Synchronization Of Oscillators Research Articles

Atypical oscillatory and aperiodic signatures of visual sampling in developmental dyslexia

Temporal processing deficits in Developmental Dyslexia (DD) have been documented extensively at the behavioral level, leading to the formulation of neural theories positing that such anomalies in parsing multisensory input rely on aberrant synchronization of neural oscillations or to an excessive level of neural noise. Despite reading being primarily supported by visual functions, experimental evidence supporting these theories remains scarce. Here, we tested 26 adults with DD (9 females) and 31 neurotypical controls (16 females) with a temporal segregation/integration task that required participants to either integrate or segregate two rapidly presented displays while their EEG activity was recorded. We confirmed behaviorally a temporal sampling deficit in DD, which affected specifically the rapid segregation of visual input. While the ongoing alpha frequency and the excitation/inhibition (E/I) ratio (i.e., an index of neural noise quantified by the aperiodic exponent) were differently modulated based on task demands in typical readers, DD participants exhibited an impairment in alpha speed modulation and an altered E/I ratio that affected their rapid visual sampling. Nonetheless, an association between visual temporal sampling accuracy and both alpha frequency and the E/I ratio measured at rest were evident in the DD group, further confirming an anomalous interplay between alpha synchronization, the E/I ratio and active visual sampling. These results provide evidence that both trait- and state-like differences in alpha-band synchronization and neural noise levels coexist in the dyslexic brain and are synergistically responsible for cascade effects on visual sampling and reading.

Mutual Synchronization of Spintronic Nano-Oscillator Ensembles

Introduction. The use of spintronic components significantly enhances the performance, reduces the size, and lowers the power consumption of modern electronic devices. The spintronic oscillator (SO) is an integral part of spintronic devices. Connecting several SOs (> 100) into ensembles with subsequent synchronization mitigates such SO drawbacks as low output power and high phase noise. These drawbacks appear as a result of an increase in the output power of an SO ensemble compared to a single oscillator under a simultaneous decrease in the spectral linewidth of the ensemble.Aim. To investigate the impact of connection topologies, synchronization mechanisms, and oscillator failures on the synchronization of oscillator ensembles.Materials and methods. The Kuramoto phase model was used to simplify the numerical modeling of synchronization of SOs connected into an ensemble.Results. A Kuramoto equation for phases of SOs connected in an ensemble was derived, and the influence of connection topologies and oscillator failures on the synchronization parameters of an ensemble of N connected oscillators was demonstrated.Conclusion. In order to ensure the shortest transition time of an SO ensemble to the synchronous mode, topologies with a higher number of connections between oscillators (e.g., "all-to-all") are preferable. The results obtained confirm the advantages of local connection of an SO ensemble by a common current, thus forming an "all-to-all" topology. This makes the transition time of the SO ensemble to the synchronous mode less dependent on both oscillator failures and the number of synchronized SOs.

Epigenetic Mechanisms of the Anti-inflammatory Action of the Opioid Peptide Leutragin: Role of Sirtuin 1

Sirtuin 1 (SIRT1) is a class III histone deacetylase that plays a key role in resolving inflammation through known epigenetic mechanisms involving histone and nuclear factor κB (NF-κB) deacetylation. Deacetylation reduces the transcriptional activity of NF-κB and the associated production of pro-inflammatory cytokines such as interleukin 1β (IL-1β), tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6). In the present study, we show for the first time that biomodeling of acute lung inflammation by a single injection of lipopolysaccharide (LPS) induces synchronous oscillations of mRNA levels of cytokines IL-1β, TNF-α, IL-6 and SIRT1 deacetylase in the lungs, the maximum amplitudes of cytokine mRNA oscillations are observed between 1.5 and 5 hours, whereas high levels of SIRT1 mRNA are observed up to 24 hours, when cytokine mRNA oscillations have already faded, which is consistent with the hypothesis about the role of SIRT1 as a factor acting in the phase of inflammation resolution. The study shows that the mechanism of anti-inflammatory action of inhaled hexapeptide Leutragin, a δ-opioid receptor agonist, is related to its ability to increase SIRT1 mRNA expression and decrease the amplitudes of IL-1β, TNF-α, and IL-6 mRNA oscillations in the lungs, which generally leads to the resolution of inflammation in the conditions of biomodeling of acute lung inflammation.

