Principal component analysis of the EEG spectrum can provide yes-or-no criteria for demarcation of boundaries between NREM sleep stages

New bands in the sleep stages of spider monkeys (Ateles geoffroyi): Electroencephalographic correlations and spatial distribution

The study of electroencephalographic (EEG) signals in nonhuman primates has led to important discoveries in neurophysiology and sleep behavior. Several studies have analyzed digital EEG data from primate species with prehensile tails, like the spider monkey, and principal component analysis has led to the identification of new EEG bands and their spatial distribution during sleep and wakefulness in these monkeys. However, the spatial location of the EEG correlations of these new bands during the sleep-wake cycle in the spider monkey has not yet been explored. Thus, the objective of this study was to determine the spatial distribution of EEG correlations in the new bands during wakefulness, rapid eye movement (REM) sleep, and non-REM sleep in this species. EEG signals were obtained from the scalp of six monkeys housed in experimental conditions in a laboratory setting. Regarding the 1-21 Hz band, a significant correlation between left frontal and central regions was recorded during non-REM 2 sleep. In the REM sleep, a significant correlation between these cortical areas was seen in two bands: 1-3 and 3-13 Hz. This reflects a modification of the degree of coupling between the cortical areas studied, associated with the distinct stages of sleep. The intrahemispheric EEG correlation found between left perceptual and motor regions during sleep in the spider monkey could indicate activation of a neural circuit for the processing of environmental information that plays a critical role in monitoring the danger of nocturnal predation.

Topographic distribution of the EEG ad hoc broad bands during sleep and wakefulness in the spider monkey (Ateles Geoffroyi)

There is evidence that research on sleep among New World monkeys may provide important knowledge related to the evolution of sleep more broadly in the primate order. Digital electroencephalographic (EEG) analyses provide essential knowledge on sleep in the spider monkey. Recently, specific EEG bands related to sleep in these animals have been obtained using principal component analysis, but the exact spatio-temporal distribution of these EEG bands in this species has not yet been analyzed. This study determined the topographic distribution of the EEG spectral power of ad hoc broad bands during rapid eye movement sleep, nonrapid eye movement sleep, and wakefulness. Superficial EEG activity was obtained from the occipital, frontal, and central areas of six young adult male monkeys housed in a laboratory. During wakefulness, occipital areas showed high absolute power in the 1-3, 3-12, and 11-30 Hz ranges, while during nonrapid eye movement 1 sleep the highest absolute power was in the 13-30 Hz range. During nonrapid eye movement 3 sleep, frontal and central areas showed a high absolute power in the 18-19 Hz range. Finally, the right central area showed a high absolute power in the 20-30 Hz range during rapid eye movement sleep. This topographic distribution of EEG bands could represent the brain organization required for arousal and mnemonic processing during sleep in the spider monkey.

Principal component analysis of electroencephalographic activity during sleep and wakefulness in the spider monkey (Ateles geoffroyi)

The study of electroencephalographic (EEG) activity during sleep in the spider monkey has provided new insights into primitive arboreal sleep physiology and behavior in anthropoids. Nevertheless, studies conducted to date have maintained the frequency ranges of the EEG bands commonly used with humans. The aim of the present work was to determine the EEG broad bands that characterize sleep and wakefulness in the spider monkey using principal component analysis (PCA). The EEG activity was recorded from the occipital, central, and frontal EEG derivations of six young-adult male spider monkeys housed in a laboratory setting. To determine which frequencies covaried and which were orthogonally independent during sleep and wakefulness, the power EEG spectra and interhemispheric and intrahemispheric EEG correlations from 1 to 30 Hz were subjected to PCA. Findings show that the EEG bands detection differed from those reported previously in both spider monkeys and humans, and that the 1-3 and 2-13 Hz frequency ranges concur with the oscillatory activity elucidated by cellular recordings of subcortical regions. Results show that applying PCA to the EEG spectrum during sleep and wakefulness in the spider monkey led to the identification of frequencies that covaried with, and were orthogonally independent of, other frequencies in each behavioral vigilance state. The new EEG bands differ from those used previously with both spider monkeys and humans. The 1-3 and 2-13 Hz frequency ranges are in accordance with the oscillatory activity elucidated by cellular recordings of subcortical regions in other mammals.

Percept-related EEG classification using machine learning approach and features of functional brain connectivity

Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature extraction and selection techniques reducing the dimensionality of input data. Dimensionality reduction is usually implemented via the mathematical approaches (e.g., principal component analysis, linear discriminant analysis, etc.) regardless of the origin of analyzed data. We hypothesize that since EEG features are determined by certain neurophysiological processes, they should have distinctive characteristics in spatiotemporal domain. If so, it is possible to specify the set of EEG principal features based on the prior knowledge about underlying neurophysiological processes. To test this hypothesis, we consider the classification of EEG trials related to the perception of ambiguous visual stimuli. We observe that EEG features, underlying the different ambiguous stimuli interpretations, are defined by the network properties of neuronal activity. Having analyzed functional neural interactions, we specify the brain area in which neural network architecture exhibits differences for different classes of EEG trials. We optimize the feedforward multilayer perceptron and develop a strategy for the training set selection to maximize the classification accuracy, being 85% when all channels are used. The revealed localization of the percept-related features allows about 95% accuracy, when the number of channels is reduced up to 90%. Obtained results can be used for classification of EEG trials associated with more complex cognitive tasks. Taking into account that cognitive activity is subserved by a distributed functional cortical network, its topological properties have to be considered when selecting optimal features for EEG trial classification.

