Coherence Network Research Articles

Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm

Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task due to the mixture of trend, seasonal and residual components in time series and the nonlinear relationships between Chla and the hydro-environmental factors. Here we developed a hybrid approach for long-term trend forecast of Chla in lakes, taking the Lake Taihu as an instantiation case, by the integration of Seasonal and Trend decomposition using Loess (STL), wavelet coherence, and Convolutional Neural Network with Bidirectional Long Short-Term Memory (CNN-BiLSTM). The results showed that long-term trends of Chla and the hydro-environmental factors could be effectively separated from the seasonal and residual terms by STL method, thereby enhancing the characterization of long-term variation. The resonance pattern and time lag between Chla and the hydro-environmental factors in the time-frequency domain were accurately identified by wavelet coherence. Chla responded quickly to variations in TP, but showed a time lag response to variations in WT in Lake Taihu. The forecasting method using multivariate and CNN-BiLSTM largely outperformed the other methods for Lake Taihu with regards to R2, RMSE, IOA and peak capture capability, owning to the combination of CNN for extracting local features and the integration of bidirectional propagation mechanism for the acquisition of higher-level features. The proposed hybrid deep learning approach offers an effective solution for the long-term trend forecast of algal blooms in eutrophic lakes and is capable of addressing the complex attributes of hydro-environmental data.

Early cortico-muscular coherence and cortical network changes in Parkinson's patients treated with MRgFUS.

To investigate cortical network changes using Magnetoencephalography (MEG) signals in Parkinson's disease (PD) patients undergoing Magnetic Resonance-guided Focused Ultrasound (MRgFUS) thalamotomy. We evaluated the MEG signals in 16 PD patients with drug-refractory tremor before and after 12-month from MRgFUS unilateral lesion of the ventralis intermediate nucleus (Vim) of the thalamus contralateral to the most affected body side. We recorded patients 24 h before (T0) and 24 h after MRgFUS (T1). We analyzed signal epochs recorded at rest and during the isometric extension of the hand contralateral to thalamotomy. We evaluated cortico-muscular coherence (CMC), the out-strength index from non-primary motor areas to the pre-central area and connectivity indexes, using generalized partial directed coherence. Statistical analysis was performed using RMANOVA and post hoct-tests. Most changes found at T1 compared to T0 occurred in the beta band and included: (1) a re-adjustment of CMC distribution; (2) a reduced out-strength from non-primary motor areas toward the precentral area; (3) strongly reduced clustering coefficient values. These differences mainly occurred during motor activation and with few statistically significant changes at rest. Correlation analysis showed significant relationships between changes of out-strength and clustering coefficient in non-primary motor areas and the changes in clinical scores. One day after MRgFUS thalamotomy, PD patients showed a topographically reordered CMC and decreased cortico-cortical flow, together with a reduced local connection between different nodes. These findings suggest that the reordered cortico-muscular and cortical-networks in the beta band may represent an early physiological readjustment related to MRgFUS Vim lesion.

Volatility contagion between cryptocurrencies, gold and stock markets pre-and-during COVID-19: evidence using DCC-GARCH and cascade-correlation network

The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets, particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic. This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets, focusing on gold and six major stock markets (Japan, USA, UK, China, Germany, and France) using daily data from January 2, 2011, to June 2, 2022, with 2958 daily observations. We employ DCC-GARCH, wavelet coherence, and cascade-correlation network models to analyze the relationship between Bitcoin and those markets. Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty. We also find evidence of long-term contagion between Bitcoin and the six stock markets, with short-term contagion observed in Chinese and Japanese markets during COVID-19. These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets. Hedge funds, mutual funds, and individual and institutional investors can benefit from using our findings in their risk management strategies. Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions. It holds significant implications for stock market investors and policymakers, providing evidence for potentially using cryptocurrencies for hedging, for diversification, or as a safe haven.