Collective behaviors of globally coupled fractional oscillators driven by different fluctuating potentials

The synchronization, stability and stochastic resonance (SR) behaviors of a globally coupled fractional oscillators driven by different fluctuating potentials are investigated. The statistical synchronicity between oscillators’ displacements is derived by reasonably grouping variables and using the moment method. Based on this, the output amplitude gain (OAG) and the necessary and sufficient condition for the system stability are obtained. Then we verified the theoretical results through numerical simulation, and mainly characterize the effects of the number of oscillators N, coupling strength ɛ, fractional order α, and noise intensity σ2 on these collective dynamic behaviors. We found that different from the fractional order coupled system with identical noise, the coupling strength ɛ and system size N also play important roles in the evolution of the system. The larger the number of oscillators N or the coupling strength ɛ is, the larger the stability region is. The synchronization speed is mainly controlled by α and has the same shape as the fractional order kernel function. Increasing the coupling strength and number of oscillators can speed up oscillators synchronization. Furthermore, all oscillators show SR phenomenon. The changes of SR with parameters were analyzed from the perspective of the strength of system randomness. This indicated that we can control the SR of the coupled fractional dynamic model by properly adjusting the system parameters within a certain range.

Seeing the piles of the velvet bending under our finger sliding over a tactile stimulator improves the feeling of the fabric.

Using friction modulation to simulate fabrics with a tactile stimulator (i.e. virtual surface) is not sufficient to render fabric touch and even more so for hairy fabrics. We hypothesized that seeing the pile of the velvet darken or lighten depending on changes in the finger movement direction on the virtual surface should improve the velvet fabric rendering. Participants actively rubbed a tactile device or a velvet fabric looking at a screen that showed a synthesized image of a velvet that either remained static (V-static) or darkening/lightening with the direction of touch (V-moving). We showed that in V-moving condition, the touched surface was always perceived rougher, which is a descriptor of a real velvet (Experiment 1). Using electroencephalography and sources localization analyses, we found increased activity in the occipital and inferior parietal lobes (Experiment 2) when seeing dark and shining traces during back-and-forth finger movements over the virtual surface. This suggests that these two posterior cortical regions work together to evaluate visuo-tactile congruence between the seen and the felt (tactile). The visuo-tactile binding, evidenced by neural synchronization (specifically, theta band (5-7 Hz) oscillation) in the left inferior posterior parietal lobule, is consistent with enhanced integration of information and probably contributed to the emergence of a more realistic velvet representation.

Gap Junctions in Different Neuronal Groups Exert Different Effects on Sharp Wave-ripples of a Neuronal Network in CA1

Abstract Gap junctions are indispensable for achieving brain functions. The direct coupling between neurons connected by gap junctions may contribute to synchronization of neuronal firing and emergence of sharp wave-ripples(SWR), which affect brain functions such as memory consolidation. However, considering the heterogeneity of excitatory and inhibitory neurons, it is not quite clear whether gap junctions have the same effect on the emergence of SWR in network activity in different neuron types. In order to explore the above problems, we constructed a neuronal network located in CA1 region of hippocampus, which contains excitatory pyramidal cells, PV+BCs and axo-axonic cells. Taking into account diverse connections between neurons and properties of neurons, we investigated effects of gap junctions on SWR in different kinds of neuronal populations in the constructed network with chemical synaptic connections by neurodynamical modeling. Numerical results show that gap junctions within pyramidal neurons and PV+BCs promote the emergence of SWR, whereas gap junctions within axo-axonic cells suppress it. At the same time, it is revealed that gap junctions in axo-axonic cells play a dominant role in modulating SWR. We hope that these findings provide some inspiration for studies on neuronal heterogeneity and the enhancement of synchronicity of oscillations by gap junctions.

Quantized current steps due to the synchronization of microwaves with Bloch oscillations in small Josephson junctions.

Synchronization of Bloch oscillations in small Josephson junctions (JJs) under microwave radiation, which leads to current quantization, has been proposed as an effect that is dual to the appearance of Shapiro steps. This current quantization was recently demonstrated in superconducting nanowires in a compact high-impedance environment. Direct observation of current quantization in JJs would confirm the synchronization of Bloch oscillations with microwaves and help with the realisation of the metrological current standard. Here, we place JJs in a high-impedance environment and demonstrate dual Shapiro steps for frequencies up to 24 GHz (I = 7.7 nA). Current quantization exists, however, only in a narrow range of JJ parameters. We carry out a systematic study to explain this by invoking the model of a JJ in the presence of thermal noise. The findings are important for fundamental physics and application in quantum metrology.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics.

