Key Points
-
Computer modelling of epilepsy is a branch of systems biology, a science that aims to combine the discoveries made by reductionist approaches into systems in order to understand how primary pathologies and secondary reactions interact to produce disease. Epilepsy, a dynamical disease of the brain, is well suited to study from the perspective of dynamical systems.
-
Epilepsy is a complex set of syndromes with the commonality of recurrent seizures. Not only do the many individual epilepsy syndromes have different causes, but most epilepsies develop owing to the interaction of many causes at molecular, cellular, network and developmental levels, defying efforts to define simple cause-and-effect relations and suggesting the need for computer modelling.
-
Knowledge discovery and data mining provides the substrate and support for dynamical modelling and allows the findings to be applied back to the research and clinical settings. The various dynamical modelling techniques that are used include stochastic models, low-dimensional (lumped) deterministic models and detailed neuronal network models.
-
Computer models are applied across the range of epilepsy phenomenology, from the molecular to the clinical. At the patient level, Markov models have been used to assess patterns of remission and relapse in pediatric epilepsy. At the molecular level, deterministic models can predict alterations in cellular activity with ion-channel mutations.
-
Many seizure models simulate activity at the network level. Some of these are lumped models, which use mean-field approximations to reduce the activity of many neurons to simple oscillators that are then coupled to produce complex activity patterns. Other models incorporate the details of neural activity and synaptic interactions, in order to reach down to the molecular level at which drug effects take place.
-
Uncommonly among areas of neuroscience research, computer modelling is immediately accessible through downloads of established models. An intrinsically collaborative activity, the future of the endeavour lies in the cooperative efforts of clinicians, experimentalists and modellers.
Abstract
Epilepsy is a complex set of disorders that can involve many areas of the cortex, as well as underlying deep-brain systems. The myriad manifestations of seizures, which can be as varied as déjà vu and olfactory hallucination, can therefore give researchers insights into regional functions and relations. Epilepsy is also complex genetically and pathophysiologically: it involves microscopic (on the scale of ion channels and synaptic proteins), macroscopic (on the scale of brain trauma and rewiring) and intermediate changes in a complex interplay of causality. It has long been recognized that computer modelling will be required to disentangle causality, to better understand seizure spread and to understand and eventually predict treatment efficacy. Over the past few years, substantial progress has been made in modelling epilepsy at levels ranging from the molecular to the socioeconomic. We review these efforts and connect them to the medical goals of understanding and treating the disorder.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
References
Haut, S. R., Bigal, M. E. & Lipton, R. B. Chronic disorders with episodic manifestations: focus on epilepsy and migraine. Lancet Neurol. 5, 148–157 (2006).
Sejnowski, T. J., Koch, C. & Churchland, P. S. Computational neuroscience. Science 241, 1299–1306 (1988).
Meeren, H. K., Pijn, J. P., Van Luijtelaar, E. L., Coenen, A. M. L. & Lopes da Silva, F. H. Cortical focus drives widespread corticothalamic networks during spontaneous absence seizures in rats. J. Neurosci. 22, 1480–1495 (2002). This study showed that seizures that seem to arise instantaneously actually have a distinct spatial course of spread.
Bouwman, B. M., Suffczynski, P., Lopes da Silva, F. H., Maris, E. & van Rijn, C. M. Gabaergic mechanisms in absence epilepsy: a computational model of absence epilepsy simulating spike and wave discharges after vigabatrin in wag/rij rats. Eur. J. Neurosci. 25, 2783–2790 (2007).
Destexhe, A. Spike-and-wave oscillations. Scholarpedia [online] (2007).
Luhmann, H. J., Mittmann, T., Luijtelaar, G. & Heinemann, U. Impairment of intracortical GABAergic inhibition in a rat model of absence epilepsy. Epilepsy Res. 22, 43–51 (1995).
Chang, B. S. & Lowenstein, D. H. Epilepsy. N. Engl. J. Med. 349, 1257–1266 (2003). This paper provides an excellent brief introduction to the many different types of epilepsy and to current clinical and experimental issues.
Scharfman, H. E. & Schwarcz, R. in Epilepsy: A Comprehensive Textbook 2nd edn (eds Engel, J., Pedley, T. A., Aicardi, J., Dichter, M. A. & Moshe, S.) 289–306 (Lippincott, New York, 2007).
Bear, D. M. & Fedio, P. Quantitative analysis of interictal behavior in temporal lobe epilepsy. Arch. Neurol. 34, 454–467 (1977).
