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Boleti APDA, Cardoso PHDO, Frihling BEF, de Moraes LFRN, Nunes EAC, Mukoyama LTH, Nunes EAC, Carvalho CME, Macedo MLR, Migliolo L. Pathophysiology to Risk Factor and Therapeutics to Treatment Strategies on Epilepsy. Brain Sci 2024; 14:71. [PMID: 38248286 PMCID: PMC10813806 DOI: 10.3390/brainsci14010071] [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: 12/18/2023] [Revised: 12/30/2023] [Accepted: 01/06/2024] [Indexed: 01/23/2024] Open
Abstract
Epilepsy represents a condition in which abnormal neuronal discharges or the hyperexcitability of neurons occur with synchronicity, presenting a significant public health challenge. Prognostic factors, such as etiology, electroencephalogram (EEG) abnormalities, the type and number of seizures before treatment, as well as the initial unsatisfactory effects of medications, are important considerations. Although there are several third-generation antiepileptic drugs currently available, their multiple side effects can negatively affect patient quality of life. The inheritance and etiology of epilepsy are complex, involving multiple underlying genetic and epigenetic mechanisms. Different neurotransmitters play crucial roles in maintaining the normal physiology of different neurons. Dysregulations in neurotransmission, due to abnormal transmitter levels or changes in their receptors, can result in seizures. In this review, we address the roles played by various neurotransmitters and their receptors in the pathophysiology of epilepsy. Furthermore, we extensively explore the neurological mechanisms involved in the development and progression of epilepsy, along with its risk factors. Furthermore, we highlight the new therapeutic targets, along with pharmacological and non-pharmacological strategies currently employed in the treatment of epileptic syndromes, including drug interventions employed in clinical trials related to epilepsy.
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Affiliation(s)
- Ana Paula de Araújo Boleti
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande 79117-900, Brazil; (A.P.d.A.B.); (P.H.d.O.C.); (B.E.F.F.); (L.F.R.N.d.M.); (E.A.C.N.); (L.T.H.M.); (E.A.C.N.); (C.M.E.C.)
- Laboratório de Purificação de Proteínas e Suas Funções Biológicas, Unidade de Tecnologia de Alimentos e da Saúde Pública, Universidade Federal de Mato Grosso do Sul, Campo Grande 79070-900, Brazil;
| | - Pedro Henrique de Oliveira Cardoso
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande 79117-900, Brazil; (A.P.d.A.B.); (P.H.d.O.C.); (B.E.F.F.); (L.F.R.N.d.M.); (E.A.C.N.); (L.T.H.M.); (E.A.C.N.); (C.M.E.C.)
| | - Breno Emanuel Farias Frihling
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande 79117-900, Brazil; (A.P.d.A.B.); (P.H.d.O.C.); (B.E.F.F.); (L.F.R.N.d.M.); (E.A.C.N.); (L.T.H.M.); (E.A.C.N.); (C.M.E.C.)
| | - Luiz Filipe Ramalho Nunes de Moraes
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande 79117-900, Brazil; (A.P.d.A.B.); (P.H.d.O.C.); (B.E.F.F.); (L.F.R.N.d.M.); (E.A.C.N.); (L.T.H.M.); (E.A.C.N.); (C.M.E.C.)
| | - Ellynes Amancio Correia Nunes
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande 79117-900, Brazil; (A.P.d.A.B.); (P.H.d.O.C.); (B.E.F.F.); (L.F.R.N.d.M.); (E.A.C.N.); (L.T.H.M.); (E.A.C.N.); (C.M.E.C.)
- Programa de Pós-graduação em Bioquímica, Universidade Federal do Rio Grande do Norte, Natal 59078-970, Brazil
| | - Lincoln Takashi Hota Mukoyama
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande 79117-900, Brazil; (A.P.d.A.B.); (P.H.d.O.C.); (B.E.F.F.); (L.F.R.N.d.M.); (E.A.C.N.); (L.T.H.M.); (E.A.C.N.); (C.M.E.C.)
| | - Ellydberto Amancio Correia Nunes
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande 79117-900, Brazil; (A.P.d.A.B.); (P.H.d.O.C.); (B.E.F.F.); (L.F.R.N.d.M.); (E.A.C.N.); (L.T.H.M.); (E.A.C.N.); (C.M.E.C.)