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Identifying Arnold's tongue for digital oscillators through event-based control in phase-locked loops.

Digital phase-locked loops (PLLs) are essential feedback circuits for synchronizing signals in digital communication systems. While amplitude and phase vary continuously in analog oscillators, the amplitude remains constant in digital oscillators with dynamical variations manifesting exclusively through changes in the timing of signal transitions. In this work, we introduce a novel analytically solvable event-based model for phase-locking in digital PLLs that leverages the discrete nature of digital signals. By employing a sampled control strategy, we demonstrate one-to-one and higher ratios of frequency locking under positive and negative feedback. By discretizing the continuous control signal, we drive a discrete iterative map, which we then use to derive analytical expressions for bifurcation curves, analogous to Arnold's tongue in analog oscillators. This mathematical framework provides an analytical approach for the analysis of synchronization and phase-locking in digital oscillators. Furthermore, the event-based control presented in this work for digital oscillators paves the way for energy-efficient circuit design and optimized control strategies for future digital communication systems.

Coordinated NREM sleep oscillations among hippocampal subfields modulate synaptic plasticity in humans

The integration of hippocampal oscillations during non-rapid eye movement (NREM) sleep is crucial for memory consolidation. However, how cardinal sleep oscillations bind across various subfields of the human hippocampus to promote information transfer and synaptic plasticity remains unclear. Using human intracranial recordings from 25 epilepsy patients, we find that hippocampal subfields, including DG/CA3, CA1, and SUB, all exhibit significant delta and spindle power during NREM sleep. The DG/CA3 displays strong coupling between delta and ripple oscillations with all the other hippocampal subfields. In contrast, the regions of CA1 and SUB exhibit more precise coordination, characterized by event-level triple coupling between delta, spindle, and ripple oscillations. Furthermore, we demonstrate that the synaptic plasticity within the hippocampal circuit, as indexed by delta-wave slope, is linearly modulated by spindle power. In contrast, ripples act as a binary switch that triggers a sudden increase in delta-wave slope. Overall, these results suggest that different subfields of the hippocampus regulate one another through diverse layers of sleep oscillation synchronization, collectively facilitating information processing and synaptic plasticity during NREM sleep.

IGF-I concentration determines cell fate by converting signaling dynamics as a bifurcation parameter in L6 myoblasts

Insulin-like growth factor (IGF)-I mediates long-term activities that determine cell fate, including cell proliferation and differentiation. This study aimed to characterize the mechanisms by which IGF-I determines cell fate from the aspect of IGF-I signaling dynamics. In L6 myoblasts, myogenic differentiation proceeded under low IGF-I levels, whereas proliferation was enhanced under high levels. Mathematical and experimental analyses revealed that IGF-I signaling oscillated at low IGF-I levels but remained constant at high levels, suggesting that differences in IGF-I signaling dynamics determine cell fate. We previously reported that differential insulin receptor substrate (IRS)-1 levels generate a driving force for cell competition. Computational simulations and immunofluorescence analyses revealed that asynchronous IRS-1 protein oscillations were synchronized during myogenic processes through cell competition. Disturbances of cell competition impaired signaling synchronization and cell fusion, indicating that synchronization of IGF-I signaling oscillation is critical for myoblast cell fusion to form multinucleate myotubes.

The phase coherence of cortical oscillations predicts dynamic changes in perceived visibility.

The phase synchronization of brain oscillations plays an important role in visual processing, perceptual awareness, and performance. Yet, the cortical mechanisms underlying modulatory effects of post-stimulus phase coherence and frequency-specific oscillations associated with different aspects of vision are still subject to debate. In this study, we aimed to identify the post-stimulus phase coherence of cortical oscillations associated with perceived visibility and contour discrimination. We analyzed electroencephalogram data from two masking experiments where target visibility was manipulated by the contrast ratio or polarity of the mask under various onset timing conditions (stimulus onset asynchronies, SOAs). The behavioral results indicated an SOA-dependent suppression of target visibility due to masking. The time-frequency analyses revealed significant modulations of phase coherence over occipital and parieto-occipital regions. We particularly identified modulations of phase coherence in the (i) 2-5Hz frequency range, which may reflect feedforward-mediated contour detection and sustained visibility; and (ii) 10-25Hz frequency range, which may be associated with suppressed visibility through inhibitory interactions between and within synchronized neural pathways. Taken together, our findings provide evidence that oscillatory phase alignments, not only in the pre-stimulus but also in the post-stimulus window, play a crucial role in shaping perceived visibility and dynamic vision.