Feil, B., Fauser, S., Wuwer, Y., Glocker, F. X. & Schulze-Bonhage, A. Changes in intracortical excitability after successful epilepsy surgery. Epilepsy Res. 79, 55–62 (2008).
Kalynchuk, L. E. Long-term amygdala kindling in rats as a model for the study of interictal emotionality in temporal lobe epilepsy. Neurosci. Biobehav. Rev. 24, 691–704 (2000).
Lytton, W. W., Orman, R. & Stewart, M. Computer simulation of epilepsy: implications for seizure spread and behavioral dysfunction. Epilepsy Behav. 7, 336–344 (2005).
Pedley, T. & Scharfman, H. E. in Neurobiology of Disease (ed. Gilman, S.) 347–367 (Academic, New York, 2006).
Schramm, J., Aliashkevich, A. F. & Grunwald, T. Multiple subpial transections: outcome and complications in 20 patients who did not undergo resection. J. Neurosurg. 97, 39–47 (2002).
Soltesz, I. & Staley, K. Computational Neuroscience in Epilepsy (Academic, San Diego, 2008). This book is an excellent compendium of recent results.
Fisher, R. S. et al. Epileptic seizures and epilepsy: definitions proposed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE). Epilepsia 46, 470–472 (2005).
Baraban, S. C. et al. A large-scale mutagenesis screen to identify seizure-resistant zebrafish. Epilepsia 48, 1151–1157 (2007).
Song, J. & Tanouye, M. A. From bench to drug: human seizure modeling using Drosophila. Prog. Neurobiol. 84, 182–191 (2008).
Engel, J. ILAE classification of epilepsy syndromes. Epilepsy Res. 70, S5–S10 (2006).
Engel, J. Report of the ILAE classification core group. Epilepsia 47, 1558–1568 (2006).
Engel, J. et al. A proposed diagnostic scheme for people with epileptic seizures and with epilepsy: report of the ILAE task force on classification and terminology. Epilepsia 42, 796–803 (2001).
Fisher, R. S. et al. Of cabbages and kings: some considerations on classifications, diagnostic schemes, semiology, and concepts. Epilepsia 44, 1–13 (2003).
ILAE. Proposal for revised classification of epilepsies and epileptic syndromes. Commission on classification and terminology of the International League Against Epilepsy. Epilepsia 30, 389–399 (1989).
Nicholl, J. S. Cabbages and kings in the classification of seizures and the epilepsies. Epilepsia 44, 988 (2003).
Wieser, H. G. ILAE commission report. Epilepsia 45, 695–714 (2004).
Crampin, E. J. et al. Computational physiology and the physiome project. Exp. Physiol. 89, 1–26 (2004).
Yu, A. C. Methods in biomedical ontology. J. Biomed. Inform. 39, 252–266 (2006).
Glasscock, E., Qian, J., Yoo, J. W. & Noebels, J. L. Masking epilepsy by combining two epilepsy genes. Nature Neurosci. 10, 1554–1558 (2007). In a remarkable case of genetic nonlinearity, this study showed how combining two seizure genes reduced seizure propensity.
Berg, A. T. & Shinnar, S. Do seizures beget seizures? An assessment of the clinical evidence in humans. J. Clin. Neurophysiol. 14, 102–110 (1997).
Berkovic, S. F., Reutens, D. C., Andermann, E. & Andermann, F. in Epileptic Seizures and Syndromes (ed. Wolf, P.) 25–37 (Libbey Eurotext, Paris, 1994).
Mulley, J. C., Scheffer, I. E., Petrou, S. & Berkovic, S. F. Channelopathies as a genetic cause of epilepsy. Curr. Opin. Neurol. 16, 171–176 (2003).
Stafstrom, C. E. Epilepsy: a review of selected clinical syndromes and advances in basic science. J. Cereb. Blood Flow Metab. 26, 983–1004 (2006).
Franks, K. M., Bartol, T. M. & Sejnowski, T. J. A Monte Carlo model reveals independent signaling at central glutamatergic synapses. Biophys. J. 83, 2333–2348 (2002).
Nunez, P. L. Neocortical dynamics and human EEG rhythms (Oxford Univ. Press, New York, 1995).
Lytton, W. W. & Omurtag, A. Tonic-clonic transitions in computer simulation. J. Clin. Neurophysiol. 24, 175–181 (2007).
Frigg, R. & Hartmann, S. Models in science. Stanford Encyclopedia of Philosophy [online], (2008).