- Programa de Pós-graduação em Bioquímica, Universidade Federal do Rio Grande do Norte, Natal 59078-970, Brazil
| | - Cristiano Marcelo Espinola Carvalho
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande 79117-900, Brazil; (A.P.d.A.B.); (P.H.d.O.C.); (B.E.F.F.); (L.F.R.N.d.M.); (E.A.C.N.); (L.T.H.M.); (E.A.C.N.); (C.M.E.C.)
| | - Maria Lígia Rodrigues Macedo
- Laboratório de Purificação de Proteínas e Suas Funções Biológicas, Unidade de Tecnologia de Alimentos e da Saúde Pública, Universidade Federal de Mato Grosso do Sul, Campo Grande 79070-900, Brazil;
| | - Ludovico Migliolo
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande 79117-900, Brazil; (A.P.d.A.B.); (P.H.d.O.C.); (B.E.F.F.); (L.F.R.N.d.M.); (E.A.C.N.); (L.T.H.M.); (E.A.C.N.); (C.M.E.C.)
- Programa de Pós-graduação em Bioquímica, Universidade Federal do Rio Grande do Norte, Natal 59078-970, Brazil
- Programa de Pós-graduação em Biologia Celular e Molecular, Universidade Federal da Paraíba, João Pessoa 58051-900, Brazil
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Schoeters R, Tarnaud T, Weyn L, Joseph W, Raedt R, Tanghe E. Quantitative analysis of the optogenetic excitability of CA1 neurons. Front Comput Neurosci 2023; 17:1229715. [PMID: 37649730 PMCID: PMC10465168 DOI: 10.3389/fncom.2023.1229715] [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: 05/26/2023] [Accepted: 07/27/2023] [Indexed: 09/01/2023] Open
Abstract
Introduction Optogenetics has emerged as a promising technique for modulating neuronal activity and holds potential for the treatment of neurological disorders such as temporal lobe epilepsy (TLE). However, clinical translation still faces many challenges. This in-silico study aims to enhance the understanding of optogenetic excitability in CA1 cells and to identify strategies for improving stimulation protocols. Methods Employing state-of-the-art computational models coupled with Monte Carlo simulated light propagation, the optogenetic excitability of four CA1 cells, two pyramidal and two interneurons, expressing ChR2(H134R) is investigated. Results and discussion The results demonstrate that confining the opsin to specific neuronal membrane compartments significantly improves excitability. An improvement is also achieved by focusing the light beam on the most excitable cell region. Moreover, the perpendicular orientation of the optical fiber relative to the somato-dendritic axis yields superior results. Inter-cell variability is observed, highlighting the importance of considering neuron degeneracy when designing optogenetic tools. Opsin confinement to the basal dendrites of the pyramidal cells renders the neuron the most excitable. A global sensitivity analysis identified opsin location and expression level as having the greatest impact on simulation outcomes. The error reduction of simulation outcome due to coupling of neuron modeling with light propagation is shown. The results promote spatial confinement and increased opsin expression levels as important improvement strategies. On the other hand, uncertainties in these parameters limit precise determination of the irradiance thresholds. This study provides valuable insights on optogenetic excitability of CA1 cells useful for the development of improved optogenetic stimulation protocols for, for instance, TLE treatment.
