This PDF file contains the front matter associated with SPIE Proceedings Volume 11847, including the Title Page, Copyright information, and Table of Contents

The role of propagating waves of neuronal activity in different brain areas, particularly in cerebral cortex, is still debated in modern neuroscience. We consider spiking neural network (SNN) model with spike timing dependent plasticity (STDP). It is shown that in the SNN the topological type of neural activity depends on the average length of connections between neurons - the connectivity radius. At the small radius (mostly local connections), the activity spreads in the form of traveling waves with well shaped leading and trailing edge. Such simple wave activity can be treated as a network spike. With increase in the connectivity radius, blurring of the leading and trailing edges is observed up to the "instant" activation of the network. In this case, the sparse neurons first generate spikes, and then all neurons in the network generate multiple spikes. This type of activity can be considered as a network burst. We also analyze the velocity of neural activity propagation depending on the connectivity radius. In the case of the network burst mode, the velocity is predominantly limited by axonal delays. In turn, in the case of the network spike mode it is defined by synaptic delays. In addition, we found that the STDP facilitates network synchronization in SNNs with a predominance of local connections.

Absence epilepsy is a widespread among children and adolescents form of disease, manifestating itself on EEG with spike-wave discharges (SWDs). It was shown, just as a proof of concept, that SWDs can be considered as a long transient process rather than attractor in a phase space. In this work, we investigate this hypothesis in detail, studying a small class of simple mathematical models consisting of 14 identical FitzHugh–Nagumo neurons, organized in accordance with modern concepts of the thalamo-cortical network of the brain. Some models belonging to the class studied demonstrate rather long transient processes in response to a short in time external driving from an individual neuron modeling the trigeminal nerve.

In theoretical consideration of the collective calcium dynamics in astrocytes, an adequate description of the properties of intercellular connections is as important as the details of intracellular dynamics. We propose and test two alternative approaches to connectivity modeling in a two-dimensional projection spatially detailed model of astrocytic networks. Both approaches are based on the creation of a system of densely packed cells with further detailing the diffusive exchange along the contact lines. The simplest way to organize intercellular communication is to change the degree of connectivity by changing the area of contact of cells without adding special case dynamics at the cell borders. Arguably a more advanced approach is to define a specialized model variant specifically for perimembrane layer at the neighboring cell interfaces. At the same time, we separately describe the dynamics of gap contacts. We compare the simulation results for both approaches and conclude that both methods can be useful at different levels of abstraction.

Most of the existing models of calcium dynamics of astrocytes do not take into account the effect of the Na/Ca- exchanger on the formation of calcium oscillations and their properties. However, recent experimental studies show that at an increased sodium concentration, the Na/Ca-exchanger works in the reverse mode, providing an additional calcium entry into the cell cytosol. In this work we study a model that simultaneously takes into account the IP₃-mediated exchange with intracellular [Ca²⁺] stores and bidirectional [Ca²⁺] flow through Na/Ca-exchanger. We analyze the behavior of the system under stimulation with external glutamate- of the cell, explore the possible behavior regimes and reveal the effect of the cytosolic sodium concentration, the maximum current through the Na/Ca-exchanger, and the stimulus amplitude on the astrocytic calcium dynamics.

The discovery of meningeal lymphatic vessels [Louveau et al, 2015] is one of the most impressive breakthroughs of neurophysiology of last years, and the problem of its functioning is a hot topic far from the complete resolution. In addition, first video recordings in vivo, which allows revealing spatio-temporal dynamics potentially possible, are made available only recently [Castranova et al., 2020]. This talk comprises two main issues in this respect, both of which address the practical sides of computational biophysics and data analysis: i) the computational analysis of the mentioned video records by means of MATLAB's tools for video and image processing as well as a presentation of its quantitative results; ii) the methodology of numerical simulations of the proposed nonlinear partial differential equation that generalizes the Barenblatt-Pattle's solution for moving fronts spread in porous media on the case of distributed sources and channels with a finite width associated with lymphatic micro-vessels.

Lung tuberculosis caused by Micobacterium tuberculosis is still one of the leading killers today. Different computational models have been developed to find new clues to understand this mortal disease. Up until now these models lacked up-to-date visualization tools that allow dynamic exploratory data analysis. We have developed model based on the study of “manipulation" by immune signalling and biochemical metabolic pathways by M. tuberculosis that reveal key factors contributing either to the beginning of reparative changes or the transition of the disease to a chronic form. We have defined deploying the mathematical model and being based on published clinical observations, "key parameters" stimulating the restoration of the structure and functions of tissues, as well as analyzed the dynamics of cellular populations for the diagnosis of functional and pathological conditions of the host.

