1
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Saggio ML, Jirsa V. Bifurcations and bursting in the Epileptor. PLoS Comput Biol 2024; 20:e1011903. [PMID: 38446814 PMCID: PMC10947678 DOI: 10.1371/journal.pcbi.1011903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/18/2024] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
The Epileptor is a phenomenological model for seizure activity that is used in a personalized large-scale brain modeling framework, the Virtual Epileptic Patient, with the aim of improving surgery outcomes for drug-resistant epileptic patients. Transitions between interictal and ictal states are modeled as bifurcations, enabling the definition of seizure classes in terms of onset/offset bifurcations. This establishes a taxonomy of seizures grounded in their essential underlying dynamics and the Epileptor replicates the activity of the most common class, as observed in patients with focal epilepsy, which is characterized by square-wave bursting properties. The Epileptor also encodes an additional mechanism to account for interictal spikes and spike and wave discharges. Here we use insights from a more generic model for square-wave bursting, based on the Unfolding Theory approach, to guide the bifurcation analysis of the Epileptor and gain a deeper understanding of the model and the role of its parameters. We show how the Epileptor's parameters can be modified to produce activities for other seizures classes of the taxonomy, as observed in patients, so that the large-scale brain models could be further personalized. Some of these classes have already been described in the literature in the Epileptor, others, predicted by the generic model, are new. Finally, we unveil how the interaction with the additional mechanism for spike and wave discharges alters the bifurcation structure of the main burster.
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Affiliation(s)
- Maria Luisa Saggio
- Institut de Neurosciences des Systemes INS UMR1106, AMU, INSERM, Marseille, France
| | - Viktor Jirsa
- Institut de Neurosciences des Systemes INS UMR1106, AMU, INSERM, Marseille, France
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2
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Saggio ML, Crisp D, Scott JM, Karoly P, Kuhlmann L, Nakatani M, Murai T, Dümpelmann M, Schulze-Bonhage A, Ikeda A, Cook M, Gliske SV, Lin J, Bernard C, Jirsa V, Stacey WC. A taxonomy of seizure dynamotypes. eLife 2020; 9:55632. [PMID: 32691734 PMCID: PMC7375810 DOI: 10.7554/elife.55632] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 06/12/2020] [Indexed: 01/02/2023] Open
Abstract
Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties. Epileptic seizures have been recognized for centuries. But it was only in the 1930s that it was realized that seizures are the result of out-of-control electrical activity in the brain. By placing electrodes on the scalp, doctors can identify when and where in the brain a seizure begins. But they cannot tell much about how the seizure behaves, that is, how it starts, stops or spreads to other areas. This makes it difficult to control and prevent seizures. It also helps explain why almost a third of patients with epilepsy continue to have seizures despite being on medication. Saggio, Crisp et al. have now approached this problem from a new angle using methods adapted from physics and engineering. In these fields, “dynamics research” has been used with great success to predict and control the behavior of complex systems like electrical power grids. Saggio, Crisp et al. reasoned that applying the same approach to the brain would reveal the dynamics of seizures and that such information could then be used to categorize seizures into groups with similar properties. This would in effect create for seizures what the periodic table is for the elements. Applying the dynamics research method to seizure data from more than a hundred patients from across the world revealed 16 types of seizure dynamics. These “dynamotypes” had distinct characteristics. Some were more common than others, and some tended to occur together. Individual patients showed different dynamotypes over time. By constructing a way to classify seizures based on the relationships between the dynamotypes, Saggio, Crisp et al. provide a new tool for clinicians and researchers studying epilepsy. Previous clinical tools have focused on the physical symptoms of a seizure (referred to as the phenotype) or its potential genetic causes (genotype). The current approach complements these tools by adding the dynamotype: how seizures start, spread and stop in the brain. This approach has the potential to lead to new branches of research and better understanding and treatment of seizures.