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Affiliation(s)
- Ruben Schoeters
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
- 4BRAIN, Department of Neurology, Institute for Neuroscience, Ghent University, Ghent, Belgium
| | - Thomas Tarnaud
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
| | - Laila Weyn
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
- 4BRAIN, Department of Neurology, Institute for Neuroscience, Ghent University, Ghent, Belgium
| | - Wout Joseph
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
| | - Robrecht Raedt
- 4BRAIN, Department of Neurology, Institute for Neuroscience, Ghent University, Ghent, Belgium
| | - Emmeric Tanghe
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
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Schach S, Rings T, Bregulla M, Witt JA, Bröhl T, Surges R, von Wrede R, Lehnertz K, Helmstaedter C. Electrodermal Activity Biofeedback Alters Evolving Functional Brain Networks in People With Epilepsy, but in a Non-specific Manner. Front Neurosci 2022; 16:828283. [PMID: 35310086 PMCID: PMC8927283 DOI: 10.3389/fnins.2022.828283] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/10/2022] [Indexed: 11/13/2022] Open
Abstract
There is evidence that biofeedback of electrodermal activity (EDA) can reduce seizure frequency in people with epilepsy. Prior studies have linked EDA biofeedback to a diffuse brain activation as a potential functional mechanism. Here, we investigated whether short-term EDA biofeedback alters EEG-derived large-scale functional brain networks in people with epilepsy. In this prospective controlled trial, thirty participants were quasi-randomly assigned to one of three biofeedback conditions (arousal, sham, or relaxation) and performed a single, 30-min biofeedback training while undergoing continuous EEG recordings. Based on the EEG, we derived evolving functional brain networks and examined their topological, robustness, and stability properties over time. Potential effects on attentional-executive functions and mood were monitored via a neuropsychological assessment and subjective self-ratings. Participants assigned to the relaxation group seemed to be most successful in meeting the task requirements for this specific control condition (i.e., decreasing EDA). Participants in the sham group were more successful in increasing EDA than participants in the arousal group. However, only the arousal biofeedback training was associated with a prolonged robustness-enhancing effect on networks. Effects on other network properties were mostly unspecific for the different groups. None of the biofeedback conditions affected attentional-executive functions or subjective behavioral measures. Our results suggest that global characteristics of evolving functional brain networks are modified by EDA biofeedback. Some alterations persisted after the single training session; however, the effects were largely unspecific across the different biofeedback protocols. Further research should address changes of local network characteristics and whether multiple training sessions will result in more specific network modifications.
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Affiliation(s)
- Sophia Schach
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- *Correspondence: Sophia Schach,
| | - Thorsten Rings
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | | | | | - Timo Bröhl
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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Caron D, Canal-Alonso Á, Panuccio G. Mimicking CA3 Temporal Dynamics Controls Limbic Ictogenesis. BIOLOGY 2022; 11:371. [PMID: 35336745 PMCID: PMC8944954 DOI: 10.3390/biology11030371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 02/22/2022] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
Mesial temporal lobe epilepsy (MTLE) is the most common partial complex epilepsy in adults and the most unresponsive to medications. Electrical deep brain stimulation (DBS) of the hippocampus has proved effective in controlling seizures in epileptic rodents and in drug-refractory MTLE patients. However, current DBS paradigms implement arbitrary fixed-frequency or patterned stimuli, disregarding the temporal profile of brain electrical activity. The latter, herein included hippocampal spontaneous firing, has been shown to follow lognormal temporal dynamics. Here, we present a novel paradigm to devise DBS protocols based on stimulation patterns fashioned as a surrogate brain signal. We focus on the interictal activity originating in the hippocampal subfield CA3, which has been shown to be anti-ictogenic. Using 4-aminopyridine-treated hippocampus-cortex slices coupled to microelectrode array, we pursue three specific aims: (1) address whether lognormal temporal dynamics can describe the CA3-driven interictal pattern, (2) explore the possibility of restoring the non-seizing state by mimicking the temporal dynamics of this anti-ictogenic pattern with electrical stimulation, and (3) compare the performance of the CA3-surrogate against periodic stimulation. We show that the CA3-driven interictal activity follows lognormal temporal dynamics. Further, electrical stimulation fashioned as a surrogate interictal pattern exhibits similar efficacy but uses less pulses than periodic stimulation. Our results support the possibility of mimicking the temporal dynamics of relevant brain signals as a straightforward DBS strategy to ameliorate drug-refractory epilepsy. Further, they herald a paradigm shift in neuromodulation, wherein a compromised brain signal can be recreated by the appropriate stimuli distribution to bypass trial-and-error studies and attain physiologically meaningful DBS operating modes.
