Phase Synchronization Research Articles

Effects of transcranial direct current stimulation on brain activity and cortical functional connectivity in children with autism spectrum disorders.

Transcranial direct current stimulation (tDCS) has emerged as a therapeutic option to mitigate symptoms in individuals with autism spectrum disorder (ASD). Our study investigated the effects of a two-week regimen of tDCS targeting the left dorsolateral prefrontal cortex (DLPFC) in children with ASD, examining changes in rhythmic brain activity and alterations in functional connectivity within key neural networks: the default mode network (DMN), sensorimotor network (SMN), and dorsal attention network (DAN). We enrolled twenty-six children with ASD and assigned them randomly to either an active stimulation group (n=13) or a sham stimulation group (n=13). The active group received tDCS at an intensity of 1mA to the left DLPFC for a combined duration of 10 days. Differences in electrical brain activity were pinpointed using standardized low-resolution brain electromagnetic tomography (sLORETA), while functional connectivity was assessed via lagged phase synchronization. Compared to the typically developing children, children with ASD exhibited lower current source density across all frequency bands. Post-treatment, the active stimulation group demonstrated a significant increase in both current source density and resting state network connectivity. Such changes were not observed in the sham stimulation group. tDCS targeting the DLPFC may bolster brain functional connectivity in patients with ASD, offering a substantive groundwork for potential clinical applications.

Review on Phase Synchronization Methods for Spaceborne Multistatic Synthetic Aperture Radar.

Multistatic synthetic aperture radar (SAR) is a special mode of SAR system. The radar transmitter and receiver are located on different satellites, which brings many advantages, such as flexible baseline configuration, diverse receiving modes, and more detailed ground object classification information. The multistatic SAR has been widely used in interferometry, moving target detection, three-dimensional imaging, and other fields. The frequency offset between different oscillators will cause a modulation phase error in the signal. Therefore, phase synchronization is one of the most critical problems to be addressed in distributed SAR systems. This article reviews phase synchronization techniques, which are mainly divided into two methods: synchronization by direct microwave link and synchronization by a data-based estimation algorithm. Furthermore, the future development of synchronization technology is anticipated.

Cardiorespiratory Response to Workload Volume and Ergonomic Risk: Automotive Assembly Line Operators’ Adaptations

Repetitive tasks can lead to long-term cardiovascular problems due to continuous strain and inadequate recovery. The automobile operators on the assembly line are exposed to these risks when workload volume changes according to the workstation type. However, the current ergonomic assessments focus primarily on observational and, in some cases, biomechanical methods that are subjective and time-consuming, overlooking cardiorespiratory adaptations. This study aimed to analyze the cardiorespiratory response to distinct workload volumes and ergonomic risk (ER) scores for an automotive assembly line. Sixteen male operators (age = 38 ± 8 years; BMI = 25 ± 3 kg·m2) volunteered from three workstations (H1, H2, and H3) with specific work cycle duration (1, 3, and 5 min respectively). Electrocardiogram (ECG), respiratory inductance plethysmography (RIP), and accelerometer (ACC) data were collected during their shift. The results showed significant differences from the first to the last 10 min, where H3 had its SDRRi reduced (p = 0.014), H1’s phase synchrony and H2’s coordination between thoracic and abdominal movements decreased (p < 0.001, p = 0.039). In terms of ergonomic risk, the moderate-high rank showed a reduction in SDRRi (p = 0.037) and moderate-risk activities had diminished phase synchrony (p = 0.018) and correlation (p = 0.004). Thus, the explored parameters could have the potential to develop personalized workplace adaptation and risk assessment systems.

Spatiotemporal patterns corresponding to phase synchronization and generalized synchronization states of thermoacoustic instability.

