Cannabinoids and Sleep/Wake Control.

The sleep-wake cycle is a complex process that includes wake (W), non-rapid-eye-movement (NREM) and rapid-eye-movement (REM) sleep. Each phase is regulated by specialized brain structures that, by means of different neurotransmitters, maintain the constant expression of the sleep-wake cycle. Molecules like orexin, serotonin, noradrenaline, histamine, for waking; GABA, adenosine, prostaglandins, for NREM sleep and acetylcholine and glutamate for REM sleep, among other molecules are responsible for the expression and maintenance of each phase. When the endocannabinoid system was being described for the first time, almost three decades ago, oleamide's sleep promoting properties were highlighted. Nowadays, enough evidence has been cumulated to support the endocannabinoid system role in the sleep-wake cycle regulation. The endocannabinoids oleamide anandamide, and 2-arachidonylglycerol promote NREM and/or REM sleep via the CB1R, thereby making this system a target to treat sleep disorders, such as insomnia.

A-phase classification using convolutional neural networks.

A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem remains open. In this paper, a different approach is proposed: instead of attempting to design a general classifier for all subjects, we propose to train ad-hoc classifiers for each subject using as little data as possible, in order to drastically reduce the amount of time required from the expert. The proposed classifiers are based on deep convolutional neural networks using the log-spectrogram of the EEG signal as input data. Results are encouraging, achieving average accuracies of 80.31% when discriminating between A-phases and non A-phases, and 71.87% when classifying among A-phase sub-types, with only 25% of the total A-phases used for training. When additional expert-validated data is considered, the sub-type classification accuracy increases to 78.92%. These results show that a semi-automatic annotation system with assistance from an expert could provide a better alternative to fully automatic classifiers. Graphical abstract A/N Deep Learning Classifier.

Comparación del sueño ligero y profundo por medio de la variabilidad de la frecuencia cardíaca

El análisis de la variabilidad de la frecuencia cardiaca (VFC) permite evaluar de forma no invasiva la actividad cardiovascular. La VFC presenta diferencias entre el sueño MOR y NMOR. Aunque, existen inconsistencias en el procedimiento de evaluación de la VFC en el sueño NMOR, hay estudios que lo dividen en sueño ligero y profundo mientras que otros no lo hacen. Nuestro objetivo fue determinar si había diferencias entre estos dos tipos de sueño en 12 medidas de la VFC. Se obtuvo la polisomnografía de 24 voluntarios sanos durante dos noches consecutivas. Se encontraron diferencias significativas entre ambos sueños en las medidas del dominio de tiempo DENN y LogVFC y en las del dominio no lineal DE2 y α1. Estas medidas se caracterizan por proporcionar indicadores de la variabilidad en el funcionamiento cardiaco. Se concluye que al menos con estas medidas se justificaría la división del sueño NMOR en ligero y profundo

Heart rate variability (HRV) analysis allows a non-invasive assessment of the cardiovascular activity. It has been reported that HRV shows differences between REM and NREM sleep. However, there are inconsistencies in the HRV evaluation of NREM sleep, since there are studies that divide it into light and deep sleep and others do not. The objective of this research was to determine if there were differences between these two types of sleep in 12 measures of HRV. Polysomnography of 24 healthy volunteers was obtained during two consecutive nights. Significant differences were found between both sleep types in the measurements of the time domain SDNN and LogHRV, also in measurements of the non-linear domain SD2 and α1. These measures are characterized by providing indicators of the variability in cardiac function. It is concluded that at least these measures would justify the division of the NREM sleep in light and deep sleep

Subjective Assessment of Sleep in Infantile Autism: A Comparative Study.

Sleep disturbances are very common in children with autism; it is for this reason that instruments that facilitate their evaluation are necessary. OBJECTIVES: Perform sleep assessment from a subjective perspective in a group of children with primary autism and compare them with a control group, using the Sleep Habits in Children Survey (CSHQ), with the purpose of determining sleep disturbances according to the subscales used. METHOD: A prospective cross-sectional study was conducted in a group of 21 patients with primary autism. For the evaluation of sleep disturbances, we chose the CSHQ survey. The differences between the independent groups were calculated by applying a Mann⁻Whitney U test. RESULTS: In the group of children with autism, higher values of the total scale were observed in comparison with the control group (p = 0.00) which It is congruent with a large sleep dysfunction. Significant differences were observed for all subscales (p = 0.00), with the exception of the subscale number 7. CONCLUSIONS: A high presence of sleep disturbances was observed in children with primary autism, with the exception of sleep breathing disorders, which did not show significant differences between the groups.

Does more sleep matter? Differential effects of NREM- and REM-dominant sleep on sleepiness and vigilance.