EEG cortical network reveals the temporo-spatial mechanism of visual search

This study aims to explore a method based on brain networks for implicit attention by using wavelet coherence as feature to identify individual targets in the visual field, find the optimal classification rhythm and time window, and investigate the relationship between the optimal rhythm and N2pc event-related potential. The study uses a weighted minimum norm estimate to locate the sources of the scalp EEG and reconstructs the source time series. The functional connectivity between brain areas during the visual search process is evaluated using wavelet coherence analysis, and a lateral difference network is constructed based on the difference in coherence values between the left and right visual fields. A support vector machine classifier is trained based on the wavelet coherence network features to identify the target in the left or right visual field. We also extract N2pc from the source activity data of the parieto-occipital brain region and record the time period in which N2pc occurred. The study finds that the best classification performance is achieved in the theta rhythm from 200 to 400 ms and achieved an average classification accuracy of 87% (chance level: 51.07%) in a serial search task. And this time window corresponds to the time period when N2pc appeared. The results show that the use of wavelet coherence analysis to evaluate the functional connectivity between brain areas during the visual search process provides a new approach for analyzing brain activity. The study's findings regarding the relationship between the N2pc and theta rhythm and the effectiveness of using wavelet coherence network features based on the theta rhythm for visual search classification contribute to the understanding of the neural mechanisms underlying visual search.

Within and between electrophysiology networks for EEG and EMG under different thumb forces

ObjectiveCompared to the research on the single central or peripheral nervous system, there is still a lack of systematic information on the relationship between the two. It is necessary to clarify whether there is information interaction between the central nervous system and the peripheral motor system via brain-muscle coupling. ApproachIn this study, we constructed the coherence networks within and between the brain and muscles at different levels of thumb force. We then developed a model that can accurately identify different levels of thumb force based on their distinct network features. Main resultsIn the internal coherence network of EEG or EMG, high-level force activity evokes stronger and more concentrated network patterns than low-level force activity. In the corticomuscular coherence network (CMCN) between EEG and EMG, there is a stronger coupling relationship between non-contralateral motor brain regions and most muscle groups for high-level force compared to low-level force. The CMCN features led to significant improvements in identifying different thumb forces, resulting in an accuracy rate of 99.00 %, which is at least 4.00 % higher than using EEG or EMG alone. SignificanceThis study indicates that there is information interaction between the central nervous system and the peripheral motor system during movement, which can have important implications for improving rehabilitation and motor control methods.

Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury.

Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can provide more scientific and effective treatment solutions for SCI patients. According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group. The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%. The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.

The Impact of COVID-19 on the Relationship between Non-Renewable Energy and Saudi Stock Market Sectors Using Wavelet Coherence Approach and Neural Networks

In this study, we examine the impact of COVID-19 on the relationship between non-renewable energy and Saudi stock market sectors for the period 11 January 2017–22 January 2022. We apply wavelet coherence and Radial Basis Function Neural Network (RBFNN) models. Our results provide evidence that COVID-19 led to an increase in the strength of the relationship between oil as a main non-renewable energy source and Saudi stock market sectors and affected the nature and direction of this relationship. The relationships between oil and commercial and professional services, materials, banks, energy, and transportation sectors are the most affected. Our results will help hedge funds, mutual funds, and individual investors, forecast the direction of Saudi stock market sectors and the use of oil for hedging or diversification during periods of uncertainty and crisis. It will also help decision and policymakers in Saudi Arabia to make the necessary decisions and actions to maintain the stability of the stock market sectors during these periods.

Predicting individual muscle fatigue tolerance by resting-state EEG brain network *

