1
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Zendrikov D, Paraskevov A. The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks. Neural Netw 2024; 180:106589. [PMID: 39217864 DOI: 10.1016/j.neunet.2024.106589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 07/06/2024] [Accepted: 07/28/2024] [Indexed: 09/04/2024]
Abstract
Thin pancake-like neuronal networks cultured on top of a planar microelectrode array have been extensively tried out in neuroengineering, as a substrate for the mobile robot's control unit, i.e., as a cyborg's brain. Most of these attempts failed due to intricate self-organizing dynamics in the neuronal systems. In particular, the networks may exhibit an emergent spatial map of steady nucleation sites ("n-sites") of spontaneous population spikes. Being unpredictable and independent of the surface electrode locations, the n-sites drastically change local ability of the network to generate spikes. Here, using a spiking neuronal network model with generative spatially-embedded connectome, we systematically show in simulations that the number, location, and relative activity of spontaneously formed n-sites ("the vitals") crucially depend on the samplings of three distributions: (1) the network distribution of neuronal excitability, (2) the distribution of connections between neurons of the network, and (3) the distribution of maximal amplitudes of a single synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about 4%) of non-pacemaker neurons having the highest excitability was enough to completely suppress the occurrence of population spikes and their n-sites. This key result is explained theoretically. Remarkably, the n-sites occur taking into account only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity. As the spiking network model used in this study is strictly deterministic, all simulation results can be accurately reproduced. The model, which has already demonstrated a very high richness-to-complexity ratio, can also be directly extended into the three-dimensional case, e.g., for targeting peculiarities of spiking dynamics in cerebral (or brain) organoids. We recommend the model as an excellent illustrative tool for teaching network-level computational neuroscience, complementing a few benchmark models.
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Affiliation(s)
- Dmitrii Zendrikov
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland.
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2
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Meneghetti N, Vannini E, Mazzoni A. Rodents' visual gamma as a biomarker of pathological neural conditions. J Physiol 2024; 602:1017-1048. [PMID: 38372352 DOI: 10.1113/jp283858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 01/23/2024] [Indexed: 02/20/2024] Open
Abstract
Neural gamma oscillations (indicatively 30-100 Hz) are ubiquitous: they are associated with a broad range of functions in multiple cortical areas and across many animal species. Experimental and computational works established gamma rhythms as a global emergent property of neuronal networks generated by the balanced and coordinated interaction of excitation and inhibition. Coherently, gamma activity is strongly influenced by the alterations of synaptic dynamics which are often associated with pathological neural dysfunctions. We argue therefore that these oscillations are an optimal biomarker for probing the mechanism of cortical dysfunctions. Gamma oscillations are also highly sensitive to external stimuli in sensory cortices, especially the primary visual cortex (V1), where the stimulus dependence of gamma oscillations has been thoroughly investigated. Gamma manipulation by visual stimuli tuning is particularly easy in rodents, which have become a standard animal model for investigating the effects of network alterations on gamma oscillations. Overall, gamma in the rodents' visual cortex offers an accessible probe on dysfunctional information processing in pathological conditions. Beyond vision-related dysfunctions, alterations of gamma oscillations in rodents were indeed also reported in neural deficits such as migraine, epilepsy and neurodegenerative or neuropsychiatric conditions such as Alzheimer's, schizophrenia and autism spectrum disorders. Altogether, the connections between visual cortical gamma activity and physio-pathological conditions in rodent models underscore the potential of gamma oscillations as markers of neuronal (dys)functioning.