Dubitzky, W. Understanding the computational methodologies of systems biology. Brief. Bioinform. 7, 315–317 (2006).
Goel, G., Chou, I. C. & Voit, E. O. Biological systems modeling and analysis: a biomolecular technique of the twenty-first century. J. Biomol. Tech. 17, 252–269 (2006).
Noble, D. The rise of computational biology. Nature Rev. Mol. Cell Biol. 3, 459–463 (2002).
Noble, D. Modeling the heart–from genes to cells to the whole organ. Science 295, 1678–1682 (2002).
Kitano, H. Computational systems biology. Nature 420, 206–210 (2002).
Bornholdt, S. Systems biology. less is more in modeling large genetic networks. Science 310, 449–451 (2005).
Bosl, W. J. Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery. Bmc Syst. Biol. 1, 13 (2007).
King, R. D., Garrett, S. M. & Coghill, G. M. On the use of qualitative reasoning to simulate and identify metabolic pathways. Bioinformatics 21, 2017–2026 (2005).
Reggia, J. A. in Proc. 2nd Ann. Symp. Comp. Application Med. Care 254–260 (IEEE, 1978).
Chute, C. G. Clinical classification and terminology: some history and current observations. J. Am. Med. Inform. Assoc. 7, 298–303 (2000).
Chute, C. G. in Medical Informatics. Knowledge Management and Data Mining in Biomedicine Vol. 8 (eds Chen, H., Fuller, S. S., Friedman, C. & Hersch, W.) 163–182 (Springer, 2005).
Bertone, P. & Gerstein, M. Integrative data mining: the new direction in bioinformatics. IEEE Eng. Med. Biol. Mag. 20, 33–40 (2001).
Cannon, R. C., Howell, F. W., Goddard, N. H. & De Schutter, E. Non-curated distributed databases for experimental data and models in neuroscience. Network 13, 415–428 (2002).
Strogatz, S. H. Exploring complex networks. Nature 410, 268–276 (2001).
Wolfram, S. Computer software in science and mathematics. Sci. Am. 251, 188–204 (1984).
Prinz, A. A., Bucher, D. & Marder, E. Similar network activity from disparate circuit parameters. Nature Neurosci. 7, 1345–1352 (2004).
Morgan, R. J., Santhakumar, V. & Soltesz, I. Modeling the dentate gyrus. Prog. Brain Res. 163, 639–658 (2007).
Lytton, W. W. Neural query system: data-mining from within the neuron simulator. Neuroinformatics 4, 163–176 (2006).
Lytton, W. W. & Stewart, M. in Neuroinformatics (ed. Crasto, C.) 155–166 (Humana, New York, 2007).
Pon, L. S., Sun, M., Scheuer, M. L., Li, C. C. & Sclabassi, R. J. in 4th Int. Symp. Uncert. Model. Anal. 262–267 (IEEE, 2003).
Ullah, M. & Wolkenhauer, O. Family tree of Markov models in systems biology. IET Syst. Biol. 1, 247–254 (2007).
Ermentrout, B. Neural networks as spatio-temporal pattern-forming systems. Rep. Prog. Phys. 61, 353–430 (1998).
Holmes, W. & Rall, W. Estimating the electrotonic structure of neurons with compartmental models. J. Neurophysiol. 68, 1438–1452 (1992).
Beggs, J. M. & Plenz, D. Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167–11177 (2003).
Delorme, A. & Thorpe, S. J. Spikenet: an event-driven simulation package for modelling large networks of spiking neurons. Network 14, 613–627 (2003).
Lytton, W. W. & Stewart, M. Rule-based firing for network simulations. Neurocomputing 69, 1160–1164 (2006).
Mattia, M. & Del Giudice, P. Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses. Neural Comput. 12, 2305–2329 (2000).
Rudolph, M. & Destexhe, A. Analytical integrate-and-fire neuron models with conductance-based dynamics for event-driven simulation strategies. Neural Comput. 18, 2146–2210 (2006).
Watts, L. in Advances in neural information processing systems vol. 6 (eds Cowan, J. D., Tesauro, G. & Alspector, J.) 927–934 (Morgan Kaufmann, 1994).
Milton, J. G., Gotman, J., Remillard, G. M. & Andermann, F. Timing of seizure recurrence in adult epileptic patients: a statistical analysis. Epilepsia 28, 471–478 (1987).
