Intracranial EEG; Pharmacoresistance; Bayesian Approach; Human Epilepsy
Rummel Christian, Slavova Nedelina, Seiler Andrea, Abela Eugenio, Hauf Martinus, Burren Yuliya, Weisstanner Christian, Vulliemoz Serge, Seeck Margitta, Schindler Kaspar, Wiest Roland (2017), Personalized structural image analysis in patients with temporal lobe epilepsy., in Scientific reports
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Lopes Marinho A, Richardson Mark P, Abela Eugenio, Rummel Christian, Schindler Kaspar, Goodfellow Marc, Terry John R (2017), An optimal strategy for epilepsy surgery: Disruption of the rich-club?, in PLoS computational biology
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Steimer Andreas, Müller Michael, Schindler Kaspar (2017), Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients., in Human brain mapping
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Andrzejak Ralph G, Rummel Christian, Mormann Florian, Schindler Kaspar (2016), All together now: Analogies between chimera state collapses and epileptic seizures., in Scientific reports
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Goodfellow M., Rummel C., Abela E., Richardson M. P., Schindler K., Terry J. R. (2016), Estimation of brain network ictogenicity predicts outcome from epilepsy surgery, in Scientific Reports
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Schindler Kaspar, Rummel Christian, Andrzejak Ralph G., Goodfellow Marc, Zubler Frédéric, Abela Eugenio, Wiest Roland, Pollo Claudio, Steimer Andreas, Gast Heidemarie (2016), Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone, in Clinical Neurophysiology
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Steimer Andreas, Zubler Frédéric, Schindler Kaspar (2015), Chow-Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series, in NeuroImage
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Steimer Andreas, Schindler Kaspar (2015), Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States., in PloS one
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Rummel Christian, Abela Eugenio, Andrzejak Ralph G, Hauf Martinus, Pollo Claudio, Müller Markus, Weisstanner Christian, Wiest Roland, Schindler Kaspar (2015), Resected Brain Tissue, Seizure Onset Zone and Quantitative EEG Measures: Towards Prediction of Post-Surgical Seizure Control., in PloS one
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Background: The primary treatment goal in patients suffering from epilepsy is complete seizure freedom. However, seizure freedom is still not achieved in 20-30% of the patients. It is therefore mandatory to find biomarkers, i.e. measurable characteristics of seizure generation that have the potential to improve current therapies and to guide the design of novel approaches like electric brain stimulation. Here we set out to invoke the powerful tools offered by Bayesian inference methods to assess seizure dynamics, i.e. the spatiotemporal characteristics of intracranial EEG (iEEG) that are critical for seizure onset, seizure propagation and seizure termination. A crucial aspect of our approach is the implementation of versatile, realistic models of multi-channel iEEG time series, which should allow to predict how specific iEEG signals, generated in different parts of the epileptic brain, influence overall iEEG seizure dynamics. We expect that the Bayesian inference approach will clearly surpass the limitations of state-of-the-art methods of iEEG analysis, such as functional networks, which are purely descriptive only and which do not allow to model the effect of localized changes onto the overall iEEG patterns. Working hypothesis:Using multi-channel iEEG time series, the Bayesian inference approach allows for realistic predictions regarding the probability of seizure occurrence. In particular, this approach allows to test, how specific iEEG signals in different parts of the epileptic brain influence overall iEEG seizure dynamics and seizure probability.Patients and methods:In patients with pharmaco-resistant epilepsies who undergo invasive monitoring, iEEG will be prospectively recorded with high spatio-temporal resolution using macroelectrodes and microwires. Bayesian inference -in particular Probabilistic Graphical Models (PGMs) that accurately represent seizure and non-seizure brain states- will be learned from iEEG signals. The PGMs then allow to extensively test, how changes of one or more iEEG signals influence seizure dynamics and thereby help to identify the brain regions and electric brain activities that are key for seizure control. To further validate the PGMs, we will retrospectively analyze an existing database consisting of >200 iEEG seizures from patients with known post-surgical outcomes. Expected value of the project:By invoking the powerful framework of Bayesian inference applied to iEEG signals this project will deepen the understanding of how localized changes of electric activity influence the overall dynamics of human epileptic brains during seizures. We expect the results of this study to potentially be key to better predict the outcome of current resective epilepsy surgeries. In addition, they may also provide guidance for future neuromodulatory therapies, by helping to improve target selection and identifying those iEEG signals that should be induced by therapeutic interventions.