Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model

Ahmadi, Negar; Pei, Yulong; Pechenizkiy, Mykola

doi:10.1016/j.physa.2019.01.003

Cited by 11 publications

(12 citation statements)

References 59 publications

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“…Therefore, the information on EEG signals is obtained just by analyzing the characteristics of the graph. In our previous works, we showed that the synchronization measure based on the HVG algorithm is a robust measure for finding correlation among chaotic, noisy and stochastic signals [37], and also this measure is less sensitive to the brain volume conduction effect and is able to predict the coupling degree correctly even with strongly overlapping signals [38]. This synchronization measure is presented here shortly.…”

Section: Functional Network Analysismentioning

confidence: 81%

EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

et al. 2020

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Epilepsy and psychogenic non-epileptic seizures (PNES) often show over-lap in symptoms, especially at an early disease stage. During a PNES, the electrical activity of the brain remains normal but in case of an epileptic seizure the brain will show epileptiform discharges on the electroencephalogram (EEG). In many cases an accurate diagnosis can only be achieved after a long-term video monitoring combined with EEG recording which is quite expensive and time-consuming. In this paper using short-term EEG data, the classification of epilepsy and PNES subjects is analyzed based on signal, functional network and EEG microstate features. Our results showed that the beta-band is the most useful EEG frequency sub-band as it performs best for classifying subjects. Also the results depicted that when the coverage feature of the EEG microstate analysis is calculated in beta-band, the classification shows fairly high accuracy and precision. Hence, the beta-band and the coverage are the most important features for classification of epilepsy and PNES patients.

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Section: Functional Network Analysismentioning

confidence: 81%

EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

et al. 2020

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“…In order to simulate the neuronal current propagation from the source regions within the brain to the EEG electrodes, several electrical models have been proposed: from simple n-sphere models (in which each concentric spherical region represents different brain layers) to more accurate models based on information from other neuroimaging techniques [18]. To simulate the source activity, also different procedures have been used, such as dipole generators, potential meshes, and oscillators [10,18,19]. The most extended electrical model in EEG source analysis is the boundary element method (BEM) [20].…”

Section: Introductionmentioning

confidence: 99%

“…skin, skull, cerebrospinal fluid (CSF), gray and white matter) are inside the head [22,23]. However, these methods do not allow the characterization of the influence of volume conduction in coupling estimators, since the simulation of ideal scenarios without volume conduction is not feasible [10,19]. A first approach to evaluate the influence of EEG volume conduction in different connectivity metrics was performed by Stam et al [10], and another recent study replicated this one with the same limitations [19].…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods do not allow the characterization of the influence of volume conduction in coupling estimators, since the simulation of ideal scenarios without volume conduction is not feasible [10,19]. A first approach to evaluate the influence of EEG volume conduction in different connectivity metrics was performed by Stam et al [10], and another recent study replicated this one with the same limitations [19]. In both studies, brain source activity was simulated using the well-known Kuramoto model [24], a set of globally coupled limit-cycle oscillators centered at a single frequency of 10 Hz.…”

Section: Introductionmentioning

confidence: 99%

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Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer’s disease continuum

et al. 2019

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Objective. The aim of this study was to evaluate the effect of electroencephalographic (EEG) volume conduction in different measures of functional connectivity and to characterize the EEG coupling alterations at the different stages of dementia due to Alzheimer’s disease (AD). Approach. Magnitude squared coherence (MSCOH), imaginary part of coherence (iCOH), lagged coherence (lagCOH), amplitude envelope correlation (AEC), synchronization likelihood (SL), phase lag index (PLI), phase locking value (PLV), and corrected imaginary PLV (ciPLV) were applied to: (i) synthetic signals generated with a Kuramoto-based model of several coupled oscillators; and (ii) a resting-state EEG database of real recordings from 51 cognitively healthy controls, 51 mild cognitive impairment (MCI) subjects, 51 mild AD (ADmil) patients, 50 moderate AD (ADmod) patients, and 50 severe AD (ADsev) patients. Main results. Our results using synthetic signals showed that PLI was the least affected parameter by spurious influences in a simulated volume conduction environment. Results using real EEG recordings showed that spontaneous activity of MCI patients is characterized by a significant coupling increase in the band. As dementia progresses, this increase in the band became more pronounced, and a significant widespread decrease in band appeared at the last stage of dementia. Significance. Our results revealed that the estimation of functional EEG connectivity using PLI could reduce the bias introduced by the spurious influence of volume conduction, and it could increase the insight into the underlying brain dynamics at different stages of the AD continuum.

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“…We seek to take a step toward integrating the key strengths of both of these approaches by proposing a communication-inspired variation of the Kuramoto-Sakaguchi (K-S) model. The K-S model is a wellstudied coupled oscillator model incorporating phase delays [9,20], and has been used to capture BOLD [7,10,[21][22][23], MEG [24], and EEG [25,26] dynamics. Individual oscillators are set at an intrinsic frequency of 40-60 Hz, which can be understood to replicate lo-cally synchronous neural firing in the gamma range.…”

Section: Introductionmentioning

confidence: 99%

Co-Evolving Dynamics and Topology in a Coupled Oscillator Model of Resting Brain Function

Pope

Seguin

Varley

et al. 2023

Preprint

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Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel structural edge time series it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function. Keywords: Structure-function relationship, generative modeling, communication modeling, dynamics, functional connectivity, structural connectivity, fMRI, network science.

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Effect of linear mixing in EEG on synchronization and complex network measures studied using the Kuramoto model

Cited by 11 publications

References 59 publications

EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features

Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer’s disease continuum

Co-Evolving Dynamics and Topology in a Coupled Oscillator Model of Resting Brain Function

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