Haut, S. R. Seizure clustering. Epilepsy Behav. 8, 50–55 (2006).
Haut, S. R., Lipton, R. B., LeValley, A. J., Hall, C. B. & Shinnar, S. Identifying seizure clusters in patients with epilepsy. Neurology 65, 1313–1315 (2005).
Iasemidis, L. D., Olson, L. D., Savit, R. S. & Sackellares, J. C. Time dependencies in the occurrences of epileptic seizures. Epilepsy Res. 17, 81–94 (1994).
Albert, P. S. A two-state Markov mixture model for a time series of epileptic seizure counts. Biometrics 47, 1371–1381 (1991).
Hopkins, A., Davies, P. & Dobson, C. Mathematical models of patterns of seizures. Their use in the evaluation of drugs. Arch. Neurol. 42, 463–467 (1985).
Le, N. D., Leroux, B. G. & Puterman, M. L. Exact likelihood evaluation in a Markov mixture model for time series of seizure counts. Biometrics 48, 317–323 (1992).
Sunderam, S., Osorio, I., Frei, A. & Watkins, J. F. Stochastic modeling and prediction of experimental seizures in sprague-dawley rats. J. Clin. Neurophysiol. 18, 275–282 (2001). This study applied a Markov model to the underlying states to be predicted by a seizure-prediction algorithm.
Wong, S., Gardner, A. B., Krieger, A. M. & Litt, B. A stochastic framework for evaluating seizure prediction algorithms using hidden Markov models. J. Neurophysiol. 97, 2525–2532 (2007).
Haut, S. R., Hall, C. B., Le, V. & Lipton, R. B. Can patients with epilepsy predict their seizures? Neurology 68, 262–266 (2007).
Haut, S. R., Shinnar, S. & Moshe, S. L. Seizure clustering: risks and outcomes. Epilepsia 46, 146–149 (2005).
Berg, A. T. et al. Modeling remission and relapse in pediatric epilepsy: application of a Markov process. Epilepsy Res. 60, 31–40 (2004).
Blumenfeld, H. et al. Early treatment suppresses the development of spike-wave epilepsy in a rat model. Epilepsia 49, 400–409 (2008).
Glass, L. & Mackey, M. C. From Clocks to Chaos: the Rhythms of Life (Princeton Univ. Press, 1988).
Li, T. Y. & Yorke, J. A. Period three implies chaos. Amer. Math. Monthly 82, 985–992 (1975).
Nunez, P. L. & Srinivasan, R. Electric Fields of the Brain: the Neurophysics of EEG 2nd edn (Oxford Univ. Press, New York, 2005).
Freeman, W. J. Models of the dynamics of neural populations. Electroencephalogr. Clin. Neurophysiol. Suppl. 34, 9–18 (1978).
Lopes da Silva, F. H., Hoeks, A., Smits, H. & Zetterberg, L. H. Model of brain rhythmic activity. The alpha-rhythm of the thalamus. Kybernetik 15, 27–37 (1974).
Lopes da Silva, F. H., van Rotterdam, A., Barts, P., van Heusden, E. & Burr, W. Models of neuronal populations: the basic mechanisms of rhythmicity. Prog. Brain Res. 45, 281–308 (1976).
Wilson, H. R. & Cowan, J. D. Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12, 1–24 (1972).
Chakravarthy, N., Sabesan, S., Iasemidis, L. & Tsakalis, K. Controlling synchronization in a neuron-level population model. Int. J. Neural Syst. 17, 123–138 (2007).
Ermentrout, B. & Saunders, D. Phase resetting and coupling of noisy neural oscillators. J. Comput. Neurosci. 20, 179–190 (2006).
Strogatz, S. H. From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators. Physica D 143, 1–20 (2000).
Tsakalis, K., Chakravarthy, N. & Iasemidis, L. in 44th IEEE Conf. Decision Control 2975–2981 (IEEE, 2005).
Williams, T. L. Phase coupling by synaptic spread in chains of coupled neuronal oscillators. Science 258, 662–665 (1992).
Winfree, A. T. Biological rhythms and the behavior of populations of coupled oscillators. J. Theor. Biol. 16, 15–42 (1967).
Liley, D. T. & Bojak, I. Understanding the transition to seizure by modeling the epileptiform activity of general anesthetic agents. J. Clin. Neurophysiol. 22, 300–313 (2005).
Robinson, P. A., Rennie, C. J. & Rowe, D. L. Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 65, 041924 (2002).
