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Brogin JAF, Faber J, Reyes-Garcia SZ, Cavalheiro EA, Bueno DD. Epileptic seizure suppression: A computational approach for identification and control using real data. PLoS One 2024; 19:e0298762. [PMID: 38416729 PMCID: PMC10901337 DOI: 10.1371/journal.pone.0298762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 01/31/2024] [Indexed: 03/01/2024] Open
Abstract
Epilepsy affects millions of people worldwide every year and remains an open subject for research. Current development on this field has focused on obtaining computational models to better understand its triggering mechanisms, attain realistic descriptions and study seizure suppression. Controllers have been successfully applied to mitigate epileptiform activity in dynamic models written in state-space notation, whose applicability is, however, restricted to signatures that are accurately described by them. Alternatively, autoregressive modeling (AR), a typical data-driven tool related to system identification (SI), can be directly applied to signals to generate more realistic models, and since it is inherently convertible into state-space representation, it can thus be used for the artificial reconstruction and attenuation of seizures as well. Considering this, the first objective of this work is to propose an SI approach using AR models to describe real epileptiform activity. The second objective is to provide a strategy for reconstructing and mitigating such activity artificially, considering non-hybrid and hybrid controllers - designed from ictal and interictal events, respectively. The results show that AR models of relatively low order represent epileptiform activities fairly well and both controllers are effective in attenuating the undesired activity while simultaneously driving the signal to an interictal condition. These findings may lead to customized models based on each signal, brain region or patient, from which it is possible to better define shape, frequency and duration of external stimuli that are necessary to attenuate seizures.
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Affiliation(s)
- João A. F. Brogin
- Department of Mechanical Engineering, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
| | - Jean Faber
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Selvin Z. Reyes-Garcia
- Departamento de Ciencias Morfológicas, Facultad de Ciencias Médicas, Universidad Nacional Autónoma de Honduras, Tegucigalpa, Honduras
| | - Esper A. Cavalheiro
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Douglas D. Bueno
- Department of Mathematics, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
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Araújo NS, Reyes-Garcia SZ, Brogin JAF, Bueno DD, Cavalheiro EA, Scorza CA, Faber J. Chaotic and stochastic dynamics of epileptiform-like activities in sclerotic hippocampus resected from patients with pharmacoresistant epilepsy. PLoS Comput Biol 2022; 18:e1010027. [PMID: 35417449 PMCID: PMC9037954 DOI: 10.1371/journal.pcbi.1010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 04/25/2022] [Accepted: 03/16/2022] [Indexed: 11/30/2022] Open
Abstract
The types of epileptiform activity occurring in the sclerotic hippocampus with highest incidence are interictal-like events (II) and periodic ictal spiking (PIS). These activities are classified according to their event rates, but it is still unclear if these rate differences are consequences of underlying physiological mechanisms. Identifying new and more specific information related to these two activities may bring insights to a better understanding about the epileptogenic process and new diagnosis. We applied Poincaré map analysis and Recurrence Quantification Analysis (RQA) onto 35 in vitro electrophysiological signals recorded from slices of 12 hippocampal tissues surgically resected from patients with pharmacoresistant temporal lobe epilepsy. These analyzes showed that the II activity is related to chaotic dynamics, whereas the PIS activity is related to deterministic periodic dynamics. Additionally, it indicates that their different rates are consequence of different endogenous dynamics. Finally, by using two computational models we were able to simulate the transition between II and PIS activities. The RQA was applied to different periods of these simulations to compare the recurrences between artificial and real signals, showing that different ranges of regularity-chaoticity can be directly associated with the generation of PIS and II activities. Temporal lobe epilepsy (TLE) is the most prevalent type of epilepsy in adults and hippocampal sclerosis is the major pathophysiological substrate of pharmaco-refractory TLE. Different patterns of epileptiform-like activity have been described in human hippocampal sclerosis, but the standard analysis applied to characterize the activities usually do not consider the nonlinear features that epileptiform patterns exhibit. Here, using Poincaré map and Recurrence Quantitative Analysis we characterized the most prevalent type of epileptiform-like activities—interictal-like events (II) and periodic ictal spiking (PIS), recorded in vitro from resected hippocampi of pharmacoresistant patients with TLE—according to their levels of stochasticity, chaoticity and determinism. The II activities showed to be more chaotic with complex rhythmicity than PIS activities. The nonlinear dynamic differences between II and PIS leads us to conjecture that they are expressions of different seizure susceptibility. We also identified that each hippocampal subfield expresses II and PIS activities in a specific and different way. Finally, from the modulation of internal parameters of two computational models, we show the conversion of one type of activity into the other, showing how specific neuron networks synchronize over time, leading to II and PIS activities and then into a generalized seizure.