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Affiliation(s)
- Maria Luisa Saggio
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Dakota Crisp
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Jared M Scott
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Philippa Karoly
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Levin Kuhlmann
- Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Faculty of Information Technology, Monash University, Clayton, Australia
| | - Mitsuyoshi Nakatani
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Tomohiko Murai
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Mark Cook
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia.,Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - Stephen V Gliske
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Jack Lin
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Christophe Bernard
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - William C Stacey
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States.,Department of Neurology, University of Michigan, Ann Arbor, United States
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3
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Kalitzin S, Petkov G, Suffczynski P, Grigorovsky V, Bardakjian BL, Lopes da Silva F, Carlen PL. Epilepsy as a manifestation of a multistate network of oscillatory systems. Neurobiol Dis 2019; 130:104488. [DOI: 10.1016/j.nbd.2019.104488] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 06/03/2019] [Accepted: 06/04/2019] [Indexed: 12/18/2022] Open
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4
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Sandler RA, Geng K, Song D, Hampson RE, Witcher MR, Deadwyler SA, Berger TW, Marmarelis VZ. Designing Patient-Specific Optimal Neurostimulation Patterns for Seizure Suppression. Neural Comput 2018; 30:1180-1208. [PMID: 29566356 DOI: 10.1162/neco_a_01075] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Neurostimulation is a promising therapy for abating epileptic seizures. However, it is extremely difficult to identify optimal stimulation patterns experimentally. In this study, human recordings are used to develop a functional 24 neuron network statistical model of hippocampal connectivity and dynamics. Spontaneous seizure-like activity is induced in silico in this reconstructed neuronal network. The network is then used as a testbed to design and validate a wide range of neurostimulation patterns. Commonly used periodic trains were not able to permanently abate seizures at any frequency. A simulated annealing global optimization algorithm was then used to identify an optimal stimulation pattern, which successfully abated 92% of seizures. Finally, in a fully responsive, or closed-loop, neurostimulation paradigm, the optimal stimulation successfully prevented the network from entering the seizure state. We propose that the framework presented here for algorithmically identifying patient-specific neurostimulation patterns can greatly increase the efficacy of neurostimulation devices for seizures.
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Affiliation(s)
- Roman A Sandler
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Kunling Geng
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Mark R Witcher
- Department of Neurosurgery, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Sam A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
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5
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DelPozo-Banos M, Travieso CM, Alonso JB, John A. Evidence of a Task-Independent Neural Signature in the Spectral Shape of the Electroencephalogram. Int J Neural Syst 2017; 28:1750035. [PMID: 28835183 DOI: 10.1142/s0129065717500356] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Genetic and neurophysiological studies of electroencephalogram (EEG) have shown that an individual's brain activity during a given cognitive task is, to some extent, determined by their genes. In fact, the field of biometrics has successfully used this property to build systems capable of identifying users from their neural activity. These studies have always been carried out in isolated conditions, such as relaxing with eyes closed, identifying visual targets or solving mathematical operations. Here we show for the first time that the neural signature extracted from the spectral shape of the EEG is to a large extent independent of the recorded cognitive task and experimental condition. In addition, we propose to use this task-independent neural signature for more precise biometric identity verification. We present two systems: one based on real cepstrums and one based on linear predictive coefficients. We obtained verification accuracies above 89% on 4 of the 6 databases used. We anticipate this finding will create a new set of experimental possibilities within many brain research fields, such as the study of neuroplasticity, neurodegenerative diseases and brain machine interfaces, as well as the mentioned genetic, neurophysiological and biometric studies. Furthermore, the proposed biometric approach represents an important advance towards real world deployments of this new technology.