Thermoacoustic instability in turbulent combustion systems emerges from the complex interplay among the flame, flow, and acoustic subsystems. While the onset of thermoacoustic instability exhibits a global order, the characteristics of local interactions between subsystems responsible for this order are not well understood. Here, we utilize the framework of synchronization to elucidate the spatiotemporal interactions among heat release rate fluctuations in the flame, velocity fluctuations in the flow, and acoustic pressure fluctuations in a turbulent combustor, across the bluff-body stabilized flame. We examine two forms of thermoacoustic instability, characterized by phase synchronization and generalized synchronization of the acoustic pressure and global heat release rate oscillations. Despite the presence of global synchrony, we uncover a coexistence of frequency synchrony and desynchrony in the local interaction of these oscillations within the reaction field. In regions of frequency-locked oscillations, various phase-locking patterns occur, including phase synchrony and partial phase synchrony. We observe that the local formation of small pockets of phase synchrony and strong amplitude correlation between these oscillations is sufficient to trigger the state of global phase synchronization. As the global dynamics approach generalized synchronization, these local regions of synchrony expand in the reaction field. Additionally, through coupled analysis of acoustic pressure and local flow velocity fluctuations, we infer that the spatial region of flow-acoustic synchrony plays a significant role in governing thermoacoustic instabilities. Our findings imply that, in turbulent combustors, an intrinsic local balance between order, partial order, and disorder within the coupled subsystems sustains the global order during thermoacoustic instability.

Phase Synchronization With Intermittent Locomotion and Nonoverlapping Sensing and Actuation

Phase Synchronization With Intermittent Locomotion and Nonoverlapping Sensing and Actuation

Emergence of phase clusters and coexisting states reveals the structure-function relationship.

The Brain Connectome Project has made significant strides in uncovering the structural connections within the brain on various levels. This has led to the question of how brain structure and function are related. Our research explores this relationship in an adaptive neural network in which synaptic conductance between neurons follows spike-time synaptic plasticity rules. By adjusting the plasticity boundary, the network exhibits diverse collective behaviors, including phase synchronization, phase locking, hierarchical synchronization (phase clusters), and coexisting states. Using graph theory, we found that hierarchical synchronization is related to the community structure, while coexisting states are related to the hierarchical self-organizing and core-periphery structure. The network evolves into several tightly connected modules, with sparsely intermodule connections resulting in the formation of phase clusters. In addition, the hierarchical self-organizing structure facilitates the emergence of coexisting states. The coexistence state promotes the evolution of the core-periphery structure. Our results point towards the equivalence between function and structure, with function emerging from structure, and structure being influenced by function in a complex dynamic process.

Optimal input reverberation and homeostatic self-organization toward the edge of synchronization.

Transient or partial synchronization can be used to do computations, although a fully synchronized network is sometimes related to the onset of epileptic seizures. Here, we propose a homeostatic mechanism that is capable of maintaining a neuronal network at the edge of a synchronization transition, thereby avoiding the harmful consequences of a fully synchronized network. We model neurons by maps since they are dynamically richer than integrate-and-fire models and more computationally efficient than conductance-based approaches. We first describe the synchronization phase transition of a dense network of neurons with different tonic spiking frequencies coupled by gap junctions. We show that at the transition critical point, inputs optimally reverberate through the network activity through transient synchronization. Then, we introduce a local homeostatic dynamic in the synaptic coupling and show that it produces a robust self-organization toward the edge of this phase transition. We discuss the potential biological consequences of this self-organization process, such as its relation to the Brain Criticality hypothesis, its input processing capacity, and how its malfunction could lead to pathological synchronization and the onset of seizure-like activity.

