ArticlePLoS computational biology2021
Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- High-resolution Bayesian Virtual Epileptic Patient using neural field models.Network neuroscience (Cambridge, Mass.) · 2026Article
- Virtual brain twins for stimulation in epilepsy.Nature computational science · 2025Article
- Neighbourhood topology unveils pathological hubs in the brain networks of epilepsy-surgery patients.Brain communications · 2025Article
- Parameter estimation in a whole-brain network model of epilepsy: Comparison of parallel global optimization solvers.PLoS computational biology · 2024Article
- Bifurcations and bursting in the Epileptor.PLoS computational biology · 2024Article
- Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in epilepsy.Network neuroscience (Cambridge, Mass.) · 2024Article
- Epilepsy lesion localization method based on brain function network.Frontiers in human neuroscience · 2024Article
- Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study.Network neuroscience (Cambridge, Mass.) · 2024Article
- The role of additive and diffusive coupling on the dynamics of neural populations.Scientific reports · 2023Article
- The role of epidemic spreading in seizure dynamics and epilepsy surgery.Network neuroscience (Cambridge, Mass.) · 2023Article
- Diffusion tractography predicts propagated high-frequency activity during epileptic spasms.Epilepsia · 2022Article
- Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings.Scientific reports · 2022Article
- Computational modeling of seizure spread on a cortical surface.Journal of computational neuroscience · 2022Article
- Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis.Frontiers in neural circuits · 2022Article
- Critical dynamics in the spread of focal epileptic seizures: Network connectivity, neural excitability and phase transitions.PloS one · 2022Article
- Identifying spatio-temporal seizure propagation patterns in epilepsy using Bayesian inference.Communications biology · 2021Article
- Optimization of epilepsy surgery through virtual resections on individual structural brain networks.Scientific reports · 2021Article
- Prediction of Seizure Recurrence. A Note of Caution.Frontiers in neurology · 2021Article
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10 authors.
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Abstract
Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.
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