Objective. Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and fatigue tolerance varies across individuals. Various studies have emphasized the close relationships between muscle fatigue and the brain. However, the relationships between the resting-state electroencephalogram (rsEEG) brain network and individual muscle fatigue tolerance remain unexplored. Approach. Eighteen elite water polo athletes took part in our experiment. Five-minute before- and after-fatigue-exercise rsEEG and fatiguing task (i.e. elbow flexion and extension) electromyography (EMG) data were recorded. Based on the graph theory, we constructed the before- and after-task rsEEG coherence network and compared the network differences between them. Then, the correlation between the before-fatigue rsEEG network properties and the EMG fatigue indexes when a subject cannot keep on exercising anymore was profiled. Finally, a prediction model based on the before-fatigue rsEEG network properties was established to predict fatigue tolerance. Main results. Results of this study revealed the significant differences between the before- and after-exercise rsEEG brain network and found significant high correlations between before-exercise rsEEG network properties in the beta band and individual muscle fatigue tolerance. Finally, an efficient support vector regression (SVR) model based on the before-exercise rsEEG network properties in the beta band was constructed and achieved the accurate prediction of individual fatigue tolerance. Similar results were also revealed on another 30 subject swimmer data set further demonstrating the reliability of predicting fatigue tolerance based on the rsEEG network. Significance. Our study investigates the relationship between the rsEEG brain network and individual muscle fatigue tolerance and provides a potential objective physiological biomarker for tolerance prediction and the regulation of muscle fatigue.

Socioeconomic Determinants of Social Capital Inequality among Older Adults of Tehran: A Population-Based Study (Urban HEART-2).

Background: Social capital (SC) is an essential concept of communities, and there is more SC inequality. In the current study, we studied SC Inequality concerning the explanatory socioeconomic factors. Methods: In a cross-sectional observational study, the household data were retrieved from an Urban Health Equity Assessment and Response Tool survey in 2011. Over 5000 elderlies in Tehran (> 60 years old) consented toparticipate in the study and filled out 2 SC questionnaires (SCQ) and a household properties questionnaire (HPQ). Subsequently, the collected HPQ data were then statistically analyzed and used to measure the economic status of households. Besides, the statistical concentration index of the SC was applied to measure socioeconomic inequality and decomposed into its determinants using both SCQ and HPQ data collections. The concentration index and the decomposition analysis were used to analyze the study data. Results: The overall concentration index of the SC in Tehran senior citizens was 0.059 (95%CI,0.044-0.076). Among the SC dimensions, collective activity, social coherence, voluntary help, and social network were more concentrated in the poor older adults. Simultaneously, reciprocity was more focused onthe wealthy class, and there was no inequality in trust. The decomposition of the concentration index showed that economic status made the most considerable contribution to the SC inequality among citizens (69.11%), followed by the level of education (12.695) and the elderlies'job type (9.58%). Conclusion: Given that the economic status and level of education are the 2 main determinants of SC inequality, a holistic policy approach should be adopted to address the socioeconomic inequalities that are taken root in Tehran's senior communities.

Action Coherence Network for Weakly-Supervised Temporal Action Localization

Weakly-supervised Temporal Action Localization (W-TAL) aims at simultaneously classifying and locating all action instances with only video-level supervision. However, current W-TAL methods have two limitations. First, they ignore the difference in video representations between an action instance and its surrounding background when generating and scoring action proposals. Second, the unique characteristics of the RGB frames and optical flow are largely ignored when fusing these two modalities. To address these problems, an Action Coherence Network (ACN) is proposed in this paper. Its core is a new coherence loss which exploits both classification predictions and video content representations to supervise action boundary regression and thus leads to more accurate action localization results. Besides, the proposed ACN explicitly takes into account the specific characteristics of RGB frames and optical flow by training two separate sub-networks, each of which is able to generate modality-specific action proposals independently. Finally, to take advantage of the complementary action proposals generated by two streams, a novel fusion module is introduced to reconcile them and obtain the final action localization results. Experiments on the THUMOS14 and ActivityNet datasets show that our ACN outperforms the state-of-the-art W-TAL methods, and is even comparable to some recent fully-supervised methods. Particularly, ACN achieves a mean average precision of 26.4% on the THUMOS14 dataset under the IoU threshold 0.5.

Topology of quantum coherence networks in singlet fission: mapping exciton states into real space and the dislocation induced three dimensional manifolds.

An understanding of the global structure of quantum coherence networks in coupled multistate systems is of great importance for the development of emerging quantum technologies such as quantum control and quantum materials design. Here, we study the topology of a quantum coherence network of a typical singlet exciton fission system by mapping the exciton states into crystal structures in real space. The defects in crystals could lead to changes in the topological structures, and also fission dynamics. In particular, we demonstrate that the dislocation induced three dimensional manifold, which differs from its lower dimensional counterparts globally, could generate exotic global structures, such as chiral spirals, and modulate singlet fission substantially. The findings may shed light on the new possibilities of engineering effective structures for fission materials.