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Affiliation(s)
- Noemi S. Araújo
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Selvin Z. Reyes-Garcia
- Departamento de Ciencias Morfológicas, Facultad de Ciencias Médicas, Universidad Nacional Autónoma de Honduras, Tegucigalpa, Honduras
| | - João A. F. Brogin
- Department of Mechanical Engineering, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
| | - Douglas D. Bueno
- Department of Mathematics, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, Ilha Solteira, São Paulo, Brazil
| | - Esper A. Cavalheiro
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Carla A. Scorza
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
| | - Jean Faber
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), São Paulo, São Paulo, Brazil
- * E-mail:
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3
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Földi T, Lőrincz ML, Berényi A. Temporally Targeted Interactions With Pathologic Oscillations as Therapeutical Targets in Epilepsy and Beyond. Front Neural Circuits 2021; 15:784085. [PMID: 34955760 PMCID: PMC8693222 DOI: 10.3389/fncir.2021.784085] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/10/2021] [Indexed: 11/13/2022] Open
Abstract
Self-organized neuronal oscillations rely on precisely orchestrated ensemble activity in reverberating neuronal networks. Chronic, non-malignant disorders of the brain are often coupled to pathological neuronal activity patterns. In addition to the characteristic behavioral symptoms, these disturbances are giving rise to both transient and persistent changes of various brain rhythms. Increasing evidence support the causal role of these "oscillopathies" in the phenotypic emergence of the disease symptoms, identifying neuronal network oscillations as potential therapeutic targets. While the kinetics of pharmacological therapy is not suitable to compensate the disease related fine-scale disturbances of network oscillations, external biophysical modalities (e.g., electrical stimulation) can alter spike timing in a temporally precise manner. These perturbations can warp rhythmic oscillatory patterns via resonance or entrainment. Properly timed phasic stimuli can even switch between the stable states of networks acting as multistable oscillators, substantially changing the emergent oscillatory patterns. Novel transcranial electric stimulation (TES) approaches offer more reliable neuronal control by allowing higher intensities with tolerable side-effect profiles. This precise temporal steerability combined with the non- or minimally invasive nature of these novel TES interventions make them promising therapeutic candidates for functional disorders of the brain. Here we review the key experimental findings and theoretical background concerning various pathological aspects of neuronal network activity leading to the generation of epileptic seizures. The conceptual and practical state of the art of temporally targeted brain stimulation is discussed focusing on the prevention and early termination of epileptic seizures.
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Affiliation(s)
- Tamás Földi
- MTA-SZTE "Momentum" Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, Hungary.,Neurocybernetics Excellence Center, University of Szeged, Szeged, Hungary.,HCEMM-USZ Magnetotherapeutics Research Group, University of Szeged, Szeged, Hungary.,Child and Adolescent Psychiatry, Department of the Child Health Center, University of Szeged, Szeged, Hungary
| | - Magor L Lőrincz
- MTA-SZTE "Momentum" Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, Hungary.,Neurocybernetics Excellence Center, University of Szeged, Szeged, Hungary.,Department of Physiology, Anatomy and Neuroscience, Faculty of Sciences University of Szeged, Szeged, Hungary.,Neuroscience Division, Cardiff University, Cardiff, United Kingdom
| | - Antal Berényi
- MTA-SZTE "Momentum" Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, Hungary.,Neurocybernetics Excellence Center, University of Szeged, Szeged, Hungary.,HCEMM-USZ Magnetotherapeutics Research Group, University of Szeged, Szeged, Hungary.,Neuroscience Institute, New York University, New York, NY, United States
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Müller J, Yang H, Eberlein M, Leonhardt G, Uckermann O, Kuhlmann L, Tetzlaff R. Coherent false seizure prediction in epilepsy, coincidence or providence? Clin Neurophysiol 2021; 133:157-164. [PMID: 34844880 DOI: 10.1016/j.clinph.2021.09.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rather to intrinsic changes in the data. METHODS We evaluated two algorithms on three datasets by computing the correlation of false predictions and estimating the information transfer between both classification methods. RESULTS For 9 out of 12 individuals both methods showed a performance better than chance. For all individuals we observed a positive correlation in predictions. For individuals with strong correlation in false predictions we were able to boost the performance of one method by excluding test samples based on the results of the second method. CONCLUSIONS Substantially different algorithms exhibit a highly consistent performance and a strong coherency in false and missing alarms. Hence, changing the underlying hypothesis of a preictal state of fixed time length prior to each seizure to a proictal state is more helpful than further optimizing classifiers. SIGNIFICANCE The outcome is significant for the evaluation of seizure prediction algorithms on continuous data.
