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Motor imagery is a widely used technique in neurophysiological research and brain–computer interface applications for restoring lost motor functions. This study aimed to analyze the neural correlates of motor imagery at the level of brain functional networks using electroencephalography (EEG). Thirty subjects performed motor executions, quasi-movements, and imaginary movements while EEG data were recorded. We explored the correlation between the latency of motor imagery and the characteristics of brain functional networks in the alpha- and beta-frequency ranges. The analysis revealed a number of nodes within the network whose local cluster coefficients negatively correlated with the time of motor imagination onset. This indicates that a decrease in the corresponding networks metrics would lead to improved rate of motor imagination. These nodes were predominantly located in the frontal cortex, parietal, and temporal lobes. The identified nodes, particularly the Frontal Mid L zone, hold promise as potential targets for non-invasive brain stimulation techniques such as transcranial magnetic stimulation or transcranial direct current stimulation. Stimulating these areas may enhance motor imagination ability and facilitate the rehabilitation process, especially for stroke patients with severe motor impairment.

Tactile perception is a fundamental sensory system, playing a pivotal role in our understanding of the surrounding environment and aiding in motor control. In this study, we investigated the distinct neural underpinnings of discriminative touch, affective touch (specifically the C tactile system), and knismesis. We developed a paradigm of EEG experiment consisted of three types of touch tuned in terms of their force and velocity for different submodalities: discriminative touch (haptics or fast touch), affective touch (C-tactile or slow touch), and knismesis (alerting tickle or ultralight touch). Touch was delivered with a special high-precision robotic rotary touch stimulation device. Thirty nine healthy individuals participated in the study. Utilizing functional brain networks derived from EEG data, we examined the patterns of brain connectivity associated with fast, slow, and ultralight touches. Our findings revealed significant differences in functional connectivity patterns between these touch conditions, with the majority of variations occurring in the theta frequency range. Notably, connections in frontal, frontal-central, frontal-parietal, and occipitotemporal regions exhibited distinct activation strengths. Pairwise statistical comparisons further highlighted the unique characteristics of each touch modality. The theta band, in particular, played a prominent role in distinguishing ultralight from slow touches. These results shed light on the interplay between different touch submodalities and their distinctive processing in the brain, contributing to a comprehensive understanding of tactile perception. This research bridges a critical gap in our knowledge of the neural mechanisms underlying tactile perception and its role in shaping our perception of the world.

The incidence of Alzheimer’s disease (AD) is steadily increasing worldwide every year. It is predicted that the number of people with AD will reach 152 million by 2050 if no effective treatment is found. Despite this, traditional pharmacological interventions for Alzheimer’s disease have not proven to be both effective and safe. As a result, the search for non-pharmacological approaches to the treatment of Alzheimer's disease has become a pressing concern in the field of medicine. Recently, sleep has emerged as a new potential indicator of AD and a target for therapy. In this context, we present a pioneering concept involving the use of phototherapy for AD specifically during sleep. Our research shows that administration of photobiomodulation (at 1050 nm, 3.5 kJ/cm 2 ) during deep sleep, as opposed to wakefulness, significantly enhances the clearance of amyloid-beta from the brain lymphatic system, leading to improved metabolic function and cognitive performance in AD-affected mice. These innovative findings shed light on the mechanism of sleep's restorative powers and provide a valuable foundation for the advancement of cutting-edge technologies aimed at treating Alzheimer's disease while the patient sleeps.

We performed a wavelet analysis of oscillatory dynamics in brain activity of patients with obstructive sleep apnoea (OSA) ( N = 10 , age  52.8 ± 13  years, median 49 years; male/female ratio: 73), compared with a group of apparently healthy participants ( N = 15 , age  51.5 ± 29.5  years, median 42 years; malefemale ratio: 87), based on the calculation of patterns from electroencephalographic (EEG) signals of ***nighttime polysomnography (PSG) recordings. It was shown that there were no statistical differences in the number and duration of nocturnal sleep stages in patients of the two groups. The distributions of the number  N and duration  T of oscillatory wavelet patterns of EEG signals in bands Δ f i = [ i ; i + 2 ] , where i takes values from 2 to 38, have been estimated. Statistically significant differences in the characteristics of the distributions of the number and duration of patterns for the high-frequency bands  Δ f 17  –  Δ f 19  (32 – 38 Hz) are shown. It is demonstrated that estimation of the coordinates of the height and the value of the maximum point of the distribution of the considered quantitative characteristics of the patterns allows clustering of the EEG processing results and demonstrates the separation of the nocturnal sleep characteristics of OSA patients and healthy volunteers. Evaluation based on the Mann–Whitney U-test shows statistically significant differences between  N and  T patterns assessed from nocturnal EEG recordings. The number and duration of high-frequency patterns are significantly reduced in the EEG of OSA patients compared to essentially healthy participants. It is possible that such a change in high-frequency activity is related to known structural changes in the brain.