Detection of weak seismic sequences based on arrival time coherence and empiric network detectability: an application at a near fault observatory

SUMMARYMicroseismic monitoring is a primary tool for understanding and tracking the progress of mechanical processes occurring in active rock fracture systems. In geothermal or hydrocarbon fields or along seismogenic fault systems, the detection and location of microseismicity facilitates resolution of the fracture system geometry and the investigation of the interaction between fluids and rocks, in response of stress field perturbations. Seismic monitoring aims to detect locate and characterize seismic sources. The detection of weak signals is often achieved at the cost of increasing the number of false detections, related to transient signals generated by a range of noise sources, or related to instrumental problems, ambient conditions or human activity that often affect seismic records. A variety of fast and automated methods has been recently proposed to detect and locate microseismicity based on the coherent detection of signal anomalies, such as increase in amplitude or coherent polarization, at dense seismic networks. While these methods proved to be very powerful to detect weak events and to reduce the magnitude of completeness, a major problem remains to discriminate among weak seismic signals produced by microseismicity and false detections. In this work, the microseimic data recorded along the Irpinia fault zone (Southern Apennines, Italy) are analysed to detect weak, natural earthquakes using one of such automated, migration-based, method. We propose a new method for the automatic discrimination of real vs false detections, which is based on empirical data and information about the detectability (i.e. detection capability) of the seismic network. Our approach allows obtaining high performances in detecting earthquakes without requiring a visual inspection of the seismic signals and minimizing analyst intervention. The proposed methodology is automated, self-updating and can be tuned at different success rates.

Early Electrophysiological Disintegration of Hippocampal Neural Networks in a Novel Locus Coeruleus Tau-Seeding Mouse Model of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive, neurodegenerative disease characterized by loss of synapses and disrupted functional connectivity (FC) across different brain regions. Early in AD progression, tau pathology is found in the locus coeruleus (LC) prior to amyloid-induced exacerbation of clinical symptoms. Here, a tau-seeding model in which preformed synthetic tau fibrils (K18) were unilaterally injected into the LC of P301L mice, equipped with multichannel electrodes for recording EEG in frontal cortical and CA1-CA3 hippocampal areas, was used to longitudinally quantify over 20 weeks of functional network dynamics in (1) power spectra; (2) FC using intra- and intersite phase-amplitude theta-gamma coupling (PAC); (3) coherence, partial coherence, and global coherent network efficiency (Eglob) estimates; and (4) the directionality of functional connectivity using extended partial direct coherence (PDC). A sustained leftward shift in the theta peak frequency was found early in the power spectra of hippocampal CA1 networks ipsilateral to the injection site. Strikingly, hippocampal CA1 coherence and Eglob measures were impaired in K18-treated animals. Estimation of instantaneous EEG amplitudes revealed deficiency in the propagation directionality of gamma oscillations in the CA1 circuit. Impaired PAC strength evidenced by decreased modulation of the theta frequency phase on gamma frequency amplitude further confirms impairments of the neural CA1 network. The present results demonstrate early dysfunctional hippocampal networks, despite no spreading tau pathology to the hippocampus and frontal cortex. The ability of the K18 seed in the brainstem LC to elicit such robust functional alterations in distant hippocampal structures in the absence of pathology challenges the classic view that tau pathology spread to an area is necessary to elicit functional impairments in that area.

Low-Theta Electroencephalography Coherence Predicts Cigarette Craving in Nicotine Addiction.

