Computational models of epilepsy

Stefanescu, Roxana A.; Shivakeshavan, R.G.; Talathi, Sachin S.

doi:10.1016/j.seizure.2012.08.012

Cited by 75 publications

(49 citation statements)

References 107 publications

(105 reference statements)

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“…Recent efforts to design and implement closed-loop control of epileptic networks have focused on the site and stimulation parameters (Stefanescu et al, 2012; Fisher and Velasco, 2014). Ideally, electrical stimulation should be done solely on the epileptogenic network, and with accurate seizure foci localization techniques (Burns et al, 2012, 2014), it may be possible to implement micro electrode arrays at the epileptogenic brain site and measure spike trains as required by our controller scheme.…”

Section: Discussionmentioning

confidence: 99%

“…Computational modeling is a powerful tool for understanding the underlying mechanisms involved in seizure genesis and its dynamics, and has proven to be very useful in aiding with the design of more efficient seizure onset detectors and brain stimulation protocols (Stefanescu et al, 2012). We recently constructed a neuronal network model of the epileptic cortex that qualitatively captures the heterogeneity in neurons observed in patients during seizure events (Sritharan and Sarma, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Closed-loop control of a fragile network: application to seizure-like dynamics of an epilepsy model

2015

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It has recently been proposed that the epileptic cortex is fragile in the sense that seizures manifest through small perturbations in the synaptic connections that render the entire cortical network unstable. Closed-loop therapy could therefore entail detecting when the network goes unstable, and then stimulating with an exogenous current to stabilize the network. In this study, a non-linear stochastic model of a neuronal network was used to simulate both seizure and non-seizure activity. In particular, synaptic weights between neurons were chosen such that the network's fixed point is stable during non-seizure periods, and a subset of these connections (the most fragile) were perturbed to make the same fixed point unstable to model seizure events; and, the model randomly transitions between these two modes. The goal of this study was to measure spike train observations from this epileptic network and then apply a feedback controller that (i) detects when the network goes unstable, and then (ii) applies a state-feedback gain control input to the network to stabilize it. The stability detector is based on a 2-state (stable, unstable) hidden Markov model (HMM) of the network, and detects the transition from the stable mode to the unstable mode from using the firing rate of the most fragile node in the network (which is the output of the HMM). When the unstable mode is detected, a state-feedback gain is applied to generate a control input to the fragile node bringing the network back to the stable mode. Finally, when the network is detected as stable again, the feedback control input is switched off. High performance was achieved for the stability detector, and feedback control suppressed seizures within 2 s after onset.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Closed-loop control of a fragile network: application to seizure-like dynamics of an epilepsy model

2015

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“…Also, recently, both healthy and epilepticform brain activity have been simulated using cellular automata with long-range interactions [55]. Other mathematical and computational approaches have also been considered and the understanding and modelling of the mechanisms of epilepsy is an active research field nowadays [56].…”

Section: Discussionmentioning

confidence: 99%

Firing patterns in a random network cellular automata model of the brain

Acedo¹,

Lamprianidou²,

Moraño³

et al. 2015

Physica A: Statistical Mechanics and its Applications

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One of the main challenges in the simulation of even reduced areas of the brain is the presence of a large number of neurons and a large number of connections among them. Even from a theoretical point of view, the behaviour of dynamical models of complex networks with high connectivity is unknown, precisely because the cost of computation is still unaffordable and it will likely be in the near future. In this paper we discuss the simulation of a cellular automata network model of the brain including up to one million sites with a maximum average of three hundred connections per neuron. This level of connectivity was achieved thanks to a distributed computing environment based on the BOINC (Berkeley Open Infrastructure for Network Computing) platform. Moreover, in this work we consider the interplay among excitatory neurons (which induce the excitation of their neighbours) and inhibitory neurons (which prevent resting neurons from firing and induce firing neurons to pass to the refractory state). Our objective is to classify the normal (noisy but asymptotically constant patterns) and the abnormal (high oscillations with spindle-like behaviour) patterns of activity in the model brain and their stability and parameter * e-mail: luiacrod@imm.upv.es 1 ranges in order to determine the role of excitatory and inhibitory compensatory effects in healthy and diseased individuals.

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“…Attempting to include all of these cells and cytokines in the model would introduce a number of unknown or not-well-defined parameters into the model. Simplifications are a critical component in the construction of effective and rigorous models and can help investigators to concentrate on a limited number of essential components in the simulated system [55]. This approach may sacrifice the power of simulating the subtle differences of different cytokines but yield a much clearer understanding of the critical components that lead to in-stent restenosis.…”

Section: Discussionmentioning

confidence: 99%

An Agent-Based Model of the Response to Angioplasty and Bare-Metal Stent Deployment in an Atherosclerotic Blood Vessel

Curtin

Zhou

2014

PLoS ONE

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PurposeWhile animal models are widely used to investigate the development of restenosis in blood vessels following an intervention, computational models offer another means for investigating this phenomenon. A computational model of the response of a treated vessel would allow investigators to assess the effects of altering certain vessel- and stent-related variables. The authors aimed to develop a novel computational model of restenosis development following an angioplasty and bare-metal stent implantation in an atherosclerotic vessel using agent-based modeling techniques. The presented model is intended to demonstrate the body’s response to the intervention and to explore how different vessel geometries or stent arrangements may affect restenosis development.MethodsThe model was created on a two-dimensional grid space. It utilizes the post-procedural vessel lumen diameter and stent information as its input parameters. The simulation starting point of the model is an atherosclerotic vessel after an angioplasty and stent implantation procedure. The model subsequently generates the final lumen diameter, percent change in lumen cross-sectional area, time to lumen diameter stabilization, and local concentrations of inflammatory cytokines upon simulation completion. Simulation results were directly compared with the results from serial imaging studies and cytokine levels studies in atherosclerotic patients from the relevant literature.ResultsThe final lumen diameter results were all within one standard deviation of the mean lumen diameters reported in the comparison studies. The overlapping-stent simulations yielded results that matched published trends. The cytokine levels remained within the range of physiological levels throughout the simulations.ConclusionWe developed a novel computational model that successfully simulated the development of restenosis in a blood vessel following an angioplasty and bare-metal stent deployment based on the characteristics of the vessel cross-section and stent. A further development of this model could ultimately be used as a predictive tool to depict patient outcomes and inform treatment options.

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Computational models of epilepsy

Abstract: We conclude with a discussion on the utility of computational models for the development of new epilepsy treatment protocols.

Cited by 75 publications

References 107 publications

Closed-loop control of a fragile network: application to seizure-like dynamics of an epilepsy model

Closed-loop control of a fragile network: application to seizure-like dynamics of an epilepsy model

Firing patterns in a random network cellular automata model of the brain

An Agent-Based Model of the Response to Angioplasty and Bare-Metal Stent Deployment in an Atherosclerotic Blood Vessel

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