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Affiliation(s)
- Marcos DelPozo-Banos
- * Division of Digital Signal Processing, IDeTIC, University of Las Palmas de Gran Canaria, Las Palmas 35017, Spain.,† College of Medicine, Swansea University, Swansea, SA2 8PP, Wales, UK
| | - Carlos M Travieso
- * Division of Digital Signal Processing, IDeTIC, University of Las Palmas de Gran Canaria, Las Palmas 35017, Spain
| | - Jesus B Alonso
- * Division of Digital Signal Processing, IDeTIC, University of Las Palmas de Gran Canaria, Las Palmas 35017, Spain
| | - Ann John
- † College of Medicine, Swansea University, Swansea, SA2 8PP, Wales, UK
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6
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Bauer PR, Thijs RD, Lamberts RJ, Velis DN, Visser GH, Tolner EA, Sander JW, Lopes da Silva FH, Kalitzin SN. Dynamics of convulsive seizure termination and postictal generalized EEG suppression. Brain 2017; 140:655-668. [PMID: 28073789 DOI: 10.1093/brain/aww322] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 10/31/2016] [Indexed: 12/21/2022] Open
Abstract
It is not fully understood how seizures terminate and why some seizures are followed by a period of complete brain activity suppression, postictal generalized EEG suppression. This is clinically relevant as there is a potential association between postictal generalized EEG suppression, cardiorespiratory arrest and sudden death following a seizure. We combined human encephalographic seizure data with data of a computational model of seizures to elucidate the neuronal network dynamics underlying seizure termination and the postictal generalized EEG suppression state. A multi-unit computational neural mass model of epileptic seizure termination and postictal recovery was developed. The model provided three predictions that were validated in EEG recordings of 48 convulsive seizures from 48 subjects with refractory focal epilepsy (20 females, age range 15-61 years). The duration of ictal and postictal generalized EEG suppression periods in human EEG followed a gamma probability distribution indicative of a deterministic process (shape parameter 2.6 and 1.5, respectively) as predicted by the model. In the model and in humans, the time between two clonic bursts increased exponentially from the start of the clonic phase of the seizure. The terminal interclonic interval, calculated using the projected terminal value of the log-linear fit of the clonic frequency decrease was correlated with the presence and duration of postictal suppression. The projected terminal interclonic interval explained 41% of the variation in postictal generalized EEG suppression duration (P < 0.02). Conversely, postictal generalized EEG suppression duration explained 34% of the variation in the last interclonic interval duration. Our findings suggest that postictal generalized EEG suppression is a separate brain state and that seizure termination is a plastic and autonomous process, reflected in increased duration of interclonic intervals that determine the duration of postictal generalized EEG suppression.
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Affiliation(s)
- Prisca R Bauer
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Roland D Thijs
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.,Department of Neurology, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Robert J Lamberts
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Demetrios N Velis
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Gerhard H Visser
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Else A Tolner
- Department of Neurology, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Josemir W Sander
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.,Epilepsy Society, Chalfont St Peter SL9 0RJ, UK
| | - Fernando H Lopes da Silva
- Center of Neurosciences, Swammerdam Institute of Life Sciences, University of Amsterdam, P.O. Box 94215 1090 GE, The Netherlands.,Instituto Superior Técnico, University of Lisbon, 1049-001, Lisbon, Portugal
| | - Stiliyan N Kalitzin
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,Image Sciences Institute, University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
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7
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Yang Y, Solis-Escalante T, van der Helm FCT, Schouten AC. A Generalized Coherence Framework for Detecting and Characterizing Nonlinear Interactions in the Nervous System. IEEE Trans Biomed Eng 2016; 63:2629-2637. [DOI: 10.1109/tbme.2016.2585097] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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8
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Koppert M, Kalitzin S, Velis D, Lopes Da Silva F, Viergever MA. Preventive and Abortive Strategies for Stimulation Based Control of Epilepsy: A Computational Model Study. Int J Neural Syst 2016; 26:1650028. [DOI: 10.1142/s0129065716500283] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy is a condition in which periods of ongoing normal EEG activity alternate with periods of oscillatory behavior characteristic of epileptic seizures. The dynamics of the transitions between the two states are still unclear. Computational models provide a powerful tool to explore the underlying mechanisms of such transitions, with the purpose of eventually finding therapeutic interventions for this debilitating condition. In this study, the possibility to postpone seizures elicited by a decrease of inhibition is investigated by using external stimulation in a realistic bistable neuronal model consisting of two interconnected neuronal populations representing pyramidal cells and interneurons. In the simulations, seizures are induced by slowly decreasing the conductivity of GABA[Formula: see text] synaptic channels over time. Since the model is bistable, the system will change state from the initial steady state (SS) to the limit cycle (LS) state because of internal noise, when the inhibition falls below a certain threshold. Several state-independent stimulations paradigms are simulated. Their effectiveness is analyzed for various stimulation frequencies and intensities in combination with periodic and random stimulation sequences. The distributions of the time to first seizure in the presence of stimulation are compared with the situation without stimulation. In addition, stimulation protocols targeted to specific subsystems are applied with the objective of counteracting the baseline shift due to decreased inhibition in the system. Furthermore, an analytical model is used to investigate the effects of random noise. The relation between the strength of random noise stimulation, the control parameter of the system and the transitions between steady state and limit cycle are investigated. The study shows that it is possible to postpone epileptic activity by targeted stimulation in a realistic neuronal model featuring bistability and that it is possible to stop seizures by random noise in an analytical model.