Sleep is as important for good health as diet and exercise. It is particularly important for children and adolescents, as they need to grow and develop. Quality sleep is necessary to ensure the plasticity of the brain, growth and maturation, and development and improvement of mental abilities, to prevent some chronic diseases. Younger the child, the more is the time he sleeps. It is well known that each behavioral state, characterized by certain physiological parameters, electroencephalography (EEG) activity and phenomena, is changed across the life span. An advanced EEG monitoring can improve our understanding of the brain neuronal activity through studying the sleep patterns. In age-related neurophysiology, importance is attached to the formation and temporary changes in such EEG patterns such as slow-wave activity (SWA), sleep spindles (SSs), vertex waves (V-waves), K-complexes (KKs) and positive occipital sharp transients (POSTs). The features of the formation of bioelectrical activity during sleep and various sleep EEG pattern in newborns depending on the conceptual age and the further maturation of the sleep EEG as the child grows and develops are widely discussed. However, one of the main sleep EEG phenomena which occur in non-rapid eye movement (non-REM) sleep 2, associated with a wide range of brain functions, such as memory and neuroplasticity, general intelligence and cognitive performance, and undergo changes throughout life, are SSs. This review attempts to summarize the available literature data on the formation of the main EEG sleep patterns in childhood and adolescence, especially SSs, and also identifies some studies conducted at the our Scientific center on age-related neurophysiology and EEG sleep characteristics and their associations with some diseases in middle adolescence. Modern methods of sleep assessment and its EEG patterns are the next step in understanding the neurophysiological ontogenetic aspects of the sleep–wake cycle . All this will open up perspectives and «windows of opportunity» in predicting postnatal maturation, understanding the mechanisms of brain neuroplasticity and memory consolidation in sleep, which is one of the tasks of modern somnology and neurophysiology.

Sleep is quantitatively described by subdividing polysomnographic records into intervals each of which is allocated to one of the just 5 all-or-nothing variables called “sleep–wake stages”. What are the mechanisms governing the establishment of such 5 relatively stable stages and rapid transitions between them? We modeled these stages as resulting from the competing interactions between the mutually inhibiting drives for wake, NREM sleep, and REM sleep that are proposed to work in a similar way as two-, two-, and one-way switch, respectively. The electromechanical counterparts of the stages were visualized as 5 variants of an electrical circuit connecting these switches with three lamps. During W and transient state N1, two sleep switches are switched off, and three lamps are turned off. During other transitions, one of these lamps is turning on after changing in on–off state of one or two of three switches. During transitions to N2, one (N2) lamp is turning on. During the following transitions to N3, one more (N3) lamp is turning on. During transitions from N2 to R, the N2 lamp is turning off, while the R lamp is turning on. Estimates of stage-specific scores on the 1st and 2nd principal components of the electroencephalographic spectra provided empirical evidence for such on–off states of these switches.

We proposed mathematical models for the electrocardiogram and photoplethysmogram signals with functionality to preset the pattern of synchronization between the phases of the low-frequency oscillations, which are related to the sympathetic neuronal control of circulation. The simulated phase difference reproduced the statistical and spectral characteristics of the experimental data, including the alternating horizontal and sloped sections, corresponding to the intervals of synchronous and asynchronous behavior. Using the proposed model, we tested and tuned an algorithm for detection of the phase synchronization between the parts of the sympathetic control of circulation, improving both sensitivity and specificity of the algorithm.

The meningeal lymphatics (MLVs) play an important role in immunity and maintaining homeostasis of the central nervous system. Sleep is a natural state when lymphatic drainage becomes maximally active and, along with the flow of fluids, toxins and metabolites are removed from the brain. A decrease in function of MLVs is a key mechanism in the development of glioblastoma (GBM), which leads to the accumulation of excess fluid in the brain and an increase in intracranial pressure. Recently, promising methods of transcranial photostimulation (tPBM) of MLVs have been developed. It is interesting to note that tPBM of MLVs during sleep vs. wakefulness stimulates lymphatic drainage of brain tissues much more effectively. It is logical to expect that tPBM of MLVs during sleep will significantly increase the effectiveness of the GBM therapy. This review highlights new trends in the treatment of GBM, including tPBM of MLVs during sleep as a promising strategy in suppression of tumor progression.

Studies of hemispheric asymmetry in humans during sleep and falling asleep give contradictory results—there is evidence of the dominance of both the right and left hemispheres when falling asleep. Such a discrepancy in the results may be due to both the high heterogeneity of asymmetry patterns and the difficulty of ensuring homogeneous experimental conditions in neurophysiological testing. 102 healthy participants repeatedly performed a monotonous bimanual psychomotor test at home. 227 trials were selected for analysis. We extracted sequences of microsleep episodes, indicating which hand executed motor activity last before falling asleep, and calculated various complexity measures (Shannon entropy, ordinary/normalized Lempel–Ziv complexity, ordinary/conditional permutation entropy, Petrosyan dimension). After clustering the data, the extracted clusters were pairwise compared according to the indicators of the psychomotor test (total tap number, number of sleep episodes, total sleep duration, inter-tap interval). We obtained four clusters based on the complexity measures estimates. They are characterized by similar psychomotor characteristics of the research participants. The third cluster is of the greatest interest, characterized by the absence of “global” asymmetry (i.e., without a clearly expressed dominance of one of the hands), high drowsiness level and the existence of temporary hand dominances for relatively short time intervals. The results indicate against the existence of a pronounced hemispheric asymmetry when falling asleep, while at the same time testifying to the existence of short-term hand dominance episodes, possibly due to the local dynamics of functional connectivity during the wake–sleep transition.

During sleep, we not only restore our energy spent during the day, but it is also the time for the formation of new synaptic contacts, cleansing brain tissue of toxins and metabolites, and active cell growth. [...]of this revolutionary step, pioneering technologies for assessing BBB only by the EEG parameters and smart sleep portable gadgets for the stimulation of lymphatic removal of toxins from the brain are being created [9, 10]. [18] propose the quantitative EEG characteristics of the patterns in OSA patients demonstrating different clusters in the density estimation space of the number and duration distributions of oscillatory patterns for high-frequency bands. The first model does not consider the structure of the cardiovascular system, but provides quantitative simulation of the shape and spectral properties of real electrocardiogram and photoplethysmogram signals and was used to test and tune an algorithm for estimation of a promising health index, based on the phase dynamics analysis.