qEEG Analysis in the Diagnosis of Alzheimers Disease; a Comparison of Functional Connectivity and Spectral Analysis

Alzheimers disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain, causing a decline in cognitive abilities and difficulties in engaging in day-to-day activities. This study compares an FFT-based spectral analysis against a functional connectivity analysis based on phase synchronization, for finding known differences between AD patients and Healthy Control (HC) subjects. Both of these quantitative analysis methods were applied on a dataset comprising bipolar EEG montages values from 20 diagnosed AD patients and 20 age-matched HC subjects. Additionally, an attempt was made to localize the identified AD-induced brain activity effects in AD patients. The obtained results showed the advantage of the functional connectivity analysis method compared to a simple spectral analysis. Specifically, while spectral analysis could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting that the AD patients brains are in a phase-locked state. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized in the centro-parietal and centro-temporal areas in the theta frequency band (4-8 Hz). The contribution of this study is that it applies a neural metric for Alzheimers detection from a data science perspective rather than from a neuroscience one. The study shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.

Cortical traveling waves reflect state-dependent hierarchical sequencing of local regions in the human connectome network

Recent human studies using electrocorticography have demonstrated that alpha and theta band oscillations form traveling waves on the cortical surface. According to neural synchronization theories, the cortical traveling waves may group local cortical regions and sequence them by phase synchronization; however these contributions have not yet been assessed. This study aimed to evaluate the functional contributions of traveling waves using connectome-based network modeling. In the simulation, we observed stable traveling waves on the entire cortical surface wherein the topographical pattern of these phases was substantially correlated with the empirically obtained resting-state networks, and local radial waves also appeared within the size of the empirical networks (< 50 mm). Importantly, individual regions in the entire network were instantaneously sequenced by their internal frequencies, and regions with higher intrinsic frequency were seen in the earlier phases of the traveling waves. Based on the communication-through-coherence theory, this phase configuration produced a hierarchical organization of each region by unidirectional communication between the arbitrarily paired regions. In conclusion, cortical traveling waves reflect the intrinsic frequency-dependent hierarchical sequencing of local regions, global traveling waves sequence the set of large-scale cortical networks, and local traveling waves sequence local regions within individual cortical networks.

Resonant waves drive long-range correlations in fMRI signals

The fundamental principles driving spontaneous long-range correlations between distant brain areas - known as intrinsic functional connectivity - remain unclear. To investigate this, we develop an ultrafast functional Magnetic Resonance Imaging (fMRI) approach with unprecedented temporal resolution (38 milliseconds) in the rat brain. We detect a repertoire of principal components exhibiting standing wave properties, i.e., with phase relationships varying gradually across space and oscillating in time, driving in- and anti-phase synchronization across distinct cortical and subcortical structures. The spatial configuration, stability and peak frequency of these standing waves is found to depend on the sedation/anaesthesia state, with medetomidine sedation revealing the most stable (i.e., less damped) standing waves, resonating at frequencies extending up to 0.25 Hz. Our findings show that the complex activity patterns observed in resting-state fMRI signals result from the superposition of standing waves, supporting the hypothesis that intrinsic functional connectivity is inherently associated to resonance phenomena.

Synchronization or cluster synchronization in coupled Van der Pol oscillators networks with different topological types

In this paper, we try to discuss the mechanism of synchronization or cluster synchronization in the coupled Van der Pol oscillator networks with different topology types by using the theory of rotating periodic solutions. The synchronous solutions here are transformed into rotating periodic solutions of some dynamical systems. By analyzing the bifurcation of rotating periodic solutions, the critical conditions of synchronous solutions are given in three different networks. We use the rotating periodic matrix in the rotating periodic theory to judge various types of synchronization phenomena, such as complete synchronization, anti-phase synchronization, periodic synchronization, or cluster synchronization. All rotating periodic matrices which satisfy the exchange invariance of multiple oscillators form special groups in these networks. By using the conjugate classes of these groups, we obtain various possible synchronization solutions in the three networks. In particular, we find symmetry has different effects on synchronization in different networks. The network with better symmetry has more elements in the corresponding group, which may have more types of synchronous solutions. However, different types of symmetry may get the same type of synchronous solutions or different types of synchronous solutions, depending on whether their corresponding rotating periodic matrices are similar.

Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network

Pre-Bötzinger complex (PBC) is a necessary condition for the generation of respiratory rhythm. Due to the existence of synaptic gaps, delay plays a key role in the synchronous operation of coupled neurons. In this study, the relationship between synchronization and correlation degree is established for the first time by using ISI bifurcation and correlation coefficient, and the relationship between synchronization and correlation degree is discussed under the conditions of no delay, symmetric delay, and asymmetric delay. The results show that the phase synchronization of two coupling PBCs is closely related to the weak correlation, that is, the weak phase synchronization may occur under the condition of incomplete synchronization. Moreover, the time delay and coupling strength are controlled in the modified PBC network model, which not only reveals the law of PBC firing transition but also reveals the complex synchronization behavior in the coupled chaotic neurons. Especially, when the two coupled neurons are nonidentical, the complete synchronization will disappear. These results fully reveal the dynamic behavior of the PBC neural system, which is helpful to explore the signal transmission and coding of PBC neurons and provide theoretical value for further understanding respiratory rhythm.