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Affiliation(s)
- Marc Koppert
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Stiliyan Kalitzin
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
- Image Sciences Institute, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
| | - Demetrios Velis
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
- Department of Neurosurgery, Free University Medical Center Amsterdam, 1007 MB Amsterdam, The Netherlands
| | - Fernando Lopes Da Silva
- Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 SM Amsterdam, The Netherlands
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001, Lisboa, Portugal
| | - Max A. Viergever
- Image Sciences Institute, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
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9
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Milanowski P, Suffczynski P. Seizures Start without Common Signatures of Critical Transition. Int J Neural Syst 2016; 26:1650053. [PMID: 27633895 DOI: 10.1142/s0129065716500532] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Complex dynamical systems may exhibit sudden autonomous changes from one state to another. Such changes that occur rapidly in comparison to the regular dynamics have been termed critical transitions. Examples of such phenomena can be found in many complex systems: changes in climate and ocean circulation, changes in wildlife populations, financial crashes, as well as in medical conditions like asthma attacks and depression. It has been recognized that critical transitions, even if they arise in completely different contexts and situations, share several common attributes and also generic early-warning signals that indicate that a critical transition is approaching. In the present study, we review briefly the general characteristics that have been observed in systems prior to critical transitions and apply these general indicators to nearly 300 epileptic seizures collected from human subjects using invasive EEG. Only in about 8% of the patients was evidence of critical transitions found. In the remaining majority of cases no early warning signals that behaved consistently prior to seizures were observed. These results do not rule out the possibility of critical transitions to seizure but point to limited relevance of their early warning signals in the context of human epilepsy observed using intracranial EEG recordings.
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Affiliation(s)
- Piotr Milanowski
- 1 Department of Biomedical Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
| | - Piotr Suffczynski
- 1 Department of Biomedical Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
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10
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Giraldo-Suarez E, Martinez-Vargas JD, Castellanos-Dominguez G. Reconstruction of Neural Activity from EEG Data Using Dynamic Spatiotemporal Constraints. Int J Neural Syst 2016; 26:1650026. [PMID: 27354190 DOI: 10.1142/s012906571650026x] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We present a novel iterative regularized algorithm (IRA) for neural activity reconstruction that explicitly includes spatiotemporal constraints, performing a trade-off between space and time resolutions. For improving the spatial accuracy provided by electroencephalography (EEG) signals, we explore a basis set that describes the smooth, localized areas of potentially active brain regions. In turn, we enhance the time resolution by adding the Markovian assumption for brain activity estimation at each time period. Moreover, to deal with applications that have either distributed or localized neural activity, the spatiotemporal constraints are expressed through [Formula: see text] and [Formula: see text] norms, respectively. For the purpose of validation, we estimate the neural reconstruction performance in time and space separately. Experimental testing is carried out on artificial data, simulating stationary and non-stationary EEG signals. Also, validation is accomplished on two real-world databases, one holding Evoked Potentials and another with EEG data of focal epilepsy. Moreover, responses of functional magnetic resonance imaging for the former EEG data have been measured in advance, allowing to contrast our findings. Obtained results show that the [Formula: see text]-based IRA produces a spatial resolution that is comparable to the one achieved by some widely used sparse-based estimators of brain activity. At the same time, the [Formula: see text]-based IRA outperforms other similar smooth solutions, providing a spatial resolution that is lower than the sparse [Formula: see text]-based solution. As a result, the proposed IRA is a promising method for improving the accuracy of brain activity reconstruction.
