The neuronal transition probability (NTP) model for the dynamic progression of non-REM sleep EEG: the role of the suprachiasmatic nucleus

A Novel Continuous Sleep State Artificial Neural Network Model Based on Multi-Feature Fusion of Polysomnographic Data

Purpose

Sleep structure is crucial in sleep research, characterized by its dynamic nature and temporal progression. Traditional 30-second epochs falter in capturing the intricate subtleties of various micro-sleep states. This paper introduces an innovative artificial neural network model to generate continuous sleep depth value (SDV), utilizing a novel multi-feature fusion approach with EEG data, seamlessly integrating temporal consistency.

Methods

The study involved 50 normal and 100 obstructive sleep apnea-hypopnea syndrome (OSAHS) participants. After segmenting the sleep data into 3-second intervals, a diverse array of 38 feature values were meticulously extracted, including power, spectrum entropy, frequency band duration and so on. The ensemble random forest model calculated the timing fitness value for all the features, from which the top 7 time-correlated features were selected to create detailed sleep sample values ranging from 0 to 1. Subsequently, an artificial neural network (ANN) model was trained to delineate sleep continuity details, unravel concealed patterns, and far surpassed the traditional 5-stage categorization (W, N1, N2, N3, and REM).

Results

The SDV changes from wakeful stage (mean 0.7021, standard deviation 0.2702) to stage N3 (mean 0.0396, standard deviation 0.0969). During the arousal epochs, the SDV increases from the range (0.1 to 0.3) to the range around 0.7, and decreases below 0.3. When in the deep sleep (≤0.1), the probability of arousal of normal individuals is less than 10%, while the average arousal probability of OSA patients is close to 30%.

Conclusion

A sleep continuity model is proposed based on multi-feature fusion, which generates SDV ranging from 0 to 1 (representing deep sleep to wakefulness). It can capture the nuances of the traditional five stages and subtle differences in microstates of sleep, considered as a complement or even an alternative to traditional sleep analysis.

Fundamentals of sleep regulation: Model and benchmark values for fractal and oscillatory neurodynamics

Homeostatic, circadian and ultradian mechanisms play crucial roles in the regulation of sleep. Evidence suggests that ratios of low-to-high frequency power in the electroencephalogram (EEG) spectrum indicate the instantaneous level of sleep pressure, influenced by factors such as individual sleep-wake history, current sleep stage, age-related differences and brain topography characteristics. These effects are well captured and reflected in the spectral exponent, a composite measure of the constant low-to-high frequency ratio in the periodogram, which is scale-free and exhibits lower interindividual variability compared to slow wave activity, potentially serving as a suitable standardization and reference measure. Here we propose an index of sleep homeostasis based on the spectral exponent, reflecting the level of membrane hyperpolarization and/or network bistability in the central nervous system in humans. In addition, we advance the idea that the U-shaped overnight deceleration of oscillatory slow and fast sleep spindle frequencies marks the biological night, providing somnologists with an EEG-index of circadian sleep regulation. Evidence supporting this assertion comes from studies based on sleep replacement, forced desynchrony protocols and high-resolution analyses of sleep spindles. Finally, ultradian sleep regulatory mechanisms are indicated by the recurrent, abrupt shifts in dominant oscillatory frequencies, with spindle ranges signifying non-rapid eye movement and non-spindle oscillations - rapid eye movement phases of the sleep cycles. Reconsidering the indicators of fundamental sleep regulatory processes in the framework of the new Fractal and Oscillatory Adjustment Model (FOAM) offers an appealing opportunity to bridge the gap between the two-process model of sleep regulation and clinical somnology.

Review: Cav2.3 R-type Voltage-Gated Ca2+ Channels - Functional Implications in Convulsive and Non-convulsive Seizure Activity

Background:

Researchers have gained substantial insight into mechanisms of synaptic transmission, hyperexcitability, excitotoxicity and neurodegeneration within the last decades. Voltage-gated Ca²⁺ channels are of central relevance in these processes. In particular, they are key elements in the etiopathogenesis of numerous seizure types and epilepsies. Earlier studies predominantly targeted on Ca_v2.1 P/Q-type and Ca_v3.2 T-type Ca²⁺ channels relevant for absence epileptogenesis. Recent findings bring other channels entities more into focus such as the Ca_v2.3 R-type Ca²⁺ channel which exhibits an intriguing role in ictogenesis and seizure propagation. Ca_v2.3 R-type voltage gated Ca²⁺ channels (VGCC) emerged to be important factors in the pathogenesis of absence epilepsy, human juvenile myoclonic epilepsy (JME), and cellular epileptiform activity, e.g. in CA1 neurons. They also serve as potential target for various antiepileptic drugs, such as lamotrigine and topiramate.

