Strength Of Synchronization Research Articles

Synchronization Of Autonomic Control Loops Of Blood Circulation In Patients With COVID-19

This study aims to investigate the strength of synchronization between the autonomic control loops of the cardiovascular system (CVS) in patients with COronaVIrus Disease 2019 (COVID-19). Methods — We calculated the total percent of phase synchronization index (S index) between the loops of autonomic control of heart rate and vascular tone in two sample groups: healthy individuals and COVID-19 patients. Results — The group-average value of the S index in COVID-19 patients is lower comparing to healthy individuals, the Mann-Whitney U-test confirmed that the differences are statistically significant. Conclusion — Obtained results suggest that the decreased value of the S index reflects the presence of a viral disease, and the S index is a promising basis for non-invasive screening diagnostics of viral diseases, particularly of COVID-19.

Parkinson's Disease Rating Scale Using Synchronization Analysis of Gait Dynamics.

Analysis of gait dynamics is a noninvasive and totally painless test, and it can be an ideal method for the diagnosis of neurodegenerative diseases. In this study, based on the strength of synchronization between dynamics of strides, we have suggested a rating scale method for Parkinson's disease (PD). Methods. The sample included 15 persons with PD (age: 66.8 ± 10.9 years) and 16 healthy persons (age: 39.3 ± 18.5 years) which were recruited from the Neurology Outpatient Clinic at Massachusetts General Hospital and were instructed to walk a 77 m long, straight hallway. The time interval of strides and subphases of strides were measured. Using the Hilbert transformation method, we obtained the data phase and used mean absolute error (MAE) to calculate the synchronization strength of the data phase. Results. In order to check the accuracy of our method, we measured the correlation between our numerical results (MAE) and values of the Hoehn-Yahr scale. Spearman's rank correlation coefficients (r) and the P values were calculated. MAE of left and right stride intervals (LRSI) significantly correlates with the Hoehn-Yahr scale for the subjects with PD (with r = 0.60 and P = 0.025 < 0.05). Conclusion. We have revealed that the synchronization weakness of LRSI shows the severity of PD. This method seems to be well suited as a rating scale for people with PD.

Increase in the Strength of Synchronization between the Baroreflex Waves of Blood Pressure and Heart Rate Due to the Lower Body Negative Pressure Effect

Increase in the Strength of Synchronization between the Baroreflex Waves of Blood Pressure and Heart Rate Due to the Lower Body Negative Pressure Effect

Network‐Synchronization Analysis Reveals the Weakening Tropical Circulations

AbstractThe impact of climate change on extreme weather is one of the most concerning problems. Climate change could cause more frequent and unexpected extreme weather. Still, synchronization features of extreme weather under climate change are not fully understood. Here, we develop a climate network approach to study global synchronization patterns of extreme events based on the 500‐hPa geopotential field. We find that a positive event synchronization (PES) network is associated with the tropical circulations in the tropics; a negative event synchronization network is associated with the large‐scale atmospheric Rossby waves in mid‐latitudes. By studying temporal evolution of the PES network, we find that the synchronization strengths in the tropics are weakening under climate change in relation to the slowing down of tropical circulations. Furthermore, our results show that the strength of synchronization in Africa and the Atlantic Ocean decrease faster than other regions.

Cortical gamma-band resonance preferentially transmits coherent input.

SUMMARYSynchronization has been implicated in neuronal communication, but causal evidence remains indirect. We use optogenetics to generate depolarizing currents in pyramidal neurons of the cat visual cortex, emulating excitatory synaptic inputs under precise temporal control, while measuring spike output. The cortex transforms constant excitation into strong gamma-band synchronization, revealing the well-known cortical resonance. Increasing excitation with ramps increases the strength and frequency of synchronization. Slow, symmetric excitation profiles reveal hysteresis of power and frequency. White-noise input sequences enable causal analysis of network transmission, establishing that the cortical gamma-band resonance preferentially transmits coherent input components. Models composed of recurrently coupled excitatory and inhibitory units uncover a crucial role of feedback inhibition and suggest that hysteresis can arise through spike-frequency adaptation. The presented approach provides a powerful means to investigate the resonance properties of local circuits and probe how these properties transform input and shape transmission.

