1
|
Fietkiewicz C, Loparo KA. Analysis and Enhancements of a Prolific Macroscopic Model of Epilepsy. SCIENTIFICA 2016; 2016:3628247. [PMID: 27144054 PMCID: PMC4838812 DOI: 10.1155/2016/3628247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 03/16/2016] [Indexed: 06/05/2023]
Abstract
Macroscopic models of epilepsy can deliver surprisingly realistic EEG simulations. In the present study, a prolific series of models is evaluated with regard to theoretical and computational concerns, and enhancements are developed. Specifically, we analyze three aspects of the models: (1) Using dynamical systems analysis, we demonstrate and explain the presence of direct current potentials in the simulated EEG that were previously undocumented. (2) We explain how the system was not ideally formulated for numerical integration of stochastic differential equations. A reformulated system is developed to support proper methodology. (3) We explain an unreported contradiction in the published model specification regarding the use of a mathematical reduction method. We then use the method to reduce the number of equations and further improve the computational efficiency. The intent of our critique is to enhance the evolution of macroscopic modeling of epilepsy and assist others who wish to explore this exciting class of models further.
Collapse
Affiliation(s)
- Christopher Fietkiewicz
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Kenneth A. Loparo
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA
| |
Collapse
|
2
|
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]
|
3
|
Early detection of epileptic seizures based on parameter identification of neural mass model. Comput Biol Med 2013; 43:1773-82. [PMID: 24209923 DOI: 10.1016/j.compbiomed.2013.08.022] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2012] [Revised: 08/21/2013] [Accepted: 08/23/2013] [Indexed: 11/20/2022]
Abstract
Physiologically based models are attractive for seizure detection, as their parameters can be explicitly related to neurological mechanisms. We propose an early seizure detection algorithm based on parameter identification of a neural mass model. The occurrence of a seizure is detected by analysing the time shift of key model parameters. The algorithm was evaluated against the manual scoring of a human expert on intracranial EEG samples from 16 patients suffering from different types of epilepsy. Results suggest that the algorithm is best suited for patients suffering from temporal lobe epilepsy (sensitivity was 95.0% ± 10.0% and false positive rate was 0.20 ± 0.22 per hour).
Collapse
|
4
|
Shayegh F, Sadri S, Amirfattahi R, Ansari-Asl K. Proposing a two-level stochastic model for epileptic seizure genesis. J Comput Neurosci 2013; 36:39-53. [PMID: 23733322 DOI: 10.1007/s10827-013-0457-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2012] [Revised: 04/26/2013] [Accepted: 04/29/2013] [Indexed: 11/25/2022]
Abstract
By assuming the brain as a multi-stable system, different scenarios have been introduced for transition from normal to epileptic state. But, the path through which this transition occurs is under debate. In this paper a stochastic model for seizure genesis is presented that is consistent with all scenarios: a two-level spontaneous seizure generation model is proposed in which, in its first level the behavior of physiological parameters is modeled with a stochastic process. The focus is on some physiological parameters that are essential in simulating different activities of ElectroEncephaloGram (EEG), i.e., excitatory and inhibitory synaptic gains of neuronal populations. There are many depth-EEG models in which excitatory and inhibitory synaptic gains are the adjustable parameters. Using one of these models at the second level, our proposed seizure generator is complete. The suggested stochastic model of first level is a hidden Markov process whose transition matrices are obtained through analyzing the real parameter sequences of a seizure onset area. These real parameter sequences are estimated from real depth-EEG signals via applying a parameter identification algorithm. In this paper both short-term and long-term validations of the proposed model are done. The long-term synthetic depth-EEG signals simulated by this model can be taken as a suitable tool for comparing different seizure prediction algorithms.
Collapse
Affiliation(s)
- F Shayegh
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111, Isfahan, Iran,
| | | | | | | |
Collapse
|
5
|
Shayegh F, Bellanger JJ, Sadri S, Amirfattahi R, Ansari-Asl K, Senhadji L. Analysis of the behavior of a seizure neural mass model using describing functions. JOURNAL OF MEDICAL SIGNALS & SENSORS 2013; 3:2-14. [PMID: 24083132 PMCID: PMC3785066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2012] [Accepted: 01/05/2013] [Indexed: 11/10/2022]
Abstract
Neural mass models are computational nonlinear models that simulate the activity of a population of neurons as an average neuron, in such a way that different inhibitory post-synaptic potential and excitatory post-synaptic potential signals could be reproduced. These models have been developed either to simulate the recognized neural mechanisms or to predict some physiological facts that are not easy to realize naturally. The role of the excitatory and inhibitory activity variation in seizure genesis has been proved, but it is not evident how these activities influence appearance of seizure like signals. In this paper a population model is considered in which the physiological inter-relation of the pyramidal and inter-neurons of the hippocampus has been appropriately modeled. The average neurons of this model have been assumed to act as a linear filter followed by a nonlinear function. By changing the gain of excitatory and inhibitory activities that are modeled by the gain of the filters, seizure-like signals could be generated. In this paper through the analysis of this nonlinear model by means of the describing function concepts, it is theoretically shown that not only the gains of the excitatory and inhibitory activities, but also the time constants may play an efficient role in seizure genesis.