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Affiliation(s)
- Davide Caron
- Enhanced Regenerative Medicine, Istituto Italiano di Tecnologia, 16163 Genova, Italy;
| | - Ángel Canal-Alonso
- BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain;
- Institute for Biomedical Research of Salamanca, University of Salamanca, 37008 Salamanca, Spain
| | - Gabriella Panuccio
- Enhanced Regenerative Medicine, Istituto Italiano di Tecnologia, 16163 Genova, Italy;
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Schoeters R, Tarnaud T, Martens L, Joseph W, Raedt R, Tanghe E. Double Two-State Opsin Model With Autonomous Parameter Inference. Front Comput Neurosci 2021; 15:688331. [PMID: 34220478 PMCID: PMC8243001 DOI: 10.3389/fncom.2021.688331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/21/2021] [Indexed: 11/13/2022] Open
Abstract
Optogenetics has a lot of potential to become an effective neuromodulative therapy for clinical applications. Selecting the correct opsin is crucial to have an optimal optogenetic tool. With computational modeling, the neuronal response to the current dynamics of an opsin can be extensively and systematically tested. Unlike electrical stimulation where the effect is directly defined by the applied field, the stimulation in optogenetics is indirect, depending on the selected opsin's non-linear kinetics. With the continuous expansion of opsin possibilities, computational studies are difficult due to the need for an accurate model of the selected opsin first. To this end, we propose a double two-state opsin model as alternative to the conventional three and four state Markov models used for opsin modeling. Furthermore, we provide a fitting procedure, which allows for autonomous model fitting starting from a vast parameter space. With this procedure, we successfully fitted two distinctive opsins (ChR2(H134R) and MerMAID). Both models are able to represent the experimental data with great accuracy and were obtained within an acceptable time frame. This is due to the absence of differential equations in the fitting procedure, with an enormous reduction in computational cost as result. The performance of the proposed model with a fit to ChR2(H134R) was tested, by comparing the neural response in a regular spiking neuron to the response obtained with the non-instantaneous, four state Markov model (4SB), derived by Williams et al. (2013). Finally, a computational speed gain was observed with the proposed model in a regular spiking and sparse Pyramidal-Interneuron-Network-Gamma (sPING) network simulation with respect to the 4SB-model, due to the former having two differential equations less. Consequently, the proposed model allows for computationally efficient optogenetic neurostimulation and with the proposed fitting procedure will be valuable for further research in the field of optogenetics.
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Affiliation(s)
- Ruben Schoeters
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
| | - Thomas Tarnaud
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
| | - Luc Martens
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
| | - Wout Joseph
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
| | - Robrecht Raedt
- 4BRAIN, Department of Neurology, Institute for Neuroscience, Ghent University, Ghent, Belgium
| | - Emmeric Tanghe
- WAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium
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Wei W, Wei X, Zuo M, Yu T, Li Y. Seizure control in a neural mass model by an active disturbance rejection approach. INT J ADV ROBOT SYST 2019. [DOI: 10.1177/1729881419890152] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
A closed-loop neuromodulation automatically adjusts stimuli according to brain response in real time. It is viewed as a promising way to control medically intractable epilepsy. A suitable closed-loop modulation strategy, which is robust enough to unknown nonlinearities, dynamics, and disturbances, is in great need in the clinic. For the specialization of epilepsy, the Jansen’s neural mass model is utilized to simulate the undesired high amplitudes epileptic activities, and active disturbance rejection control is designed to suppress the high amplitudes of epileptiform discharges. With the help of active disturbance rejection control, closed-loop roots of the system are far from the imaginary axis. Time domain response shows that active disturbance rejection control is able to control seizure no matter whether disturbances exist or not. At the same time, frequency domain response presents that enough stability margins and a broader range of tunable controller parameters can be obtained. Stable regions have also been presented to provide guidance to choose the parameters of active disturbance rejection control. Numerical results show that, compared with proportional-integral control, more accurate modulation with less energy can be achieved by active disturbance rejection control. It confirms that the active disturbance rejection control-based neuromodulation solution is able to achieve a desired performance. It is a promising closed-loop neuromodulation strategy in seizure control.