Most of human endogenous processes are subject to the circadian rhythm. This rhythm, in turn, demonstrates the adaptation of the body to the cycle of day and night, that is, it is regulated by illumination. Normally, the sleep / wake cycle is synchronized with the circadian rhythm, but sometimes, for various reasons, the phenomenon of desynchronization occurs, which can take a different form. We present a model study of such a phenomenon by introducing both random and periodic forcing on the neuronal nuclei responsible for sleep and wakefulness. The results of the study confirmed the experimental data on changes in sleep patterns, and also made it possible to predict that the effect on the homeostatic process associated with the sleep / wake cycle may lead to desynchronization.

A mathematical model of COVID-19 pandemic based on a two-parameter nonlinear first-order ordinary differential equation with retarded time argument is proposed. The model uses two parameters: the time of possible dissemination of infection by an individual virus carrier and the probability of contamination of a healthy population member in a contact with an infected one per unit time. The parameters can be given functions of time, which is particularly important in describing multi-peak pandemic. The model is applicable to any community (country, city, etc.) and provides an optimal balance between the adequate description of a pandemic inherent in the known SIR model and the relative simplicity for practical estimates. Examples of the model application are in qualitative agreement with the dynamics of COVID-19 pandemic.

Analysis of the pulse wave shape variability is an important step in the tasks of assessing the cardiovascular regulatory mechanisms and reconstruction of the central pulse. The conventional approach to the quantification of the pulse wave is based on the assessment of the features of its shape within each beat to beat heart interval. Usually, a set of indices is calculated, which are determined by the reference points of the wave contour. We have developed an alternative method aimed to analyze the variability of the pulse waveform. Being combined with adaptive baseline removal algorithm, our method is able to analyze the pulse wave shape variability regardless of the variability of its rhythm.

In this report we proposed a method for identifying the individual characteristics of motor activity based on the recurrence analysis as applied to the encephalography of the human brain. The analysis was carried out according to the real and imaginary movements of the subjects and compared with each other. This approach makes it possible to identify for each subject connections between channels in which the frequency increases at the moment of motor activity and/or a stable pattern corresponding to this movement is formed. The proposed method has a large number of applications in medicine and neurophysiology.

Wavelet transformation uses a special basis widely known for its unique properties, the most important of which are its compactness and multi-resolution analysis of original signal. However, for a standard discrete and continuous wavelet transform (CWT), the extracted set of feature may be not optimal for solving given inverse problem. If no inverse transformation is needed, the values of transition and dilation coefficients may be determined during network training, and the windows corresponding to various wavelet functions may overlap. In this study, we suggest Adaptive Window Wavelet Neural Network (AWWNN) with bottom to top strategy of iterative neighboring windows merging, designed primarily for signal processing. The efficiency of proposed algorithm was compared on the example of the inverse problem (IP) of Raman spectroscopy of complex solutions of inorganic salts. The IP was solved using a dense neural network based on features generated using the proposed approach and a standard CWT.

The paper presents the results of a harmonic analysis of the indicators of photoplethysmograms of the human index finger, which characterize the pulse blood filling and tone of large diameter arteries (blood distribution vessels), as well as indicators of the tone of small diameter arteries. Fourier analysis of periodic changes in the tone of regional arteries of various diameters was carried out with natural breathing, in a test with deep breathing (hyperventilation), with the voluntary breathing at three fixed frequencies (0.2Hz, 0.1Hz; 0.05Hz). The tests were performed under spirographic control. The advantage of the spectral analysis of photoplethysmographic indicators over the traditional harmonic analysis of PPG in studies of the relationship between changes in the tone of regional arteries of various diameters on the one hand, and the depth and frequency of breathing of the subject on the other hand is shown. Spectral analysis of photoplethysmographic indicators is more effective in observing the dynamics of transient changes in the tone of regional arteries in people with neurocirculatory disorders of blood circulation regulation.

Long-term continuous registration of photoplethysmograms allows observing periodic fluctuations of regional vascular tone, which is important for non-invasive functional diagnostics of a number of diseases of the vascular system. But the relief of the photoplethysmogram within the pulse cycle in some functional states is poorly expressed, which significantly reduces the possibility of a quantitative assessment of the indicators of vascular tone. We propose a new algorithm for detecting special points of the contour of photoplethysmograms, based on the discovered relationship between the position of these special points and the duration of the pulse cycle. The efficiency of the new algorithm in cases where there is no diastolic rise has been demonstrated. It is shown that the average relative error does not exceed 5%. The proposed algorithm can be used in computer monitoring systems for vascular tone.