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Affiliation(s)
- Nicolò Meneghetti
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence for Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Eleonora Vannini
- Neuroscience Institute, National Research Council (CNR), Pisa, Italy
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence for Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
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3
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Panarese A, Vissani M, Meneghetti N, Vannini E, Cracchiolo M, Micera S, Caleo M, Mazzoni A, Restani L. Disruption of layer-specific visual processing in a model of focal neocortical epilepsy. Cereb Cortex 2022; 33:4173-4187. [PMID: 36089833 DOI: 10.1093/cercor/bhac335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 07/25/2022] [Accepted: 07/26/2022] [Indexed: 11/12/2022] Open
Abstract
The epileptic brain is the result of a sequence of events transforming normal neuronal populations into hyperexcitable networks supporting recurrent seizure generation. These modifications are known to induce fundamental alterations of circuit function and, ultimately, of behavior. However, how hyperexcitability affects information processing in cortical sensory circuits is not yet fully understood. Here, we investigated interlaminar alterations in sensory processing of the visual cortex in a mouse model of focal epilepsy. We found three main circuit dynamics alterations in epileptic mice: (i) a spreading of visual contrast-driven gamma modulation across layers, (ii) an increase in firing rate that is layer-unspecific for excitatory units and localized in infragranular layers for inhibitory neurons, and (iii) a strong and contrast-dependent locking of firing units to network activity. Altogether, our data show that epileptic circuits display a functional disruption of layer-specific organization of visual sensory processing, which could account for visual dysfunction observed in epileptic subjects. Understanding these mechanisms paves the way to circuital therapeutic interventions for epilepsy.
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Affiliation(s)
- Alessandro Panarese
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy
| | - Matteo Vissani
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy
| | - Nicolò Meneghetti
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy
| | - Eleonora Vannini
- Neuroscience Institute, National Research Council (CNR), via G. Moruzzi 1, 56124 Pisa, Italy
| | - Marina Cracchiolo
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy
| | - Silvestro Micera
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy.,Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, 1202 Geneva, Switzerland
| | - Matteo Caleo
- Neuroscience Institute, National Research Council (CNR), via G. Moruzzi 1, 56124 Pisa, Italy.,Department of Biomedical Sciences, University of Padua, via G. Colombo 3, 35121 Padua, Italy
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy
| | - Laura Restani
- Neuroscience Institute, National Research Council (CNR), via G. Moruzzi 1, 56124 Pisa, Italy
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4
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Gerster M, Taher H, Škoch A, Hlinka J, Guye M, Bartolomei F, Jirsa V, Zakharova A, Olmi S. Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation. Front Syst Neurosci 2021; 15:675272. [PMID: 34539355 PMCID: PMC8440880 DOI: 10.3389/fnsys.2021.675272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 07/07/2021] [Indexed: 11/13/2022] Open
Abstract
Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.
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Affiliation(s)
- Moritz Gerster
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
| | - Halgurd Taher
- Inria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, Valbonne, France
| | - Antonín Škoch
- National Institute of Mental Health, Klecany, Czechia
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czechia
| | - Jaroslav Hlinka
- National Institute of Mental Health, Klecany, Czechia
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
| | - Maxime Guye
- Faculté de Médecine de la Timone, Centre de Résonance Magnétique et Biologique et Médicale (CRMBM, UMR CNRS-AMU 7339), Medical School of Marseille, Aix-Marseille Université, Marseille, France
- Assistance Publique -Hôpitaux de Marseille, Hôpital de la Timone, Pôle d'Imagerie, Marseille, France
| | - Fabrice Bartolomei
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Université, Inserm, Institut de Neurosciences des Systèmes, UMRS 1106, Marseille, France
| | - Anna Zakharova
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
| | - Simona Olmi
- Inria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, Valbonne, France
- Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy
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5
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Abdelnour F, Dayan M, Devinsky O, Thesen T, Raj A. Algebraic relationship between the structural network's Laplacian and functional network's adjacency matrix is preserved in temporal lobe epilepsy subjects. Neuroimage 2020; 228:117705. [PMID: 33385550 DOI: 10.1016/j.neuroimage.2020.117705] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 12/16/2020] [Accepted: 12/21/2020] [Indexed: 11/19/2022] Open
Abstract
The relationship between anatomic and resting state functional connectivity of large-scale brain networks is a major focus of current research. In previous work, we introduced a model based on eigen decomposition of the Laplacian which predicts the functional network from the structural network in healthy brains. In this work, we apply the eigen decomposition model to two types of epilepsy; temporal lobe epilepsy associated with mesial temporal sclerosis, and MRI-normal temporal lobe epilepsy. Our findings show that the eigen relationship between function and structure holds for patients with temporal lobe epilepsy as well as normal individuals. These results suggest that the brain under TLE conditions reconfigures and rewires the fine-scale connectivity (a process which the model parameters are putatively sensitive to), in order to achieve the necessary structure-function relationship.