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Affiliation(s)
- E. Giraldo-Suarez
- Department of Electrical Engineering, Universidad Tecnológica de Pereira, Colombia
| | - J. D. Martinez-Vargas
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
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11
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Miller P. Itinerancy between attractor states in neural systems. Curr Opin Neurobiol 2016; 40:14-22. [PMID: 27318972 DOI: 10.1016/j.conb.2016.05.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 05/20/2016] [Accepted: 05/27/2016] [Indexed: 11/25/2022]
Abstract
Converging evidence from neural, perceptual and simulated data suggests that discrete attractor states form within neural circuits through learning and development. External stimuli may bias neural activity to one attractor state or cause activity to transition between several discrete states. Evidence for such transitions, whose timing can vary across trials, is best accrued through analyses that avoid any trial-averaging of data. One such method, hidden Markov modeling, has been effective in this context, revealing state transitions in many neural circuits during many tasks. Concurrently, modeling efforts have revealed computational benefits of stimulus processing via transitions between attractor states. This review describes the current state of the field, with comments on how its perceived limitations have been addressed.
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Affiliation(s)
- Paul Miller
- Volen National Center for Complex Systems, Brandeis University, Waltham, MA 02454-9110, USA
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12
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Wang J, Niebur E, Hu J, Li X. Suppressing epileptic activity in a neural mass model using a closed-loop proportional-integral controller. Sci Rep 2016; 6:27344. [PMID: 27273563 PMCID: PMC4895166 DOI: 10.1038/srep27344] [Citation(s) in RCA: 18] [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: 10/15/2015] [Accepted: 05/18/2016] [Indexed: 11/09/2022] Open
Abstract
Closed-loop control is a promising deep brain stimulation (DBS) strategy that could be used to suppress high-amplitude epileptic activity. However, there are currently no analytical approaches to determine the stimulation parameters for effective and safe treatment protocols. Proportional-integral (PI) control is the most extensively used closed-loop control scheme in the field of control engineering because of its simple implementation and perfect performance. In this study, we took Jansen's neural mass model (NMM) as a test bed to develop a PI-type closed-loop controller for suppressing epileptic activity. A graphical stability analysis method was employed to determine the stabilizing region of the PI controller in the control parameter space, which provided a theoretical guideline for the choice of the PI control parameters. Furthermore, we established the relationship between the parameters of the PI controller and the parameters of the NMM in the form of a stabilizing region, which provided insights into the mechanisms that may suppress epileptic activity in the NMM. The simulation results demonstrated the validity and effectiveness of the proposed closed-loop PI control scheme.
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Affiliation(s)
- Junsong Wang
- School of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, China
| | - Ernst Niebur
- Zanvyl Krieger Mind/Brain Institute and Solomon Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jinyu Hu
- Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Xiaoli Li
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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13
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O'Sullivan-Greene E, Kuhlmann L, Nurse ES, Freestone DR, Grayden DB, Cook M, Burkitt A, Mareels I. Probing to Observe Neural Dynamics Investigated with Networked Kuramoto Oscillators. Int J Neural Syst 2016; 27:1650038. [PMID: 27596927 DOI: 10.1142/s0129065716500386] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The expansion of frontiers in neural engineering is dependent on the ability to track, detect and predict dynamics in neural tissue. Recent innovations to elucidate information from electrical recordings of brain dynamics, such as epileptic seizure prediction, have involved switching to an active probing paradigm using electrically evoked recordings rather than traditional passive measurements. This paper positions the advantage of probing in terms of information extraction, by using a coupled oscillator Kuramoto model to represent brain dynamics. While active probing performs better at observing underlying system synchrony in Kuramoto networks, especially in non-Gaussian measurement environments, the benefits diminish with increasing relative size of electrode spatial resolution compared to synchrony area. This suggests probing will be useful for improved characterization of synchrony for suitably dense electrode recordings.