Suffczynski, P., Kalitzin, S. & Lopes da Silva, F. H. Dynamics of non-convulsive epileptic phenomena modeled by a bistable neuronal network. Neuroscience 126, 467–484 (2004).
Wendling, F. Neurocomputational models in the study of epileptic phenomena. J. Clin. Neurophysiol. 22, 285–287 (2005).
Wilson, M. T., Sleigh, J. W., Steyn-Ross, D. A. & Steyn-Ross, M. L. General anesthetic-induced seizures can be explained by a mean-field model of cortical dynamics. Anesthesiology 104, 588–593 (2006).
Lopes da Silva, F. H. et al. Dynamical diseases of brain systems: different routes to epileptic seizures. IEEE Trans. Biomed. Eng. 50, 540–548 (2003). This paper provides an excellent review of lumped models.
Suffczynski, P. et al. Dynamics of epileptic phenomena determined from statistics of ictal transitions. IEEE Trans. Biomed. Eng. 53, 524–532 (2006).
Ferlazzo, E., Zifkin, B. G., Andermann, E. & Andermann, F. Cortical triggers in generalized reflex seizures and epilepsies. Brain 128, 700–710 (2005).
Inouye, Y. Higher brain function as precipitant of seizure. Neurol. Asia 12, 1–5 (2007).
Wendling, F., Bartolomei, F., Bellanger, J. J. & Chauvel, P. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur. J. Neurosci. 15, 1499–1508 (2002). This study applied a lumped model to MTLE.
Lemasson, G., Marder, E. & Abbott, L. F. Activity-dependent regulation of conductances in model neurons. Science 259, 1915–1917 (1993). This classic modelling study demonstrated the interaction of fast and slow processes.
Izhikevich, E. M. & Edelman, G. M. Large-scale model of mammalian thalamocortical systems. Proc. Natl Acad. Sci. USA 105, 3593–3598 (2008).
Lytton, W. W. & Sejnowski, T. J. Computer model of ethosuximide's effect on a thalamic neuron. Ann. Neurol. 32, 131–139 (1992).
Spampanato, J., Aradi, I., Soltesz, I. & Goldin, A. L. Increased neuronal firing in computer simulations of sodium channel mutations that cause generalized epilepsy with febrile seizures plus. J. Neurophysiol. 91, 2040–2050 (2004).
Traub, R. D. et al. Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J. Neurophysiol. 93, 2194–2232 (2005). This paper provides a detailed model of the operation of a single column, with applications in epilepsy and normal activity.
Traub, R. D., Contreras, D. & Whittington, M. A. Combined experimental/simulation studies of cellular and network mechanisms of epileptogenesis in vitro and in vivo. J. Clin. Neurophysiol. 22, 330–342 (2005).
Traub, R. D., Jefferys, J. G. R. & Whittington, M. A. Fast Oscillations in Cortical Circuits (MIT Press, Cambridge, Massachusetts, 1999).
Traub, R. D., Miles, R. & Wong, R. K. S. Model of the origin of rhythmic population oscillations in the hippocampal slice. Science 243, 1319–1325 (1989).
Destexhe, A. & Sejnowski, T. J. Thalamocortical Assemblies: How Ion Channels, Single Neurons and Large-Scale Networks Organize Sleep Oscillations (Oxford Univ. Press, New York, 2001). This book details the application of paired experimentation and modelling to the dynamics behind absence seizures and sleep spindles.
Bal, T., Debay, D. & Destexhe, A. Cortical feedback controls the frequency and synchrony of oscillations in the visual thalamus. J. Neurosci. 20, 7478–7488 (2000).
Blumenfeld, H. & McCormick, D. A. Corticothalamic inputs control the pattern of activity generated in thalamocortical networks. J. Neurosci. 20, 5153–5162 (2000).
Destexhe, A., Contreras, D. & Steriade, M. Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states. J. Neurosci. 19, 4595–4608 (1999).
Dudek, F. E. & Sutula, T. P. Epileptogenesis in the dentate gyrus: a critical perspective. Prog. Brain Res. 163, 755–773 (2007).
Houser, C. R., Miyashiro, J. E., Swartz, B. E., Walsh, G. O. & Rich, J. R. Altered patterns of dynorphin immunoreactivity suggest mossy fiber reorganization in human hippocampal epilepsy. J. Neurosci. 10, 267–282 (1990).
Mathern, G. W. et al. Childhood generalized and mesial temporal epilepsies demonstrate different amounts and patterns of hippocampal neuron loss and mossy fibre synaptic reorganization. Brain 119, 965–987 (1996).