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Affiliation(s)
- Farras Abdelnour
- Radiology and Biomedical Imaging Graduate Program in BioEngineering UCSF, San Francisco, CA, USA.
| | - Michael Dayan
- Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland
| | | | - Thomas Thesen
- Department of Physiology, Neuroscience & Behavioral Sciences, St. George's University, Grenada, West Indies
| | - Ashish Raj
- Radiology and Biomedical Imaging Graduate Program in BioEngineering UCSF, San Francisco, CA, USA
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6
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Deeba F, Sanz-Leon P, Robinson PA. Effects of physiological parameter evolution on the dynamics of tonic-clonic seizures. PLoS One 2020; 15:e0230510. [PMID: 32240175 PMCID: PMC7117716 DOI: 10.1371/journal.pone.0230510] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 03/03/2020] [Indexed: 02/02/2023] Open
Abstract
The temporal and spectral characteristics of tonic-clonic seizures are investigated using a neural field model of the corticothalamic system in the presence of a temporally varying connection strength between the cerebral cortex and thalamus. Increasing connection strength drives the system into ∼ 10 Hz seizure oscillations once a threshold is passed and a subcritical Hopf bifurcation occurs. In this study, the spectral and temporal characteristics of tonic-clonic seizures are explored as functions of the relevant properties of physiological connection strengths, such as maximum strength, time above threshold, and the ramp rate at which the strength increases or decreases. Analysis shows that the seizure onset time decreases with the maximum connection strength and time above threshold, but increases with the ramp rate. Seizure duration and offset time increase with maximum connection strength, time above threshold, and rate of change. Spectral analysis reveals that the power of nonlinear harmonics and the duration of the oscillations increase as the maximum connection strength and the time above threshold increase. A secondary limit cycle at ∼ 18 Hz, termed a saddle-cycle, is also seen during seizure onset and becomes more prominent and robust with increasing ramp rate. If the time above the threshold is too small, the system does not reach the 10 Hz limit cycle, and only exhibits 18 Hz saddle-cycle oscillations. It is also seen that the time to reach the saturated large amplitude limit-cycle seizure oscillation from both the instability threshold and from the end of the saddle-cycle oscillations is inversely proportional to the square root of the ramp rate.
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Affiliation(s)
- F. Deeba
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
- * E-mail: ,
| | - P. Sanz-Leon
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - P. A. Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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7
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Deeba F, Sanz-Leon P, Robinson PA. Unified dynamics of interictal events and absence seizures. Phys Rev E 2019; 100:022407. [PMID: 31574631 DOI: 10.1103/physreve.100.022407] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Indexed: 01/09/2023]
Abstract
The dynamics of interictal events between absence seizures and their relationship to seizures themselves are investigated by employing a neural field model of the corticothalamic system. Interictal events are modeled as being due to transient parameter excursions beyond the seizure threshold, in the present case by sufficiently temporally varying the connection strength between the cerebral cortex and the thalamus. Increasing connection strength drives the system into ∼3-Hz seizure oscillations via a supercritical Hopf bifurcation once the linear instability threshold is passed. Depending on the time course of the excursion above threshold, different interictal activity event dynamics are seen in the time series of corticothalamic fields. These resemble experimental interictal time series observed via electroencephalography. It is found that the morphology of these events depends on the magnitude and duration of the excursion above threshold. For a large-amplitude excursion of short duration, events resemble interictal spikes, where one large spike is seen, followed by small damped oscillations. For a short excursion with long duration, events like observed interictal periodic sharp waves are seen. When both amplitude and duration above threshold are large, seizure oscillations are seen. Using these outcomes, proximity to seizure can be estimated and tracked.
