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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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Grigorovsky V, Jacobs D, Breton VL, Tufa U, Lucasius C, Del Campo JM, Chinvarun Y, Carlen PL, Wennberg R, Bardakjian BL. Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression. Brain Commun 2020; 2:fcaa182. [PMID: 33376988 PMCID: PMC7750942 DOI: 10.1093/braincomms/fcaa182] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 09/22/2020] [Accepted: 09/24/2020] [Indexed: 12/15/2022] Open
Abstract
Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21–41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5–4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5–1.5 Hz signal and amplitude of 30–50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier—a hidden Markov model—was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.
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Affiliation(s)
| | - Daniel Jacobs
- Institute of Biomedical Engineering, University of Toronto, Canada
| | | | - Uilki Tufa
- Institute of Biomedical Engineering, University of Toronto, Canada
| | - Christopher Lucasius
- Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Canada
| | | | - Yotin Chinvarun
- Comprehensive Epilepsy Program and Neurology Unit, Phramongkutklao Hospital, Thailand
| | - Peter L Carlen
- Institute of Biomedical Engineering, University of Toronto, Canada.,Department of Physiology, University of Toronto, Canada.,Division of Neurology, Toronto Western Hospital, Canada
| | | | - Berj L Bardakjian
- Institute of Biomedical Engineering, University of Toronto, Canada.,Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Canada
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Liu Y, Grigorovsky V, Bardakjian B. Excitation and Inhibition Balance Underlying Epileptiform Activity. IEEE Trans Biomed Eng 2020; 67:2473-2481. [PMID: 31902751 DOI: 10.1109/tbme.2019.2963430] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE The phenomenon of postictal generalized EEG suppression state (PGES) - a period with suppressed activity following seizure termination and has been found to be associated with sudden unexpected death in epilepsy - remains poorly understood. This article aims to examine the how the balance of excitation and inhibition (E/I balance) affect the dynamics of seizure and PGES. METHODS A network of 1000 Izhikevich model neurons was developed and only the strengths of synaptic connections were adjusted to recreate the dynamics observed in recordings of seizure and PGES from human patients. RESULTS A rapid rise followed by a slow decay of dominant frequency was observed in iEEG recordings of ictal periods and reproduced in the simulated local field potential by changing the E/I balance of the model network. The rate of this dominant frequency evolution was quantified by a single measure, β, which was found to have a significant rank correlation with the duration of PGES in iEEG data and the rate of E/I balance shift in the model. Significance and Conclusion: (i) highlighting the importance of E/I balance in the dynamics of seizure and PGES; (ii) suggesting the measure, β, as a marker for PGES and the shift in E/I balance as a neural correlate for this marker.
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Grigorovsky V, Bardakjian BL. Neuro-Glial Network Model Of Postictal Generalized EEG Suppression (PGES). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:2044-2047. [PMID: 30440803 DOI: 10.1109/embc.2018.8512661] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Over the past couple of decades, glial cells have been highlighted as active agents in hyperexcitability of neuronal networks, specifically playing key roles in seizure onset and termination. In particular, microglia have been suggested to have both neuroprotective and neurotoxic effects on the brain. Investigation into seizure termination is of particular interest, as it is sometimes followed by a postictal generalized EEG suppression (PGES) - a low activity state that is potentially associated with sudden unexpected death in epilepsy. In this study, we attempt to link glial effects - synaptic pruning and astrocytic potassium clearance - to the duration of spontaneous epileptiform discharges (SEDs) as well as interSED intervals (iSEDs). We build upon an earlier model of a neuroglial network by translating it into the cortical paradigm and including microglial units. Preliminary findings of our model demonstrated that the duration of SEDs is largely determined by the astrocytic potassium clearance, whereas iSEDs significantly increased with microglial-driven synaptic pruning. In our model, astrocytic potassium clearance itself did not bring a PGES-like state, whereas microglial effects did, which suggests a potential biomarker for PGES phenomena.
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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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González OC, Krishnan GP, Timofeev I, Bazhenov M. Ionic and synaptic mechanisms of seizure generation and epileptogenesis. Neurobiol Dis 2019; 130:104485. [PMID: 31150792 DOI: 10.1016/j.nbd.2019.104485] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 05/23/2019] [Accepted: 05/27/2019] [Indexed: 01/09/2023] Open
Abstract
The biophysical mechanisms underlying epileptogenesis and the generation of seizures remain to be better understood. Among many factors triggering epileptogenesis are traumatic brain injury breaking normal synaptic homeostasis and genetic mutations disrupting ionic concentration homeostasis. Impairments in these mechanisms, as seen in various brain diseases, may push the brain network to a pathological state characterized by increased susceptibility to unprovoked seizures. Here, we review recent computational studies exploring the roles of ionic concentration dynamics in the generation, maintenance, and termination of seizures. We further discuss how ionic and synaptic homeostatic mechanisms may give rise to conditions which prime brain networks to exhibit recurrent spontaneous seizures and epilepsy.