Parent, J. M. Adult neurogenesis in the intact and epileptic dentate gyrus. Prog. Brain Res. 163, 529–540 (2007).
Parent, J. M. et al. Dentate granule cell neurogenesis is increased by seizures and contributes to aberrant network reorganization in the adult rat hippocampus. J. Neurosci. 17, 3727–3738 (1997). This study showed that cells are added to the hippocampus during epileptogenesis.
Sutula, T., Cascino, G., Cavazos, J., Parada, I. & Ramirez, L. Mossy fiber synaptic reorganization in the epileptic human temporal lobe. Ann. Neurol. 26, 321–330 (1989).
Sloviter, R. S. The functional organization of the hippocampal dentate gyrus and its relevance to the pathogenesis of temporal lobe epilepsy. Ann. Neurol. 35, 640–654 (1994).
Lytton, W. W., Hellman, K. M. & Sutula, T. P. Computer models of hippocampal circuit changes of the kindling model of epilepsy. Artif. Intel. Med. 13, 81–98 (1998).
Ratzliff, A. H., Howard, A. L., Santhakumar, V., Osapay, I. & Soltesz, I. Rapid deletion of mossy cells does not result in a hyperexcitable dentate gyrus: implications for epileptogenesis. J. Neurosci. 24, 2259–2269 (2004).
Bradley, D. C., Mascaro, M. & Santhakumar, S. A relational database for trial-based behavioral experiments. J. Neurosci. Methods 141, 75–82 (2005).
Morgan, R. J. & Soltesz, I. Nonrandom connectivity of the epileptic dentate gyrus predicts a major role for neuronal hubs in seizures. Proc. Natl Acad. Sci. USA 105, 6179–6184 (2008). A study showing how graph theory can make predictions that can be confirmed in an exploration of neuronal wiring.
Dyhrfjeld-Johnsen, J. et al. Topological determinants of epileptogenesis in large-scale structural and functional models of the dentate gyrus derived from experimental data. J. Neurophysiol. 97, 1566–1587 (2007).
Santhakumar, V., Aradi, I. & Soltesz, I. Role of mossy fiber sprouting and mossy cell loss in hyperexcitability: a network model of the dentate gyrus incorporating cell types and axonal topography. J. Neurophysiol. 93, 437–453 (2005).
Watts, D. J. & Strogatz, S. H. Collective dynamics of 'small-world' networks. Nature 393, 440–442 (1998).
Lytton, W. W. From Computer to Brain (Springer, New York, 2002).
Scharfman, H. E. The neurobiology of epilepsy. Curr. Neurol. Neurosci. Rep. 7, 348–354 (2007).
Bazhenov, M., Timofeev, I., Steriade, M. & Sejnowski, T. J. Computational models of thalamocortical augmenting responses. J. Neurosci. 18, 6444–6465 (1998).
Destexhe, A. Can GABAA conductances explain the fast oscillation frequency of absence seizures in rodents? Eur. J. Neurosci. 11, 2175–2181 (1999).
Destexhe, A., McCormick, D. A. & Sejnowski, T. J. Thalamic and thalamocortical mechanisms underlying 3 Hz spike-and-wave discharges. Prog. Brain Res. 121, 289–307 (1999).
Lytton, W. W., Destexhe, A. & Sejnowski, T. J. Control of slow oscillations in the thalamocortical neuron: a computer model. Neuroscience 70, 673–684 (1996). This review traces absence manifestations from genes to the whole animal.
Crunelli, V. & Leresche, N. Childhood absence epilepsy: genes, channels, neurons and networks. Nature Rev. Neurosci. 3, 371–382 (2002).
Lytton, W. W. & Sejnowski, T. J. Inhibitory interneurons may help synchronize oscillations in cortical pyramidal neurons. J. Neurophysiol. 66, 1059–1079 (1991).
Cossart, R., Bernard, C. & Ben-Ari, Y. Multiple facets of GABAergic neurons and synapses: multiple fates of GABA signalling in epilepsies. Trends Neurosci. 28, 108–115 (2005). This paper provides a useful introduction to the roles and controversies of GABA signalling.
Bernard, C. Dogma and dreams: experimental lessons for epilepsy mechanism chasers. Cell. Mol. Life Sci. 62, 1177–1181 (2005).