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Affiliation(s)
- F Deeba
- Department of Physics, Dhaka University of Engineering and Technology, Gazipur 1700, Bangladesh; School of Physics, University of Sydney, New South Wales 2006, Australia; and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - P Sanz-Leon
- School of Physics, University of Sydney, New South Wales 2006, Australia, and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, New South Wales 2006, Australia, and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
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8
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Buchin A, Kerr CC, Huberfeld G, Miles R, Gutkin B. Adaptation and Inhibition Control Pathological Synchronization in a Model of Focal Epileptic Seizure. eNeuro 2018; 5:ENEURO.0019-18.2018. [PMID: 30302390 PMCID: PMC6173584 DOI: 10.1523/eneuro.0019-18.2018] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 06/07/2018] [Accepted: 06/07/2018] [Indexed: 01/12/2023] Open
Abstract
Pharmacoresistant epilepsy is a common neurological disorder in which increased neuronal intrinsic excitability and synaptic excitation lead to pathologically synchronous behavior in the brain. In the majority of experimental and theoretical epilepsy models, epilepsy is associated with reduced inhibition in the pathological neural circuits, yet effects of intrinsic excitability are usually not explicitly analyzed. Here we present a novel neural mass model that includes intrinsic excitability in the form of spike-frequency adaptation in the excitatory population. We validated our model using local field potential (LFP) data recorded from human hippocampal/subicular slices. We found that synaptic conductances and slow adaptation in the excitatory population both play essential roles for generating seizures and pre-ictal oscillations. Using bifurcation analysis, we found that transitions towards seizure and back to the resting state take place via Andronov-Hopf bifurcations. These simulations therefore suggest that single neuron adaptation as well as synaptic inhibition are responsible for orchestrating seizure dynamics and transition towards the epileptic state.
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Affiliation(s)
- Anatoly Buchin
- University of Washington, Department of Physiology and Biophysics (United States, Seattle), 1959 NE Pacific St, 98195
| | - Cliff C. Kerr
- University of Sydney, School of Physics (Australia, Sydney), Physics Rd, NSW 2006
| | - Gilles Huberfeld
- Sorbonne Université-UPMC, Pitié-Salpêtrière Hô, Neurophysiology Department (France, Paris), 47-83 Boulevard de l’Hôpital, 75013
- Institut national de la santé et de la recherche médicale Unit 1129 “Infantile Epilepsies and Brain Plasticity”, Paris Descartes University, Sorbonne Paris Cité University group, (France, Paris), 149 rue de Sévres 75015
| | - Richard Miles
- Brain and Spine Institute, Cortex and Epilepsie Group (France, Paris), 47 Boulevard Hôpital, 75013
| | - Boris Gutkin
- Paris Sciences & Lettres Research University, Laboratoire des Neurosciences Cognitives, Group for Neural Theory (France, Paris), 29, rue d'Ulm, 75005 France
- National Research University Higher School of Economics, Center for Cognition and Decision Making (Russia, Moscow), 20 Myasnitskaya, 109316
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9
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Deeba F, Sanz-Leon P, Robinson PA. Dependence of absence seizure dynamics on physiological parameter evolution. J Theor Biol 2018; 454:11-21. [PMID: 29807025 DOI: 10.1016/j.jtbi.2018.05.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 05/22/2018] [Accepted: 05/24/2018] [Indexed: 12/30/2022]
Abstract
A neural field model of the corticothalamic system is applied to investigate the temporal and spectral characteristics of absence seizures in the presence of a temporally varying connection strength between the cerebral cortex and thalamus. Increasing connection strength drives the system into an absence seizure-like state once a threshold is passed and a supercritical Hopf bifurcation occurs. The dynamics and spectral characteristics of the resulting model seizures are explored as functions of maximum connection strength, time above threshold, and the rate at which the connection strength increases (ramp rate). Our results enable spectral and temporal characteristics of seizures to be related to changes in the underlying physiological evolution of connections via nonlinear dynamics and neural field theory. Spectral analysis reveals that the power of the harmonics and the duration of the oscillations increase as the maximum connection strength and the time above threshold increase. It is also found that the time to reach the stable limit-cycle seizure oscillation from the instability threshold decreases with the square root of the ramp rate.
