ArticlePLoS computational biology2021
On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.
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, 49 citations in OpenAlex.
- Normative Modeling of Static and Dynamic Functional Connectivity.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- High-resolution Bayesian Virtual Epileptic Patient using neural field models.Network neuroscience (Cambridge, Mass.) · 2026Article
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- Dynamic causal modelling in probabilistic programming languages.Journal of the Royal Society, Interface · 2025Article
- Mapping Brain Lesions to Conduction Delays: The Next Step for Personalized Brain Models in Multiple Sclerosis.Human brain mapping · 2025Article
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- Genomic Prediction of Semen Traits in Boars Incorporating Biological Interactions.International journal of molecular sciences · 2024Article
- The virtual multiple sclerosis patient.iScience · 2024Article
- Parameter estimation in a whole-brain network model of epilepsy: Comparison of parallel global optimization solvers.PLoS computational biology · 2024Article
- Virtual brain twins: from basic neuroscience to clinical use.National science review · 2024Review
- Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference.PLoS computational biology · 2024Article
- Effects of the spatial resolution of the Virtual Epileptic Patient on the identification of epileptogenic networks.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in epilepsy.Network neuroscience (Cambridge, Mass.) · 2024Article
- Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients.Brain communications · 2024Article
- The role of epidemic spreading in seizure dynamics and epilepsy surgery.Network neuroscience (Cambridge, Mass.) · 2023Article
- Towards an efficient validation of dynamical whole-brain models.Scientific reports · 2022Article
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Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.
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