We investigated effects of NREM and REM predominant sleep periods on sleepiness and psychomotor performances measured with visual analog scales and the psychomotor vigilance task, respectively. After one week of stable sleep-wake rhythms, 18 healthy sleepers slept 3hours of early sleep and 3hours of late sleep, under polysomnographic control, spaced by two hours of sustained wakefulness between sleep periods in a within subjects split-night, sleep interruption protocol. Power spectra analysis was applied for sleep EEG recordings and sleep phase-relative power proportions were computed for six different frequency bands (delta, theta, alpha, sigma, beta and gamma). Both sleep periods presented with similar sleep duration and efficiency. As expected, phasic NREM and REM predominances were obtained for early and late sleep conditions, respectively. Albeit revealing additive effects of total sleep duration, our results showed a systematic discrepancy between psychomotor performances and sleepiness levels. In addition, sleepiness remained stable throughout sustained wakefulness during both conditions, whereas psychomotor performances even decreased after the second sleep period. Disregarding exchanges for frequency bands in NREM or stability in REM, correlations between outcome measures and EEG power proportions further evidenced directional divergence with respect to sleepiness and psychomotor performances, respectively. Showing that the functional correlation pattern changed with respect to early and late sleep condition, the relationships between EEG power and subjective or behavioral outcomes might however essentially be related to total sleep duration rather than to the phasic predominance of REM or NREM sleep.

Ecology and neurophysiology of sleep in two wild sloth species.

STUDY OBJECTIVES: Interspecific variation in sleep measured in captivity correlates with various physiological and environmental factors, including estimates of predation risk in the wild. However, it remains unclear whether prior comparative studies have been confounded by the captive recording environment. Herein we examine the effect of predation pressure on sleep in sloths living in the wild. DESIGN: Comparison of two closely related sloth species, one exposed to predation and one free from predation. SETTING: Panamanian mainland rainforest (predators present) and island mangrove (predators absent). PARTICIPANTS: Mainland (Bradypus variegatus, five males and four females) and island (Bradypus pygmaeus, six males) sloths. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Electroencephalographic (EEG) and electromyographic (EMG) activity was recorded using a miniature data logger. Although both species spent between 9 and 10 h per day sleeping, the mainland sloths showed a preference for sleeping at night, whereas island sloths showed no preference for sleeping during the day or night. Standardized EEG activity during nonrapid eye movement (NREM) sleep showed lower low-frequency power, and increased spindle and higher frequency power in island sloths when compared to mainland sloths. CONCLUSIONS: In sloths sleeping in the wild, predation pressure influenced the timing of sleep, but not the amount of time spent asleep. The preference for sleeping at night in mainland sloths may be a strategy to avoid detection by nocturnal cats. The pronounced differences in the NREM sleep EEG spectrum remain unexplained, but might be related to genetic or environmental factors.

Epilepsia y sueño / Epilepsy and sleep

Existe una interacción importante entre la epilepsia y el sueño, ya que este último tiene influencia en el momento de iniciación, la frecuencia y las características de las crisis, así como los hallazgos electroencefalográficos. La privación del sueño también desempeña un papel fundamental de otra parte, la epilepsia y los medicamentos anticonvulsivantes alteran el patrón de sueño. Sin embargo, se desconocen los mecanismos neurofisiológicos que intervienen en esta interacción y su esclarecimiento es de vital importancia para controlar las crisis y mejorar la calidad de vida de los pacientes. En este escrito se describen los diferentes tipos de crisis, su relación con las etapas del sueño y sus patrones electroencefalográfico.

There is a significant interaction between epilepsy and sleep, since the latter has influence in the onset, frequency and characteristics of the seizures, as well as EEG findings, and sleep deprivation also plays an important role; on the other hand, epilepsy and anticonvulsant drugs alter the pattern of sleep. However, neurophysiological mechanisms involved in this interaction are yet unknown and its clarification is vital to control seizures and improve the quality of life of patients. In this paper are described the different types of seizures, their relationship with the stages of sleep, and their electroencephalographic patterns.

Sleep in brain development

With the discovery of rapid eye movement (REM) sleep, sleep was no longer considered a homogeneous state of passive rest for the brain. On the contrary, sleep, and especially REM sleep, appeared as an active condition of intense cerebral activity. The fact that we get large amounts of sleep in early life suggested that sleep may play a role in brain maturation. This idea has been investigated for many years through a large number of animal and human studies, but evidence remains fragmented. The hypothesis proposed was that REM sleep would provide an endogenous source of activation, possibly critical for structural maturation of the central nervous system. This proposal led to a series of experiments looking at the role of REM sleep in brain development. In particular, the influence of sleep in developing the visual system has been highlighted. More recently, non-REM (NREM) sleep state has become a major focus of attention. The current data underscore the importance of both REM sleep and NREM sleep states in normal synaptic development and lend support to their functional roles in brain maturation. Both sleep states appear to be important for neuronal development, but the corresponding contribution is likely to be different.