ArticleJournal of computational neuroscience2022
Computational modeling of seizure spread on a cortical surface.
Article in Journal of computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI-Driven Mapping of Seizure Spread Patterns.Annals of neurology · 2026Article
- Multi-expert consensus annotations of spontaneous and stimulation-induced seizures in stereotactic EEG.medRxiv : the preprint server for health sciences · 2026Article
- Impact of ictal spread on preoperative memory and language functions and postoperative seizure outcomes in drug-resistant epilepsy.Epilepsy & behavior : E&B · 2025Article
- Neighbourhood topology unveils pathological hubs in the brain networks of epilepsy-surgery patients.Brain communications · 2025Article
- Modeling for neurosurgical laser interstitial thermal therapy with and without intracranial recording electrodes.Current research in neurobiology · 2024Article
- Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures.PLoS computational biology · 2023Article
- The role of epidemic spreading in seizure dynamics and epilepsy surgery.Network neuroscience (Cambridge, Mass.) · 2023Article
- Extracting Dynamical Understanding From Neural-Mass Models of Mouse Cortex.Frontiers in computational neuroscience · 2022Article
- Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model.Frontiers in physiology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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