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Affiliation(s)
- Oscar C González
- Neurosciences Graduate Program, University of California, San Diego, CA 92093, United States of America; Department of Medicine, University of California, San Diego, CA 92093, United States of America
| | - Giri P Krishnan
- Department of Medicine, University of California, San Diego, CA 92093, United States of America
| | - Igor Timofeev
- Centre de recherche de l'Institut universitaire en santé mentale de Québec (CRIUSMQ), 2601 de la Canardière, Québec, QC, Canada; Department of Psychiatry and Neuroscience, Université Laval, Québec, QC, Canada
| | - Maxim Bazhenov
- Neurosciences Graduate Program, University of California, San Diego, CA 92093, United States of America; Department of Medicine, University of California, San Diego, CA 92093, United States of America.
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Grigorovsky V, Bardakjian BL. Low-to-High Cross-Frequency Coupling in the Electrical Rhythms as Biomarker for Hyperexcitable Neuroglial Networks of the Brain. IEEE Trans Biomed Eng 2017; 65:1504-1515. [PMID: 28961101 DOI: 10.1109/tbme.2017.2757878] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE One of the features used in the study of hyperexcitablility is high-frequency oscillations (HFOs, >80 Hz). HFOs have been reported in the electrical rhythms of the brain's neuroglial networks under physiological and pathological conditions. Cross-frequency coupling (CFC) of HFOs with low-frequency rhythms was used to identify pathologic HFOs in the epileptogenic zones of epileptic patients and as a biomarker for the severity of seizure-like events in genetically modified rodent models. We describe a model to replicate reported CFC features extracted from recorded local field potentials (LFPs) representing network properties. METHODS This study deals with a four-unit neuroglial cellular network model where each unit incorporates pyramidal cells, interneurons, and astrocytes. Three different pathways of hyperexcitability generation-Na - ATPase pump, glial potassium clearance, and potassium afterhyperpolarization channel-were used to generate LFPs. Changes in excitability, average spontaneous electrical discharge (SED) duration, and CFC were then measured and analyzed. RESULTS Each parameter caused an increase in network excitability and the consequent lengthening of the SED duration. Short SEDs showed CFC between HFOs and theta oscillations (4-8 Hz), but in longer SEDs the low frequency changed to the delta range (1-4 Hz). CONCLUSION Longer duration SEDs exhibit CFC features similar to those reported by our team. SIGNIFICANCE First, Identifying the exponential relationship between network excitability and SED durations; second, highlighting the importance of glia in hyperexcitability (as they relate to extracellular potassium); and third, elucidation of the biophysical basis for CFC coupling features.
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Synthesis of high-complexity rhythmic signals for closed-loop electrical neuromodulation. Neural Netw 2013; 42:62-73. [DOI: 10.1016/j.neunet.2013.01.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2012] [Revised: 01/05/2013] [Accepted: 01/06/2013] [Indexed: 11/20/2022]
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Kang EE, Zalay OC, Serletis D, Carlen PL, Bardakjian BL. Markers of pathological excitability derived from principal dynamic modes of hippocampal neurons. J Neural Eng 2012; 9:056004. [PMID: 22871606 DOI: 10.1088/1741-2560/9/5/056004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Transformation of principal dynamic modes (PDMs) under epileptogenic conditions was investigated by computing the Volterra kernels in a rodent epilepsy model derived from a mouse whole hippocampal preparation, where epileptogenesis was induced by altering the concentrations of Mg(2 +) and K(+) of the perfusate for different levels of excitability. Both integrating and differentiating PDMs were present in the neuronal dynamics, and both of them increased in absolute magnitude for increased excitability levels. However, the integrating PDMs dominated at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs were shifted to higher ranges under epileptogenic conditions, from ripple activities (75-200 Hz) to fast ripple activities (200-500 Hz).
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Affiliation(s)
- Eunji E Kang
- Department of Electrical and Computer Engineering, University of Toronto, M5S 3G4 ON, Canada.