Comparison of Wearable Trackers' Ability to Estimate Sleep

Tracking physical activity and sleep patterns using wearable trackers has become a current trend. However, little information exists about the comparability of wearable trackers measuring sleep. This study examined the comparability of wearable trackers for estimating sleep measurement with a sleep diary (SD) for three full nights. A convenience sample of 78 adults were recruited in this research with a mean age of 27.6 ± 11.0 years. Comparisons between wearable trackers and sleep outcomes were analyzed using the mean absolute percentage errors, Pearson correlations, Bland⁻Altman Plots, and equivalent testing. Trackers that showed the greatest equivalence with the SD for total sleep time were the Jawbone UP3 and Fitbit Charge Heart Rate (effect size = 0.09 and 0.23, respectively). The greatest equivalence with the SD for time in bed was seen with the SenseWear Armband, Garmin Vivosmart, and Jawbone UP3 (effect size = 0.09, 0.16, and 0.07, respectively). Some of the wearable trackers resulted in closer approximations to self-reported sleep outcomes than a previously sleep research-grade device, these trackers offer a lower-cost alternative to tracking sleep in healthy populations.

The effect of unilateral forced nostril breathing on sleep in healthy right-handed men: a preliminary report

PURPOSE

Although we spend about one-third of our lives in sleep and recognize its necessity for good health, sleep has only been partially elucidated in the last century. The nasal cycle of congestion and decongestion during sleep has various effects on human physiology. The aim of the present study was to investigate the effect of unilateral forced nostril breathing on sleep.

METHODS

Twenty-one healthy male volunteers aged 18-24 years were included in the study. Only individuals with right-hand dominance were included. Subjects were observed during sleep for three nights under different conditions: no obstruction (normal sleep) on the first night, right nasal obstruction on the second night, and left nasal obstruction on the third night.

RESULTS

The main findings of our study are that sleep efficiency, NREM stage III, and total sleep duration were greater during left nasal obstruction (right nostril dominant respiration), while apnea-hypopnea index (AHI), frequency of periodic limb movements, and oxygen desaturation were higher during right nasal obstruction (left nostril dominant respiration).

CONCLUSION

The nasal cycle has a significant impact on sleep which is reflected in sleep recordings. Our result supports that nasal obstructions, due to deviations, concha hypertrophy, or congestion/decongestion, might affect the physiology of respiration and sleep. Nasal obstruction should be taken into consideration when evaluating patients in sleep laboratories and further studies are required to elucidate the situation in the patients with nasal obstruction.

The Mutual Interaction Between Sleep and Epilepsy on the Neurobiological Basis and Therapy

BACKGROUND

Sleep and epilepsy are mutually related in a complex, bidirectional manner. However, our understanding of this relationship remains unclear.

RESULTS

The literatures of the neurobiological basis of the interactions between sleep and epilepsy indicate that non rapid eye movement sleep and idiopathic generalized epilepsy share the same thalamocortical networks. Most of neurotransmitters and neuromodulators such as adenosine, melatonin, prostaglandin D2, serotonin, and histamine are found to regulate the sleep-wake behavior and also considered to have antiepilepsy effects; antiepileptic drugs, in turn, also have effects on sleep. Furthermore, many drugs that regulate the sleep-wake cycle can also serve as potential antiseizure agents. The nonpharmacological management of epilepsy including ketogenic diet, epilepsy surgery, neurostimulation can also influence sleep.

CONCLUSION

In this paper, we address the issues involved in these phenomena and also discuss the various therapies used to modify them.

Neural Plasticity Is Involved in Physiological Sleep, Depressive Sleep Disturbances, and Antidepressant Treatments

Depression, which is characterized by a pervasive and persistent low mood and anhedonia, greatly impacts patients, their families, and society. The associated and recurring sleep disturbances further reduce patient's quality of life. However, therapeutic sleep deprivation has been regarded as a rapid and robust antidepressant treatment for several decades, which suggests a complicated role of sleep in development of depression. Changes in neural plasticity are observed during physiological sleep, therapeutic sleep deprivation, and depression. This correlation might help us to understand better the mechanism underlying development of depression and the role of sleep. In this review, we first introduce the structure of sleep and the facilitated neural plasticity caused by physiological sleep. Then, we introduce sleep disturbances and changes in plasticity in patients with depression. Finally, the effects and mechanisms of antidepressants and therapeutic sleep deprivation on neural plasticity are discussed.