ArticleNeuroImage2016
Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review.Frontiers in neuroscience · 2019Pooled it
- High-resolution Bayesian Virtual Epileptic Patient using neural field models.Network neuroscience (Cambridge, Mass.) · 2026Article
- Synaptic inhibitory dynamics drive benzodiazepine response in pediatric status epilepticus.Epilepsia · 2025Article
- A survey of brain network analysis by electroencephalographic signals.Cognitive neurodynamics · 2022Article
- On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread.PLoS computational biology · 2021Article
- Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders.International journal of molecular sciences · 2021Review
- Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography.PLoS computational biology · 2021Article
- Localization of Epileptic Foci Based on Simultaneous EEG-fMRI Data.Frontiers in neurology · 2021Review
- Article
- Taking the EEG Back Into the Brain: The Power of Multiple Discrete Sources.Frontiers in neurology · 2019Article
- NMDA-receptor antibodies alter cortical microcircuit dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Dynamic effective connectivity in resting state fMRI.NeuroImage · 2018Article
- Seizure pathways: A model-based investigation.PLoS computational biology · 2018Article
- Calcium imaging and dynamic causal modelling reveal brain-wide changes in effective connectivity and synaptic dynamics during epileptic seizures.PLoS computational biology · 2018Article
- Directed functional connectivity using dynamic graphical models.NeuroImage · 2018Article
- Identifying neural drivers of benign childhood epilepsy with centrotemporal spikes.NeuroImage. Clinical · 2018Article
- Neuronal network models of epileptogenesis.Neurosciences (Riyadh, Saudi Arabia) · 2017Review
- Embodied neurology: an integrative framework for neurological disorders.Brain : a journal of neurology · 2016Article
- Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions.NeuroImage · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Seizure activity in EEG recordings can persist for hours with seizure dynamics changing rapidly over time and space. To characterise the spatiotemporal evolution of seizure activity, large data sets often need to be analysed. Dynamic causal modelling (DCM) can be used to estimate the synaptic drivers of cortical dynamics during a seizure; however, the requisite (Bayesian) inversion procedure is computationally expensive. In this note, we describe a straightforward procedure, within the DCM framework, that provides efficient inversion of seizure activity measured with non-invasive and invasive physiological recordings; namely, EEG/ECoG. We describe the theoretical background behind a Bayesian belief updating scheme for DCM. The scheme is tested on simulated and empirical seizure activity (recorded both invasively and non-invasively) and compared with standard Bayesian inversion. We show that the Bayesian belief updating scheme provides similar estimates of time-varying synaptic parameters, compared to standard schemes, indicating no significant qualitative change in accuracy. The difference in variance explained was small (less than 5%). The updating method was substantially more efficient, taking approximately 5-10min compared to approximately 1-2h. Moreover, the setup of the model under the updating scheme allows for a clear specification of how neuronal variables fluctuate over separable timescales. This method now allows us to investigate the effect of fast (neuronal) activity on slow fluctuations in (synaptic) parameters, paving a way forward to understand how seizure activity is generated.
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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.