Collapse
Affiliation(s)
- Farzaneh Shayegh
- Department of Electrical and Computer Engineering, Digital Signal Processing Research Lab, Isfahan University of Technology, Isfahan, Iran,Address for correspondence: Dr. Farzaneh Shayegh, Department of Electrical and Computer Engineering, Digital Signal Processing Research Lab, Isfahan University of Technology, Isfahan, 84156-83111, Iran. E-mail:
| | - Jean-Jacques Bellanger
- Inserm, UMR 1099, Rennes, F 35000, France,Université de Rennes 1, LTSI, Rennes, F 35000, France
| | - Saied Sadri
- Department of Electrical and Computer Engineering, Digital Signal Processing Research Lab, Isfahan University of Technology, Isfahan, Iran
| | - Rasoul Amirfattahi
- Department of Electrical and Computer Engineering, Digital Signal Processing Research Lab, Isfahan University of Technology, Isfahan, Iran
| | - Karim Ansari-Asl
- Department of Electrical and Computer Engineering, Digital Signal Processing Research Lab,Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Lotfi Senhadji
- Inserm, UMR 1099, Rennes, F 35000, France,Université de Rennes 1, LTSI, Rennes, F 35000, France
| |
Collapse
|
6
|
Goodfellow M, Schindler K, Baier G. Self-organised transients in a neural mass model of epileptogenic tissue dynamics. Neuroimage 2012; 59:2644-60. [DOI: 10.1016/j.neuroimage.2011.08.060] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2011] [Revised: 07/12/2011] [Accepted: 08/19/2011] [Indexed: 01/18/2023] Open
|
7
|
Avoli M, de Curtis M. GABAergic synchronization in the limbic system and its role in the generation of epileptiform activity. Prog Neurobiol 2011; 95:104-32. [PMID: 21802488 PMCID: PMC4878907 DOI: 10.1016/j.pneurobio.2011.07.003] [Citation(s) in RCA: 193] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2011] [Revised: 07/14/2011] [Accepted: 07/15/2011] [Indexed: 11/30/2022]
Abstract
GABA is the main inhibitory neurotransmitter in the adult forebrain, where it activates ionotropic type A and metabotropic type B receptors. Early studies have shown that GABA(A) receptor-mediated inhibition controls neuronal excitability and thus the occurrence of seizures. However, more complex, and at times unexpected, mechanisms of GABAergic signaling have been identified during epileptiform discharges over the last few years. Here, we will review experimental data that point at the paradoxical role played by GABA(A) receptor-mediated mechanisms in synchronizing neuronal networks, and in particular those of limbic structures such as the hippocampus, the entorhinal and perirhinal cortices, or the amygdala. After having summarized the fundamental characteristics of GABA(A) receptor-mediated mechanisms, we will analyze their role in the generation of network oscillations and their contribution to epileptiform synchronization. Whether and how GABA(A) receptors influence the interaction between limbic networks leading to ictogenesis will be also reviewed. Finally, we will consider the role of altered inhibition in the human epileptic brain along with the ability of GABA(A) receptor-mediated conductances to generate synchronous depolarizing events that may lead to ictogenesis in human epileptic disorders as well.
Collapse
Affiliation(s)
- Massimo Avoli
- Montreal Neurological Institute and Departments of Neurology & Neurosurgery, and of Physiology, McGill University, Montreal H3A 2B4 Quebec, Canada.
| | | |
Collapse
|
8
|
Shayegh F, AmirFattahi R, Sadri S, Ansari-Asl K. A brief survey of computational models of normal and epileptic EEG signals: A guideline to model-based seizure prediction. JOURNAL OF MEDICAL SIGNALS & SENSORS 2011. [DOI: 10.4103/2228-7477.83521] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
|
9
|
Shayegh F, Fattahi RA, Sadri S, Ansari-Asl K. A Brief Survey of Computational Models of Normal and Epileptic EEG Signals: A Guideline to Model-based Seizure Prediction. JOURNAL OF MEDICAL SIGNALS & SENSORS 2011; 1:62-72. [PMID: 22606660 PMCID: PMC3317768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In recent decades, seizure prediction has caused a lot of research in both signal processing and the neuroscience field. The researches have tried to enhance the conventional seizure prediction algorithms such that the rate of the false alarms be appropriately small, so that seizures can be predicted according to clinical standards. To date, none of the proposed algorithms have been sufficiently adequate. In this article we show that in considering the mechanism of the generation of seizures, the prediction results may be improved. For this purpose, an algorithm based on the identification of the parameters of a physiological model of seizures is introduced. Some models of electroencephalographic (EEG) signals that can also be potentially considered as models of seizure and some developed seizure models are reviewed. As an example the model of depth-EEG signals, proposed by Wendling, is studied and is shown to be a suitable model.