Predictive maintenance in electrical power systems: Thermography and statistical methods for phase synchronization analysis in disconnected substations

Phase synchronization can aid in understanding the dynamics of underlying systems. In an electric substation, disconnect, failures mostly occur due to overheating. In this work using the Hilbert transform to assess the phase synchrony of a disconnect temperature and its relationship with meteorological variables and disconnect current can be helpful in understanding temperature influences and composition. Statistical methodologies, including entropy analysis, correlation, Kalman filter, Fast Fourier Transform, Augmented Dickey–Fuller test, and Kwiatkowski–Phillips–Schmidt–Shin test, were employed. One of the differentials of this study is the use of the median filter, which was able to access the phase synchrony of the variables with disconnect temperature. An important result is that current is not the most correlated variable with disconnect temperature, instead, air temperature exhibits the highest correlation, and the highest phase synchronization is only obtained when disconnect current and air temperatures are combined. This study contributes to the advancement of power substation operations, advocating for continuous monitoring and analysis to ensure a reliable electricity supply obtaining better predictive maintenance.

Research on Brain Networks of Human Balance Based on Phase Estimation Synchronization.

Phase synchronization serves as an effective method for analyzing the synchronization of electroencephalogram (EEG) signals among brain regions and the dynamic changes of the brain. The purpose of this paper is to study the construction of the functional brain network (FBN) based on phase synchronization, with a special focus on neural processes related to human balance regulation. This paper designed four balance paradigms of different difficulty by blocking vision or proprioception and collected 19-channel EEG signals. Firstly, the EEG sequences are segmented by sliding windows. The phase-locking value (PLV) of core node pairs serves as the phase-screening index to extract the valid data segments, which are recombined into new EEG sequences. Subsequently, the multichannel weighted phase lag index (wPLI) is calculated based on the new EEG sequences to construct the FBN. The experimental results show that due to the randomness of the time points of body balance adjustment, the degree of phase synchronization of the datasets screened by PLV is more obvious, improving the effective information expression of the subsequent EEG data segments. The FBN topological structures of the wPLI show that the connectivity of various brain regions changes structurally as the difficulty of human balance tasks increases. The frontal lobe area is the core brain region for information integration. When vision or proprioception is obstructed, the EEG synchronization level of the corresponding occipital lobe area or central area decreases. The synchronization level of the frontal lobe area increases, which strengthens the synergistic effect among the brain regions and compensates for the imbalanced response caused by the lack of sensory information. These results show the brain regional characteristics of the process of human balance regulation under different balance paradigms, providing new insights into endogenous neural mechanisms of standing balance and methods of constructing brain networks.

Phenylephrine alters phase synchronization between cerebral blood velocity and blood pressure in ME/CFS with orthostatic intolerance.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) with orthostatic intolerance (OI) is characterized by neurocognitive deficits perhaps related to upright hypocapnia and loss of cerebral autoregulation (CA). We performed N-back neurocognition testing and calculated the phase synchronization index (PhSI) between arterial pressure (AP) and cerebral blood velocity (CBV) as a time-dependent measurement of cerebral autoregulation in 11 control (mean age = 24.1 yr) and 15 patients with ME/CFS (mean age = 21.8 yr). All patients with ME/CFS had postural tachycardia syndrome (POTS). A 10-min 60° head-up tilt (HUT) significantly increased heart rate (109.4 ± 3.9 vs. 77.2 ± 1.6 beats/min, P < 0.05) and respiratory rate (20.9 ± 1.7 vs. 14.2 ± 1.2 breaths/min, P < 0.05) and decreased end-tidal CO2 (ETCO2; 33.9 ± 1.1 vs. 42.8 ± 1.2 Torr, P < 0.05) in ME/CFS versus control. In ME/CFS, HUT significantly decreased CBV compared with control (-22.5% vs. -8.7%, P < 0.005). To mitigate the orthostatic CBV reduction, we administered supplemental CO2, phenylephrine, and acetazolamide and performed N-back testing supine and during HUT. Only phenylephrine corrected the orthostatic decrease in neurocognition by reverting % correct n = 4 N-back during HUT in ME/CFS similar to control (ME/CFS = 38.5 ± 5.5 vs. ME/CFS + PE= 65.6 ± 5.7 vs. Control 56.9 ± 7.5). HUT in ME/CFS resulted in increased PhSI values indicating decreased CA. Although CO2 and acetazolamide had no effect on PhSI in ME/CFS, phenylephrine caused a significant reduction in PhSI (ME/CFS = 0.80 ± 0.03 vs. ME/CFS + PE= 0.69 ± 0.04, P < 0.05) and improved cerebral autoregulation. Thus, PE improved neurocognitive function in patients with ME/CFS, perhaps related to improved neurovascular coupling, cerebral autoregulation, and maintenance of CBV.NEW & NOTEWORTHY We evaluated cognitive function before and after CO2, acetazolamide, and phenylephrine, which mitigate orthostatic reductions in cerebral blood velocity. Neither CO2 nor acetazolamide affected N-back testing (% correct answers) during an orthostatic challenge. Only phenylephrine improved upright N-back performance in ME/CFS, as it both blocked hyperventilation and increased CO2 significantly compared with those untreated. And only phenylephrine resulted in improved PSI values in both ME/CFS and control while upright, suggesting improved cerebral autoregulation.