Hereld, M., Stevens, R. L., Drongelen, W. & Lee, H. C. Developing a petascale neural simulation. Conf. Proc. IEEE Eng. Med. Biol. Soc. 6, 3999–4002 (2004).
Hereld, M., Stevens, R. L., Lee, H. C. & van Drongelen, W. Framework for interactive million-neuron simulation. J. Clin. Neurophysiol. 24, 189–196 (2007).
Hereld, M., Stevens, R. L., Teller, J. & van Drongelen, W. Large neural simulations on large parallel computers. Int. J. Bioelectromag. 7, 44–46 (2005).
Markram, H. The blue brain project. Nature Rev. Neurosci. 7, 153–160 (2006).
Migliore, M., Cannia, C., Lytton, W. W. & Hines, M. L. Parallel network simulations with neuron. J. Comput. Neurosci. 6, 119–129 (2006).
van Drongelen, W. et al. Emergent epileptiform activity in neural networks with weak excitatory synapses. IEEE Trans. Neural Syst. Rehabil. Eng. 13, 236–241 (2005).
van Drongelen, W., Lee, H. C., Stevens, R. L. & Hereld, M. Propagation of seizure-like activity in a model of neocortex. J. Clin. Neurophysiol. 24, 182–188 (2007).
Gayatri, N. A. & Livingston, J. H. Aggravation of epilepsy by anti-epileptic drugs. Dev. Med. Child. Neurol. 48, 394–398 (2006).
Vendrame, M. et al. Aggravation of seizures and/or EEG features in children treated with oxcarbazepine monotherapy. Epilepsia 48, 2116–2120 (2007).
Blois, M. S. Medicine and the nature of vertical reasoning. N. Engl. J. Med. 318, 847–851 (1988).
Ng, A., Bursteinas, B., Gao, Q., Mollison, E. & Zvelebil, M. Resources for integrative systems biology: from data through databases to networks and dynamic system models. Brief. Bioinform. 7, 318–330 (2006).
Rogawski, M. A. Diverse mechanisms of antiepileptic drugs in the development pipeline. Epilepsy Res. 69, 273–294 (2006).
Rogawski, M. A. Molecular targets versus models for new antiepileptic drug discovery. Epilepsy Res. 68, 22–28 (2006).
Rogawski, M. A. & Loscher, W. The neurobiology of antiepileptic drugs for the treatment of nonepileptic conditions. Nature Med. 10, 685–692 (2004).
Le Novere, N. The long journey to a systems biology of neuronal function. BMC Syst. Biol. 1, 28 (2007).
De Schutter, E. Why are computational neuroscience and systems biology so separate? Plos Comput. Biol. 4, e1000078 (2008).
Kellinghaus, C. et al. Specific epileptic syndromes are rare even in tertiary epilepsy centers: a patient-oriented approach to epilepsy classification. Epilepsia 45 (Suppl. 1), 268–275 (2004).
Loddenkemper, T. et al. A proposal for a five-dimensional patient-oriented epilepsy classification. Epileptic Disord. 7, 308–316 (2005).
Luders, H. et al. Semiological seizure classification. Epilepsia 39, 1006–1013 (1998).
Hines, M. L., Morse, T., Migliore, M., Carnevale, N. T. & Shepherd, G. M. Modeldb: a database to support computational neuroscience. J. Comput. Neurosci. 17, 73–77 (2004).
Gilat, A. MATLAB: An Introduction with Applications 3rd edn (Wiley, New York, 2008).
Ermentrout, B. Simulating, Analyzing, and Animating Dynamical Systems: A Guide to Xppaut for Researchers and Students (Society for Industrial Mathematics, Philadelphia, 2002).
Bower, J. & Beeman, D. The Book of Genesis 2nd edn (Springer, New York 1998).
Carnevale, N. T. & Hines, M. L. The NEURON Book (Cambridge Univ. Press, New York, 2006).
Cannon, R. C. et al. Interoperability of neuroscience modeling software: current status and future directions. Neuroinformatics 5, 127–138 (2007).
Brette, R. et al. Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23, 349–398 (2007).
Lennox, W. G. & Lennox, M. A. Epilepsy and related disorders. (Little Brown, New York, 1960).
Acknowledgements
I would like to thank S. Neymotin, J. Reggia, P. Rutecki, P. Suffczynski, W. van Drongelen and three anonymous reviewers for many helpful suggestions, and the National Institute of Health for 20 years of support.
Author information
Authors and Affiliations
Related links
Glossary
- Generalized seizure
-
A seizure that seems to start simultaneously across cortical sites.