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Affiliation(s)
- Jens Müller
- TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany.
| | - Hongliu Yang
- TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany
| | - Matthias Eberlein
- TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany
| | - Georg Leonhardt
- TU Dresden, Neurosurgery of University Hospital Carl Gustav Carus, Fetscherstr. 74, 01307 Dresden, Germany
| | - Ortrud Uckermann
- TU Dresden, Neurosurgery of University Hospital Carl Gustav Carus, Fetscherstr. 74, 01307 Dresden, Germany
| | - Levin Kuhlmann
- Department of Medicine, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Ronald Tetzlaff
- TU Dresden, Faculty of Electrical and Computer Engineering, Institute of Circuits and Systems, 01062 Dresden, Germany
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5
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Carvalho VR, Moraes MFD, Cash SS, Mendes EMAM. Active probing to highlight approaching transitions to ictal states in coupled neural mass models. PLoS Comput Biol 2021; 17:e1008377. [PMID: 33493165 PMCID: PMC7861539 DOI: 10.1371/journal.pcbi.1008377] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 02/04/2021] [Accepted: 12/02/2020] [Indexed: 01/07/2023] Open
Abstract
The extraction of electrophysiological features that reliably forecast the occurrence of seizures is one of the most challenging goals in epilepsy research. Among possible approaches to tackle this problem is the use of active probing paradigms in which responses to stimuli are used to detect underlying system changes leading up to seizures. This work evaluates the theoretical and mechanistic underpinnings of this strategy using two coupled populations of the well-studied Wendling neural mass model. Different model settings are evaluated, shifting parameters (excitability, slow inhibition, or inter-population coupling gains) from normal towards ictal states while probing stimuli are applied every 2 seconds to the input of either one or both populations. The correlation between the extracted features and the ictogenic parameter shifting indicates if the impending transition to the ictal state may be identified in advance. Results show that not only can the response to the probing stimuli forecast seizures but this is true regardless of the altered ictogenic parameter. That is, similar feature changes are highlighted by probing stimuli responses in advance of the seizure including: increased response variance and lag-1 autocorrelation, decreased skewness, and increased mutual information between the outputs of both model subsets. These changes were mostly restricted to the stimulated population, showing a local effect of this perturbational approach. The transition latencies from normal activity to sustained discharges of spikes were not affected, suggesting that stimuli had no pro-ictal effects. However, stimuli were found to elicit interictal-like spikes just before the transition to the ictal state. Furthermore, the observed feature changes highlighted by probing the neuronal populations may reflect the phenomenon of critical slowing down, where increased recovery times from perturbations may signal the loss of a systems' resilience and are common hallmarks of an impending critical transition. These results provide more evidence that active probing approaches highlight information about underlying system changes involved in ictogenesis and may be able to play a role in assisting seizure forecasting methods which can be incorporated into early-warning systems that ultimately enable closing the loop for targeted seizure-controlling interventions.