Why behaviour matters: Studying inter-brain coordination during child-caregiver interaction

Modern technology allows for simultaneous neuroimaging from interacting caregiver-child dyads. Whereas most analyses that examine the coordination between brain regions within an individual brain do so by measuring changes relative to observed events, studies that examine coordination between two interacting brains generally do this by measuring average intra-brain coordination across entire blocks or experimental conditions. In other words, they do not examine changes in inter-brain coordination relative to individual behavioural events. Here, we discuss the limitations of this approach. First, we present data suggesting that fine-grained temporal interdependencies in behaviour can leave residual artifact in neuroimaging data. We show how artifact can manifest as both power and (through that) phase synchrony effects in EEG and affect wavelet transform coherence in fNIRS analyses. Second, we discuss different possible mechanistic explanations of how inter-brain coordination is established and maintained. We argue that non-event-locked approaches struggle to differentiate between them. Instead, we contend that approaches which examine how interpersonal dynamics change around behavioural events have better potential for addressing possible artifactual confounds and for teasing apart the overlapping mechanisms that drive changes in inter-brain coordination.

A dynamic learning method for phase synchronization control in Hodgkin–Huxley neuronal networks

A dynamic learning method for phase synchronization control in Hodgkin–Huxley neuronal networks

Evoked oscillatory cortical activity during acute pain: Probing brain in pain by transcranial magnetic stimulation combined with electroencephalogram.

Temporal dynamics of local cortical rhythms during acute pain remain largely unknown. The current study used a novel approach based on transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) to investigate evoked-oscillatory cortical activity during acute pain. Motor (M1) and dorsolateral prefrontal cortex (DLPFC) were probed by TMS, respectively, to record oscillatory power (event-related spectral perturbation and relative spectral power) and phase synchronization (inter-trial coherence) by 63 EEG channels during experimentally induced acute heat pain in 24 healthy participants. TMS-EEG was recorded before, during, and after noxious heat (acute pain condition) and non-noxious warm (Control condition), delivered in a randomized sequence. The main frequency bands (α, β1, and β2) of TMS-evoked potentials after M1 and DLPFC stimulation were recorded close to the TMS coil and remotely. Cold and heat pain thresholds were measured before TMS-EEG. Over M1, acute pain decreased α-band oscillatory power locally and α-band phase synchronization remotely in parietal-occipital clusters compared with non-noxious warm (all p < .05). The remote (parietal-occipital) decrease in α-band phase synchronization during acute pain correlated with the cold (p = .001) and heat pain thresholds (p = .023) and to local (M1) α-band oscillatory power decrease (p = .024). Over DLPFC, acute pain only decreased β1-band power locally compared with non-noxious warm (p = .015). Thus, evoked-oscillatory cortical activity to M1 stimulation is reduced by acute pain in central and parietal-occipital regions and correlated with pain sensitivity, in contrast to DLPFC, which had only local effects. This finding expands the significance of α and β band oscillations and may have relevance for pain therapies.