Temporal patterns of synchrony in a pyramidal-interneuron gamma (PING) network.

Synchronization in neural systems plays an important role in many brain functions. Synchronization in the gamma frequency band (30-100 Hz) is involved in a variety of cognitive phenomena; abnormalities of the gamma synchronization are found in schizophrenia and autism spectrum disorder. Frequently, the strength of synchronization is not high, and synchronization is intermittent even on short time scales (few cycles of oscillations). That is, the network exhibits intervals of synchronization followed by intervals of desynchronization. Neural circuit dynamics may show different distributions of desynchronization durations even if the synchronization strength is fixed. We use a conductance-based neural network exhibiting pyramidal-interneuron gamma rhythm to study the temporal patterning of synchronized neural oscillations. We found that changes in the synaptic strength (as well as changes in the membrane kinetics) can alter the temporal patterning of synchrony. Moreover, we found that the changes in the temporal pattern of synchrony may be independent of the changes in the average synchrony strength. Even though the temporal patterning may vary, there is a tendency for dynamics with short (although potentially numerous) desynchronizations, similar to what was observed in experimental studies of neural synchronization in the brain. Recent studies suggested that the short desynchronizations dynamics may facilitate the formation and the breakup of transient neural assemblies. Thus, the results of this study suggest that changes of synaptic strength may alter the temporal patterning of the gamma synchronization as to make the neural networks more efficient in the formation of neural assemblies and the facilitation of cognitive phenomena.

Noise effect on the temporal patterns of neural synchrony

Neural synchrony in the brain is often present in an intermittent fashion, i.e., there are intervals of synchronized activity interspersed with intervals of desynchronized activity. A series of experimental studies showed that this kind of temporal patterning of neural synchronization may be very specific and may be correlated with behaviour (even if the average synchrony strength is not changed). Prior studies showed that a network with many short desynchronized intervals may be functionally different from a network with few long desynchronized intervals as it may be more sensitive to synchronizing input signals. In this study, we investigated the effect of channel noise on the temporal patterns of neural synchronization. We employed a small network of conductance-based model neurons that were mutually connected via excitatory synapses. The resulting dynamics of the network was studied using the same time-series analysis methods as used in prior experimental and computational studies. While it is well known that synchrony strength generally degrades with noise, we found that noise also affects the temporal patterning of synchrony. Noise, at a sufficient intensity (yet too weak to substantially affect synchrony strength), promotes dynamics with predominantly short (although potentially very numerous) desynchronizations. Thus, channel noise may be one of the mechanisms contributing to the short desynchronization dynamics observed in multiple experimental studies.

Gait coordination in overground walking with a virtual reality avatar.

Little information is currently available on interpersonal gait synchronization in overground walking. This is caused by difficulties in continuous gait monitoring over many steps while ensuring repeatability of experimental conditions. These challenges could be overcome by using immersive virtual reality (VR), assuming it offers ecological validity. To this end, this study provides some of the first evidence of gait coordination patterns for overground walking dyads in VR. Six subjects covered the total distance of 27 km while walking with a pacer. The pacer was either a real human subject or their anatomically and biomechanically representative VR avatar driven by an artificial intelligence algorithm. Side-by-side and front-to-back arrangements were tested without and with the instruction to synchronize steps. Little evidence of spontaneous gait coordination was found in both visual conditions, but persistent gait coordination patterns were found in the case of intentional synchronization. Front-to-back rather than side-by-side arrangement consistently yielded in the latter case higher mean synchronization strength index. Although the mean magnitude of synchronization strength index was overall comparable in both visual conditions when walking under the instruction to synchronize steps, quantitative and qualitative differences were found which might be associated with common limitations of VR solutions.