Remarkable Sensitivity of Young Honey Bee Workers to Multiple Non-photic, Non-thermal, Forager Cues That Synchronize Their Daily Activity Rhythms

Honey bees live in colonies containing tens of thousands of workers that coordinate their activities to produce efficient colony-level behavior. In free-foraging colonies, nest bees are entrained to the forager daily phase of activity even when experiencing conflicting light-dark illumination regime, but little is known on the cues mediating this potent social synchronization. We monitored locomotor activity in an array of individually caged bees in which we manipulated the contact with neighbour bees. We used circular statistics and coupling function analyses to estimate the degree of social synchronization. We found that young bees in cages connected to cages housing foragers showed stronger rhythms, better synchronization with each other, higher coupling strength, and a phase more similar to that of the foragers compared to similar bees in unconnected cages. These findings suggest that close distance contacts are sufficient for social synchronization or that cage connection facilitated the propagation of time-giving social cues. Coupling strength was higher for bees placed on the same tray compared with bees at a similar distance but on a different tray, consistent with the hypothesis that substrate borne vibrations mediate phase synchronization. Additional manipulation of the contact between cages showed that social synchronization is better among bees in cages connected with tube with a single mesh partition compared to sealed tubes consistent with the notion that volatile cues act additively to substrate borne vibrations. These findings are consistent with self-organization models for social synchronization of activity rhythms and suggest that the circadian system of honey bees evolved remarkable sensitivity to non-photic, non-thermal, time giving entraining cues enabling them to tightly coordinate their behavior in the dark and constant physical environment of their nests.

An Enhanced Control Strategy for Mitigation of State-Transition Oscillation Phenomena in Grid-Forming Self-Synchronized Converter System with Islanded Power System

This paper proposes an enhanced control strategy for mitigating state-transition oscillations in active and reactive power responses of self-synchronized converter system to secure the islanded power system stability. The self-synchronized converter is well known for “grid-forming” that is able to operate to stand-alone mode (SAM) providing grid voltage and frequency without phase synchronization units. Although the grid-forming (GFM) is self-synchronized, the inherent synchronization principle causes system degradation in which should maintain a point of common coupling (PCC) voltage for critical loads as well as transitions from grid-connected mode (GCM) to SAM and vice versa. Therefore, this paper focuses on resolving the inherent oscillatory issues in GFM self-synchronized converter system (especially adopted ‘synchronverter’ principle), and proposes a control strategy for controllability improvement based on stability analysis for smooth state-transition under islanded power system. The efficacy of the proposed control method is verified through a high-fidelity electromagnetic transient (EMT) simulation with case studies on 30kW synchronverter system and further experimental hardware-in-loop system (HILS) test with Opal-RT (OP-5707) platform.

The Spatial Reach of Neuronal Coherence and Spike-field Coupling across the Human Neocortex

Neuronal coherence is thought to be a fundamental mechanism of communication in the brain, where synchronized field potentials coordinate synaptic and spiking events to support plasticity and learning. Although the spread of field potentials has garnered great interest, little is known about the spatial reach of phase synchronization, or neuronal coherence. Functional connectivity between different brain regions is known to occur across long distances, but the locality of coherence within a brain region is understudied. Here we used simultaneous recordings from electrocorticography (ECoG) grids and high-density microelectrode arrays to estimate the spatial reach of neuronal coherence and spike-field coherence (SFC) across frontal, temporal, and occipital cortices during cognitive tasks in humans. We observed the strongest coherence within a 2-3 cm distance from the microelectrode arrays, potentially defining an effective range for local communication. This range was relatively consistent across brain regions, spectral frequencies, and cognitive tasks. The magnitude of coherence showed power law decay with increasing distance from the microelectrode arrays, where the highest coherence occurred between ECoG contacts, followed by coherence between ECoG and deep cortical LFP, and then SFC (i.e., ECoG > LFP > SFC). The spectral frequency of coherence also affected its magnitude. Alpha coherence (8-14 Hz) was generally higher than other frequencies for signals nearest the microelectrode arrays, whereas delta coherence (1-3 Hz) was higher for signals that were farther away. Action potentials in all brain regions were most coherent with the phase of alpha oscillations, which suggests that alpha waves could play a larger, more spatially local role in spike timing than other frequencies. These findings provide a deeper understanding of the spatial and spectral dynamics of neuronal coherence, further advancing knowledge about how activity propagates across the human brain.

Increasing the sensitivity of real-time method for diagnostic of autogenerators phase synchronization based on their non-stationary time series

Purpose of this work is to of the research – Increasing the sensitivity of a method for diagnosing phase synchronization of autogenerators based on their non-stationary time series in real time, and also a comparison of the statistical properties of the proposed modification of the method with the well-known method for diagnostics of loop synchronization, which has proven itself in the analysis of experimental data. Methods.The paper compares the probabilities of the appearance of an error of the second kind of the developed modified method for diagnostics of phase synchronization with the probabilities of occurrence of an error of the second kind of the known method at equal values of sensitivity. When comparing the methods, generated test time realizations with a priori known boundaries of the phase synchronization sections are used, which repeat the statistical properties of the experimental data. It also compares the computational complexity of the two methods. Results. A modification of the method for diagnosing phase synchronization of autonomic regulation circuits in real time is proposed. It is shown that the proposed modification provides similar values of sensitivity and probability of appearance of errors of the second kind as the previously proposed approach. The developed method has less computational complexity than the previously proposed method. The values of free parameters corresponding to different values of sensitivity and probability of appearance of errors of the second kind are obtained. Conclusion. The area of application of the developed method with modification is formulated. The low computational complexity of the proposed method, as well as the possibility of switching devices to integer computations in calculations, makes it possible to use it for wearable registrations performing calculations in real time, based on small-sized low-power processors that do not support floating-point arithmetic operations.