Addicts are often vulnerable to drug use in the presence of drug cues, which elicit significant drug cue reactivity. Mounting neuroimaging evidence suggests an association between functional magnetic resonance imaging connectivity networks and smoking cue reactivity; however, there is still little understanding of the electroencephalography (EEG) coherence basis of smoking cue reactivity. We therefore designed two independent experiments wherein nicotine-dependent smokers performed a smoking cue reactivity task during EEG recording. Experiment I showed that a low-theta EEG coherence network occurring 400–600 ms after onset during long-range (mainly between frontal and parieto-occipital) scalp regions, which was involved in smoking cue reactivity. Moreover, the average coherence of this network was significantly correlated with participants’ level of cigarette craving. In experiment II, we tested an independent group of smokers and demonstrated that the low-theta coherence network significantly predicted changes in individuals’ cigarette craving. Thus, the low-theta EEG coherence in smokers’ brains might be a biomarker of smoking cue reactivity and can predict addiction behavior.

Visual Exploration of Dynamic Multichannel EEG Coherence Networks

AbstractElectroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline‐based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole time window. In addition, we introduce the time‐annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as a first step before a complete analysis of dynamic EEG coherence networks.

Data-driven visualization of multichannel EEG coherence networks based on community structure analysis

An electroencephalography (EEG) coherence network is a representation of functional brain connectivity, and is constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Typical visualizations of coherence networks use a matrix representation with rows and columns representing electrodes and cells representing coherences between electrode signals, or a 2D node-link diagram with vertices representing electrodes and edges representing coherences. However, such representations do not allow an easy embedding of spatial information or they suffer from visual clutter, especially for multichannel EEG coherence networks. In this paper, a new method for data-driven visualization of multichannel EEG coherence networks is proposed to avoid the drawbacks of conventional methods. This method partitions electrodes into dense groups of spatially connected regions. It not only preserves spatial relationships between regions, but also allows an analysis of the functional connectivity within and between brain regions, which could be used to explore the relationship between functional connectivity and underlying brain structures. As an example application, the method is applied to the analysis of multichannel EEG coherence networks obtained from older and younger adults who perform a cognitive task. The proposed method can serve as a preprocessing step before a more detailed analysis of EEG coherence networks.

F125. Brain connectivity related to sleep-wake state: An intracranial EEG study

Introduction Understanding brain connectivity in health and disease is a major challenge for basic and translational neuroscience. Direct EEG recordings from the brain surface (electrocorticography; ECoG) in epilepsy provide unique opportunity for studying human neuro-electric connectivity with reference to the wake, sleep and epileptic states. A major conundrum is reconciling the views of sleep being a disconnected state (Massimini et al., 2005) with the hypersynchronicity of sleep that favors seizure occurrence in the partial epilepsies, implying a heightened connectivity. Using spectral analysis and graph-theoretic measures (Bullmore and Sporns, 2009) applied to ECoG recordings in 6 patients undergoing continuous monitoring, we demonstrate how a reconciliation between these two scenarios is possible. Methods ECoG data in average reference format from six patients with refractory focal epilepsy undergoing prolonged pre-surgical video-EEG telemetry with subdural grid electrodes was analyzed. Segments of wakefulness and sleep (25–30 s long) were concatenated into contiguous epochs and normalized to zero mean and unit variance. Power spectra were computed by standard methods and the amplitude spectra curve-fitted empirically with a three-parameter function. The analysis was repeated over sliding windows of 30 s duration across the whole epoch. A fuzzy C-means method was used to cluster each parameter triplet into an activity score between [0–1], with 0 representing deep sleep, and 1 being alert wakefulness. The epoch time series were then filtered into the canonical Berger δ, θ, α, β and γ EEG bands. For each band, standard graph-theoretic measures were computed over sliding window segments across whole epochs to correspond with the activity score computations. Pearson correlations between the activity score and concurrently computed graph-theoretic connectivity metrics were calculated, and statistically significant correlations following Bonferroni correction (by a factor of 30) retained. Results We found that the coherence network modularity in the beta bands – relevant to high-frequency seizure-onset rhythms – correlated positively with wakefulness, while delta, theta and alpha modularity correlated negatively. An approximately reverse relationship was observed with respect to clustering coefficient. Conclusion Our results complement those obtained by resting state fMRI (Cox et al., 2014) and cortico-cortical evoked potentials (Usami et al., 2015) of sleep. We suggest that ECoG-based brain connectivity metrics are both state (sleep-wake) dependent and time-scale (waveform frequency) dependent. It is possible for the cortex to be ‘disconnected’ with respect to frequencies ostensibly underlying conscious wakefulness in the sleep state, yet ‘hyperconnected’ on time scales relevant to the transmission of epileptic seizures.