Spectral and Anatomical Patterns of Large-Scale Synchronization Predict Human Attentional Capacity.

The capacity of visual attention determines how many visual objects may be perceived at any moment. This capacity can be investigated with multiple object tracking (MOT) tasks, which have shown that it varies greatly between individuals. The neuronal mechanisms underlying capacity limits have remained poorly understood. Phase synchronization of cortical oscillations coordinates neuronal communication within the fronto-parietal attention network and between the visual regions during endogenous visual attention. We tested a hypothesis that attentional capacity is predicted by the strength of pretarget synchronization within attention-related cortical regions. We recorded cortical activity with magneto- and electroencephalography (M/EEG) while measuring attentional capacity with MOT tasks and identified large-scale synchronized networks from source-reconstructed M/EEG data. Individual attentional capacity was correlated with load-dependent strengthening of theta (3-8Hz), alpha (8-10Hz), and gamma-band (30-120Hz) synchronization that connected the visual cortex with posterior parietal and prefrontal cortices. Individual memory capacity was also preceded by crossfrequency phase-phase and phase-amplitude coupling of alpha oscillation phase with beta and gamma oscillations. Our results show that good attentional capacity is preceded by efficient dynamic functional coupling and decoupling within brain regions and across frequencies, which may enable efficient communication and routing of information between sensory and attentional systems.

Decoding Adaptive Visuomotor Behavior Mediated by Non-linear Phase Coupling in Macaque Area MT.

The idea that a flexible behavior relies on synchronous neural activity within intra- and inter-associated cortical areas has been a matter of intense research in human and animal neuroscience. The neurophysiological mechanisms underlying this behavioral correlate of the synchronous activity are still unknown. It has been suggested that the strength of neural synchrony at the level of population is an important neural code to guide an efficient transformation of the sensory input into the behavioral action. In this study, we have examined the non-linear synchronization between neural ensembles in area MT of the macaque visual cortex by employing a non-linear cross-frequency coupling technique, namely bicoherence. We trained a macaque monkey to detect a brief change in the cued stimulus during a visuomotor detection task. The results show that the non-linear phase synchronization in the high-gamma frequency band (100–250 Hz) predicts the animal’s reaction time. The strength of non-linear phase synchronization is selective to the target stimulus location. In addition, the non-linearity characteristics of neural synchronization are selectively modulated when the monkey covertly attends to the stimulus inside the neuron’s receptive field. This additional evidence indicates that non-linear neuronal synchronization may be affected by a cognitive function like spatial attention. Our neural and behavioral observations reflect that two crucial processes may be involved in processing of visuomotor information in area MT: (I) a non-linear cortical process (measured by the bicoherence) and (II) a linear process (measured by the spectral power).

A complex-valued functional brain connectivity descriptor amenable to Riemannian geometry

Objective. We introduce a novel, phase-based, functional connectivity descriptor that encapsulates not only the synchronization strength between distinct brain regions, but also the time-lag between the involved neural oscillations. The new estimator employs complex-valued measurements and results in a brain network sketch that lives on the smooth manifold of Hermitian Positive Definite (HPD) matrices. Approach: Leveraging the HPD property of the proposed descriptor, we adapt a recently introduced dimensionality reduction methodology that is based on Riemannian Geometry and discriminatively detects the recording sites which best reflect the differences in network organization between contrasting recording conditions in order to overcome the problem of high-dimensionality, usually encountered in the connectivity patterns derived from multisite encephalographic recordings. Main results. The proposed framework is validated using an EEG dataset that refers to the challenging problem of differentiating between attentive and passive visual responses. We provide evidence that the reduced connectivity representation facilitates high classification performance and caters for neuroscientific explorations. Significance. Our paper is the very first that introduces an advanced connectivity descriptor that can take advantage of Riemannian geometry tools. The proposed descriptor, that inherently and simultaneously captures both the strength and the corresponding time-lag of the phase synchronization, is the first phase-based descriptor tailored to leverage the benefits of Riemannian geometry.