- Focal seizure
-
A seizure that starts at a particular location in the brain.
- Secondary generalization
-
A process whereby an initially focal seizure spreads to involve the entire brain.
- Dynamical model
-
A computer or physical model that reproduces change in an experimentally observable feature. In the case of dynamical models of motion, these changes would be in position and velocity.
- Tonic–clonic
-
A common pattern of convulsion that involves a phase of contraction of the extensor muscles (the tonic) followed by a phase of alternating flexor–extensor contractions (the clonic phase).
- Seizure semiology
-
The detailed study of the progress of a seizure.
- Scale model
-
A small physical model of an object, with correct proportions.
- Verbal model
-
An informal descriptive explanation of an object or phenomenon.
- Systems biology
-
The analysis of element interactions in biological systems. Owing to the complexity of these systems, the computer is often used as a tool for analysis and simulation. Objects of study include metabolic and expression pathways but extend up to the study of macroscopic systems. The goal is to insert the results of reductionist study back into the systems from which they were extracted.
- Parameter
-
In a computer model, parameters are the constant values in the set of equations that describe the model. These values are set by the user and determine the behaviour of the model.
- Stochastic model
-
A computer model that attempts to replicate phenomenology by drawing exemplars (which might be locations or time intervals) from a probability distribution. The prototypical example is the model of Brownian motion.
- Poisson model
-
A stochastic model that generates time intervals that are independently drawn from a Poisson distribution. The Poisson distribution is the limiting case of the binomial distribution for large 'n' (number of events) and small 'p' (probability of event occurrence).
- Monte Carlo model
-
A stochastic model that uses repeated random sampling from one or more distributions.
- Markov model
-
A stochastic model that uses a series of connected states with transition probabilities between them.
- Discretization
-
A process whereby continuous time is divided into timesteps, or whereby continuous space is divided into segments or compartments, in order to simulate continuous reality in the discontinuous words of computer memory.
- Finite-difference approximation
-
A process whereby the infinitesimal changes of continuous curves (in time or space) are approximated with a finite change that is based on the curve's values at a discrete timestep or spatial interval.
- State variable
-
In a dynamical model, state variables are the values that change with time.
- Trajectory
-
In a dynamical model, the trajectory is the path that is followed by the n state variables through the n-dimensional state space. This is a higher-dimensional generalization of the notion of trajectory as a term that is commonly used to describe motion. However, trajectories in models of motion include velocities as well as locations.
- Attractor
-
The set of stable trajectories of a dynamical system in state-space. If a trajectory is perturbed away from an attractor it will tend to move back to it.
- Mean-field approximation
-
An approximation that is used when large numbers of elements (for example, neurons) make it impracticable to model the influence of each element individually. Instead, the effect of a large ensemble of elements is estimated as a field, the influence of which is widely felt.
- Lumped model
-
A model that approximates the activity of a large ensemble of neurons using a single-state variable that typically represents the proportion of neurons that are active at a given time.
- Cortical minicolumn
-
A group of cortical cells that interact with each other more than they interact with neurons in neighbouring columns. Although columnar structure was originally identified physiologically as groups of neurons with shared properties, it has since been sought anatomically and variously identified as groups of 100–200 neurons (∼30 μm across).
- State space
-
The dimensionality of a dynamic system. The current state of the system can be described as a point in state-space. Also called phase space.
- Parameter space
-
The m-dimensional space in which the parameters of a system can be defined as a single point.
- Ictogenesis
-
The generation of a seizure (the ictus) by dynamical, cellular and synaptic processes.
Rights and permissions
About this article
Cite this article
Lytton, W. Computer modelling of epilepsy. Nat Rev Neurosci 9, 626–637 (2008). https://doi.org/10.1038/nrn2416
Published:
Issue Date:
DOI: https://doi.org/10.1038/nrn2416
This article is cited by
-
Examining the low-voltage fast seizure-onset and its response to optogenetic stimulation in a biophysical network model of the hippocampus
Cognitive Neurodynamics (2024)
-
Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair
Communications Biology (2023)
-
Optimization of ictal aborting stimulation using the dynamotype taxonomy
Journal of Computational Neuroscience (2023)
-
Nonlinear dynamical modeling of neural activity using volterra series with GA-enhanced particle swarm optimization algorithm
Cognitive Neurodynamics (2023)
-
Closed-loop controller based on reference signal tracking for absence seizures
Scientific Reports (2022)