Quantum phase synchronization via exciton-vibrational energy dissipation sustains long-lived coherence in photosynthetic antennas

The lifetime of electronic coherences found in photosynthetic antennas is known to be too short to match the energy transfer time, rendering the coherent energy transfer mechanism inactive. Exciton-vibrational coherence time in excitonic dimers which consist of two chromophores coupled by excitation transfer interaction, can however be much longer. Uncovering the mechanism for sustained coherences in a noisy biological environment is challenging, requiring the use of simpler model systems as proxies. Here, via two-dimensional electronic spectroscopy experiments, we present compelling evidence for longer exciton-vibrational coherence time in the allophycocyanin trimer, containing excitonic dimers, compared to isolated pigments. This is attributed to the quantum phase synchronization of the resonant vibrational collective modes of the dimer, where the anti-symmetric modes, coupled to excitonic states with fast dephasing, are dissipated. The decoupled symmetric counterparts are subject to slower energy dissipation. The resonant modes have a predicted nearly 50% reduction in the vibrational amplitudes, and almost zero amplitude in the corresponding dynamical Stokes shift spectrum compared to the isolated pigments. Our findings provide insights into the mechanisms for protecting coherences against the noisy environment.

Oscillatory Neural Network-Based Ising Machine Using 2D Memristors.

Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.

Alpha desynchronization during the filtering initiation phase reflects active processing of distractors

The ability to select task-relevant information and filter out task-irrelevant information is critical to our success in daily goal-directed behavior. Researchers call this ability filtering efficiency and divide it into three cognitive processing stages: detection of distractors, initiation of filtering, and unnecessary storage. Although researchers have conducted more studies on ERP components related to filtration efficiency, there are few studies related to neural oscillations. Alpha oscillation activity is related to the active processing of information and the suppression of distractors. In the current EEG study, we used the change detection task with distracted items to examine whether alpha activity during filtering initiation reflects reactive suppression of distractors by manipulating memory load levels and the presence or absence of distractors. Results showed that, the presence of the distractors caused an increase in the degree of desynchronization of the alpha oscillations, and in the subsequent time, the alpha activity level returned to a level consistent with the absence of interference conditions. Phase synchronization between frontal and posterior brain regions in the upper alpha oscillations found no effects associated with distractors. Based on these results, we believed that the alpha activity during the filtering initiation phase reflected the active processing of distractors, but this may also be due to lower perceptual load of the target items. In addition, we observed a dominance effect of the right hemisphere in both time-frequency results and connectivity results. We speculate that this effect is related to the activation of the right ventral frontoparietal network.

Tailoring transcranial alternating current stimulation based on endogenous event-related P3 to modulate premature responses: a feasibility study.

Transcranial alternating current stimulation (tACS) is a brain stimulation method for modulating ongoing endogenous oscillatory activity at specified frequency during sensory and cognitive processes. Given the overlap between event-related potentials (ERPs) and event-related oscillations (EROs), ERPs can be studied as putative biomarkers of the effects of tACS in the brain during cognitive/sensory task performance. This preliminary study aimed to test the feasibility of individually tailored tACS based on individual P3 (latency and frequency) elicited during a cued premature response task. Thus, tACS frequency was individually tailored to match target-P3 ERO for each participant. Likewise, the target onset in the task was adjusted to match the tACS phase and target-P3 latency. Twelve healthy volunteers underwent tACS in two separate sessions while performing a premature response task. Target-P3 latency and ERO were calculated in a baseline block during the first session to allow a posterior synchronization between the tACS and the endogenous oscillatory activity. The cue and target-P3 amplitudes, delta/theta ERO, and power spectral density (PSD) were evaluated pre and post-tACS blocks. Target-P3 amplitude significantly increased after activetACS, when compared to sham. Evoked-delta during cue-P3 was decreased after tACS. No effects were found for delta ERO during target-P3 nor for the PSD and behavioral outcomes. The present findings highlight the possible effect of phase synchronization between individualized tACS parameters and endogenous oscillatory activity, which may result in an enhancement of the underlying process (i.e., an increase of target-P3). However, an unsuccessful synchronization between tACS and EEG activity might also result in a decrease in the evoked-delta activity during cue-P3. Further studies are needed to optimize the parameters of endogenous activity and tACS synchronization. The implications of the current results for future studies, including clinical studies, are further discussed since transcranial alternating current stimulation can be individually tailored based on endogenous event-related P3 to modulate responses.

