ArticleeNeuro
A Model for the Propagation of Seizure Activity in Normal Brain Tissue.
Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms.eLife · 2026Article
- A minimal model of working memory in neural systems and neuromorphic circuits.Nature communications · 2026Article
- Neuronal silence as a predictive biomarker and target for epileptic seizures suppression.Scientific reports · 2026Article
- Modeling impairment of ionic regulation with extended Adaptive Exponential integrate-and-fire models.Journal of computational neuroscience · 2025Article
- Precision medicine for epilepsy: challenges and perspectives for an optimized clinical care pathway.Frontiers in neurology · 2025Review
- Exploring Kainic Acid-Induced Alterations in Circular Tripartite Networks with Advanced Analysis Tools.eNeuro · 2024Article
- Microscale Neuronal Activity Collectively Drives Chaotic and Inflexible Dynamics at the Macroscale in Seizures.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Epilepsies are characterized by paroxysmal electrophysiological events and seizures, which can propagate across the brain. One of the main unsolved questions in epilepsy is how epileptic activity can invade normal tissue and thus propagate across the brain. To investigate this question, we consider three computational models at the neural network scale to study the underlying dynamics of seizure propagation, understand which specific features play a role, and relate them to clinical or experimental observations. We consider both the internal connectivity structure between neurons and the input properties in our characterization. We show that a paroxysmal input is sometimes controlled by the network while in other instances, it can lead the network activity to itself produce paroxysmal activity, and thus will further propagate to efferent networks. We further show how the details of the network architecture are essential to determine this switch to a seizure-like regime. We investigated the nature of the instability involved and in particular found a central role for the inhibitory connectivity. We propose a probabilistic approach to the propagative/non-propagative scenarios, which may serve as a guide to control the seizure by using appropriate stimuli.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.