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Affiliation(s)
- Vinícius Rezende Carvalho
- Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Márcio Flávio Dutra Moraes
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Centro de Tecnologia e Pesquisa em Magneto-Ressonância, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Eduardo Mazoni Andrade Marçal Mendes
- Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Centro de Tecnologia e Pesquisa em Magneto-Ressonância, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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6
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Yang C, Liu Z, Luan G, wang Q. The extension of epileptogenicity as the driving force of the epileptogenic network evolution and complex symptoms. Brain Res 2020; 1748:147073. [DOI: 10.1016/j.brainres.2020.147073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 08/17/2020] [Accepted: 08/18/2020] [Indexed: 01/04/2023]
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7
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Neurostimulation stabilizes spiking neural networks by disrupting seizure-like oscillatory transitions. Sci Rep 2020; 10:15408. [PMID: 32958802 PMCID: PMC7506027 DOI: 10.1038/s41598-020-72335-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/26/2020] [Indexed: 12/29/2022] Open
Abstract
An improved understanding of the mechanisms underlying neuromodulatory approaches to mitigate seizure onset is needed to identify clinical targets for the treatment of epilepsy. Using a Wilson–Cowan-motivated network of inhibitory and excitatory populations, we examined the role played by intrinsic and extrinsic stimuli on the network’s predisposition to sudden transitions into oscillatory dynamics, similar to the transition to the seizure state. Our joint computational and mathematical analyses revealed that such stimuli, be they noisy or periodic in nature, exert a stabilizing influence on network responses, disrupting the development of such oscillations. Based on a combination of numerical simulations and mean-field analyses, our results suggest that high variance and/or high frequency stimulation waveforms can prevent multi-stability, a mathematical harbinger of sudden changes in network dynamics. By tuning the neurons’ responses to input, stimuli stabilize network dynamics away from these transitions. Furthermore, our research shows that such stabilization of neural activity occurs through a selective recruitment of inhibitory cells, providing a theoretical undergird for the known key role these cells play in both the healthy and diseased brain. Taken together, these findings provide new vistas on neuromodulatory approaches to stabilize neural microcircuit activity.
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8
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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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Kalitzin S, Petkov G, Suffczynski P, Grigorovsky V, Bardakjian BL, Lopes da Silva F, Carlen PL. Epilepsy as a manifestation of a multistate network of oscillatory systems. Neurobiol Dis 2019; 130:104488. [DOI: 10.1016/j.nbd.2019.104488] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 06/03/2019] [Accepted: 06/04/2019] [Indexed: 12/18/2022] Open
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10
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Kuhlmann L, Lehnertz K, Richardson MP, Schelter B, Zaveri HP. Seizure prediction - ready for a new era. Nat Rev Neurol 2019; 14:618-630. [PMID: 30131521 DOI: 10.1038/s41582-018-0055-2] [Citation(s) in RCA: 191] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.
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Affiliation(s)
- Levin Kuhlmann
- Centre for Human Psychopharmacology, Swinburne University of Technology, Melbourne, Victoria, Australia.,Department of Medicine - St. Vincent's, The University of Melbourne, Parkville, Victoria, Australia.,Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn, Bonn, Germany. .,Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany.
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Björn Schelter
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
| | - Hitten P Zaveri
- Department of Neurology, Yale University, New Haven, CT, USA
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11
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The role that choice of model plays in predictions for epilepsy surgery. Sci Rep 2019; 9:7351. [PMID: 31089190 PMCID: PMC6517411 DOI: 10.1038/s41598-019-43871-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 05/02/2019] [Indexed: 12/26/2022] Open
Abstract
Mathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism that defines the transition from background to the seizure state. Since these mechanisms are unknown, it is important to understand how predictions from alternative dynamical descriptions compare. Herein we evaluate to what extent three different dynamical models provide consistent predictions for the effect of removing nodes from networks. We show that for small, directed, connected networks the three considered models provide consistent predictions. For larger networks, predictions are shown to be less consistent. However consistency is higher in networks that have sufficiently large differences in ictogenicity between nodes. We further demonstrate that heterogeneity in ictogenicity across nodes correlates with variability in the number of connections for each node.
