Quantifying epileptogenesis in rats with spontaneous and responsive brain state dynamics

Crisp, Dakota; Cheung, Warwick; Gliske, S.; Lai, Alan; Freestone, Dean R.; Grayden, David B.; Cook, Mark; Stacey, William C.

doi:10.1093/braincomms/fcaa048

Cited by 14 publications

(14 citation statements)

References 53 publications

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“…There are many other potential applications for TSD in basic research as well. For instance, our group recently published an analysis quantifying how epileptogenesis progresses in the tetanus toxin model in rats ( Crisp et al, 2020 ). That work showed that the dynamotype evolved over time, starting with SN and moving into SNIC ( Appendix 1—figure 4 ) and sometimes SupH onsets over the course of weeks.…”

Section: Discussionmentioning

confidence: 99%

“…There were no unequivocal examples of SNIC onset in our human data. However, our group recently published data from the tetanus toxin model in rats that has excellent SNIC onsets ( Crisp et al, 2020 ). Shown are data from two rats, which were shown in Figure 5 of that paper (see that work for details on experimental procedures).…”

Section: Methodsmentioning

confidence: 99%

“…Note, there is sometimes a continuum between some of the bifurcations; here, the clonic bursts have some characteristics of a DC shift, but since they all return to baseline before the next burst this is not considered a sustained DC shift. Additional details are discussed in Crisp et al, 2020 . Offset Classification : Seizure offset is not seen in this view.…”

Section: Methodsmentioning

confidence: 99%

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A taxonomy of seizure dynamotypes

Saggio

Crisp

Scott

et al. 2020

eLife

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102

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Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A taxonomy of seizure dynamotypes

Saggio

Crisp

Scott

et al. 2020

eLife

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102

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“…Humans exhibit a range of dynamotypes when monitored over long periods, suggesting that brain states can vary over the period of days or months (Saggio et al, 2020). In a rodent model of epilepsy, the dynamotypes had similar changes during the progression of epileptogenesis in multiple animals, which corresponded to alterations in the response to perturbing stimuli (Crisp et al, 2020b). These results suggest that the brain does not exhibit a single pathway into seizure, and that dynamotype is influenced by a changing interictal brain state.…”

Section: Introductionmentioning

confidence: 86%

Carbamazepine and GABA have distinct effects on seizure onset dynamics in mouse brain slices

Crisp

Parent

Nakatani

et al. 2020

Preprint

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Optimizing antiepileptic drug therapy is very challenging due to the absence of a reliable method to assess how brain activity changes between seizures. This work uses the Taxonomy of Seizure Dynamics (Saggio et al., 2020) to investigate how anticonvulsants influence seizure onset dynamotypes. The no Mg2+/high K+ mouse brain-slice seizure model (N = 92) was used to generate consistent epileptiform onsets. We compared the onset bifurcations of controls with slices treated with either GABA or carbamazepine. Each anticonvulsant uniquely changed the types of bifurcations in the slices. This experiment provides proof-of-concept evidence that brain states exist on a "map" of seizure dynamics, and that antiepileptic drugs with different mechanisms can change the positioning of the brain states on the map.

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“…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%

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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Quantifying epileptogenesis in rats with spontaneous and responsive brain state dynamics

Cited by 14 publications

References 53 publications

A taxonomy of seizure dynamotypes

A taxonomy of seizure dynamotypes

Carbamazepine and GABA have distinct effects on seizure onset dynamics in mouse brain slices

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

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