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Eslami F, Djedovic A, Loeb JA. Modeling the Interictal Epileptic State for Therapeutic Development with Tetanus Toxin. Brain Sci 2024; 14:634. [PMID: 39061375 PMCID: PMC11274369 DOI: 10.3390/brainsci14070634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024] Open
Abstract
Focal forms of epilepsy can result from a wide range of insults and can vary from focal symptoms to generalized convulsions. Most drugs that have been developed for epilepsy focus on the prevention of seizures. On Electroencephalography (EEG), seizures are characterized by a repetitive buildup of epileptic waveforms that can spread across the brain. Brain regions that produce seizures generate far more frequent 'interictal' spikes seen between seizures, and in animal models, these spikes occur prior to the development of seizures. Interictal spiking by itself has been shown to have significant adverse clinical effects on cognition and behavior in both patients and animal models. While the exact relationships between interictal spiking and seizures are not well defined, interictal spikes serve as an important biomarker that, for some forms of epilepsy, can serve as a surrogate biomarker and as a druggable target. While there are many animal models of seizures for drug development, here we review models of interictal spiking, focusing on tetanus toxin, to study the relationship between interictal spiking, seizures, cognition, and behavior. Studies on human cortical regions with frequent interictal spiking have identified potential therapeutic targets; therefore, having a highly consistent model of spiking will be invaluable not only for unraveling the initial stages of the pathological cascade leading to seizure development but also for testing novel therapeutics. This review offers a succinct overview of the use of tetanus toxin animal models for studying and therapeutic development for interictal spiking.
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Affiliation(s)
| | | | - Jeffrey A. Loeb
- Department of Neurology and Rehabilitation, University of Illinois Chicago, 912 S Wood Street, 174N NPI M/C 796, Chicago, IL 60612, USA; (F.E.); (A.D.)
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2
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Szuromi MP, Jirsa VK, Stacey WC. Optimization of ictal aborting stimulation using the dynamotype taxonomy. J Comput Neurosci 2023; 51:445-462. [PMID: 37667137 PMCID: PMC10754472 DOI: 10.1007/s10827-023-00859-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 08/01/2023] [Accepted: 08/09/2023] [Indexed: 09/06/2023]
Abstract
Electrical stimulation is an increasingly popular method to terminate epileptic seizures, yet it is not always successful. A potential reason for inconsistent efficacy is that stimuli are applied empirically without considering the underlying dynamical properties of a given seizure. We use a computational model of seizure dynamics to show that different bursting classes have disparate responses to aborting stimulation. This model was previously validated in a large set of human seizures and led to a description of the Taxonomy of Seizure Dynamics and the dynamotype, which is the clinical analog of the bursting class. In the model, the stimulation is realized as an applied input, which successfully aborts the burst when it forces the system from a bursting state to a quiescent state. This transition requires bistability, which is not present in all bursters. We examine how topological and geometric differences in the bistable state affect the probability of termination as the burster progresses from onset to offset. We find that the most significant determining factors are the burster class (dynamotype) and whether the burster has a DC (baseline) shift. Bursters with a baseline shift are far more likely to be terminated due to the necessary structure of their state space. Furthermore, we observe that the probability of termination varies throughout the burster's duration, is often dependent on the phase when it was applied, and is highly correlated to dynamotype. Our model provides a method to predict the optimal method of termination for each dynamotype. These results lead to the prediction that optimization of ictal aborting stimulation should account for seizure dynamotype, the presence of a DC shift, and the timing of the stimulation.