Collapse
Affiliation(s)
- Farzaneh Shayegh
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences,Departments of Electrical and Computer Engineering, Digital Signal Processing Laboratory, Isfahan University of Technology, Isfahan, Iran,Address for correspondence: Mr. Farzaneh Shayegh, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran E-mail:
| | - Rasoul Amir Fattahi
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences,Departments of Electrical and Computer Engineering, Digital Signal Processing Laboratory, Isfahan University of Technology, Isfahan, Iran
| | - Saeid Sadri
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences,Departments of Electrical and Computer Engineering, Digital Signal Processing Laboratory, Isfahan University of Technology, Isfahan, Iran
| | - Karim Ansari-Asl
- Department of Electrical, Engineering Faculty, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| |
Collapse
|
10
|
Intermittent spike-wave dynamics in a heterogeneous, spatially extended neural mass model. Neuroimage 2010; 55:920-32. [PMID: 21195779 DOI: 10.1016/j.neuroimage.2010.12.074] [Citation(s) in RCA: 88] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2010] [Revised: 12/15/2010] [Accepted: 12/24/2010] [Indexed: 12/24/2022] Open
Abstract
Generalised epileptic seizures are frequently accompanied by sudden, reversible transitions from low amplitude, irregular background activity to high amplitude, regular spike-wave discharges (SWD) in the EEG. The underlying mechanisms responsible for SWD generation and for the apparently spontaneous transitions to SWD and back again are still not fully understood. Specifically, the role of spatial cortico-cortical interactions in ictogenesis is not well studied. We present a macroscopic, neural mass model of a cortical column which includes two distinct time scales of inhibition. This model can produce both an oscillatory background and a pathological SWD rhythm. We demonstrate that coupling two of these cortical columns can lead to a bistability between out-of-phase, low amplitude background dynamics and in-phase, high amplitude SWD activity. Stimuli can cause state-dependent transitions from background into SWD. In an extended local area of cortex, spatial heterogeneities in a model parameter can lead to spontaneous reversible transitions from a desynchronised background to synchronous SWD due to intermittency. The deterministic model is therefore capable of producing absence seizure-like events without any time dependent adjustment of model parameters. The emergence of such mechanisms due to spatial coupling demonstrates the importance of spatial interactions in modelling ictal dynamics, and in the study of ictogenesis.
Collapse
|
11
|
Gnatkovsky V, Librizzi L, Trombin F, de Curtis M. Fast activity at seizure onset is mediated by inhibitory circuits in the entorhinal cortex in vitro. Ann Neurol 2008; 64:674-86. [DOI: 10.1002/ana.21519] [Citation(s) in RCA: 160] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
12
|
Wendling F. Computational models of epileptic activity: a bridge between observation and pathophysiological interpretation. Expert Rev Neurother 2008; 8:889-96. [PMID: 18505354 DOI: 10.1586/14737175.8.6.889] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Epilepsy is a neurological disorder characterized by the recurrence of seizures. It affects 50 million people worldwide. Although a considerable number of new antiepileptic drugs with reduced side effects and toxicity have been introduced since the 1950s, 30% of patients remain pharmacoresistant. Although epilepsy research is making progress, advances in understanding drug resistance have been hampered by the complexity of the underlying neuronal systems responsible for epileptic activity. In such systems where short- or long-term plasticity plays a role, pathophysiological alterations may take place at subcellular (i.e., membrane ion channels and neurotransmitter receptors), cellular (neurons), tissular (networks of neurons) and regional (networks of networks of neurons) scales. In such a context, the demand for integrative approaches is high and neurocomputational models become recognized tools for tackling the complexity of epileptic phenomena. The purpose of this report is to provide an overview on computational modeling as a way of structuring and interpreting multimodal data recorded from the epileptic brain. Some examples are briefly described, which illustrate how computational models closely related with either experimental or clinical data can markedly advance our understanding of essential issues in epilepsy such as the transition from background to seizure activity. A commentary is also made on the potential use of such models in the study of therapeutic strategies such as rational drug design or electrical stimulations.
Collapse
|