Objective:

This review provides a summary of structure, function and pharmacology of VGCCs and their fundamental role in cellular Ca²⁺ homeostasis. We elaborate the unique modulatory properties of Ca_v2.3 R-type Ca²⁺ channels and point to recent findings in the proictogenic and proneuroapoptotic role of Ca_v2.3 R-type VGCCs in generalized convulsive tonic–clonic and complex-partial hippocampal seizures and its role in non-convulsive absence like seizure activity.

Conclusion:

Development of novel Ca_v2.3 specific modulators can be effective in the pharmacological treatment of epilepsies and other neurological disorders.

Odds ratio product of sleep EEG as a continuous measure of sleep state

STUDY OBJECTIVES

To develop and validate an algorithm that provides a continuous estimate of sleep depth from the electroencephalogram (EEG).

DESIGN

Retrospective analysis of polysomnograms.

SETTING

Research laboratory.

PARTICIPANTS

114 patients who underwent clinical polysomnography in sleep centers at the University of Manitoba (n = 58) and the University of Calgary (n = 56).

INTERVENTIONS

None.

MEASUREMENTS AND RESULTS

Power spectrum of EEG was determined in 3-second epochs and divided into delta, theta, alpha-sigma, and beta frequency bands. The range of powers in each band was divided into 10 aliquots. EEG patterns were assigned a 4-digit number that reflects the relative power in the 4 frequency ranges (10,000 possible patterns). Probability of each pattern occurring in 30-s epochs staged awake was determined, resulting in a continuous probability value from 0% to 100%. This was divided by 40 (% of epochs staged awake) producing the odds ratio product (ORP), with a range of 0-2.5. In validation testing, average ORP decreased progressively as EEG progressed from wakefulness (2.19 ± 0.29) to stage N3 (0.13 ± 0.05). ORP < 1.0 predicted sleep and ORP > 2.0 predicted wakefulness in > 95% of 30-s epochs. Epochs with intermediate ORP occurred in unstable sleep with a high arousal index (> 70/h) and were subject to much interrater scoring variability. There was an excellent correlation (r(2) = 0.98) between ORP in current 30-s epochs and the likelihood of arousal or awakening occurring in the next 30-s epoch.

CONCLUSIONS

Our results support the use of the odds ratio product (ORP) as a continuous measure of sleep depth.

Electrical source imaging of sleep spindles

To identify and compare cortical source generators of slow and fast sleep spindles in healthy subjects, electroencephalographic (EEG) signals were obtained from 256 channels, and sources on neuroanatomical Montreal Neurological Institute (MNI) space estimated with low-resolution brain electromagnetic tomography analysis (LORETA). Spindle activity was recorded in 18 healthy volunteers during daytime napping. Because of lack of sleep or excessive artifacts, data from 13 subjects were analyzed off-line. Spindles were visually scored, marked, and bandpass filtered (slow 10-12 Hz or fast 12-14 Hz). EEG was segmented on the marker, and segments separately averaged. LORETA projected cortical sources on the MNI brain. Maximal intra- and inter-individual intensities were compared using the Wilcoxon test (P < .05) and cortical sources distribution compared using a chi2 test. Two to three slow spindles generators were consistently identified in frontal lobes, with additional sources in parietal and limbic lobes in half cases. Fast spindles had multiple temporo-parietal sources, with an inconstant frontal source. Inter-individual (P = 0.44), and intra-individual (P = 0.09 slow and P = 0.10 fast spindles) source intensities were comparable. Slow spindles sources were preferentially concentrated over frontal cortices in comparison with fast spindles (P = 0.0009). Our results demonstrate multiple, synchronous, and equipotent spindles cortical generators in healthy subjects, with more anterior generators for slow spindles.