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Affiliation(s)
- Matthew P. Szuromi
- Department of Biomedical Engineering and Biointerfaces Institute, University of Michigan, Ann Arbor, USA
- Department of Neurology, University of Michigan, Ann Arbor, USA
| | - Viktor K. Jirsa
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systémes, Marseille, France
| | - William C. Stacey
- Department of Biomedical Engineering and Biointerfaces Institute, University of Michigan, Ann Arbor, USA
- Department of Neurology, University of Michigan, Ann Arbor, USA
- Division of Neurology, Ann Arbor VA Hospital System, Ann Arbor, USA
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3
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Depannemaecker D, Ezzati A, Wang H, Jirsa V, Bernard C. From phenomenological to biophysical models of seizures. Neurobiol Dis 2023; 182:106131. [PMID: 37086755 DOI: 10.1016/j.nbd.2023.106131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 04/24/2023] Open
Abstract
Epilepsy is a complex disease that requires various approaches for its study. In this short review, we discuss the contribution of theoretical and computational models. The review presents theoretical frameworks that underlie the understanding of certain seizure properties and their classification based on their dynamical properties at the onset and offset of seizures. Dynamical system tools are valuable resources in the study of seizures. By analyzing the complex, dynamic behavior of seizures, these tools can provide insights into seizure mechanisms and offer a framework for their classification. Additionally, computational models have high potential for clinical applications, as they can be used to develop more accurate diagnostic and personalized medicine tools. We discuss various modeling approaches that span different scales and levels, while also questioning the neurocentric view, and emphasize the importance of considering glial cells. Finally, we explore the epistemic value provided by this type of approach.
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Affiliation(s)
- Damien Depannemaecker
- Institut de Neurosciences des Syst' emes, Aix-Marseille University, INSERM, Marseille, France.
| | - Aitakin Ezzati
- Institut de Neurosciences des Syst' emes, Aix-Marseille University, INSERM, Marseille, France
| | - Huifang Wang
- Institut de Neurosciences des Syst' emes, Aix-Marseille University, INSERM, Marseille, France
| | - Viktor Jirsa
- Institut de Neurosciences des Syst' emes, Aix-Marseille University, INSERM, Marseille, France
| | - Christophe Bernard
- Institut de Neurosciences des Syst' emes, Aix-Marseille University, INSERM, Marseille, France.
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4
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Recruitment of interictal- and ictal-like discharges in posterior piriform cortex by delta-rate (1–4 Hz) focal bursts in anterior piriform cortex in vivo. Epilepsy Res 2022; 187:107032. [DOI: 10.1016/j.eplepsyres.2022.107032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 09/10/2022] [Accepted: 10/06/2022] [Indexed: 11/23/2022]
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5
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Bosl WJ, Leviton A, Loddenkemper T. Prediction of Seizure Recurrence. A Note of Caution. Front Neurol 2021; 12:675728. [PMID: 34054713 PMCID: PMC8155381 DOI: 10.3389/fneur.2021.675728] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/20/2021] [Indexed: 12/31/2022] Open
Abstract
Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.
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Affiliation(s)
- William J Bosl
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Health Informatics Program, University of San Francisco, San Francisco, CA, United States
| | - Alan Leviton
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Tobias Loddenkemper
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
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6
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The Kainic Acid Models of Temporal Lobe Epilepsy. eNeuro 2021; 8:ENEURO.0337-20.2021. [PMID: 33658312 PMCID: PMC8174050 DOI: 10.1523/eneuro.0337-20.2021] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 01/14/2021] [Accepted: 01/24/2021] [Indexed: 12/14/2022] Open
Abstract
Experimental models of epilepsy are useful to identify potential mechanisms of epileptogenesis, seizure genesis, comorbidities, and treatment efficacy. The kainic acid (KA) model is one of the most commonly used. Several modes of administration of KA exist, each producing different effects in a strain-, species-, gender-, and age-dependent manner. In this review, we discuss the advantages and limitations of the various forms of KA administration (systemic, intrahippocampal, and intranasal), as well as the histologic, electrophysiological, and behavioral outcomes in different strains and species. We attempt a personal perspective and discuss areas where work is needed. The diversity of KA models and their outcomes offers researchers a rich palette of phenotypes, which may be relevant to specific traits found in patients with temporal lobe epilepsy.
