleep shows more fragmented cortical dynamics.     

The dimensionality of human's electroencephalogram during sleep

In order to perform an analysis of nonlinear EEG-dynamics we investigated the EEG of ten male probands during sleep. According to Rechtschaffen and Kales (1968) we scored the sleep-EEG and applied an algorithm, proposed by Grassberger and Proccaccia (1983) to compute the correlation dimension of different sleep stages. The correlation dimension characterizes the dynamics of the EEG signal and estimates the degrees of freedom of the signal under study. We could demonstrate, that the EEG of slow wave sleep stages depicts a dimensionality, which is two units smaller than that of light or REM sleep.     

Regional slow waves and spindles in human sleep

The most prominent EEG events in sleep are slow waves, reflecting a slow (<1 Hz) oscillation between up and down states in cortical neurons. It is unknown whether slow oscillations are synchronous across the majority or the minority of brain regions--are they a global or local phenomenon? To examine this, we recorded simultaneously scalp EEG, intracerebral EEG, and unit firing in multiple brain regions of neurosurgical patients. We find that most sleep slow waves and the underlying active and inactive neuronal states occur locally. Thus, especially in late sleep, some regions can be active while others are silent. We also find that slow waves can propagate, usually from medial prefrontal cortex to the medial temporal lobe and hippocampus. Sleep spindles, the other hallmark of NREM sleep EEG, are likewise predominantly local. Thus, intracerebral communication during sleep is constrained because slow and spindle oscillations often occur out-of-phase in different brain regions.     

Reticular activation and the dynamics of neuronal networks

It is postulated that during arousal the cortical system is driven by a spatially and temporally noisy signal arising from non-specific reticulo-cortical pathways. An elementary unit of cortical neuroanatomy is assumed, which permits non-linear dynamics to be represented by stochastic linear equations. Under these assumptions the resonant modes of the system of cortical dendrites approach thermodynamic equilibrium. Specific sensory signals perturb the dendritic system about equilibrium, generate low frequency, linear, non-dispersive waves corresponding to the EEG, which in turn regulate action potential sequences, and instantiate internal inputs to the dendritic field. A large and distributed memory capacity in axo-synaptic couplings, resistance to interference between functionally separate logical operations, and a very large next-state function set emerge as properties of the network. The model is able to explain the close association of the EEG with cognition, the channel of low capacity corresponding to the field of immediate attention, the low overall correlation of action potentials with EEG, and specificity of action potentials in some neurons during particular cognitive activity. Predictions made from hypothesis include features of thermal equilibrium in EEG (determinable by autoregression) and expectation that the cortical evoked response can be accounted for as the response to a sensory impulse of specific time characteristics.     

Human gamma oscillations during slow wave sleep

Neocortical local field potentials have shown that gamma oscillations occur spontaneously during slow-wave sleep (SWS). At the macroscopic EEG level in the human brain, no evidences were reported so far. In this study, by using simultaneous scalp and intracranial EEG recordings in 20 epileptic subjects, we examined gamma oscillations in cerebral cortex during SWS. We report that gamma oscillations in low (30-50 Hz) and high (60-120 Hz) frequency bands recurrently emerged in all investigated regions and their amplitudes coincided with specific phases of the cortical slow wave. In most of the cases, multiple oscillatory bursts in different frequency bands from 30 to 120 Hz were correlated with positive peaks of scalp slow waves ("IN-phase" pattern), confirming previous animal findings. In addition, we report another gamma pattern that appears preferentially during the negative phase of the slow wave ("ANTI-phase" pattern). This new pattern presented dominant peaks in the high gamma range and was preferentially expressed in the temporal cortex. Finally, we found that the spatial coherence between cortical sites exhibiting gamma activities was local and fell off quickly when computed between distant sites. Overall, these results provide the first human evidences that gamma oscillations can be observed in macroscopic EEG recordings during sleep. They support the concept that these high-frequency activities might be associated with phasic increases of neural activity during slow oscillations. Such patterned activity in the sleeping brain could play a role in off-line processing of cortical networks.