The main goal of this project was to identify the patterns of muscular activity of a person in the process of his interaction with the environment, as well as to identify mechanisms that make it possible to adapt behavior in response to changing external conditions. For this, we conducted a series of experiments with subjects placed in an unstable state. We carried out statistical analysis for the received signals of muscle activity. Based on the results of the analysis of behavioral characteristics, we revealed positive dynamics when subjects were reaching a state of balance and a pattern associated with training.

This method can be used to detect signs of inhibitory neurons among a set of cell activity data in the spike train format. It is known that inhibitory neurons prevent the activity of related neurons for some time. This method detects the absence of spikes in a given period of time. The result is the level of suppression from inhibitory neuron. This method also contains an example of sorting the suppression level matrix.

We propose to use the chimera-like state for stimulus classification in a spiking neural network of bistable HH neurons. As a stimulus, we use an external pulsed current applied to the network. Additive noise makes the neurons nonidentical so that the external pulse switches only a part of the neurons from the resting to the oscillatory state depending on the pulse amplitude. For classification, we use the neural network and two output neurons. The network is trained on two external pulses with different amplitudes to adjust coupling strengths between neurons in the main network and output neurons. We investigate influence of inhibitory coupling between output neurons on classification of input signal with different amplitudes.

Here we report on bi-directional control of spin waves propagated in yttrium iron garnet (YIG) waveguide with Fe-Rh stripe placed on top of the central part of YIG. We use the micromagnetic numerical simulation to investigate spin-wave transport in multimode regime by the numerical solution of Landau-Lifshitz-Gilbert equation. Furthermore, we have explained the evolution of spin-wave signal in the proposed structure by means of 2D Fourier analysis revealing the spin-wave dispersion transformation. The transformation of the spin-wave transmission spectra demonstrates that the proposed structure will enable the control of spin-wave mode propagation by varying the temperature range of Fe-Rh close to the room temperature. Furthermore, the spin-wave signal can switch back and forth via a small variation of the temperature in Fe-Rh slab provided by the means of laser radiation. Analysis of those spectra revealed that YIG/Fe-Rh bilayer structure can be used as a functional unit in planar magnonic networks performing the spatio-frequency demultiplexing and spin-wave mode filtration regime.

We report here on dipolar coupling of spin waves propagating as guided modes of adjacent yttrium iron garnet stripes. Three stripes are placed parallel to each other and separated by gaps that are small enough to provide nearest-neighbor coupling. The origin of the coupling is the dipole field of the precessing magnetization vector. The micromagnetic numerical simulation, yielded spectra of spin waves through the magnonic structure. Analysis of those spectra revealed that the lateral structure can be used as a functional unit in planar magnonic networks – they can be utilized as a directional coupler, spin-wave multiplexer, or microwave power divider. Using Brillouin light scattering spectroscopy, we experimentally demonstrated spin-wave transport along the lateral stripes. We were able to control the spin-wave routing between the stripes by varying the bias angle of the magnetic field.

This paper examined the statistical features of the alpha rhythm in the EEG signal of subjects with sleep disorders in comparison with conventionally healthy people. The experiment included subjects with insomnia and normal sleep patterns (as a control group). The experiment was a simple response to a audio stimulus. The response time to the stimulus correlates with the amplitude of the alpha rhythm in all subjects. It was shown that in subjects suffering from insomnia there are a number of statistical features for the amount of energy falling within the frequency range of the alpha rhythm in the EEG signal. The data obtained indicate that subjects with insomnia better retain their attention to the task at hand during the experiment and cannot relax even when recording a EEG signal before the start of the experiment (when the subject is at rest with closed eyes).

In this work presents a study of the features of alpha rhythm formation before and after cognitive load in children aged 9-10 years. Experimental studies were conducted for two groups of children of 5 people (a group with visual disabilities up to 25%, a group with almost normal vision from 80%). In General, a decrease in the alpha-rhythm power was demonstrated for the group of disabilities children in comparison with the group of practically healthy subjects. Spatial maps of the alpha rhythm distribution are presented, showing the features of localization of this rhythm during passive wakefulness and solving cognitive tasks.

In the present paper, we consider the dynamics of the time-frequency activity of ECoG data in WAG/Rij rats, well known as experimental models of absence epilepsy. With the help of the wavelet technique of tracking the duration and number of oscillatory patterns, it is possible to observe that with an increase in the amount of epileptic activity, a qualitative restructuring of oscillatory activity in the ECoG occurs during sleep and wakefulness.