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Affiliation(s)
- Elma O'Sullivan-Greene
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Levin Kuhlmann
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.,† Brain & Psychological Sciences Research Centre, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
| | - Ewan S Nurse
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.,‡ Department of Medicine, The University of Melbourne, Parkville, VIC 3010, Australia.,§ St. Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Dean R Freestone
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.,‡ Department of Medicine, The University of Melbourne, Parkville, VIC 3010, Australia.,§ St. Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - David B Grayden
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Mark Cook
- ‡ Department of Medicine, The University of Melbourne, Parkville, VIC 3010, Australia.,§ St. Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Anthony Burkitt
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Iven Mareels
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
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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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Abstract
We study the existence of chimera states in pulse-coupled networks of bursting Hindmarsh-Rose neurons with nonlocal, global, and local (nearest neighbor) couplings. Through a linear stability analysis, we discuss the behavior of the stability function in the incoherent (i.e., disorder), coherent, chimera, and multichimera states. Surprisingly, we find that chimera and multichimera states occur even using local nearest neighbor interaction in a network of identical bursting neurons alone. This is in contrast with the existence of chimera states in populations of nonlocally or globally coupled oscillators. A chemical synaptic coupling function is used which plays a key role in the emergence of chimera states in bursting neurons. The existence of chimera, multichimera, coherent, and disordered states is confirmed by means of the recently introduced statistical measures and mean phase velocity.
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Affiliation(s)
- Bidesh K Bera
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata-700108, India
| | - Dibakar Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata-700108, India
| | - M Lakshmanan
- Centre for Nonlinear Dynamics, School of Physics, Bharathidasan University, Tiruchirapalli-620024, India
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16
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Abstract
The difficulty to understand, diagnose, and treat neurological disorders stems from the great complexity of the central nervous system on different levels of physiological granularity. The individual components, their interactions, and dynamics involved in brain development and function can be represented as molecular, cellular, or functional networks, where diseases are perturbations of networks. These networks can become a useful research tool in investigating neurological disorders if they are properly tailored to reflect corresponding mechanisms. Here, we review approaches to construct networks specific for neurological disorders describing disease-related pathology on different scales: the molecular, cellular, and brain level. We also briefly discuss cross-scale network analysis as a necessary integrator of these scales.
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17
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Hsu WY. Assembling A Multi-Feature EEG Classifier for Left–Right Motor Imagery Data Using Wavelet-Based Fuzzy Approximate Entropy for Improved Accuracy. Int J Neural Syst 2015; 25:1550037. [DOI: 10.1142/s0129065715500379] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
An EEG classifier is proposed for application in the analysis of motor imagery (MI) EEG data from a brain–computer interface (BCI) competition in this study. Applying subject-action-related brainwave data acquired from the sensorimotor cortices, the system primarily consists of artifact and background removal, feature extraction, feature selection and classification. In addition to background noise, the electrooculographic (EOG) artifacts are also automatically removed to further improve the analysis of EEG signals. Several potential features, including amplitude modulation, spectral power and asymmetry ratio, adaptive autoregressive model, and wavelet fuzzy approximate entropy (wfApEn) that can measure and quantify the complexity or irregularity of EEG signals, are then extracted for subsequent classification. Finally, the significant sub-features are selected from feature combination by quantum-behaved particle swarm optimization and then classified by support vector machine (SVM). Compared with feature extraction without wfApEn on MI data from two data sets for nine subjects, the results indicate that the proposed system including wfApEn obtains better performance in average classification accuracy of 88.2% and average number of commands per minute of 12.1, which is promising in the BCI work applications.
