A taxonomy of seizure dynamotypes

Saggio, Maria Luisa; Crisp, Dakota; Scott, Jared M.; Karoly, Philippa J.; Kuhlmann, Levin; Nakatani, Mitsuyoshi; Murai, Toshifumi; Dümpelmann, Matthias; Schulze‐Bonhage, Andreas; Ikeda, Akio; Cook, Mark; Gliske, S.; Lin, Jack J.; Bernard, Christophe; Jirsa, Viktor K.; Stacey, William C.

doi:10.7554/elife.55632

Cited by 104 publications

(116 citation statements)

References 82 publications

(183 reference statements)

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“…We do not know how seizures start (the causal event), but as an axiom, we can propose that a threshold must be reached for a transition between normal and epileptic activity to occur (Figure 7B). Many theoretical and experimental studies support the existence of such a threshold, independently from the assumed mathematical mechanism underlying seizure genesis 84–89 . Let us consider this image.…”

Section: The More Hypothesismentioning

confidence: 98%

“…Clinical data demonstrate that individual patients express different types of seizures in terms of dynamics 89 and spatial patterns of propagation 93 . Of interest, seizures with similar propagation patterns tend to be clustered in time 93 .…”

Section: Interpreting Clinical Data Within the More Hypothesismentioning

confidence: 99%

“…Finally, when recorded over extended periods, individuals with epilepsy and experimental models display different types of seizures 89,121 . It will be interesting to test whether specific types of seizures occur at specific circadian times and whether their temporal distribution is similar in humans and rodents.…”

Section: Using the More Hypothesis To Explore Mechanismsmentioning

confidence: 99%

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Circadian/multidien Molecular Oscillations and Rhythmicity of Epilepsy (MORE)

Bernard

2020

Epilepsia

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The occurrence of seizures at specific times of the day has been consistently observed for centuries in individuals with epilepsy. Electrophysiological recordings provide evidence that seizures have a higher probability of occurring at a given time during the night and day cycle in individuals with epilepsy here referred to as the seizure rush hour. Which mechanisms underlie such circadian rhythmicity of seizures? Why don't they occur every day at the same time? Which mechanisms may underlie their occurrence outside the rush hour? In this commentary, I present a hypothesis: MORE – Molecular Oscillations and Rhythmicity of Epilepsy, a conceptual framework to study and understand the mechanisms underlying the circadian rhythmicity of seizures and their probabilistic nature. The core of the hypothesis is the existence of ~24‐hour oscillations of gene and protein expression throughout the body in different cells and organs. The orchestrated molecular oscillations control the rhythmicity of numerous body events, such as feeding and sleep. The concept developed here is that molecular oscillations may favor seizure genesis at preferred times, generating the condition for a seizure rush hour. However, the condition is not sufficient, as other factors are necessary for a seizure to occur. Studying these molecular oscillations may help us understand seizure genesis mechanisms and find new therapeutic targets and predictive biomarkers. The MORE hypothesis can be generalized to comorbidities and the slower multidien (week/month period) rhythmicity of seizures, a phenomenon addressed in another article in this issue of Epilepsia.

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Section: The More Hypothesismentioning

confidence: 98%

Section: Interpreting Clinical Data Within the More Hypothesismentioning

confidence: 99%

Section: Using the More Hypothesis To Explore Mechanismsmentioning

confidence: 99%

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Circadian/multidien Molecular Oscillations and Rhythmicity of Epilepsy (MORE)

Bernard

2020

Epilepsia

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“…This framework has been generalized by Saggio and col. [11,12]. A phenomenological mathematical model, called The Epileptor, describes the dynamics of a majority of seizures recorded in drug-resistant patients, and most seizures recorded in experimental models [1,12]. A qualitative analysis of the Epileptor reveals that seizures, SE and DB co-exist, and that multiple types of transitions from one type of activity to the other are possible, as verified experimentally [11,13,14].…”

Section: Introductionmentioning

confidence: 99%

A unified physiological framework of transitions between seizures, sustained ictal activity and depolarization block at the single neuron level

Depannemaecker

Ivanov

Lillo

et al. 2020

Preprint

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The majority of seizures recorded in humans and experimental animal models can be described by a generic phenomenological mathematical model, The Epileptor. In this model, seizure-like events (SLEs) are driven by a slow variable and occur via saddle node (SN) and homoclinic bifurcations at seizure onset and offset, respectively. Here we investigated SLEs at the single cell level using a biophysically relevant neuron model including a slow/fast system of four equations. The two equations for the slow subsystem describe ion concentration variations and the two equations of the fast subsystem delineate the electrophysiological activities of the neuron. Using extracellular K+ as a slow variable, we report that SLEs with SN/homoclinic bifurcations can readily occur at the single cell level when extracellular K+ reaches a critical value. In patients and experimental models, seizures can also evolve into status epilepticus (SE) and depolarization block (DB), activities which are also parts of the dynamic repertoire of the Epileptor. Increasing extracellular concentration of K+ in the model to values found during experimental SE and DB, we show that SE-like events and DB can also occur at the single cell level. Thus, seizures, SE and DB, which have been first identified as network events, can exist in a unified framework of a biophysical model at the single neuron level and exhibit similar dynamics as observed in the Epileptor.

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“…The knowledge of the bifurcation type that the system is going through might then guide the development of a mathematical model of the observed activity (Kuznetsov, 1998;Izhikevich, 2010;Touboul et al, 2011;Jirsa et al, 2014). A systematic analysis of onset and offset bifurcations then permits the construction of a seizure taxonomy based on purely dynamic features (Saggio et al, 2017(Saggio et al, , 2020.…”

mentioning

confidence: 99%

Computational modeling of seizure spread on a cortical surface

Sip

Guye

Bartolomei

et al. 2020

Preprint

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In the field of computational epilepsy, neural field models helped to understand some large-scale features of seizure dynamics. These insights however remain on general levels, without translation to the clinical settings via personalization of the model with the patient-specific structure. In particular, a link was suggested between epileptic seizures spreading across the cortical surface and the so-called theta-alpha activity (TAA) pattern seen on intracranial electrographic signals, yet this link was not demonstrated on a patient-specific level. Here we present a single patient computational study linking the seizure spreading across the patient-specific cortical surface with a specific instance of the TAA pattern recorded in the patient. Using the realistic geometry of the cortical surface we perform the simulations of seizure dynamics in The Virtual Brain platform, and we show that the simulated electrographic signals qualitatively agree with the recorded signals. Furthermore, the comparison with the simulations performed on surrogate surfaces reveals that the best quantitative fit is obtained for the real surface. The work illustrates how the patient-specific cortical geometry can be utilized in The Virtual Brain for personalized model building, and the importance of such approach.

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A taxonomy of seizure dynamotypes

Cited by 104 publications

References 82 publications

Circadian/multidien Molecular Oscillations and Rhythmicity of Epilepsy (MORE)

Circadian/multidien Molecular Oscillations and Rhythmicity of Epilepsy (MORE)

A unified physiological framework of transitions between seizures, sustained ictal activity and depolarization block at the single neuron level

Computational modeling of seizure spread on a cortical surface

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