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Affiliation(s)
- Wei Wei
- Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, China
| | - Xiaofang Wei
- Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, China
| | - Min Zuo
- Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, China
| | - Tao Yu
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yan Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
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Precursors of seizures due to specific spatial-temporal modifications of evolving large-scale epileptic brain networks. Sci Rep 2019; 9:10623. [PMID: 31337840 PMCID: PMC6650408 DOI: 10.1038/s41598-019-47092-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 07/10/2019] [Indexed: 12/25/2022] Open
Abstract
Knowing when, where, and how seizures are initiated in large-scale epileptic brain networks remains a widely unsolved problem. Seizure precursors – changes in brain dynamics predictive of an impending seizure – can now be identified well ahead of clinical manifestations, but either the seizure onset zone or remote brain areas are reported as network nodes from which seizure precursors emerge. We aimed to shed more light on the role of constituents of evolving epileptic networks that recurrently transit into and out of seizures. We constructed such networks from more than 3200 hours of continuous intracranial electroencephalograms recorded in 38 patients with medication refractory epilepsy. We succeeded in singling out predictive edges and predictive nodes. Their particular characteristics, namely edge weight respectively node centrality (a fundamental concept of network theory), from the pre-ictal periods of 78 out of 97 seizures differed significantly from the characteristics seen during inter-ictal periods. The vast majority of predictive nodes were connected by most of the predictive edges, but these nodes never played a central role in the evolving epileptic networks. Interestingly, predictive nodes were entirely associated with brain regions deemed unaffected by the focal epileptic process. We propose a network mechanism for a transition into the pre-seizure state, which puts into perspective the role of the seizure onset zone in this transition and highlights the necessity to reassess current concepts for seizure generation and seizure prevention.
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Markert MS, Fisher RS. Neuromodulation - Science and Practice in Epilepsy: Vagus Nerve Stimulation, Thalamic Deep Brain Stimulation, and Responsive NeuroStimulation. Expert Rev Neurother 2018; 19:17-29. [DOI: 10.1080/14737175.2019.1554433] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Matthew S. Markert
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert S. Fisher
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
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Vitale F, Shen W, Driscoll N, Burrell JC, Richardson AG, Adewole O, Murphy B, Ananthakrishnan A, Oh H, Wang T, Lucas TH, Cullen DK, Allen MG, Litt B. Biomimetic extracellular matrix coatings improve the chronic biocompatibility of microfabricated subdural microelectrode arrays. PLoS One 2018; 13:e0206137. [PMID: 30383805 PMCID: PMC6211660 DOI: 10.1371/journal.pone.0206137] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Accepted: 10/08/2018] [Indexed: 01/15/2023] Open
Abstract
Intracranial electrodes are a vital component of implantable neurodevices, both for acute diagnostics and chronic treatment with open and closed-loop neuromodulation. Their performance is hampered by acute implantation trauma and chronic inflammation in response to implanted materials and mechanical mismatch between stiff synthetic electrodes and pulsating, natural soft host neural tissue. Flexible electronics based on thin polymer films patterned with microscale conductive features can help alleviate the mechanically induced trauma; however, this strategy alone does not mitigate inflammation at the device-tissue interface. In this study, we propose a biomimetic approach that integrates microscale extracellular matrix (ECM) coatings on microfabricated flexible subdural microelectrodes. Taking advantage of a high-throughput process employing micro-transfer molding and excimer laser micromachining, we fabricate multi-channel subdural microelectrodes primarily composed of ECM protein material and demonstrate that the electrochemical and mechanical properties match those of standard, uncoated controls. In vivo ECoG recordings in rodent brain confirm that the ECM microelectrode coatings and the protein interface do not alter signal fidelity. Astrogliotic, foreign body reaction to ECM coated devices is reduced, compared to uncoated controls, at 7 and 30 days, after subdural implantation in rat somatosensory cortex. We propose microfabricated, flexible, biomimetic electrodes as a new strategy to reduce inflammation at the device-tissue interface and improve the long-term stability of implantable subdural electrodes.