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Sip V, Scholly J, Guye M, Bartolomei F, Jirsa V. Evidence for spreading seizure as a cause of theta-alpha activity electrographic pattern in stereo-EEG seizure recordings. PLoS Comput Biol 2021; 17:e1008731. [PMID: 33635864 PMCID: PMC7946361 DOI: 10.1371/journal.pcbi.1008731] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 03/10/2021] [Accepted: 01/21/2021] [Indexed: 02/07/2023] Open
Abstract
Intracranial electroencephalography is a standard tool in clinical evaluation of patients with focal epilepsy. Various early electrographic seizure patterns differing in frequency, amplitude, and waveform of the oscillations are observed. The pattern most common in the areas of seizure propagation is the so-called theta-alpha activity (TAA), whose defining features are oscillations in the θ - α range and gradually increasing amplitude. A deeper understanding of the mechanism underlying the generation of the TAA pattern is however lacking. In this work we evaluate the hypothesis that the TAA patterns are caused by seizures spreading across the cortex. To do so, we perform simulations of seizure dynamics on detailed patient-derived cortical surfaces using the spreading seizure model as well as reference models with one or two homogeneous sources. We then detect the occurrences of the TAA patterns both in the simulated stereo-electroencephalographic signals and in the signals of recorded epileptic seizures from a cohort of fifty patients, and we compare the features of the groups of detected TAA patterns to assess the plausibility of the different models. Our results show that spreading seizure hypothesis is qualitatively consistent with the evidence available in the seizure recordings, and it can explain the features of the detected TAA groups best among the examined models.
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Affiliation(s)
- Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Scholly
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Maxime Guye
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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8
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Bernard C. Circadian/multidien Molecular Oscillations and Rhythmicity of Epilepsy (MORE). Epilepsia 2020; 62 Suppl 1:S49-S68. [PMID: 33063860 DOI: 10.1111/epi.16716] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/15/2020] [Accepted: 09/15/2020] [Indexed: 12/26/2022]
Abstract
The occurrence of seizures at specific times of the day has been consistently observed for centuries in individuals with epilepsy. Electrophysiological recordings provide evidence that seizures have a higher probability of occurring at a given time during the night and day cycle in individuals with epilepsy here referred to as the seizure rush hour. Which mechanisms underlie such circadian rhythmicity of seizures? Why don't they occur every day at the same time? Which mechanisms may underlie their occurrence outside the rush hour? In this commentary, I present a hypothesis: MORE - Molecular Oscillations and Rhythmicity of Epilepsy, a conceptual framework to study and understand the mechanisms underlying the circadian rhythmicity of seizures and their probabilistic nature. The core of the hypothesis is the existence of ~24-hour oscillations of gene and protein expression throughout the body in different cells and organs. The orchestrated molecular oscillations control the rhythmicity of numerous body events, such as feeding and sleep. The concept developed here is that molecular oscillations may favor seizure genesis at preferred times, generating the condition for a seizure rush hour. However, the condition is not sufficient, as other factors are necessary for a seizure to occur. Studying these molecular oscillations may help us understand seizure genesis mechanisms and find new therapeutic targets and predictive biomarkers. The MORE hypothesis can be generalized to comorbidities and the slower multidien (week/month period) rhythmicity of seizures, a phenomenon addressed in another article in this issue of Epilepsia.
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Affiliation(s)
- Christophe Bernard
- Inserm, INS, Institut de Neurosciences des Systèmes, Aix Marseille Univ, Marseille, France
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9
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Saggio ML, Crisp D, Scott JM, Karoly P, Kuhlmann L, Nakatani M, Murai T, Dümpelmann M, Schulze-Bonhage A, Ikeda A, Cook M, Gliske SV, Lin J, Bernard C, Jirsa V, Stacey WC. A taxonomy of seizure dynamotypes. eLife 2020; 9:55632. [PMID: 32691734 PMCID: PMC7375810 DOI: 10.7554/elife.55632] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 06/12/2020] [Indexed: 01/02/2023] Open
Abstract
Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties. Epileptic seizures have been recognized for centuries. But it was only in the 1930s that it was realized that seizures are the result of out-of-control electrical activity in the brain. By placing electrodes on the scalp, doctors can identify when and where in the brain a seizure begins. But they cannot tell much about how the seizure behaves, that is, how it starts, stops or spreads to other areas. This makes it difficult to control and prevent seizures. It also helps explain why almost a third of patients with epilepsy continue to have seizures despite being on medication. Saggio, Crisp et al. have now approached this problem from a new angle using methods adapted from physics and engineering. In these fields, “dynamics research” has been used with great success to predict and control the behavior of complex systems like electrical power grids. Saggio, Crisp et al. reasoned that applying the same approach to the brain would reveal the dynamics of seizures and that such information could then be used to categorize seizures into groups with similar properties. This would in effect create for seizures what the periodic table is for the elements. Applying the dynamics research method to seizure data from more than a hundred patients from across the world revealed 16 types of seizure dynamics. These “dynamotypes” had distinct characteristics. Some were more common than others, and some tended to occur together. Individual patients showed different dynamotypes over time. By constructing a way to classify seizures based on the relationships between the dynamotypes, Saggio, Crisp et al. provide a new tool for clinicians and researchers studying epilepsy. Previous clinical tools have focused on the physical symptoms of a seizure (referred to as the phenotype) or its potential genetic causes (genotype). The current approach complements these tools by adding the dynamotype: how seizures start, spread and stop in the brain. This approach has the potential to lead to new branches of research and better understanding and treatment of seizures.
