Poster No:
1888
Submission Type:
Abstract Submission
Authors:
Maria Luisa Saggio1, Dakota Crisp2, Jared Scott2, Philippa Karoly3, Levin Kuhlmann4, Mitsuyoshi Nakatani5, Tomohiko Murai6, Matthias Dumpelmann7, Andreas Schulze-Bonhage7, Akio Ikeda8, Mark Cook3, Stephen Gliske9, Jack Lin10, Christophe Bernard5, Viktor Jirsa11, William Stacey12
Institutions:
1Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, 2University of Michigan, Ann Arbor, MI, 3Graeme Clark Institute, The University of Melbourne, Melbourne, Australia, 4Department of Medicine, St. Vincent’s Hospital, The University of Melbourne, Melbourne, Australia, 5Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Syst`emes, Marseille, France, 6Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medic, Kyoto, Japan, 7Epilepsy Center, Medical Center – University of Freiburg, Freiburg im Breisgau, Germany, 8Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medic, Kyoyo, Japan, 9Department of Neurology, University of Michigan, Ann Arboor, MI, 10Department of Neurology, University of Michigan, Ann Arbor, MI, 11Institut de Neurosciences des Systèmes, Marseille, France, 12Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, MI
First Author:
Maria Luisa Saggio
Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes
Marseille, France
Co-Author(s):
Philippa Karoly
Graeme Clark Institute, The University of Melbourne
Melbourne, Australia
Levin Kuhlmann
Department of Medicine, St. Vincent’s Hospital, The University of Melbourne
Melbourne, Australia
Mitsuyoshi Nakatani
Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Syst`emes
Marseille, France
Tomohiko Murai
Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medic
Kyoto, Japan
Matthias Dumpelmann
Epilepsy Center, Medical Center – University of Freiburg
Freiburg im Breisgau, Germany
Akio Ikeda
Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medic
Kyoyo, Japan
Mark Cook
Graeme Clark Institute, The University of Melbourne
Melbourne, Australia
Stephen Gliske
Department of Neurology, University of Michigan
Ann Arboor, MI
Jack Lin
Department of Neurology, University of Michigan
Ann Arbor, MI
Christophe Bernard
Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Syst`emes
Marseille, France
Viktor Jirsa
Institut de Neurosciences des Systèmes
Marseille, France
William Stacey
Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan
Ann Arbor, MI
Introduction:
Epilepsy is one of the most common neurological disorders and is characterized by spontaneously recurring seizures. Different ways of classifying seizures have been proposed. However, in the absence of fundamental knowledge, the current classification is based on an operational (i.e. practical) system [Fisher et al., 2017a]. A complementary approach leverages on the idea that electrographic seizures can be classified using knowledge from dynamical system theory [Jirsa et al. 2014, Wang et al. 2017]. It is possible to create a taxonomy of sixteen classes, the dynamotypes, based on the bifurcations pair allowing for the transitions between healthy and ictal state and viceversa [Izhikevich 2000, Jirsa et al. 2014]. Based on that initial work, we here expanded and validated the Taxonomy of Seizure Dynamotypes, through the analysis of a large cohort of human data. Our analysis was guided by insights gained from the study of a minimal model for bursting that captures the different dynamotypes in a single mathematical representation [Saggio et al, 2017]. The model establishes a hierarchy among the classes of the taxonomy and introduces relationship among them, with the possibility that patients could exhibit more than one seizure type, with some pairings being more likely too occur.
Methods:
We analyzed seizures from 120 patients with focal onset seizures, recorded on intracranial EEG in seven centers worldwide, to identify the bifurcations at onset and offset. The canonical features necessary to distinguish the bifurcations are the trends of the amplitude and interspike intervals (ISI), with some bifurcations exhibiting specific scaling laws, and the presence/absence of a direct current shift. We first validated our procedure, using three human reviewers and an automated algorithm, on a synthetic dataset obtained through the model. We then compared these same methods on 120 human seizures. We found that concordance was also reliable in human data.
Results:
We identified 12 different dynamotypes, with some bifurcations being more common than others. To test whether individuals display different types of seizures over time, analysed longitudinal data, with over 2000 seizures from 13 patients. Individual patients displayed different dynamotypes over time. This finding challenges the traditional view of stereotyped seizures, suggesting a more nuanced understanding of seizure dynamics is necessary. In a few cases, we also observed transitions between classes occurring during a single seizure. This findings were reproducible within our model, together with some other 'unusual' seizures, highlighting the role of processes acting on at least three different timescales in the generation, evolution and termination of a seizure.
Conclusions:
The introduction of TSD provides a new perspective in the study and classification of epileptic seizures. It does not describe all possible seizure features, but relies on seizure onset and offset classification, complementing existing clinical tools by focusing on the dynamic aspects of seizures. TSD establishes a principled method for characterizing seizure dynamics that has the potential to lead to new branches of research, improved understanding, and better treatment of seizures. The patient specific dynamotype(s) can be used to improve the predictive power of large-scale brain models that are being developed to improve surgery outcome in drug-resistant epilepsy patients. One of these approaches, the Virtual Epileptic Patient (VEP), is currently under clinical trial in France and uses a model encoding one of the most common dynamotypes [Jirsa et al. 2014]. However, our analysis suggests that there is variability among patients and that patient specificity may be improved by modelling the appropriate dynamotype, which which may affect the global dynamics of the VEP and alter predictions on treatment.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Modeling and Analysis Methods:
Methods Development 1
Other Methods
Keywords:
Data analysis
Epilepsy
Machine Learning
Modeling
1|2Indicates the priority used for review
Provide references using author date format
Fisher, R. S. et al. (2017). Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia, 58(4), 522-530.
Izhikevich, E. M. (2000). Neural excitability, spiking and bursting. Interna- tional journal of bifurcation and chaos, 10(06), 1171-1266.
Jirsa, V. K. et al. (2014). On the nature of seizure dynamics. Brain, 137(8), 2210-2230.
Wang, Y. et al. (2017). Mechanisms underlying different onset patterns of focal seizures. PLoS computational biology, 13(5), e1005475.
Saggio, M. L. et al. (2017). Fast–slow bursters in the unfolding of a high codimension singularity and the ultra-slow transitions of classes. The Journal of Mathematical Neuroscience, 7, 1-47.
Saggio, M. L. et al. (2020). A taxonomy of seizure dynamotypes. Elife, 9, e55632.