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Affiliation(s)
- F Deeba
- School of Physics, University of Sydney, NSW 2006, Australia; Center for Integrative Brain Function, University of Sydney, NSW 2006, Australia.
| | - Paula Sanz-Leon
- School of Physics, University of Sydney, NSW 2006, Australia; Center for Integrative Brain Function, University of Sydney, NSW 2006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, NSW 2006, Australia; Center for Integrative Brain Function, University of Sydney, NSW 2006, Australia
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10
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Hall SP, Traub RD, Adams NE, Cunningham MO, Schofield I, Jenkins AJ, Whittington MA. Enhanced interlaminar excitation or reduced superficial layer inhibition in neocortex generates different spike-and-wave-like electrographic events in vitro. J Neurophysiol 2018; 119:49-61. [PMID: 28954894 PMCID: PMC5866469 DOI: 10.1152/jn.00516.2017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 09/26/2017] [Accepted: 09/26/2017] [Indexed: 12/19/2022] Open
Abstract
Acute in vitro models have revealed a great deal of information about mechanisms underlying many types of epileptiform activity. However, few examples exist that shed light on spike-and-wave (SpW) patterns of pathological activity. SpW are seen in many epilepsy syndromes, both generalized and focal, and manifest across the entire age spectrum. They are heterogeneous in terms of their severity, symptom burden, and apparent anatomical origin (thalamic, neocortical, or both), but any relationship between this heterogeneity and underlying pathology remains elusive. In this study we demonstrate that physiological delta-frequency rhythms act as an effective substrate to permit modeling of SpW of cortical origin and may help to address this issue. For a starting point of delta activity, multiple subtypes of SpW could be modeled computationally and experimentally by either enhancing the magnitude of excitatory synaptic events ascending from neocortical layer 5 to layers 2/3 or selectively modifying superficial layer GABAergic inhibition. The former generated SpW containing multiple field spikes with long interspike intervals, whereas the latter generated SpW with short-interval multiple field spikes. Both types had different laminar origins and each disrupted interlaminar cortical dynamics in a different manner. A small number of examples of human recordings from patients with different diagnoses revealed SpW subtypes with the same temporal signatures, suggesting that detailed quantification of the pattern of spikes in SpW discharges may be a useful indicator of disparate underlying epileptogenic pathologies. NEW & NOTEWORTHY Spike-and-wave-type discharges (SpW) are a common feature in many epilepsies. Their electrographic manifestation is highly varied, as are available genetic clues to associated underlying pathology. Using computational and in vitro models, we demonstrate that distinct subtypes of SpW are generated by lamina-selective disinhibition or enhanced interlaminar excitation. These subtypes could be detected in at least some noninvasive patient recordings, suggesting more detailed analysis of SpW may be useful in determining clinical pathology.
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Affiliation(s)
- Stephen P Hall
- Hull York Medical School, University of York , Heslington , United Kingdom
| | - Roger D Traub
- Department of Physical Sciences, IBM Thomas J. Watson Research Center , Yorktown Heights, New York
| | - Natalie E Adams
- Hull York Medical School, University of York , Heslington , United Kingdom
| | - Mark O Cunningham
- Institute of Neuroscience, Newcastle University , Newcastle upon Tyne , United Kingdom
| | - Ian Schofield
- Department of Clinical Neurophysiology, Royal Victoria Infirmary, Newcastle upon Tyne , United Kingdom
| | - Alistair J Jenkins
- Department of Clinical Neurophysiology, Royal Victoria Infirmary, Newcastle upon Tyne , United Kingdom
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11
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Kinjo ER, Rodríguez PXR, Dos Santos BA, Higa GSV, Ferraz MSA, Schmeltzer C, Rüdiger S, Kihara AH. New Insights on Temporal Lobe Epilepsy Based on Plasticity-Related Network Changes and High-Order Statistics. Mol Neurobiol 2017; 55:3990-3998. [PMID: 28555345 DOI: 10.1007/s12035-017-0623-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 05/16/2017] [Indexed: 12/21/2022]
Abstract
Epilepsy is a disorder of the brain characterized by the predisposition to generate recurrent unprovoked seizures, which involves reshaping of neuronal circuitries based on intense neuronal activity. In this review, we first detailed the regulation of plasticity-associated genes, such as ARC, GAP-43, PSD-95, synapsin, and synaptophysin. Indeed, reshaping of neuronal connectivity after the primary, acute epileptogenesis event increases the excitability of the temporal lobe. Herein, we also discussed the heterogeneity of neuronal populations regarding the number of synaptic connections, which in the theoretical field is commonly referred as degree. Employing integrate-and-fire neuronal model, we determined that in addition to increased synaptic strength, degree correlations might play essential and unsuspected roles in the control of network activity. Indeed, assortativity, which can be described as a condition where high-degree correlations are observed, increases the excitability of neural networks. In this review, we summarized recent topics in the field, and data were discussed according to newly developed or unusual tools, as provided by mathematical graph analysis and high-order statistics. With this, we were able to present new foundations for the pathological activity observed in temporal lobe epilepsy.