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Affiliation(s)
- Wei-Yen Hsu
- Department of Information Management, National Chung Cheng University, No. 168, Sec. 1, University Rd., Min-Hsiung Township, Chiayi County 621, Taiwan
- Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University, No. 168, Sec. 1, University Rd. Min-Hsiung Township, Chiayi County 621, Taiwan
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18
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Donnarumma F, Prevete R, Chersi F, Pezzulo G. A Programmer–Interpreter Neural Network Architecture for Prefrontal Cognitive Control. Int J Neural Syst 2015; 25:1550017. [DOI: 10.1142/s0129065715500173] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input–output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter–programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer–interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchies.
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Affiliation(s)
- Francesco Donnarumma
- Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Via S. Martino della Battaglia 44-00185 Roma, Italy
| | - Roberto Prevete
- Università degli Studi di Napoli Federico II, Dipartimento di Ingegneria Elettrica e Tecnologie dell'Informazione (DIETI), Via Claudio, 21, 80125 Napoli, Italy
| | - Fabian Chersi
- University College London, Institute of Cognitive Neuroscience, 17 Queen Square, London, WC1N 3AR, England
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Via S. Martino della Battaglia 44-00185 Rome, Italy
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19
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Helling RM, Koppert MMJ, Visser GH, Kalitzin SN. Gap Junctions as Common Cause of High-Frequency Oscillations and Epileptic Seizures in a Computational Cascade of Neuronal Mass and Compartmental Modeling. Int J Neural Syst 2015; 25:1550021. [DOI: 10.1142/s0129065715500215] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
High frequency oscillations (HFO) appear to be a promising marker for delineating the seizure onset zone (SOZ) in patients with localization related epilepsy. It remains, however, a purely observational phenomenon and no common mechanism has been proposed to relate HFOs and seizure generation. In this work we show that a cascade of two computational models, one on detailed compartmental scale and a second one on neural mass scale can explain both the autonomous generation of HFOs and the presence of epileptic seizures as emergent properties. To this end we introduce axonal–axonal gap junctions on a microscopic level and explore their impact on the higher level neural mass model (NMM). We show that the addition of gap junctions can generate HFOs and simultaneously shift the operational point of the NMM from a steady state network into bistable behavior that can autonomously generate epileptic seizures. The epileptic properties of the system, or the probability to generate epileptic type of activity, increases gradually with the increase of the density of axonal–axonal gap junctions. We further demonstrate that ad hoc HFO detectors used in previous studies are applicable to our simulated data.
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Affiliation(s)
- Robert M. Helling
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW, Heemstede, The Netherlands
- Faculty of Technical Medicine, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands
| | - Marc M. J. Koppert
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW, Heemstede, The Netherlands
| | - Gerhard H. Visser
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW, Heemstede, The Netherlands
| | - Stiliyan N. Kalitzin
- Stichting Epilepsie Instellingen Nederland, Achterweg 5, 2103 SW, Heemstede, The Netherlands
- Image Sciences Institute – University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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20
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Kugiumtzis D, Kimiskidis VK. Direct Causal Networks for the Study of Transcranial Magnetic Stimulation Effects on Focal Epileptiform Discharges. Int J Neural Syst 2015; 25:1550006. [DOI: 10.1142/s0129065715500069] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Background: Transcranial magnetic stimulation (TMS) can have inhibitory effects on epileptiform discharges (EDs) of patients with focal seizures. However, the brain connectivity before, during and after EDs, with or without the administration of TMS, has not been extensively explored. Objective: To investigate the brain network of effective connectivity during ED with and without TMS in patients with focal seizures. Methods: For the effective connectivity a direct causality measure is applied termed partial mutual information from mixed embedding (PMIME). TMS-EEG data from two patients with focal seizures were analyzed. Each EEG record contained a number of EDs in the majority of which TMS was administered over the epileptic focus. As a control condition, sham stimulation over the epileptogenic zone or real TMS at a distance from the epileptic focus was also performed. The change in brain connectivity structure was investigated from the causal networks formed at each sliding window. Conclusion: The PMIME could detect distinct changes in the network structure before, within, and after ED. The administration of real TMS over the epileptic focus, in contrast to sham stimulation, terminated the ED prematurely in a node-specific manner and regained the network structure as if it would have terminated spontaneously.