The CaV2.3 R-type voltage-gated Ca2+ channel in mouse sleep architecture

STUDY OBJECTIVES

Voltage-gated Ca(2+) channels (VGCCs) are key elements in mediating thalamocortical rhythmicity. Low-voltage activated (LVA) CaV 3 T-type Ca(2+) channels have been related to thalamic rebound burst firing and to generation of non-rapid eye movement (NREM) sleep. High-voltage activated (HVA) CaV 1 L-type Ca(2+) channels, on the opposite, favor the tonic mode of action associated with higher levels of vigilance. However, the role of the HVA Non-L-type CaV2.3 Ca(2+) channels, which are predominantly expressed in the reticular thalamic nucleus (RTN), still remains unclear. Recently, CaV2.3(-/-) mice were reported to exhibit altered spike-wave discharge (SWD)/absence seizure susceptibility supported by the observation that CaV2.3 mediated Ca(2+) influx into RTN neurons can trigger small-conductance Ca(2+)-activated K(+)-channel type 2 (SK2) currents capable of maintaining thalamic burst activity. Based on these studies we investigated the role of CaV2.3 R-type Ca(2+) channels in rodent sleep.

METHODS

The role of CaV2.3 Ca(2+) channels was analyzed in CaV2.3(-/-) mice and controls in both spontaneous and artificial urethane-induced sleep, using implantable video-EEG radiotelemetry. Data were analyzed for alterations in sleep architecture using sleep staging software and time-frequency analysis.

RESULTS

CaV2.3 deficient mice exhibited reduced wake duration and increased slow-wave sleep (SWS). Whereas mean sleep stage durations remained unchanged, the total number of SWS epochs was increased in CaV2.3(-/-) mice. Additional changes were observed for sleep stage transitions and EEG amplitudes. Furthermore, urethane-induced SWS mimicked spontaneous sleep results obtained from CaV2.3 deficient mice. Quantitative Real-time PCR did not reveal changes in thalamic CaV3 T-type Ca(2+) channel expression. The detailed mechanisms of SWS increase in CaV2.3(-/-) mice remain to be determined.

CONCLUSIONS

Low-voltage activated CaV2.3 R-type Ca(2+) channels in the thalamocortical loop and extra-thalamocortical circuitries substantially regulate rodent sleep architecture thus representing a novel potential target for pharmacological treatment of sleep disorders in the future.

Sleep-wake characterization of double MT₁/MT₂ receptor knockout mice and comparison with MT₁ and MT₂ receptor knockout mice

The neurohormone melatonin activates two G-protein coupled receptors, MT1 and MT2. Melatonin is implicated in circadian rhythms and sleep regulation, but the role of its receptors remains to be defined. We have therefore characterized the spontaneous vigilance states in wild-type (WT) mice and in three different types of transgenic mice: mice with genetic inactivation of MT1 (MT1(-/-)), MT2 (MT2(-/-)) and both MT1/MT2 (MT1(-/-)/MT2(-/-)) receptors. Electroencephalographic (EEG) and electromyographic sleep-wake patterns were recorded across the 24-h light-dark cycle. MT1(-/-)mice displayed a decrease (-37.3%) of the 24-h rapid eye movement sleep (REMS) time whereas MT2(-/-)mice showed a decrease (-17.3%) of the 24-h non rapid eye movement sleep (NREMS) time and an increase in wakefulness time (14.8%). These differences were the result of changes occurring in particular during the light/inactive phase. Surprisingly, MT1(-/-)/MT2(-/-) mice showed only an increase (8.9%) of the time spent awake during the 24-h. These changes were correlated to a decrease of the REMS EEG theta power in MT1(-/-)mice, of the NREMS EEG delta power in MT2(-/-)mice, and an increase of the REMS and wakefulness EEG theta power in MT1(-/-)/MT2(-/-) mice. Our results show that the genetic inactivation of both MT1 and MT2 receptors produces an increase of wakefulness, likely as a result of reduced NREMS due to the lack of MT2 receptors, and reduced REMS induced by the lack of MT1 receptors. Therefore, each melatonin receptor subtype differently regulates the vigilance states: MT2 receptors mainly NREMS, whereas MT1 receptors REMS.