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12
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Koppert M, Kalitzin S, Velis D, Lopes Da Silva F, Viergever MA. Preventive and Abortive Strategies for Stimulation Based Control of Epilepsy: A Computational Model Study. Int J Neural Syst 2016; 26:1650028. [DOI: 10.1142/s0129065716500283] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy is a condition in which periods of ongoing normal EEG activity alternate with periods of oscillatory behavior characteristic of epileptic seizures. The dynamics of the transitions between the two states are still unclear. Computational models provide a powerful tool to explore the underlying mechanisms of such transitions, with the purpose of eventually finding therapeutic interventions for this debilitating condition. In this study, the possibility to postpone seizures elicited by a decrease of inhibition is investigated by using external stimulation in a realistic bistable neuronal model consisting of two interconnected neuronal populations representing pyramidal cells and interneurons. In the simulations, seizures are induced by slowly decreasing the conductivity of GABA[Formula: see text] synaptic channels over time. Since the model is bistable, the system will change state from the initial steady state (SS) to the limit cycle (LS) state because of internal noise, when the inhibition falls below a certain threshold. Several state-independent stimulations paradigms are simulated. Their effectiveness is analyzed for various stimulation frequencies and intensities in combination with periodic and random stimulation sequences. The distributions of the time to first seizure in the presence of stimulation are compared with the situation without stimulation. In addition, stimulation protocols targeted to specific subsystems are applied with the objective of counteracting the baseline shift due to decreased inhibition in the system. Furthermore, an analytical model is used to investigate the effects of random noise. The relation between the strength of random noise stimulation, the control parameter of the system and the transitions between steady state and limit cycle are investigated. The study shows that it is possible to postpone epileptic activity by targeted stimulation in a realistic neuronal model featuring bistability and that it is possible to stop seizures by random noise in an analytical model.
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Affiliation(s)
- Marc Koppert
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Stiliyan Kalitzin
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
- Image Sciences Institute, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
| | - Demetrios Velis
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
- Department of Neurosurgery, Free University Medical Center Amsterdam, 1007 MB Amsterdam, The Netherlands
| | - Fernando Lopes Da Silva
- Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 SM Amsterdam, The Netherlands
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001, Lisboa, Portugal
| | - Max A. Viergever
- Image Sciences Institute, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
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13
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Goodfellow M, Rummel C, Abela E, Richardson MP, Schindler K, Terry JR. Estimation of brain network ictogenicity predicts outcome from epilepsy surgery. Sci Rep 2016; 6:29215. [PMID: 27384316 PMCID: PMC4935897 DOI: 10.1038/srep29215] [Citation(s) in RCA: 104] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 06/13/2016] [Indexed: 02/01/2023] Open
Abstract
Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal brain lesions or the presence of epileptiform rhythms, do not necessarily predict the best resection strategy. We validate our framework by analysing electrocorticogram (ECoG) recordings from patients who have undergone epilepsy surgery. We find that when post-operative outcome is good, model predictions for optimal strategies align better with the actual surgery undertaken than when post-operative outcome is poor. Crucially, this allows the prediction of optimal surgical strategies and the provision of quantitative prognoses for patients undergoing epilepsy surgery.
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Affiliation(s)
- M Goodfellow
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.,Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
| | - C Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Switzerland
| | - E Abela
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Switzerland
| | - M P Richardson
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - K Schindler
- Department of Neurology, University of Bern, Switzerland
| | - J R Terry
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.,Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
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14
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Computational models of epileptiform activity. J Neurosci Methods 2016; 260:233-51. [DOI: 10.1016/j.jneumeth.2015.03.027] [Citation(s) in RCA: 123] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Revised: 03/23/2015] [Accepted: 03/24/2015] [Indexed: 12/24/2022]
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15
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Helling RM, Koppert MMJ, Visser GH, Kalitzin SN. Gap Junctions as Common Cause of High-Frequency Oscillations and Epileptic Seizures in a Computational Cascade of Neuronal Mass and Compartmental Modeling. Int J Neural Syst 2015; 25:1550021. [DOI: 10.1142/s0129065715500215] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
High frequency oscillations (HFO) appear to be a promising marker for delineating the seizure onset zone (SOZ) in patients with localization related epilepsy. It remains, however, a purely observational phenomenon and no common mechanism has been proposed to relate HFOs and seizure generation. In this work we show that a cascade of two computational models, one on detailed compartmental scale and a second one on neural mass scale can explain both the autonomous generation of HFOs and the presence of epileptic seizures as emergent properties. To this end we introduce axonal–axonal gap junctions on a microscopic level and explore their impact on the higher level neural mass model (NMM). We show that the addition of gap junctions can generate HFOs and simultaneously shift the operational point of the NMM from a steady state network into bistable behavior that can autonomously generate epileptic seizures. The epileptic properties of the system, or the probability to generate epileptic type of activity, increases gradually with the increase of the density of axonal–axonal gap junctions. We further demonstrate that ad hoc HFO detectors used in previous studies are applicable to our simulated data.