Study of the Synchronization and Transmission of Intracellular Signaling Oscillations in Cells Using Bispectral Analysis.

The oscillation synchronization analysis in biological systems will expand our knowledge about the response of living systems to changes in environmental conditions. This knowledge can be used in medicine (diagnosis, therapy, monitoring) and agriculture (increasing productivity, resistance to adverse effects). Currently, the search is underway for an informative, accurate and sensitive method for analyzing the synchronization of oscillatory processes in cell biology. It is especially pronounced in analyzing the concentration oscillations of intracellular signaling molecules in electrically nonexcitable cells. The bispectral analysis method could be applied to assess the characteristics of synchronized oscillations of intracellular mediators. We chose endothelial cells from mouse microvessels as model cells. Concentrations of well-studied calcium and nitric oxide (NO) were selected for study in control conditions and well-described stress: heating to 40 °C and hyperglycemia. The bispectral analysis allows us to accurately evaluate the proportion of synchronized cells, their synchronization degree, and the amplitude and frequency of synchronized calcium and NO oscillations. Heating to 40 °C increased cell synchronization for calcium but decreased for NO oscillations. Hyperglycemia abolished this effect. Heating to 40 °C changed the frequencies and increased the amplitudes of synchronized oscillations of calcium concentration and the NO synthesis rate. The first part of this paper describes the principles of the bispectral analysis method and equations and modifications of the method we propose. In the second part of this paper, specific examples of the application of bispectral analysis to assess the synchronization of living cells in vitro are presented. The discussion compares the capabilities of bispectral analysis with other analytical methods in this field.

Generation of Synchronous Unpredictable Oscillations by Coupled Hopfield Neural Networks

Generation of Synchronous Unpredictable Oscillations by Coupled Hopfield Neural Networks

Beta bursts in the parkinsonian cortico-basal ganglia network form spatially discrete ensembles

Defining spatial synchronisation of pathological beta oscillations is important, given that many theories linking them to parkinsonian symptoms propose a reduction in the dimensionality of the coding space within and/or across cortico-basal ganglia structures. Such spatial synchronisation could arise from a single process, with widespread entrainment of neurons to the same oscillation. Alternatively, the partially segregated structure of cortico-basal ganglia loops could provide a substrate for multiple ensembles that are independently synchronized at beta frequencies. Addressing this question requires an analytical approach that identifies groups of signals with a statistical tendency for beta synchronisation, which is unachievable using standard pairwise measures. Here, we utilized such an approach on multichannel recordings of background unit activity (BUA) in the external globus pallidus (GP) and subthalamic nucleus (STN) in parkinsonian rats. We employed an adapted version of a principle and independent component analysis-based method commonly used to define assemblies of single neurons (i.e., neurons that are synchronized over short timescales). This analysis enabled us to define whether changes in the power of beta oscillations in local ensembles of neurons (i.e., the BUA recorded from single contacts) consistently covaried over time, forming a “beta ensemble”. Multiple beta ensembles were often present in single recordings and could span brain structures. Membership of a beta ensemble predicted significantly higher levels of short latency (<5 ms) synchrony in the raw BUA signal and phase synchronisation with cortical beta oscillations, suggesting that they comprised clusters of neurons that are functionally connected at multiple levels, despite sometimes being non-contiguous in space. Overall, these findings suggest that beta oscillations do not comprise of a single synchronisation process, but rather multiple independent activities that can bind both spatially contiguous and non-contiguous pools of neurons within and across structures. As previously proposed, such ensembles provide a substrate for beta oscillations to constrain the coding space of cortico-basal ganglia circuits.

