Background: One of the challenges today is that the quality of sleep has weakened by many external factors, which we are not even aware of and which directly affect sleep. Sleep quality has an essential role in maintaining the cognitive function and memory consolidation of individuals. So far, various studies have been done to improve the quality of sleep by using external electrical stimulation, vestibular and olfactory system stimulation. Methods: In this study, the increase in sleep quality was considered by simultaneous acoustic stimulation in a deep sleep to increase the density of slow oscillations. Slow oscillations are the important events recorded in electroencephalography (EEG) and hallmark deep sleep. Acoustic stimulation of pink noise with random frequency ranging from 0.8 to 1.1 Hz was used to improve sleep quality. Results: Eight healthy adults (mean age: 28.4±7.8 years) studied in 3 nights under 3 conditions: accommodation night, stimulation night (STIM) and no stimulation night (SHAM), in counterbalanced order. Significant characteristics of the objective and subjective quality of sleep have been extracted from the acquired EEG and compared in the last 2 nights. Also, the arousal and cyclic alternating pattern characteristics have been measured to assess sleep stability over 2 nights of STIM and SHAM. Conclusion: Our findings confirm this goal of the study that applying designed acoustic stimulation simultaneously in the slow wave sleep (SWS) stage increases the duration of deep sleep and ultimately improves overall sleep stability and quality. Keywords: Sleep quality enhancement; Acoustic stimulation; Slow wave sleep; CAP & arousals; Sleep stability; EEG
Alzheimer's disease (AD) is a complex neurodegenerative disorder with a progressive template leading to neural damage as well as cognitive and memory deficit. The present study designed to investigate the neuroprotective effects of Centella Asiatica (CA) in STZ-induced rat model of memory impairment and neuronal damage. ICV infusion of STZ (3 mg/kg) or saline (as vehicle) were performed on days 1 and 3. CA (150 and 300 mg/kg/day) was administered through oral gavage for 21 days after model induction. Y-maze test was carried out to assess working memory related performances of animals. Rats were then sacrificed and the hippocampi were harvested for evaluation of neuronal density in CA1, CA2, CA3, and DG regions using stereology technique. ICV infusion of STZ caused significant working memory impairment in Y-maze apparatus as indicated with a significant decrease in alternative behavior compared to control animals (40.67 ± 2.04 vs. 73.00 ± 1.88, p < 0.0001). Oral administration of CA (150 and 300 mg/kg each day) for 21 days significantly (55.33 ± 3.34 and 57.17 ± 3.81 vs. 40.67 ± 2.04, p < 0.013, p < 0.004) improved STZ-induced working memory deficit. Furthermore, 21 days consecutive administration of CA significantly ameliorated STZ-induced neuronal loss in the CA1, CA2, and DG subfields of the hippocampus. Overall, these data demonstrate that CA increases neuronal density and improves cognitive impairment in STZ- induced rat model of AD, thereby has a promising therapeutic potential for neurodegenerative disorders. Accordingly, further studies are needed to determine the exact molecular mechanism of CA protective effects in brain disorders particularly AD
Sleep deprivation can cause hyperalgesia and interfere with analgesic treatments. The aim of the present study was to establish an obligatory sleep-abstinence model and also evaluate the effects of Intracerebroventricular (ICV) injection of crocin on pain perception in Wistar rats. Methods: In this experimental study, 35 adult male Wistar rats were randomly divided into 5 groups (n=7). The intra-ventricular cannulation was done for all rats before sleep deprivation. Sleep deprivation was performed by placing animals on a chamber equipped with an automatic animated conveyor (5 s with an interval of 3 min) for 72 h. Subsequently, the sleep-deprived animals received ICV injection of saline (MOD), Morphine 10 µg (MOR), Crocin 10 ug (Cr10), and Crocin40 µg (Cr40) using a microsyringe. Besides, a non-sleep-deprived group was allocated as a Control Group (NC) and only received an ICV injection of saline. Fifteen minutes after the ICV injections, pain perception was evaluated by the hot plate test (54±0.4 • C). Results: Compared with the NC group, latency significantly decreased in the MOD group (6.28±0.48 vs. 4.28± 0.48, P<0.0001). In comparison with the MOD group, both morphine (8.42±1.53) and crocin (7.60±1.45 for Cr10 and 8.14±0.89 for Cr40) could significantly increase latency in the sleep-deprived animals (P<0.0001). There was no statistically significant difference between the Cr10 and Cr40 (P=0.42), Cr10, and MOR (P=0.059) and Cr40 with MOR (P=0.86) groups. Conclusion: Our results indicated that crocin could attenuate hyperalgesia induced by sleep deprivation in rats.
In recent neuroimaging research, there has been considerable interest in identifying neuromarkers of sleep. Automatic slow wave sleep (SWS) and rapid eye movement (REM) are two known phases of sleep. However, the level by which those changes contribute to brain interactions has not been well characterized. In recent years, it has been shown that brain connectivity measuring can be helpful in investigation of behavioral states of the brain. By considering the fact that brains have different states in different stages of sleep, the present work employs effective connectivity and machine-learning analysis to quantify and classify SWS and REM stages of sleep. We examine low-density 12-channel EEG data from 8 healthy participants during a full night of sleep. Data were epoched into 30-s windows and SWS and REM stages were labeled by a sleep consultant. Effective connectivity was quantified using a directed metric, generalized partial directed coherence, and measures were used as input features for a machine-learning system. A support vector machine classifier was used to solve 2 binary problems of REM vs. nREM and SWS vs. nSWS. Findings revealed an excellent balanced accuracy of 89.80% in REM detection and 87.32% in SWS detection. Overall, our work demonstrates a successful application of effective connectivity analysis and machine learning for sleep neuromarkers in EEG.
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