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Affiliation(s)
- Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Vasilios K. Kimiskidis
- Laboratory of Clinical Neurophysiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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21
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Kimiskidis VK, Koutlis C, Tsimpiris A, Kälviäinen R, Ryvlin P, Kugiumtzis D. Transcranial Magnetic Stimulation Combined with EEG Reveals Covert States of Elevated Excitability in the Human Epileptic Brain. Int J Neural Syst 2015; 25:1550018. [PMID: 25986753 DOI: 10.1142/s0129065715500185] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
BACKGROUND Transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) can be used to explore the dynamical state of neuronal networks. In patients with epilepsy, TMS can induce epileptiform discharges (EDs) with a stochastic occurrence despite constant stimulation parameters. This observation raises the possibility that the pre-stimulation period contains multiple covert states of brain excitability some of which are associated with the generation of EDs. OBJECTIVE To investigate whether the interictal period contains "high excitability" states that upon brain stimulation produce EDs and can be differentiated from "low excitability" states producing normal appearing TMS-EEG responses. METHODS In a cohort of 25 patients with Genetic Generalized Epilepsies (GGE) we identified two subjects characterized by the intermittent development of TMS-induced EDs. The high-excitability in the pre-stimulation period was assessed using multiple measures of univariate time series analysis. Measures providing optimal discrimination were identified by feature selection techniques. The "high excitability" states emerged in multiple loci (indicating diffuse cortical hyperexcitability) and were clearly differentiated on the basis of 14 measures from "low excitability" states (accuracy = 0.7). CONCLUSION In GGE, the interictal period contains multiple, quasi-stable covert states of excitability a class of which is associated with the generation of TMS-induced EDs. The relevance of these findings to theoretical models of ictogenesis is discussed.
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Affiliation(s)
- Vasilios K Kimiskidis
- Laboratory of Clinical Neurophysiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Christos Koutlis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Alkiviadis Tsimpiris
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Reetta Kälviäinen
- Kuopio Epilepsy Center, Department of Neurology, Kuopio University Hospital, Kuopio, Finland.,Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
| | - Philippe Ryvlin
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon, Lyon, France.,Department of Clinical Neurosciences, CHUV, Lausanne, Switzerland
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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22
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Petkov G, Goodfellow M, Richardson MP, Terry JR. A critical role for network structure in seizure onset: a computational modeling approach. Front Neurol 2014; 5:261. [PMID: 25538679 PMCID: PMC4259126 DOI: 10.3389/fneur.2014.00261] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Accepted: 11/23/2014] [Indexed: 12/30/2022] Open
Abstract
Recent clinical work has implicated network structure as critically important in the initiation of seizures in people with idiopathic generalized epilepsies. In line with this idea, functional networks derived from the electroencephalogram (EEG) at rest have been shown to be significantly different in people with generalized epilepsy compared to controls. In particular, the mean node degree of networks from the epilepsy cohort was found to be statistically significantly higher than those of controls. However, the mechanisms by which these network differences can support recurrent transitions into seizures remain unclear. In this study, we use a computational model of the transition into seizure dynamics to explore the dynamic consequences of these differences in functional networks. We demonstrate that networks with higher mean node degree are more prone to generating seizure dynamics in the model and therefore suggest a mechanism by which increased mean node degree of brain networks can cause heightened ictogenicity.
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Affiliation(s)
- George Petkov
- College of Engineering, Mathematics and Physical Sciences, University of Exeter , Exeter , UK
| | - Marc Goodfellow
- College of Engineering, Mathematics and Physical Sciences, University of Exeter , Exeter , UK
| | | | - John R Terry
- College of Engineering, Mathematics and Physical Sciences, University of Exeter , Exeter , UK
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