Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: Application to epilepsy seizure evolution

Nevado‐Holgado, Alejo J.; Marten, Frank; Richardson, Mark P.; Terry, John R.

doi:10.1016/j.neuroimage.2011.08.111

Cited by 66 publications

(85 citation statements)

References 39 publications

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“…Indeed, this is the prescribed treatment for resistance to ethosuximide (Katzung et al 2004;Shorvon 2010). Studies on the application of bifurcation analysis and phase diagrams to seizures has also been done in similar models (Crunelli et al 2011;Nevado-Holgado et al 2012).…”

Section: Drug Actionsmentioning

confidence: 99%

Generalized seizures in a neural field model with bursting dynamics

Zhao

Robinson

2015

J Comput Neurosci

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The mechanisms underlying generalized seizures are explored with neural field theory. A corticothalamic neural field model that has accounted for multiple brain activity phenomena and states is used to explore changes leading to pathological seizure states. It is found that absence seizures arise from instabilities in the system and replicate experimental studies in numerous animal models and clinical studies.

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Section: Drug Actionsmentioning

confidence: 99%

Generalized seizures in a neural field model with bursting dynamics

Zhao

Robinson

2015

J Comput Neurosci

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“…However the eventual goal of the work presented here is aligned closely to one of the goals of Nevado-Holgado et al (2012): to determine quantitatively underlying parameters. In comparison to Nevado-Holgado et al (2012), we present a probabilistic approach to handle a stochastic model and use a wider variety of features in the feature set encompassing not only time domain features but also frequency domain features, wavelet features, and features of neighboring ECoG channels.…”

Section: Introductionmentioning

confidence: 96%

“…In Kramer et al (2005Kramer et al ( , 2007, pathways to seizure regions identified with bifurcation analysis through the particular mesoscale model used in this work were explored qualitatively to determine potential directions through seizure and enumerate seizure regions. Using a different but related mathematical model, Nevado-Holgado et al (2012) explores a more quantitative approach to determining parameter pathways, concentrating on time domain waveforms and attempting to match specific waveforms in measured EEG to waveforms generated by a neural mass model. Similarly (Wang et al 2012) uses a minimalist model to analyze seizure waves and (Aarabi and He 2014) leverages a neural mass model for seizure prediction.…”

Section: Introductionmentioning

confidence: 99%

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A probabilistic method for determining cortical dynamics during seizures

et al. 2015

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This work presents a probabilistic method for inferring the parameter ranges in a biologically relevant mathematical model of the cortex most likely to be producing seizures observed in an electrocorticogram (ECoG) signal from a human subject. Additionally, this method produces a probabilistic pathway of the temporal evolution of physiological state in the cortex over the course of individual seizures, leveraging a model of the cortex that describes cortical physiology. We describe ways in which these methods and results offer insights into seizure etiology and have the potential to suggest new treatment options. To directly account for the stochastic and noisy nature of the mathematical model and the ECoG signal, we use a probabilistic Bayesian framework to map features of ECoG segments onto a distribution of likelihoods over physiologically-relevant parameter states. A Hidden Markov Model (HMM) is then introduced to incorporate the belief that cortical physiology has both temporal continuity and also a degree of reproducibility between individual seizures. By inspecting the ratio of likelihoods between HMMs run under two possible parameter regions, both of which produce seizures in the model, we determine which physiological parameter regions are more likely to be causing the observed seizures. We show that between individual seizures, there is consistency in these likelihood ratios between hypothesized regions, in the temporal pathways calculated, and in the separation of seizure from non-seizure time segment likelihood maps.

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“…The framework has been used to explain epilepsy-like brain activity (Wendling et al 2000), as well as various narrow-band EEG oscillations, ranging from the delta to the gamma frequency bands (David and Friston 2003). Nevado-Holgado et al (2012) developed a multi-objective genetic algorithm that can estimate parameters of a neural mass model from clinical EEG recordings. Estimating the parameters of neural mass models generally involves the Bayesian inversion of dynamic causal models using standard variational system identification techniques.…”

Section: Introductionmentioning

confidence: 99%

Bifurcation and oscillation in a time-delay neural mass model

et al. 2014

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The neural mass model developed by Lopes da Silva et al. simulates complex dynamics between cortical areas and is able to describe a limit cycle behavior for alpha rhythms in electroencephalography (EEG). In this work, we propose a modified neural mass model that incorporates a time delay. This time-delay model can be used to simulate several different types of EEG activity including alpha wave, interictal EEG, and ictal EEG. We present a detailed description of the model's behavior with bifurcation diagrams. Through simulation and an analysis of the influence of the time delay on the model's oscillatory behavior, we demonstrate that a time delay in neuronal signal transmission could cause seizure-like activity in the brain. Further study of the bifurcations in this new neural mass model could provide a theoretical reference for the understanding of the neurodynamics in epileptic seizures.

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Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: Application to epilepsy seizure evolution

Cited by 66 publications

References 39 publications

Generalized seizures in a neural field model with bursting dynamics

Generalized seizures in a neural field model with bursting dynamics

A probabilistic method for determining cortical dynamics during seizures

Bifurcation and oscillation in a time-delay neural mass model

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