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Affiliation(s)
- Flavia Vitale
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia PA, United States of America
- Department of Physical Medicine & Rehabilitation, University of Pennsylvania, Philadelphia PA, United States of America
- Center for Neurotrauma, Neurodegeneration & Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia PA, United States of America
| | - Wendy Shen
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia PA, United States of America
| | - Nicolette Driscoll
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA, United States of America
| | - Justin C. Burrell
- Center for Neurotrauma, Neurodegeneration & Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA, United States of America
- Department of Neurosurgery, University of Pennsylvania, Philadelphia PA, United States of America
| | - Andrew G. Richardson
- Department of Neurosurgery, University of Pennsylvania, Philadelphia PA, United States of America
| | - Oladayo Adewole
- Center for Neurotrauma, Neurodegeneration & Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA, United States of America
| | - Brendan Murphy
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA, United States of America
| | - Akshay Ananthakrishnan
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia PA, United States of America
| | - Hanju Oh
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia PA, United States of America
| | - Theodore Wang
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA, United States of America
| | - Timothy H. Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, United States of America
- Department of Neurosurgery, University of Pennsylvania, Philadelphia PA, United States of America
| | - D. Kacy Cullen
- Center for Neurotrauma, Neurodegeneration & Restoration, Corporal Michael J. Crescenz VA Medical Center, Philadelphia PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA, United States of America
- Department of Neurosurgery, University of Pennsylvania, Philadelphia PA, United States of America
| | - Mark G. Allen
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia PA, United States of America
| | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia PA, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA, United States of America
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Eissa TL, Schevon CA, Emerson RG, Mckhann GM, Goodman RR, Van Drongelen W. The Relationship Between Ictal Multi-Unit Activity and the Electrocorticogram. Int J Neural Syst 2018; 28:1850027. [PMID: 30001641 DOI: 10.1142/s0129065718500272] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
During neocortical seizures in patients with epilepsy, microelectrode array recordings from the ictal core show a strong correlation between the fast, cellular spiking activities and the low-frequency component of the potential field, reflected in the electrocorticogram (ECoG). Here, we model the relationship between the cellular spike activity and this low-frequency component as the input and output signals of a linear time invariant system. Our approach is based on the observation that this relationship can be characterized by a so-called sinc function, the unit impulse response of an ideal (brick-wall) filter. Accordingly, using a brick-wall filter, we are able to convert ictal cellular spike inputs into an output that significantly correlates with the observed seizure activity in the ECoG (r = 0.40 - 0.56,p < 0.01) , while ECoG recordings of subsequent seizures within patients also show significant, but lower, correlations (r = 0.10 - 0.30,p < 0.01) . Furthermore, we can produce seizure-like output signals using synthetic spike trains with ictal properties. We propose a possible physiological mechanism to explain the observed properties associated with an ideal filter, and discuss the potential use of our approach for the evaluation of anticonvulsant strategies.
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Affiliation(s)
- Tahra L Eissa
- 1 Committee on Neurobiology, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637, USA.,2 Department of Neurology, Columbia University, New York 10032, NY, USA
| | - Catherine A Schevon
- 3 Department of Neurology, Columbia University, 710 W 168th St, New York 10032, NY, USA
| | - Ronald G Emerson
- 3 Department of Neurology, Columbia University, 710 W 168th St, New York 10032, NY, USA.,4 Department of Neurology, Weill Cornell Medical College, New York 10021, NY, USA
| | - Guy M Mckhann
- 5 Department of Neurological Surgery, Columbia University, 710 W 168th St, New York 10032, NY, USA
| | - Robert R Goodman
- 5 Department of Neurological Surgery, Columbia University, 710 W 168th St, New York 10032, NY, USA.,6 Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
| | - Wim Van Drongelen
- 7 Department of Pediatrics, University of Chicago, 900 E 57th St, Chicago, IL 60637, USA
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11
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Random ensemble learning for EEG classification. Artif Intell Med 2018; 84:146-158. [DOI: 10.1016/j.artmed.2017.12.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 12/19/2017] [Accepted: 12/21/2017] [Indexed: 01/21/2023]
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12
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Sevcencu C, Nielsen TN, Struijk JJ. A neural blood pressure marker for bioelectronic medicines for treatment of hypertension. Biosens Bioelectron 2017. [DOI: 10.1016/j.bios.2017.06.031] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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13
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Edwards CA, Kouzani A, Lee KH, Ross EK. Neurostimulation Devices for the Treatment of Neurologic Disorders. Mayo Clin Proc 2017; 92:1427-1444. [PMID: 28870357 DOI: 10.1016/j.mayocp.2017.05.005] [Citation(s) in RCA: 86] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 04/16/2017] [Accepted: 05/01/2017] [Indexed: 12/01/2022]
Abstract
Rapid advancements in neurostimulation technologies are providing relief to an unprecedented number of patients affected by debilitating neurologic and psychiatric disorders. Neurostimulation therapies include invasive and noninvasive approaches that involve the application of electrical stimulation to drive neural function within a circuit. This review focuses on established invasive electrical stimulation systems used clinically to induce therapeutic neuromodulation of dysfunctional neural circuitry. These implantable neurostimulation systems target specific deep subcortical, cortical, spinal, cranial, and peripheral nerve structures to modulate neuronal activity, providing therapeutic effects for a myriad of neuropsychiatric disorders. Recent advances in neurotechnologies and neuroimaging, along with an increased understanding of neurocircuitry, are factors contributing to the rapid rise in the use of neurostimulation therapies to treat an increasingly wide range of neurologic and psychiatric disorders. Electrical stimulation technologies are evolving after remaining fairly stagnant for the past 30 years, moving toward potential closed-loop therapeutic control systems with the ability to deliver stimulation with higher spatial resolution to provide continuous customized neuromodulation for optimal clinical outcomes. Even so, there is still much to be learned about disease pathogenesis of these neurodegenerative and psychiatric disorders and the latent mechanisms of neurostimulation that provide therapeutic relief. This review provides an overview of the increasingly common stimulation systems, their clinical indications, and enabling technologies.
