ArticleBrain communications2020
Quantifying epileptogenesis in rats with spontaneous and responsive brain state dynamics.
Article in Brain communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Seizure recruitment properties are dependent upon dynamotype: A modeling study.PLoS computational biology · 2026Article
- Article
- Article
- Dynamic multiday seizure cycles and evolving rhythms in a tetanus toxin rat model of epilepsy.Scientific reports · 2025Article
- Cycles in seizure duration and their underlying dynamics in the tetanus toxin rat model.Brain communications · 2025Article
- Modeling the Interictal Epileptic State for Therapeutic Development with Tetanus Toxin.Brain sciences · 2024Review
- Optimization of ictal aborting stimulation using the dynamotype taxonomy.Journal of computational neuroscience · 2023Article
- Evidence for spreading seizure as a cause of theta-alpha activity electrographic pattern in stereo-EEG seizure recordings.PLoS computational biology · 2021Article
- Prediction of Seizure Recurrence. A Note of Caution.Frontiers in neurology · 2021Article
- A taxonomy of seizure dynamotypes.eLife · 2020Article
- Quantifying epileptogenesis in rats with spontaneous and responsive brain state dynamics.Brain communications · 2020Article
- Review
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Authors and funding
8 authors.
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Abstract
There is a crucial need to identify biomarkers of epileptogenesis that will help predict later development of seizures. This work identifies two novel electrophysiological biomarkers that quantify epilepsy progression in a rat model of epileptogenesis. The long-term tetanus toxin rat model was used to show the development and remission of epilepsy over several weeks. We measured the response to periodic electrical stimulation and features of spontaneous seizure dynamics over several weeks. Both biomarkers showed dramatic changes during epileptogenesis. Electrically induced responses began to change several days before seizures began and continued to change until seizures resolved. These changes were consistent across animals and allowed development of an algorithm that could differentiate which animals would later develop epilepsy. Once seizures began, there was a progression of seizure dynamics that closely follows recent theoretical predictions, suggesting that the underlying brain state was changing over time. This research demonstrates that induced electrical responses and seizure onset dynamics are useful biomarkers to quantify dynamical changes in epileptogenesis. These tools hold promise for robust quantification of the underlying epileptogenicity and prediction of later development of seizures.
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