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Affiliation(s)
- Robert M. Helling
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW, Heemstede, The Netherlands
- Faculty of Technical Medicine, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands
| | - Marc M. J. Koppert
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW, Heemstede, The Netherlands
| | - Gerhard H. Visser
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW, Heemstede, The Netherlands
| | - Stiliyan N. Kalitzin
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW, Heemstede, The Netherlands
- Image Sciences Institute – University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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16
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Kuhlmann L, Grayden DB, Wendling F, Schiff SJ. Role of multiple-scale modeling of epilepsy in seizure forecasting. J Clin Neurophysiol 2015; 32:220-6. [PMID: 26035674 PMCID: PMC4455036 DOI: 10.1097/wnp.0000000000000149] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Over the past three decades, a number of seizure prediction, or forecasting, methods have been developed. Although major achievements were accomplished regarding the statistical evaluation of proposed algorithms, it is recognized that further progress is still necessary for clinical application in patients. The lack of physiological motivation can partly explain this limitation. Therefore, a natural question is raised: can computational models of epilepsy be used to improve these methods? Here, we review the literature on the multiple-scale neural modeling of epilepsy and the use of such models to infer physiologic changes underlying epilepsy and epileptic seizures. The authors argue how these methods can be applied to advance the state-of-the-art in seizure forecasting.
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Affiliation(s)
- Levin Kuhlmann
- NeuroEngineering Laboratory, Department of Electrical & Electronic Engineering, The University of Melbourne, VIC 3010, Australia
- Brain Dynamics Unit, Brain and Psychological Sciences Research Centre, Swinburne University of Technology, Hawthorn VIC 3122, Australia
| | - David B. Grayden
- NeuroEngineering Laboratory, Department of Electrical & Electronic Engineering, The University of Melbourne, VIC 3010, Australia
- Centre for Neural Engineering, The University of Melbourne, VIC 3010, Australia
- Bionics Institute, 384 Albert St, East Melbourne, VIC 3002, Australia
- St. Vincent’s Hospital Melbourne, Fitzroy, VIC 3002, Australia
| | - Fabrice Wendling
- INSERM, U1099, Rennes, F-35000, France
- Université de Rennes, LTSI, F-35000, France
| | - Steven J. Schiff
- Center for Neural Engineering, Departments of Engineering Science and Mechanics, Neurosurgery, and Physics, The Pennsylvania State University, University Park, PA 16802, USA
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17
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Woldman W, Terry JR. Multilevel Computational Modelling in Epilepsy: Classical Studies and Recent Advances. VALIDATING NEURO-COMPUTATIONAL MODELS OF NEUROLOGICAL AND PSYCHIATRIC DISORDERS 2015. [DOI: 10.1007/978-3-319-20037-8_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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18
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Petkov G, Goodfellow M, Richardson MP, Terry JR. A critical role for network structure in seizure onset: a computational modeling approach. Front Neurol 2014; 5:261. [PMID: 25538679 PMCID: PMC4259126 DOI: 10.3389/fneur.2014.00261] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Accepted: 11/23/2014] [Indexed: 12/30/2022] Open
Abstract
Recent clinical work has implicated network structure as critically important in the initiation of seizures in people with idiopathic generalized epilepsies. In line with this idea, functional networks derived from the electroencephalogram (EEG) at rest have been shown to be significantly different in people with generalized epilepsy compared to controls. In particular, the mean node degree of networks from the epilepsy cohort was found to be statistically significantly higher than those of controls. However, the mechanisms by which these network differences can support recurrent transitions into seizures remain unclear. In this study, we use a computational model of the transition into seizure dynamics to explore the dynamic consequences of these differences in functional networks. We demonstrate that networks with higher mean node degree are more prone to generating seizure dynamics in the model and therefore suggest a mechanism by which increased mean node degree of brain networks can cause heightened ictogenicity.