The role of ventral hippocampal-medial prefrontal glutamatergic pathway on the non-affected side in post-stroke cognitive impairment

Elucidate the pathogenesis mechanism of post-stroke cognitive impairment (PSCI) can help to develop precision interventions. In this study, we established a mouse model of PSCI using the photochemical method, and behavioral tests including Y-maze and Novel object recognition task for accessing cognitive impairment were observed at week 2 post-stroke. Besides, synaptic plasticity, theta nerve oscillatory and the activity of glutamatergic neurons related to the ventral hippocampal-medial prefrontal glutamatergic neural pathway in the non-affected hemisphere (contralateral hemisphere to the lesion site) were observed. The result indicated the cognitive function declined at week 2 post-stroke. Synaptic plasticity, theta nerve oscillatory synchronization and the activity of glutamatergic neurons of the ventral hippocampal-medial prefrontal glutamatergic neural pathway in the non-affected hemisphere was down-regulated in the PSCI group compared to those of the SHAM group. Therefore, we concluded that the declined function of the ventral hippocampal-medial prefrontal glutamatergic pathway in the non-affected hemisphere is a biomarker in the occurrence of cognitive dysfunction after stroke.

Attentional selection and communication through coherence: Scope and limitations.

Synchronous neural oscillations are strongly associated with a variety of perceptual, cognitive, and behavioural processes. It has been proposed that the role of the synchronous oscillations in these processes is to facilitate information transmission between brain areas, the 'communication through coherence,' or CTC hypothesis. The details of how this mechanism would work, however, and its causal status, are still unclear. Here we investigate computationally a proposed mechanism for selective attention that directly implicates the CTC as causal. The mechanism involves alpha band (about 10 Hz) oscillations, originating in the pulvinar nucleus of the thalamus, being sent to communicating cortical areas, organizing gamma (about 40 Hz) oscillations there, and thus facilitating phase coherence and communication between them. This is proposed to happen contingent on control signals sent from higher-level cortical areas to the thalamic reticular nucleus, which controls the alpha oscillations sent to cortex by the pulvinar. We studied the scope of this mechanism in parameter space, and limitations implied by this scope, using a computational implementation of our conceptual model. Our results indicate that, although the CTC-based mechanism can account for some effects of top-down and bottom-up attentional selection, its limitations indicate that an alternative mechanism, in which oscillatory coherence is caused by communication between brain areas rather than being a causal factor for it, might operate in addition to, or even instead of, the CTC mechanism.

Inter-subject gamma oscillation synchronization as a biomarker of abnormal processing of social interaction in ADHD

Children with attention-deficit hyperactivity disorder (ADHD) have difficulties in social interactions. Studying brain activity during social interactions is difficult with conventional artificial stimuli. This pioneering study examined the neural correlates of social perception in children with ADHD and matched controls using naturalistic stimuli. We presented 20 children with ADHD and 20 age-and-sex-matched controls with tailored movies featuring high- or low-level social interactions while recording electroencephalographic signals. Both groups exhibited synchronized gamma-band oscillations, but controls demonstrated greater inter-subject correlations. Additionally, the difference in inter-subject correlations between high- and low-interaction movies was significantly larger in controls compared to ADHD patients. Between 55 and 75 Hz comparing viewing high interaction movies with low interaction moves, controls had a significantly larger weighting in the right parietal lobe, while ADHD patients had a significantly smaller weighting in the left occipital lobe. These findings reveal distinct spatiotemporal neural signatures in social interaction processing among children with ADHD and controls using naturalistic stimuli. These neural markers offer potential for group differentiation and assessing intervention efficacy, advancing our understanding ADHD-related social interaction mechanisms.

Sleep stages and brain oscillations: An in-depth analysis using quantitative and mathematical models

This study explores the intricate relationship between sleep stages and brain oscillations, utilizing quantitative EEG analysis and mathematical models. We examine the distinct EEG characteristics of light sleep, deep sleep, and REM sleep, focusing on power spectral density (PSD), coherence, and phase synchronization analyses. The Ising model and Kuramoto model provide frameworks for understanding the synchronization dynamics and phase relationships of neuronal populations across sleep stages. Our findings highlight significant variations in oscillatory activity, coherence, and phase synchronization, offering insights into the neural mechanisms underlying sleep regulation. Additionally, we discuss the implications of these findings for sleep disorders such as insomnia, sleep apnea, and narcolepsy, demonstrating how mathematical models can predict therapeutic outcomes and guide targeted interventions. This comprehensive analysis contributes to the understanding of sleep architecture and the development of novel treatments for sleep disorders.