General Anesthetic-induced Seizures Can Be Explained by a Mean-field Model of Cortical Dynamics

Wilson, Marcus T.; Sleigh, James W.; Steyn-Ross, D. Alistair; Steyn-Ross, Moira L.

doi:10.1097/00000542-200603000-00026

Cited by 60 publications

(46 citation statements)

References 14 publications

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“…We showed that the increase of the propofol concentration may render the resting state bistable (triple solution case) or monostable (single solution case). The former case has been investigated in detail in previous studies (Steyn-Ross et al 2001a;Wilson et al 2006), while the latter monostable case has been investigated by MolaeeArdekani et al (2007); Bojak and Liley (2005). Section ''The resting state'' reveals the existence of monostable states for excitatory firing thresholds lower than inhibitory thresholds at equal nonlinear gains, while the bistable resting state may occur for all other parameter sets.…”

Section: Discussionmentioning

confidence: 99%

“…This finding may be important for future modeling studies and even may be compared to experimental measurements Further the derivation of the power spectrum in section ''The general power spectrum'' takes into account both the excitatory and inhibitory membrane potentials, whose difference represents the effective membrane potential (Freeman 1992) or, equivalently, the effective dendritic current generating experimental signals such as LFPs (Nicholson and Freeman 1975) and EEG (Nunez 2000). This approach is different to most previous studies of the power spectrum of neural populations (Steyn-Ross et al 2001a;Wilson et al 2006;Molaee-Ardekani et al 2007;Bojak and Liley 2005;Liley and Bojak 2005;Rennie et al 2002;Robinson 2003;Robinson et al 2004), which consider the power spectrum of either the excitatory or inhibitory membrane potential only.…”

Section: Discussionmentioning

confidence: 99%

“…In this context it is interesting to note that Steyn-Ross et al (Steyn-Ross et al 2001a;Wilson et al 2006) suppose the biphasic power spectrum observed in general anesthesia to originate from a first-order phase transition and thus assume the triple solution case. In contrast Liley and Bojak Liley and Bojak 2005) and Molaee-Ardekani et al (Molaee-Ardekani et al 2007) treat a monostable resting state and also show biphasic power spectra indicating that first-order phase transitions are not compulsory to gain biphasic behavior.…”

Section: Discussionmentioning

confidence: 99%

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Effects of the anesthetic agent propofol on neural populations

Hutt

Longtin

2009

Cogn Neurodyn

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The neuronal mechanisms of general anesthesia are still poorly understood. Besides several characteristic features of anesthesia observed in experiments, a prominent effect is the bi-phasic change of power in the observed electroencephalogram (EEG), i.e. the initial increase and subsequent decrease of the EEG-power in several frequency bands while increasing the concentration of the anaesthetic agent. The present work aims to derive analytical conditions for this bi-phasic spectral behavior by the study of a neural population model. This model describes mathematically the effective membrane potential and involves excitatory and inhibitory synapses, excitatory and inhibitory cells, nonlocal spatial interactions and a finite axonal conduction speed. The work derives conditions for synaptic time constants based on experimental results and gives conditions on the resting state stability. Further the power spectrum of Local Field Potentials and EEG generated by the neural activity is derived analytically and allow for the detailed study of bi-spectral power changes. We find bi-phasic power changes both in monostable and bistable system regime, affirming the omnipresence of bi-spectral power changes in anesthesia. Further the work gives conditions for the strong increase of power in the d-frequency band for large propofol concentrations as observed in experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Effects of the anesthetic agent propofol on neural populations

Hutt

Longtin

2009

Cogn Neurodyn

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“…Mean-field models have a long history in computational neuroscience (e.g., [26][27][28][29], including many important applications for seizure modeling. These include, for example, mean-field models of absence seizures (30)(31)(32), of depth and surface electrode recordings from patients with temporal lobe epilepsy (33,34), seizure generalization (35), and anesthetic-induced seizures (36).…”