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Affiliation(s)
- George Petkov
- College of Engineering, Mathematics and Physical Sciences, University of Exeter , Exeter , UK
| | - Marc Goodfellow
- College of Engineering, Mathematics and Physical Sciences, University of Exeter , Exeter , UK
| | | | - John R Terry
- College of Engineering, Mathematics and Physical Sciences, University of Exeter , Exeter , UK
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19
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KALITZIN STILIYAN, KOPPERT MARCUS, PETKOV GEORGE, DA SILVA FERNANDOLOPES. MULTIPLE OSCILLATORY STATES IN MODELS OF COLLECTIVE NEURONAL DYNAMICS. Int J Neural Syst 2014; 24:1450020. [DOI: 10.1142/s0129065714500208] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In our previous studies, we showed that the both realistic and analytical computational models of neural dynamics can display multiple sustained states (attractors) for the same values of model parameters. Some of these states can represent normal activity while other, of oscillatory nature, may represent epileptic types of activity. We also showed that a simplified, analytical model can mimic this type of behavior and can be used instead of the realistic model for large scale simulations. The primary objective of the present work is to further explore the phenomenon of multiple stable states, co-existing in the same operational model, or phase space, in systems consisting of large number of interconnected basic units. As a second goal, we aim to specify the optimal method for state control of the system based on inducing state transitions using appropriate external stimulus. We use here interconnected model units that represent the behavior of neuronal populations as an effective dynamic system. The model unit is an analytical model (S. Kalitzin et al., Epilepsy Behav. 22 (2011) S102–S109) and does not correspond directly to realistic neuronal processes (excitatory–inhibitory synaptic interactions, action potential generation). For certain parameter choices however it displays bistable dynamics imitating the behavior of realistic neural mass models. To analyze the collective behavior of the system we applied phase synchronization analysis (PSA), principal component analysis (PCA) and stability analysis using Lyapunov exponent (LE) estimation. We obtained a large variety of stable states with different dynamic characteristics, oscillatory modes and phase relations between the units. These states can be initiated by appropriate initial conditions; transitions between them can be induced stochastically by fluctuating variables (noise) or by specific inputs. We propose a method for optimal reactive control, allowing forced transitions from one state (attractor) into another.
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Affiliation(s)
- STILIYAN KALITZIN
- Foundation Epilepsy Institute in The Netherlands (SEIN), Achterweg 5, Heemstede, The Netherlands
| | - MARCUS KOPPERT
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, Devon EX4 4QF, UK
| | - GEORGE PETKOV
- Foundation Epilepsy Institute in The Netherlands (SEIN), Achterweg 5, Heemstede, The Netherlands
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, Devon EX4 4QF, UK
| | - FERNANDO LOPES DA SILVA
- Swammerdam Institute for Life Sciences, Center of Neuroscience, University of Amsterdam, Amsterdam, The Netherlands
- Department of Bioengineering, Instituto Superior Técnico, Lisbon Technical University, Lisbon, Portugal
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20
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KOPPERT MARC, KALITZIN STILIYAN, VELIS DEMETRIOS, LOPES DA SILVA FERNANDO, VIERGEVER MAXA. DYNAMICS OF COLLECTIVE MULTI-STABILITY IN MODELS OF MULTI-UNIT NEURONAL SYSTEMS. Int J Neural Syst 2014; 24:1430004. [DOI: 10.1142/s0129065714300046] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this study, we investigate the correspondence between dynamic patterns of behavior in two types of computational models of neuronal activity. The first model type is the realistic neuronal model; the second model type is the phenomenological or analytical model. In the simplest model set-up of two interconnected units, we define a parameter space for both types of systems where their behavior is similar. Next we expand the analytical model to two sets of 90 fully interconnected units with some overlap, which can display multi-stable behavior. This system can be in three classes of states: (i) a class consisting of a single resting state, where all units of a set are in steady state, (ii) a class consisting of multiple preserving states, where subsets of the units of a set participate in limit cycle, and (iii) a class consisting of a single saturated state, where all units of a set are recruited in a global limit cycle. In the third and final part of the work, we demonstrate that phase synchronization of units can be detected by a single output unit.
