ArticleScientific reports2019
The role that choice of model plays in predictions for epilepsy surgery.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Bifurcations and bursting in the Epileptor.PLoS computational biology · 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
- Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG.Brain connectivity · 2022Review
- 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
- Dynamical Network Models From EEG and MEG for Epilepsy Surgery-A Quantitative Approach.Frontiers in neurology · 2022Review
- Optimization of epilepsy surgery through virtual resections on individual structural brain networks.Scientific reports · 2021Article
- Domino-like transient dynamics at seizure onset in epilepsy.PLoS computational biology · 2020Article
- The Role of Excitability and Network Structure in the Emergence of Focal and Generalized Seizures.Frontiers in neurology · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Mathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism that defines the transition from background to the seizure state. Since these mechanisms are unknown, it is important to understand how predictions from alternative dynamical descriptions compare. Herein we evaluate to what extent three different dynamical models provide consistent predictions for the effect of removing nodes from networks. We show that for small, directed, connected networks the three considered models provide consistent predictions. For larger networks, predictions are shown to be less consistent. However consistency is higher in networks that have sufficiently large differences in ictogenicity between nodes. We further demonstrate that heterogeneity in ictogenicity across nodes correlates with variability in the number of connections for each node.
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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.