Section: Signatures Of a Critical Transition In A Mean-field Model Ofmentioning

confidence: 99%

Human seizures self-terminate across spatial scales via a critical transition

Kramer

Truccolo

Eden

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

193

147

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Why seizures spontaneously terminate remains an unanswered fundamental question of epileptology. Here we present evidence that seizures self-terminate via a discontinuous critical transition or bifurcation. We show that human brain electrical activity at various spatial scales exhibits common dynamical signatures of an impending critical transition-slowing, increased correlation, and flickering-in the approach to seizure termination. In contrast, prolonged seizures (status epilepticus) repeatedly approach, but do not cross, the critical transition. To support these results, we implement a computational model that demonstrates that alternative stable attractors, representing the ictal and postictal states, emulate the observed dynamics. These results suggest that self-terminating seizures end through a common dynamical mechanism. This description constrains the specific biophysical mechanisms underlying seizure termination, suggests a dynamical understanding of status epilepticus, and demonstrates an accessible system for studying critical transitions in nature.critical slowing down | epilepsy | electrocorticogram | local field potential A lthough extensive observations and research have elucidated some mechanism of seizure initiation and maintenance, how seizures spontaneously terminate remains one of the most important, but still unanswered, questions in epileptology (1). Some of the potential mechanisms of seizure termination (2) include glutamate depletion (3), dynamic changes in ion concentrations (4-6), persistent activation of a hyperpolarization-activated cation conductance (7), changing synaptic effectiveness (5, 8), modulatory effects from subcortical structures and the cerebellum (9), and reduced pH (10). Together, these studies suggest distinct pathways to seizure termination through specific biophysical mechanisms.We propose here a dynamical understanding of seizure termination that encompasses and constrains these and other hypothesized biophysical mechanisms. Specifically, we propose that focal seizures with secondary generalization terminate in a manner consistent with crossing a critical transition or bifurcation in which the system traverses a critical threshold and shifts suddenly to an alternative, attracting dynamical regime (11). Such regime shifts between alternative stable states have been described in many real-world systems (12-14) and exhibit a repertoire of early warning indicators (15-17). The seizing brain provides a unique living system for studying the signatures of an impending critical transition as well as one in which interventions can have profound practical import.We characterize the features of seizure termination in both multiscale in vivo data from patients with epilepsy and a computational model of cortical field activity. We show that population electrical activity-observed at macroscopic spatial scales-exhibits numerous signatures of an impending critical transition, whereas spatially localized recordings of neural spiking activity possess different dynamics. These insi...

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“…Here, the individual neurons are assumed to be indistinguishable particles whose interactions produce bulk properties, just as the interactions of molecules in a liquid produce the bulk properties familiar from thermodynamics (Wilson and Cowan, 1972). These approaches have lately been applied to epilepsy as well (Wilson et al, 2006).…”

Section: Detailed Versus Simplifying Modelingmentioning

confidence: 99%

Simulation of Large Networks

Lytton¹,

Stewart²,

Hines³

2008

Computational Neuroscience in Epilepsy

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Computer models of epilepsy and seizures require simulation of large networks in order to produce emergent phenomenology, but also require inclusion of details of cellular and synaptic physiology to retain connection with pharmacological effectors for therapeutic intervention. The former constraint suggests the use of simplifying approaches: integrate-and-fire models or meanfield approximations. The latter constraint pushes one towards multi-compartment models. Compromise is required. We have developed highly-simplified event-driven complex artificial-cell models that utilize a series of rules to emulate critical underlying phenomena likely important for seizure generation and patterning. These include depolarization blockade, voltage-dependent NMDA activation, afterhyperpolarization and several others. Using these units, we can readily run simulations of 1-2 million units on a single processor. Here, we present results of data-mining 138,240 simulations of a 3,500-unit network. Parameter explorations can then relate single-unit dynamical "structure" to network function. We find that the networks are prone to latch-up, reminiscent of a seizure tonic phase. This can alternate with a slow repetitive clonic-like activity. Parameter dependence of epileptiform features was typically complex. For example, massive initial network response increased with greater unit excitability only in the context of particular AMPA and NMDA strength settings. This consequence of network complexity fits with the multi-site activity of endogenous neuroeffectors and may help explain why "dirty" drugs, those acting at multiple sites, might be particularly effective for seizure control. GOALS OF COMPUTER MODELING FOR CLINICAL DISEASEWe use the fruits of computer simulation every day when we consult a weather report. Although weather reporting accuracy remains low for periods of greater than 10 days from the present, the improvement over weather reporting of 50 years ago is striking. The big difference is size: both the size of the data set available for initial conditions and the size of the computers for running the massive simulations that are now used. In the next 50 years we will likely see comparable progress in our ability to:1. predict seizures 2. understand the genesis of seizures based on alterations in the underlying neurobiological substrate 3. intervene in directed ways to prevent a single seizure or to alter the substrate and prevent seizures from occurring.Unlike weather simulation, where the underlying factors involved (temperature, wind, humidity) are well characterized and where the interactions are fully understood, the nervous system remains full of under-or uncharacterized neurotransmitters, receptors, second and third messengers and electrical interactions. Also unlike weather, many locations where we would like to measure chemical concentrations or currents are inaccessible. For these reasons, accurate simulation will require growth not only in simulation but also in measuring capabilities.The hope and expect...

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General Anesthetic-induced Seizures Can Be Explained by a Mean-field Model of Cortical Dynamics

Cited by 60 publications

References 14 publications

Effects of the anesthetic agent propofol on neural populations

Effects of the anesthetic agent propofol on neural populations

Human seizures self-terminate across spatial scales via a critical transition

Simulation of Large Networks

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