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Affiliation(s)
- MARC KOPPERT
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - STILIYAN KALITZIN
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - DEMETRIOS VELIS
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW Heemstede, The Netherlands
- Department of Neurosurgery, Free University Medical Centre (VUmc), De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - FERNANDO LOPES DA SILVA
- Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 SM Amsterdam, The Netherlands
- Department Bioengineering, Instituto Superior Técnico, Universidade Técnica de Lisboa, Portugal
| | - MAX A. VIERGEVER
- Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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21
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Koppert MMJ, Kalitzin S, Lopes da Silva F, Viergever MA. Connectivity and phase coherence in neural network models of interconnected Z(4)-bi-stable units. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:5458-61. [PMID: 23367164 DOI: 10.1109/embc.2012.6347229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A phenomenological neural network model with bi-stable oscillatory units is used to model up- and down-states. These states have been observed in vivo in biological neuronal systems and feature oscillatory, limit cycle type of behavior in the up-states. A network is formed by a set of interconnected units. Two different types of network layouts are considered in this work: networks with hierarchical connections and hubs and networks with random connections. The phase coherence between the different units is analyzed and compared to the connectivity distance between nodes. In addition the connectivity degree of a node is associated to the average phase coherence with all other units. The results show that we may be able to identify the set of hubs in a network based on the phase coherence estimates between the different nodes. If the network is very dense or randomly connected, the underlying network structure, however, can not be derived uniquely from the phase coherence.
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Affiliation(s)
- M M J Koppert
- Foundation Epilepsy Institute of The Netherlands (SEIN), Achterweg 5, 2103 SW, Heemstede, The Netherlands.
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22
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KOPPERT MARC, KALITZIN STILIYAN, VELIS DEMETRIOS, DA SILVA FERNANDOLOPES, VIERGEVER MAXA. REACTIVE CONTROL OF EPILEPTIFORM DISCHARGES IN REALISTIC COMPUTATIONAL NEURONAL MODELS WITH BISTABILITY. Int J Neural Syst 2012; 23:1250032. [DOI: 10.1142/s0129065712500323] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We aim to derive fully autonomous seizure suppression paradigms based on reactive control of neuronal dynamics. A previously derived computational model of seizure generation describing collective degrees of freedom and featuring bistable dynamics is used. A novel technique for real-time control of epileptogenicity is introduced. The reactive control reduces practically all seizures in the model. The study indicates which parameters provide the maximal seizure reduction with minimal intervention. An adaptive scheme is proposed that optimizes the stimulation parameters in nonstationary situations.
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Affiliation(s)
- MARC KOPPERT
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - STILIYAN KALITZIN
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - DEMETRIOS VELIS
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW Heemstede, The Netherlands
- Department of Neurosurgery, Free University Medical Centre (VUmc), De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - FERNANDO LOPES DA SILVA
- Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 SM Amsterdam, The Netherlands
- Department Bioengineering, Instituto Superior Técnico, Universidade Técnica de Lisboa, Portugal
| | - MAX A. VIERGEVER
- Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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23
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Da Silva FHL, Gorter JA, Wadman WJ. Epilepsy as a dynamic disease of neuronal networks. HANDBOOK OF CLINICAL NEUROLOGY 2012; 107:35-62. [DOI: 10.1016/b978-0-444-52898-8.00003-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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24
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Kalitzin S, Zijlmans M, Petkov G, Velis D, Claus S, Visser G, Koppert M, Lopes da Silva F. Quantification of spontaneous and evoked HFO's in SEEG recording and prospective for pre-surgical diagnostics. Case study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:1024-1027. [PMID: 23366069 DOI: 10.1109/embc.2012.6346108] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
High frequency oscillations (HFO) in stereo electroencephalographic (SEEG) signals have been recently the focus of attention as biomarkers that can have potential predictive power for the spatial location and possibly the timing of the onset of epileptic seizures. In this work we present a case study where we compare two quantitative paradigms for automated detection of biomarkers, one based on spontaneous SEEG recordings of HFOs and the other using activity induced by direct electrical stimulation (relative Phase Clustering Index algorithm). We compare the performance of these automated methods with manually detected HFO ripples by a trained EEG analyst and explore their potential diagnostic relevance. Intracranial recordings from patients undergoing pre-surgical evaluation are processed with a combination of morphological filtering and the analysis of the auto-correlation function. The results were compared to those obtained by visual inspection and to results from an active paradigm involving stimulation with 20 Hz trains of biphasic pulses. The quantity of HFOs, estimated automatically, or "rippleness", was found to correspond to the findings of a trained EEG analyst. The relative phase clustering index (rPCI) obtained using periodic stimulation appeared to be associated with the closeness to the seizure onset zone (SOZ) detected from ictal epochs. The HFO estimates were also indicative for the SOZ but with less specificity.
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Affiliation(s)
- Stiliyan Kalitzin
- Foundation Epilepsy Institute of The Netherlans (SEIN), Heemstede, The Netherlands.
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