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Affiliation(s)
- Maria Luisa Saggio
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Dakota Crisp
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Jared M Scott
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - 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.,Faculty of Information Technology, Monash University, Clayton, Australia
| | - Mitsuyoshi Nakatani
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Tomohiko Murai
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), 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 Medicine, Kyoto, Japan
| | - Mark Cook
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia.,Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - Stephen V Gliske
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Jack Lin
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Christophe Bernard
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - William C Stacey
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States.,Department of Neurology, University of Michigan, Ann Arbor, United States
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10
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Crisp DN, Cheung W, Gliske SV, Lai A, Freestone DR, Grayden DB, Cook MJ, Stacey WC. Quantifying epileptogenesis in rats with spontaneous and responsive brain state dynamics. Brain Commun 2020; 2:fcaa048. [PMID: 32671339 PMCID: PMC7331126 DOI: 10.1093/braincomms/fcaa048] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 03/06/2020] [Accepted: 03/27/2020] [Indexed: 12/17/2022] Open
Abstract
There is a crucial need to identify biomarkers of epileptogenesis that will help predict later development of seizures. This work identifies two novel electrophysiological biomarkers that quantify epilepsy progression in a rat model of epileptogenesis. The long-term tetanus toxin rat model was used to show the development and remission of epilepsy over several weeks. We measured the response to periodic electrical stimulation and features of spontaneous seizure dynamics over several weeks. Both biomarkers showed dramatic changes during epileptogenesis. Electrically induced responses began to change several days before seizures began and continued to change until seizures resolved. These changes were consistent across animals and allowed development of an algorithm that could differentiate which animals would later develop epilepsy. Once seizures began, there was a progression of seizure dynamics that closely follows recent theoretical predictions, suggesting that the underlying brain state was changing over time. This research demonstrates that induced electrical responses and seizure onset dynamics are useful biomarkers to quantify dynamical changes in epileptogenesis. These tools hold promise for robust quantification of the underlying epileptogenicity and prediction of later development of seizures.
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Affiliation(s)
- Dakota N Crisp
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Warwick Cheung
- Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC 3010, Australia
- Department of Medicine, St. Vincent’s Hospital, University of Melbourne, Melbourne, VIC 3065, Australia
| | - Stephen V Gliske
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alan Lai
- Department of Medicine, St. Vincent’s Hospital, University of Melbourne, Melbourne, VIC 3065, Australia
| | - Dean R Freestone
- Department of Medicine, St. Vincent’s Hospital, University of Melbourne, Melbourne, VIC 3065, Australia
| | - David B Grayden
- Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC 3010, Australia
- Department of Medicine, St. Vincent’s Hospital, University of Melbourne, Melbourne, VIC 3065, Australia
| | - Mark J Cook
- Department of Medicine, St. Vincent’s Hospital, University of Melbourne, Melbourne, VIC 3065, Australia
| | - William C Stacey
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
- Correspondence to: William Stacey, MD PhD Departments of Neurology and Biomedical Engineering, BioInterfaces Institute, University of Michigan 1500 E. Medical Center Dr., Ann Arbor, MI 48109, USA E-mail:
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