Spectro-Temporal Processing in a Two-Stream Computational Model of Auditory Cortex.

Neural processing of sounds in the dorsal and ventral streams of the (human) auditory cortex is optimized for analyzing fine-grained temporal and spectral information, respectively. Here we use a Wilson and Cowan firing-rate modeling framework to simulate spectro-temporal processing of sounds in these auditory streams and to investigate the link between neural population activity and behavioral results of psychoacoustic experiments. The proposed model consisted of two core (A1 and R, representing primary areas) and two belt (Slow and Fast, representing rostral and caudal processing respectively) areas, differing in terms of their spectral and temporal response properties. First, we simulated the responses to amplitude modulated (AM) noise and tones. In agreement with electrophysiological results, we observed an area-dependent transition from a temporal (synchronization) to a rate code when moving from low to high modulation rates. Simulated neural responses in a task of amplitude modulation detection suggested that thresholds derived from population responses in core areas closely resembled those of psychoacoustic experiments in human listeners. For tones, simulated modulation threshold functions were found to be dependent on the carrier frequency. Second, we simulated the responses to complex tones with missing fundamental stimuli and found that synchronization of responses in the Fast area accurately encoded pitch, with the strength of synchronization depending on number and order of harmonic components. Finally, using speech stimuli, we showed that the spectral and temporal structure of the speech was reflected in parallel by the modeled areas. The analyses highlighted that the Slow stream coded with high spectral precision the aspects of the speech signal characterized by slow temporal changes (e.g., prosody), while the Fast stream encoded primarily the faster changes (e.g., phonemes, consonants, temporal pitch). Interestingly, the pitch of a speaker was encoded both spatially (i.e., tonotopically) in Slow area and temporally in Fast area. Overall, performed simulations showed that the model is valuable for generating hypotheses on how the different cortical areas/streams may contribute toward behaviorally relevant aspects of auditory processing. The model can be used in combination with physiological models of neurovascular coupling to generate predictions for human functional MRI experiments.

Dysregulation of temporal dynamics of synchronous neural activity in adolescents on autism spectrum.

Autism spectrum disorder is increasingly understood to be based on atypical signal transfer among multiple interconnected networks in the brain. Relative temporal patterns of neural activity have been shown to underlie both the altered neurophysiology and the altered behaviors in a variety of neurogenic disorders. We assessed brain network dynamics variability in autism spectrum disorders (ASD) using measures of synchronization (phase-locking) strength, and timing of synchronization and desynchronization of neural activity (desynchronization ratio) across frequency bands of resting-state electroencephalography (EEG). Our analysis indicated that frontoparietal synchronization is higher in ASD but with more short periods of desynchronization. It also indicates that the relationship between the properties of neural synchronization and behavior is different in ASD and typically developing populations. Recent theoretical studies suggest that neural networks with a high desynchronization ratio have increased sensitivity to inputs. Our results point to the potential significance of this phenomenon to the autistic brain. This sensitivity may disrupt the production of an appropriate neural and behavioral responses to external stimuli. Cognitive processes dependent on the integration of activity from multiple networks maybe, as a result, particularly vulnerable to disruption. Autism Res 2020, 13: 24-31. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Parts of the brain can work together by synchronizing the activity of the neurons. We recorded the electrical activity of the brain in adolescents with autism spectrum disorder and then compared the recording to that of their peers without the diagnosis. We found that in participants with autism, there were a lot of very short time periods of non-synchronized activity between frontal and parietal parts of the brain. Mathematical models show that the brain system with this kind of activity is very sensitive to external events.