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Affiliation(s)
- Erika Reime Kinjo
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Pedro Xavier Royero Rodríguez
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Bianca Araújo Dos Santos
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Guilherme Shigueto Vilar Higa
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
- Departamento de Fisiologia e Biofísica, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Mariana Sacrini Ayres Ferraz
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Christian Schmeltzer
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
- Institute of Physics, Humboldt University at Berlin, Berlin, Germany
| | - Sten Rüdiger
- Institute of Physics, Humboldt University at Berlin, Berlin, Germany
| | - Alexandre Hiroaki Kihara
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.
- Departamento de Fisiologia e Biofísica, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, Brazil.
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12
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Proix T, Bartolomei F, Guye M, Jirsa VK. Individual brain structure and modelling predict seizure propagation. Brain 2017; 140:641-654. [PMID: 28364550 PMCID: PMC5837328 DOI: 10.1093/brain/awx004] [Citation(s) in RCA: 142] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 12/03/2016] [Indexed: 01/03/2023] Open
Abstract
See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions.
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Affiliation(s)
- Timothée Proix
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.,Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, 13005 Marseille, France
| | - Maxime Guye
- Aix-Marseille Université, Centre de Résonance Magnétique et Biologique et Médicale (CRMBM, UMR CNRS-AMU 7339), Medical School of Marseille, 13005, Marseille, France.,Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d'Imagerie Médicale, CHU, 13005, Marseille, France
| | - Viktor K Jirsa
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
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13
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Ahn S, Jun SB, Lee HW, Lee S. Computational modeling of epileptiform activities in medial temporal lobe epilepsy combined with in vitro experiments. J Comput Neurosci 2016; 41:207-23. [PMID: 27416961 DOI: 10.1007/s10827-016-0614-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Revised: 04/20/2016] [Accepted: 06/28/2016] [Indexed: 10/21/2022]
Abstract
In this paper, we propose a comprehensive computational model that is able to reproduce three epileptiform activities. The model targets a hippocampal formation that is known to be an important lesion in medial temporal lobe epilepsy. It consists of four sub-networks consisting of excitatory and inhibitory neurons and well-known signal pathways, with consideration of propagation delay. The three epileptiform activities involve fast and slow interictal discharge and ictal discharge, and those activities can be induced in vitro by application of 4-Aminopyridine in entorhinal cortex combined hippocampal slices. We model the three epileptiform activities upon previously reported biological mechanisms and verify the simulation results by comparing them with in vitro experimental data obtained using a microelectrode array. We use the results of Granger causality analysis of recorded data to set input gains of signal pathways in the model, so that the compatibility between the computational and experimental models can be improved. The proposed model can be expanded to evaluate the suppression effect of epileptiform activities due to new treatment methods.
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Affiliation(s)
- Sora Ahn
- Department of Electronics Engineering, Ewha Womans University, Seoul, 120-750, South Korea
| | - Sang Beom Jun
- Department of Electronics Engineering, Ewha Womans University, Seoul, 120-750, South Korea
| | - Hyang Woon Lee
- Department of Neurology, Ewha Medical Research Institute, Ewha Womans University School of Medicine, Seoul, 158-710, South Korea
| | - Seungjun Lee
- Department of Electronics Engineering, Ewha Womans University, Seoul, 120-750, South Korea.