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Affiliation(s)
- Christine A Edwards
- School of Engineering, Deakin University, Geelong, Victoria, Australia; Department of Neurologic Surgery, Mayo Clinic, Rochester, MN
| | - Abbas Kouzani
- School of Engineering, Deakin University, Geelong, Victoria, Australia
| | - Kendall H Lee
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN
| | - Erika K Ross
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN; Department of Surgery, Mayo Clinic, Rochester, MN.
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14
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Li DH, Yang XF. Remote modulation of network excitability during deep brain stimulation for epilepsy. Seizure 2017; 47:42-50. [DOI: 10.1016/j.seizure.2017.02.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Revised: 01/20/2017] [Accepted: 02/28/2017] [Indexed: 12/18/2022] Open
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15
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Cao Y, Jin L, Su F, Wang J, Deng B. Principal dynamic mode analysis of neural mass model for the identification of epileptic states. CHAOS (WOODBURY, N.Y.) 2016; 26:113118. [PMID: 27908011 DOI: 10.1063/1.4967734] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The detection of epileptic seizures in Electroencephalography (EEG) signals is significant for the diagnosis and treatment of epilepsy. In this paper, in order to obtain characteristics of various epileptiform EEGs that may differentiate different states of epilepsy, the concept of Principal Dynamic Modes (PDMs) was incorporated to an autoregressive model framework. First, the neural mass model was used to simulate the required intracerebral EEG signals of various epileptiform activities. Then, the PDMs estimated from the nonlinear autoregressive Volterra models, as well as the corresponding Associated Nonlinear Functions (ANFs), were used for the modeling of epileptic EEGs. The efficient PDM modeling approach provided physiological interpretation of the system. Results revealed that the ANFs of the 1st and 2nd PDMs for the auto-regressive input exhibited evident differences among different states of epilepsy, where the ANFs of the sustained spikes' activity encountered at seizure onset or during a seizure were the most differentiable from that of the normal state. Therefore, the ANFs may be characteristics for the classification of normal and seizure states in the clinical detection of seizures and thus provide assistance for the diagnosis of epilepsy.
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Affiliation(s)
- Yuzhen Cao
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
| | - Liu Jin
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
| | - Fei Su
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Bin Deng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
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Predictability of uncontrollable multifocal seizures - towards new treatment options. Sci Rep 2016; 6:24584. [PMID: 27091239 PMCID: PMC4835791 DOI: 10.1038/srep24584] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Accepted: 03/30/2016] [Indexed: 01/03/2023] Open
Abstract
Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels of sensitivity and specificity, even in an ambulatory setting, has been repeatedly shown, however, in patients with a single seizure focus only. We did a study to assess feasibility of state-of-the-art, electroencephalogram-based seizure-prediction techniques in patients with uncontrollable multifocal seizures. We obtained significant predictive information about upcoming seizures in more than two thirds of patients. Unexpectedly, the emergence of seizure precursors was confined to non-affected brain areas. Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures. Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.
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