A Mobile-based Monitoring System for Micro Small Medium Enterprises (MSMEs) with Offline Data Synchronization

Objectives: The use of technology has become ubiquitous in different areas of businesses and plays a significant role in gathering huge amount of data. In Philippines, for example, a government agency, Department of Trade and Industry (DTI) is entrusted with a project to assist and monitor the status of Micro Small Medium Enterprises (MSMEs), some of which are located in rural or remote places. There are number of approaches that are able to address registration and monitoring of clients or business establishments records through mobile-based application. However, there may be times that poor connectivity or no network connectivity is experienced with LTE or wi-fi network. Hence, one of the major challenges in dealing with mobile computing is on how to provide the clients with the same user experience even when there are some issues on connectivity. In this study, we developed a mobile-based application that may significantly improve the process whereby clients or users have the chance to access data that is stored in a stationary database or some other data repository at any time and any place. Methods: We introduce an innovative approach for efficient tracking and monitoring data leveraging the strength of data synchronization and replication. A mobile-based platform using a centralized database was designed and developed to facilitate quick registration of clients and easy portfolio monitoring of the business establishments. Application: The mobile-based system is an application that runs through android platform necessary for MSMEs’ efficient registration and monitoring purposes whereby users in remote areas are provided with the same user experience despite issues on connectivity (i.e. from “poor” to “no network” connectivity). We deployed the system in the Department of Industry (DTI) provincial office, which covers thousands of MSMEs. Findings: Experiments reveal that our approach provides effective results, which gains high acceptability of the users. Keywords: Agile Approach, Agile Software Development Method, Context Diagram, Micro Small Medium Enterprises (MSME), Mobile-based Application, Monitoring System, Offline Data Synchronization

Simultaneous enhancement of spin-torque diode sensitivity and frequency by voltage controlled magnetic anisotropy and parametric synchronization

We propose a method for simultaneous enhancement of the Radio Frequency (RF) detection sensitivity and the detection frequency of a Magnetic Tunnel Junction (MTJ) by utilizing voltage controlled magnetic anisotropy (VCMA) and the technique of parametric synchronization. We show that the presence of VCMA enhances the coupling strength of parametric synchronization by ∼40 times, giving a very high sensitivity of the order of 105 mV/mW. We discuss the detailed field, bias, external RF power, and temperature dependence of the phenomenon for optimization of MTJ based detectors. The results will be useful for realizing highly sensitive RF detectors with high detection frequency working at low input powers.

Increasing Local Excitability of Brainstem Respiratory Nuclei Reveals a Distributed Network Underlying Respiratory Motor Pattern Formation.

The core circuit of the respiratory central pattern generator (rCPG) is located in the ventrolateral medulla, especially in the pre-Bötzinger complex (pre-BötC) and the neighboring Bötzinger complex (BötC). To test the hypothesis that this core circuit is embedded within an anatomically distributed pattern-generating network, we investigated whether local disinhibition of the nucleus tractus solitarius (NTS), the Kölliker-Fuse nuclei (KFn), or the midbrain periaqueductal gray area (PAG) can similarly affect the respiratory pattern compared to disinhibition of the pre-BötC/BötC core. In arterially-perfused brainstem preparations of rats, we recorded the three-phase respiratory pattern (inspiration, post-inspiration and late-expiration) from phrenic and vagal nerves before and after bilateral microinjections of the GABA(A)R antagonist bicuculline (50 nl, 10 mM). Local disinhibition of either NTS, pre-BötC/BötC, or KFn, but not PAG, triggered qualitatively similar disruptions of the respiratory pattern resulting in a highly significant increase in the variability of the respiratory cycle length, including inspiratory and expiratory phase durations. To quantitatively analyze these motor pattern perturbations, we measured the strength of phase synchronization between phrenic and vagal motor outputs. This analysis showed that local disinhibition of all brainstem target nuclei, but not the midbrain PAG, significantly decreased the strength of phase synchronization. The convergent perturbations of the respiratory pattern suggest that the rCPG expands rostrally and dorsally from the designated core but does not include higher mid-brain structures. Our data also suggest that excitation-inhibition balance of respiratory network synaptic interactions critically determines the network dynamics that underlie vital respiratory rhythm and pattern formation.

Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets

Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

The Orientation Selectivity of Spike-LFP Synchronization in Macaque V1 and V4.

Orientation selectivity is a fundamental property of visual cortical neurons and plays a crucial role in pattern perception. Although many studies have dedicated to explain how the orientation selectivity emerged, the mechanism underlying orientation selectivity is still not clear. In this work, we investigated the synchronization between spikes and local field potentials (LFP) in gamma band, with the aim of providing a new avenue to analyze the orientation selectivity. The experimental data were recorded by utilizing two chronically implanted multi-electrode arrays, where each array consisted of 48 electrodes and was placed over V1 and V4, respectively, in two macaques performing a selective visual attention task. An unbiased and robust measure for quantifying the synchronization between spikes and LFP was employed in the analysis process, which is termed as spike-triggered correlation matrix synchronization (SCMS) and performs reliably for limited samples of data. We observed the spike-LFP synchronization in three cases, i.e., spikes and LFP in V1, spikes and LFP in V4, spikes in V4 and LFP in V1. From the orientation tuning curves based on the spike-LFP synchronization, it is found that there is a strong correlation between the synchronization and grating orientation. The neurons in both V1 and V4 exhibit orientation selectivity, but V1 is stronger. In addition, the spike-LFP synchronization strength between V1 and V4 also shows orientation selectivity to drifting gratings. It means that the synchronization not only reflects the basic features of visual stimulation, but also describes the orientation tuning characteristics of neurons in different regions. Our results suggest that the spike-LFP synchronization can be used as an alternative and effective method to study the mechanism for generating orientation selectivity of visual neurons.

Explosive synchronization of multi-layer frequency-weighted coupled complex systems**Project supported by the National Natural Science Foundation of China (Grant No. 61771299), the Key Laboratory of Speciality Fiber Optics and Optical Access Networks, Shanghai University, China (Grant No. SKLSFO2012-14), Funding of the Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information Technology, China, and Funding of the

Synchronization is a phenomenon that is ubiquitous in engineering and natural ecosystems. The study of explosive synchronization on a single-layer network gives the critical transition coupling strength that causes explosive synchronization. However, no significant findings have been made on multi-layer complex networks. This paper proposes a frequency-weighted Kuramoto model on a two-layer network and the critical coupling strength of explosive synchronization is obtained by both theoretical analysis and numerical validation. It is found that the critical value is affected by the interaction strength between layers and the number of network oscillators. The explosive synchronization will be hindered by enhancing the interaction and promoted by increasing the number of network oscillators. Our results have importance across a range of engineering and biological research fields.

Routing information flow by separate neural synchrony frequencies allows for “functionally labeled lines” in higher primate cortex

Efficient transfer of sensory information to higher (motor or associative) areas in primate visual cortical areas is crucial for transforming sensory input into behavioral actions. Dynamically increasing the level of coordination between single neurons has been suggested as an important contributor to this efficiency. We propose that differences between the functional coordination in different visual pathways might be used to unambiguously identify the source of input to the higher areas, ensuring a proper routing of the information flow. Here we determined the level of coordination between neurons in area MT in macaque visual cortex in a visual attention task via the strength of synchronization between the neurons' spike timing relative to the phase of oscillatory activities in local field potentials. In contrast to reports on the ventral visual pathway, we observed the synchrony of spikes only in the range of high gamma (180 to 220 Hz), rather than gamma (40 to 70 Hz) (as reported previously) to predict the animal's reaction speed. This supports a mechanistic role of the phase of high-gamma oscillatory activity in dynamically modulating the efficiency of neuronal information transfer. In addition, for inputs to higher cortical areas converging from the dorsal and ventral pathway, the distinct frequency bands of these inputs can be leveraged to preserve the identity of the input source. In this way source-specific oscillatory activity in primate cortex can serve to establish and maintain "functionally labeled lines" for dynamically adjusting cortical information transfer and multiplexing converging sensory signals.