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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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Slow Spatial Recruitment of Neocortex during Secondarily Generalized Seizures and Its Relation to Surgical Outcome. J Neurosci 2015; 35:9477-90. [PMID: 26109670 DOI: 10.1523/jneurosci.0049-15.2015] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Understanding the spatiotemporal dynamics of brain activity is crucial for inferring the underlying synaptic and nonsynaptic mechanisms of brain dysfunction. Focal seizures with secondary generalization are traditionally considered to begin in a limited spatial region and spread to connected areas, which can include both pathological and normal brain tissue. The mechanisms underlying this spread are important to our understanding of seizures and to improve therapies for surgical intervention. Here we study the properties of seizure recruitment-how electrical brain activity transitions to large voltage fluctuations characteristic of spike-and-wave seizures. We do so using invasive subdural electrode arrays from a population of 16 patients with pharmacoresistant epilepsy. We find an average delay of ∼30 s for a broad area of cortex (8 × 8 cm) to be recruited into the seizure, at an estimated speed of ∼4 mm/s. The spatiotemporal characteristics of recruitment reveal two categories of patients: one in which seizure recruitment of neighboring cortical regions follows a spatially organized pattern consistent from seizure to seizure, and a second group without consistent spatial organization of activity during recruitment. The consistent, organized recruitment correlates with a more regular, compared with small-world, connectivity pattern in simulation and successful surgical treatment of epilepsy. We propose that an improved understanding of how the seizure recruits brain regions into large amplitude voltage fluctuations provides novel information to improve surgical treatment of epilepsy and highlights the slow spread of massive local activity across a vast extent of cortex during seizure.
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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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Proix T, Bartolomei F, Chauvel P, Bernard C, Jirsa VK. Permittivity coupling across brain regions determines seizure recruitment in partial epilepsy. J Neurosci 2014; 34:15009-21. [PMID: 25378166 PMCID: PMC6608363 DOI: 10.1523/jneurosci.1570-14.2014] [Citation(s) in RCA: 86] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Revised: 09/08/2014] [Accepted: 09/19/2014] [Indexed: 11/21/2022] Open
Abstract
Brain regions generating seizures in patients with refractory partial epilepsy are referred to as the epileptogenic zone (EZ). During a seizure, paroxysmal activity is not restricted to the EZ, but may recruit other brain regions and propagate activity through large brain networks, which comprise brain regions that are not necessarily epileptogenic. The identification of the EZ is crucial for candidates for neurosurgery and requires unambiguous criteria that evaluate the degree of epileptogenicity of brain regions. To obtain such criteria and investigate the mechanisms of seizure recruitment and propagation, we develop a mathematical framework of coupled neural populations, which can interact via signaling through a slow permittivity variable. The permittivity variable captures effects evolving on slow timescales, including extracellular ionic concentrations and energy metabolism, with time delays of up to seconds as observed clinically. Our analyses provide a set of indices quantifying the degree of epileptogenicity and predict conditions, under which seizures propagate to nonepileptogenic brain regions, explaining the responses to intracerebral electric stimulation in epileptogenic and nonepileptogenic areas. In conjunction, our results provide guidance in the presurgical evaluation of epileptogenicity based on electrographic signatures in intracerebral electroencephalograms.
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Affiliation(s)
- Timothée Proix
- Aix Marseille Université, Institut de Neurosciences des Systèmes, 13005 Marseille, France and INSERM, UMR 1106, 13005 Marseille, France and
| | - Fabrice Bartolomei
- Aix Marseille Université, Institut de Neurosciences des Systèmes, 13005 Marseille, France and INSERM, UMR 1106, 13005 Marseille, France and Assistance Publique-Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, 13005 Marseille, France
| | - Patrick Chauvel
- Aix Marseille Université, Institut de Neurosciences des Systèmes, 13005 Marseille, France and INSERM, UMR 1106, 13005 Marseille, France and Assistance Publique-Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, 13005 Marseille, France
| | - Christophe Bernard
- Aix Marseille Université, Institut de Neurosciences des Systèmes, 13005 Marseille, France and INSERM, UMR 1106, 13005 Marseille, France and
| | - Viktor K Jirsa
- Aix Marseille Université, Institut de Neurosciences des Systèmes, 13005 Marseille, France and INSERM, UMR 1106, 13005 Marseille, France and
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19
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Wu Y, Liu D, Song Z. Neuronal networks and energy bursts in epilepsy. Neuroscience 2014; 287:175-86. [PMID: 24993475 DOI: 10.1016/j.neuroscience.2014.06.046] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2013] [Revised: 06/16/2014] [Accepted: 06/17/2014] [Indexed: 11/16/2022]