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Chen W, Cahoy DO, Tasker JG, Chiu AWL. Kernel duration and modulation gain in a coupled oscillator model and their implications on the progression of seizures. NETWORK (BRISTOL, ENGLAND) 2012; 23:59-75. [PMID: 22571251 DOI: 10.3109/0954898x.2012.678463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The coupled oscillator model has previously been used for the simulation of neuronal activities in in vitro rat hippocampal slice seizure data and the evaluation of seizure suppression algorithms. Each model unit can be described as either an oscillator which can generate action potential spike trains without inputs, or a threshold-based unit. With the change of only one parameter, each unit can either be an oscillator or a threshold-based spiking unit. This would eliminate the need of a new set of equations for each type of unit. Previous analysis has suggested that long kernel duration and imbalance of inhibitory feedback can cause the system to intermittently transition into and out of ictal activities. The state transitions of seizure-like events were investigated here; specifically, how the system excitability may change when the system underwent transitions in the preictal and postictal processes. Analysis showed that the area of the excitation kernel is positively correlated with the mean firing rate of ictal activity. The kernel duration is also correlated to the amount of ictal activity. The transition into ictal involved the escape from the saddle point foci in the state space trajectory identified using Newton's method.
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Affiliation(s)
- Wu Chen
- Biomedical Engineering, Louisiana Tech University, Ruston, LA, United States
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COLIC SINISA, ZALAY OSBERTC, BARDAKJIAN BERJL. RESPONSIVE NEUROMODULATORS BASED ON ARTIFICIAL NEURAL NETWORKS USED TO CONTROL SEIZURE-LIKE EVENTS IN A COMPUTATIONAL MODEL OF EPILEPSY. Int J Neural Syst 2011; 21:367-83. [DOI: 10.1142/s0129065711002894] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Deep brain stimulation (DBS) has been noted for its potential to suppress epileptic seizures. To date, DBS has achieved mixed results as a therapeutic approach to seizure control. Using a computational model, we demonstrate that high-complexity, biologically-inspired responsive neuromodulation is superior to periodic forms of neuromodulation (responsive and non-responsive) such as those implemented in DBS, as well as neuromodulation using random and random repetitive-interval stimulation. We configured radial basis function (RBF) networks to generate outputs modeling interictal time series recorded from rodent hippocampal slices that were perfused with low Mg2+/high K+solution. We then compared the performance of RBF-based interictal modulation, periodic biphasic-pulse modulation, random modulation and random repetitive modulation on a cognitive rhythm generator (CRG) model of spontaneous seizure-like events (SLEs), testing efficacy of SLE control. A statistically significant improvement in SLE mitigation for the RBF interictal modulation case versus the periodic and random cases was observed, suggesting that the use of biologically-inspired neuromodulators may achieve better results for the purpose of electrical control of seizures in a clinical setting.
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Affiliation(s)
- SINISA COLIC
- Edwards S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5S 3G4, Canada
| | - OSBERT C. ZALAY
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
| | - BERJ L. BARDAKJIAN
- Edwards S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5S 3G4, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
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Zhang ZJ, Valiante TA, Carlen PL. Transition to seizure: from "macro"- to "micro"-mysteries. Epilepsy Res 2011; 97:290-9. [PMID: 22075227 DOI: 10.1016/j.eplepsyres.2011.09.025] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2011] [Revised: 09/25/2011] [Accepted: 09/27/2011] [Indexed: 01/25/2023]
Abstract
One of the most terrifying aspects of epilepsy is the sudden and apparently unpredictable transition of the brain into the pathological state of an epileptic seizure. The pathophysiology of the transition to seizure still remains mysterious. Herein we review some of the key concepts and relevant literatures dealing with this enigmatic transitioning of brain states. At the "MACRO" level, electroencephalographic (EEG) recordings at time display preictal phenomena followed by pathological high-frequency oscillations at the seizure onset. Numerous seizure prediction algorithms predicated on identifying changes prior to seizure onset have met with little success, underscoring our lack of understanding of the dynamics of transition to seizure, amongst other inherent limitation. We then discuss the concept of synchronized hyperexcited oscillatory networks underlying seizure generation. We consider these networks as weakly coupled oscillators, a concept which forms the basis of some relevant mathematical modeling of seizure transitions. Next, the underlying "MICRO" processes involved in seizure generation are discussed. The depolarization of the GABA(A) chloride reversal potential is a major concept, facilitating epileptogenesis, particularly in immature brain. Also the balance of inhibitory and excitatory local neuronal networks plays an important role in the process of transitioning to seizure. Gap junctional communication, including that which occurs between glia, as well as ephaptic interactions are increasingly recognized as critical for seizure generation. In brief, this review examines the evidence regarding the characterization of the transition to seizure at both the "MACRO" and "MICRO" levels, trying to characterize this mysterious yet critical problem of the brain state transitioning into a seizure.
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Affiliation(s)
- Z J Zhang
- Division of Fundamental Neurobiology, Toronto Western Research Institute, Toronto Western Hospital, Toronto, ON, Canada.
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Koppert MMJ, Kalitzin S, Lopes da Silva FH, Viergever MA. Plasticity-modulated seizure dynamics for seizure termination in realistic neuronal models. J Neural Eng 2011; 8:046027. [DOI: 10.1088/1741-2560/8/4/046027] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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