Parametric Frequency Divider Based Ising Machines.

We report on a new class of Ising machines (IMs) that rely on coupled parametric frequency dividers (PFDs) as macroscopic artificial spins. Unlike the IM counterparts based on subharmonic-injection locking (SHIL), PFD IMs do not require strong injected continuous-wave signals or applied dc voltages. Therefore, they show a significantly lower power consumption per spin compared to SHIL-based IMs, making it feasible to accurately solve large-scale combinatorial optimization problems that are hard or even impossible to solve by using the current von Neumann computing architectures. Furthermore, using high quality factor resonators in the PFD design makes PFD IMs able to exhibit a nanowatt-level power per spin. Also, it remarkably allows a speedup of the phase synchronization among the PFDs, resulting in shorter time to solution and lower energy to solution despite the resonators' longer relaxation time. As a proof of concept, a 4-node PFD IM has been demonstrated. This IM correctly solves a set of Max-Cut problems while consuming just 600 nanowatts per spin. This power consumption is 2 orders of magnitude lower than the power per spin of state-of-the-art SHIL-based IMs operating at the same frequency.

ArguteDUB: Deep Learning Based Distributed Uplink Beamforming in 6G-Based IoV

In the last decade, MIMO spatial multiplexing and distributed beamforming play a significant role in improving data throughput through cooperative transmission. It has been widely used in wireless communication, especially in 6 G. However, the distributed uplink beamforming is still an open problem in highly dynamic environments. However, the proposed 6 G technology represents the further integration of deep learning and wireless communication. In this paper, we propose Argute Distributed Uplink Beamforming (ArguteDUB), which uses a feedback algorithm with an offline-trained deep learning model to implement highly dynamic distributed uplink beamforming for the Internet of Vehicles (IoV) in 6 G. Specifically, each vehicle enables the base station (BS)/access point (AP) to separate different channel state information (CSI) by inserting orthogonal sequences into the sending data. The BS adopts deep learning to filter the noise and predict the beamforming weight to achieve phase synchronization. Unlike traditional distributed uplink beamforming, ArguteDUB can be adapted to the highly dynamic time-varying channels. The simple network structure ensures the fast response of ArguteDUB. In addition, we make ArguteDUB Orthogonal Frequency Division Multiplexing (OFDM) compatible so that it can be easily deployed in 6 G networks. Our evaluation shows that ArguteDUB has an signal-to-noise ratio (SNR) gain of about 5 dB to 5.3 dB over the single vehicle transmission mode.

High-Precision Time Difference of Arrival Estimation Method Based on Phase Measurement

In unmanned aerial vehicle (UAV)-based time difference of arrival (TDOA) positioning technique, baselines are limited due to communication constraints. In this case, the accuracy is highly sensitive to the TDOA measurements’ error. This article primarily addresses the problem of short-baseline high-precision time synchronization and TDOA measurement. We conducted a detailed analysis of error models in TDOA systems, considering both the time and phase measurement. We utilize the frequency division wireless phase synchronization technique in TDOA systems. Building upon this synchronization scheme, we propose a novel time delay estimation method that relies on phase measurements based on the integer least squares method. The performance of this method is demonstrated through Monte Carlo simulations and outdoor experiments. The standard deviations of synchronization and TDOA measurements in experiments are 1.12 ps and 1.66 ps, respectively. Furthermore, the circular error probable (CEP) accuracy is improved from 0.33%R to 0.02%R, offering support for the practical application of distributed short-baseline high-precision passive location techniques.