Abstract
Epilepsy can be defined as the abnormal activities of neurons. The occurrence, propagation and termination of epileptic seizures rely on the networks of neuronal cells that are connected through both synaptic- and non-synaptic interactions. These complicated interactions contain the modified functions of normal neurons and glias as well as the mediation of excitatory and inhibitory mechanisms with feedback homeostasis. Numerous spread patterns are detected in disparate networks of ictal activities. The cortical-thalamic-cortical loop is present during a general spike wave seizure. The thalamic reticular nucleus (nRT) is the major inhibitory input traversing the region, and the dentate gyrus (DG) controls CA3 excitability. The imbalance between γ-aminobutyric acid (GABA)-ergic inhibition and glutamatergic excitation is the main disorder in epilepsy. Adjustable negative feedback that mediates both inhibitory and excitatory components affects neuronal networks through neurotransmission fluctuation, receptor and transmitter signaling, and through concomitant influences on ion concentrations and field effects. Within a limited dynamic range, neurons slowly adapt to input levels and have a high sensitivity to synaptic changes. The stability of the adapting network depends on the ratio of the adaptation rates of both the excitatory and inhibitory populations. Thus, therapeutic strategies with multiple effects on seizures are required for the treatment of epilepsy, and the therapeutic functions on networks are reviewed here. Based on the high-energy burst theory of epileptic activity, we propose a potential antiepileptic therapeutic strategy to transfer the high energy and extra electricity out of the foci.
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Affiliation(s)
- Y Wu
- The Neurology Department of Third Xiangya Hospital, Medical School of Central South University, Changsha, China
| | - D Liu
- The Neurology Department of Third Xiangya Hospital, Medical School of Central South University, Changsha, China
| | - Z Song
- The Neurology Department of Third Xiangya Hospital, Medical School of Central South University, Changsha, China.
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20
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Linne ML, Jalonen TO. Astrocyte-neuron interactions: from experimental research-based models to translational medicine. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2014; 123:191-217. [PMID: 24560146 DOI: 10.1016/b978-0-12-397897-4.00005-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
In this chapter, we review the principal astrocyte functions and the interactions between neurons and astrocytes. We then address how the experimentally observed functions have been verified in computational models and review recent experimental literature on astrocyte-neuron interactions. Benefits of computational neuroscience work are highlighted through selected studies with neurons and astrocytes by analyzing the existing models qualitatively and assessing the relevance of these models to experimental data. Common strategies to mathematical modeling and computer simulation in neuroscience are summarized for the nontechnical reader. The astrocyte-neuron interactions are then further illustrated by examples of some neurological and neurodegenerative diseases, where the miscommunication between glia and neurons is found to be increasingly important.
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Affiliation(s)
- Marja-Leena Linne
- Computational Neuroscience Group, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Tuula O Jalonen
- Department of Physiology and Neuroscience, St. George's University, School of Medicine, Grenada, West Indies
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21
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Bernard C, Naze S, Proix T, Jirsa VK. Modern concepts of seizure modeling. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2014; 114:121-53. [PMID: 25078501 DOI: 10.1016/b978-0-12-418693-4.00006-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Seizures are complex phenomena spanning multiple spatial and temporal scales, from ion dynamics to communication between brain regions, from milliseconds (spikes) to days (interseizure intervals). Because of the existence of such multiple scales, the experimental evaluation of the mechanisms underlying the initiation, propagation, and termination of epileptic seizures is a difficult problem. Theoretical models and numerical simulations provide new tools to investigate seizure mechanisms at multiple scales. In this chapter, we review different theoretical approaches and their contributions to our understanding of seizure mechanisms.
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Affiliation(s)
- Christophe Bernard
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France; Inserm UMR_S 1106, Aix Marseille Universite, Marseille, France.
| | - Sebastien Naze
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France; Inserm UMR_S 1106, Aix Marseille Universite, Marseille, France
| | - Timothée Proix
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France; Inserm UMR_S 1106, Aix Marseille Universite, Marseille, France
| | - Viktor K Jirsa
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France; Inserm UMR_S 1106, Aix Marseille Universite, Marseille, France
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