The paper presents an investigation of synchronization between different rats' ECoG leads under anesthesia. It was shown, that sleeping/awake rats and rats under anesthesia demonstrate a statistically significant difference in the strength of synchronization between ECoG leads in certain frequency bands. Quantitative characteristics, that characterize the strength of synchronization between ECoG channels for different conditions: awake, sleeping, anesthesia, were measured.

Spatial changes in surface EEG activity under the influence of odor were studied. The group of subjects included young neurologically healthy men (N = 3) and men with chronic headache (N = 3). An experimental technique was proposed that made it possible to assess the average spatial activity of the brain of the subjects, which arises during the perception of aroma. On the basis of a comprehensive analysis of oscillatory EEG - activity, statistically significant changes were found for the frontal and parietal projections of the cerebral cortex that occur when exposed to odor. We described changes of the same type within the group in the ratio of the powers of the observed vibrational activity in standard neurophysiological ranges.

We describe the features of the occurrence of the “monotony” state in practically healthy young adults. The materials of processing results of experimental data of test subjects (N = 13, aged 29 ± 4.3 years, men) are presented. Mathematical processing of the reaction time of the subjects during a long test on the perception of visual objects allowed us to identify two different response scenarios of the subjects, associated with more and less resistance to the occurrence of monotony in the process of uniform cognitive load. The differentiation of the subjects into two subgroups is confirmed by other objectively calculated characteristics (the number of erroneous decisions and average reaction time). The observed effects may be associated with the ability of the nervous system to adapt to prolonged stress.

In the present study we aimed to find specific characteristic based on brain activity, that can be used to evaluate attention and, thus, can be used in brain-computer interface. We introduced a characteristic based on prestimulus beta-rhythm activity and proposed an approach to collaborative BCI aimed to enhance human-to-human interaction while performing shared visual task. We also described general setup for such BCI and its possible application in long task of classifying ambiguous visual stimuli with varying degrees of ambiguity by a group of people.

The perception of visual information includes such stages as the initial processing of sensory input and the interpretation of the received information (decision-making). The uncertainty of visual stimuli affects the neural activity during both sensory-processing and the decision-making stages. Here we analyzed spatial and temporal properties of the neural activity in the β-frequency band during the processing of ambiguous bistable stimuli. We tested how the stimulus ambiguity influenced the perceptual decision-making process.

We consider two small-world networks of Hodgkin-Huxley neurons interacting via inhibitory coupling. We found that synchronization indices (SI) in both networks oscillate periodically in time, so that time intervals of high SI alternate with time intervals of low SI. Depending on the coupling strength, the two coupled networks can be in the regime of either in-phase or anti-phase synchronization. We suppose that the inherent mechanism behind such a behavior lies in the cognitive resource redistribution between neuronal ensembles of the brain.

The analysis of neurophysiological mechanisms responsible for the imagination of movements is essential for developing brain-computer interfaces. We have carried out MEG experiments with voluntary participants performing imaginary movements. We have analyzed the features of motor imagery (imagery arms and legs movements) at the source level. We have obtained the results that confirmed two types of motor imagery: visual imagery and kinesthetic imagery. We have analyzed averaged distributions of normalized sources power for the visual imagery and kinesthetic imagery subgroups of volunteers and identified the main differences between these types of motor imagery in terms of the excitation of sources of neural activity. We have applied statistical cluster-based permutation test to identify the differences between the averaged distributions of normalized sources power for visual and kinesthetic imagery.

In the present paper, we introduce an extended machine-learning-based approach to detect inter-areal functional connectivity based on an artificial neural network (ANN). Using the concept of generalized synchronization, we show that the proposed approach is relevant to infer functional dependencies between remote brain areas of interest from multivariate EEG recordings. We verify the ANN-based method to capture the reconfiguration of functional connectivity during motor execution. The proposed model showed good ability to approximate functional relations between the electrical activity of parietal and frontal areas and motor cortex at different stages of motor execution, providing an adequate pattern of functional connectivity network.

Healthy aging affects structural and neurochemical properties of the human brain neural network. It also changes the brain functioning via the transformation of neural interactions both within and between functionally distinct brain areas. The age-related degradation of the brain functioning is evident on the behavioral level in terms of the decline in reaction time, low ability to execute and control complex motor actions, weak flexibility in learning new skills. In this paper we apply functional connectivity analysis to reveal the age-related changes in the integrative brain dynamic during the motor initiation before the dominant hand movements accompanied. Analyzing the whole-scalp electroencephalography (EEG) signals on the sensor level, we find higher theta-band coupling in the ipsilateral hemisphere.