A model for focal seizure onset, propagation, evolution, and progression

Liou, Jyun-you; Smith, Elliot H.; Bateman, Lisa M.; Bruce, Samuel L.; McKhann, Guy M.; Goodman, Robert; Emerson, Ronald G.; Schevon, Catherine A.; Abbott, LF

doi:10.7554/elife.50927

Cited by 81 publications

(96 citation statements)

References 85 publications

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“…A Turing analysis was also performed for the 2D neural field equation, given in Appendix B by (35), and a very similar bifurcation structure was found when the mean background drive η 0 = 0.5 (see Supplemental information 2 panel (a)). Increased η 0 , the Hopf and Turing-Hopf curves move down in the v-κ v plane, and they switch, such that the Turing-Hopf occurs first for low action potential propagation speeds v as the gap junction coupling strength κ v is increased (see Supplemental information 2 panel (b)).…”

Section: Two Spatial Dimensionsmentioning

confidence: 69%

“…They can also be associated with dysfunction and in particular epileptic seizures [38]. Computational modelling is a very natural way to investigate the mechanisms for their occurrence in brain tissue, as well as how they may evolve and disperse [18,19,35].…”

Section: Neural Field Modelmentioning

confidence: 99%

“…We linearise around the steady state and consider perturbations of the form (U (r, t), Ψ(r, t), R(r, t), V (r, t)) = (0, 0, R 0 , V 0 )+ (U , Ψ, R, V )e λt e ik•r for | | 1 and λ = µ + iω. Substitution into (35) and working to first order in gives the linear relationship…”

Section: Data Availabilitymentioning

confidence: 99%

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Mean-field models for EEG/MEG: from oscillations to waves

Byrne

Ross

Nicks

et al. 2020

Preprint

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Neural mass models have been actively used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomeno-logical in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit to an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

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Section: Two Spatial Dimensionsmentioning

confidence: 69%

Section: Neural Field Modelmentioning

confidence: 99%

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Mean-field models for EEG/MEG: from oscillations to waves

Byrne

Ross

Nicks

et al. 2020

Preprint

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“…• The sudden transition of a brain region i from the healthy to the seizure state is driven by continuous changes in the slow variable z i , loosely corresponding to the slow permittivity variable in the Epileptor model (Jirsa et al, 2014) or to the usage-dependent exhaustion of inhibition in the model of Liou et al (2020). All regions are initially in a healthy state, and any region starts to seize when its slow variable crosses a given threshold.…”

Section: Overview Of the Methodsmentioning

confidence: 99%

Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

Sip

Hashemi

Vattikonda

et al. 2020

Preprint

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Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of fifty patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.

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“…However, due to the many potential positive feedback loops in larger networks with extensive recurrent connections, imbalances in excitatory (E) and inhibitory (I) synaptic activity could lead to activity saturation [22,23], such as observed in epilepsy [24,25], or, in milder cases, a noise-like perturbation of the information content of internal signals, which would be disadvantageous for learning.…”

Section: Introductionmentioning

confidence: 99%

A non-spiking neuron model with dynamic leak to avoid instability in recurrent networks

Rongala

Enander

Köhler

et al. 2020

Preprint

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Elements of recurrent excitatory synaptic loops are present within sensorimotor control loops in the brain. Such positive feedback loops can cause self-amplification, which is potentially dangerous for the brain (as in epilepsy) or which may cause disruptive effects on of the information passed within the neuronal circuitry. Here we introduce a simplified nonspiking neuron model, whose output accurately reflects summated synaptic inputs, with which we performed simulations of highly connected networks composed of excitatory and inhibitory neurons in equal proportions. The neuron model could be optionally equipped with a dynamic leak, i.e. dynamic short-circuit to the resting membrane potential, equivalent to a membrane time constant. Synaptic weights were randomly assigned across the network according to a range of different types and ranges of distribution. Networks were provided with random and semi-structured sensory inputs and we studied the spread of activity through the network. We find that in neurons without dynamic leak, recurrent networks produced high frequency noise components that were not present in the input signal, across all synaptic weight distributions. The addition of dynamic leak consistently removed such high frequency noise. Conduction delays between neurons, when explicitly included, tended to worsen the high frequency noise, which was rescued by dynamic leak also in this case. Our findings suggest that neuronal dynamic short circuit of voltage deviations towards the resting potential serve the beneficial function of protecting recurrent neuronal circuitry from inducing itself into spurious high frequency signal generation, thereby permitting the brain to utilize this architectural circuitry component.

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A model for focal seizure onset, propagation, evolution, and progression

Cited by 81 publications

References 85 publications

Mean-field models for EEG/MEG: from oscillations to waves

Mean-field models for EEG/MEG: from oscillations to waves

Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

A non-spiking neuron model with dynamic leak to avoid instability in recurrent networks

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