Improving Performance of Distributed Shared Memory (DSM) on Multiprocessor Framework with Software Approach

Objectives: To design Distributed Shared Memory (DSM) for the multiprocessor distributed framework using a different software parametric approach that provides significant performance improvement against convention software based architectures. Methods/Statistical Analysis: Software distributed shared memory can be architected by using a different concept of an operating system, by utilizing a programming library and by extending underlying virtual address space architecture. It incorporates various design options like granularity, consistency model, implementation level, data organization, algorithms, protocols, etc. We have proposed few software parameter choices and impact which gives significant performance improvement compared to past designs to manage software distributed shared memory. This paper also discusses various issues that exist while moving toward software distributed shared memory implementation. Findings: There are two methodologies by which it is possible to achieve distributed shared memory design are first in hardware like cache coherence circuits and network interfaces and the second is software. Here the proposed system architecture makes major impact on programming, performance, design and cost. An algorithm is designed such a unique manner which resides in memory controller and make efficient global virtual memories. It is using variable as granularity which are shared that is more flexible for complex data structure and large database. It is defined using unique identifier which makes its mapping and retrieval more manageable using proposed consistency mechanism. Application/Improvements: Distributed shared memory optimization is a most important area of improving distributed system performance. By taking care of good choice on underlying issues and according to system’s design requirement, it possible to gain advantages of improved architecture which can be more used for various distributed applications where shared data plays a major role.

Anomaly in neural phase coherence accompanies reduced sensorimotor integration in adults who stutter

Despite advances in our understanding of the human speech system, the neurophysiological basis of stuttering remains largely unknown. Here, it is hypothesized that the speech of adults who stutter (AWS) is susceptible to disruptions in sensorimotor integration caused by neural miscommunication within the speech motor system. Human speech unfolds over rapid timescales and relies on a distributed system of brain regions working in a parallel and synchronized manner, and a breakdown in neural communication between the putative brain regions could increase susceptibility to dysfluency. Using a speech motor adaptation paradigm under altered auditory feedback with simultaneous recording of EEG, the oscillatory cortical dynamics was investigated in stuttering and fluent adults (FA). Auditory feedback perturbation involved the shifting of the formant frequencies of the target vowel sound. Reduced adaptation in response to the feedback error was observed in AWS and was accompanied by differences in EEG spectral powers and anomalies in phase coherence evolving over the course of speech motor training. It is understood that phase coherence possibly captures neural communication within speech motor networks. Thus, the phase coherence network of the two groups exhibited differences involving the EEG frequency bands. These findings in anomalous neural synchrony provide novel evidence for compromised neuronal communication at short time scales within the speech motor network of AWS.

Interferometric phase reconstruction using simplified coherence network

Interferometric time-series analysis techniques, which extend the traditional differential radar interferometry, have demonstrated a strong capability for monitoring ground surface displacement. Such techniques are able to obtain the temporal evolution of ground deformation within millimeter accuracy by using a stack of synthetic aperture radar (SAR) images. In order to minimize decorrelation between stacked SAR images, the phase reconstruction technique has been developed recently. The main idea of this technique is to reform phase observations along a SAR stack by taking advantage of a maximum likelihood estimator which is defined on the coherence matrix estimated from each target. However, the phase value of a coherence matrix element might be considerably biased when its corresponding coherence is low. In this case, it will turn to an outlying sample affecting the corresponding phase reconstruction process. In order to avoid this problem, a new approach is developed in this paper. This approach considers a coherence matrix element to be an arc in a network. A so-called simplified coherence network (SCN) is constructed to decrease the negative impact of outlying samples. Moreover, a pointed iterative strategy is designed to resolve the transformed phase reconstruction problem defined on a SCN. For validation purposes, the proposed method is applied to 29 real SAR images. The results demonstrate that the proposed method has an excellent computational efficiency and could obtain more reliable phase reconstruction solutions compared to the traditional method using phase triangulation algorithm.