nach oben

Journal of Computational Neuroscience

Erschienen in:

01.12.2014

A neural mass model based on single cell dynamics to model pathophysiology

verfasst von: Bas-Jan Zandt, Sid Visser, Michel J. A. M. van Putten, Bennie ten Haken

Erschienen in: Journal of Computational Neuroscience | Ausgabe 3/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Neural mass models are successful in modeling brain rhythms as observed in macroscopic measurements such as the electroencephalogram (EEG). While the synaptic current is explicitly modeled in current models, the single cell electrophysiology is not taken into account. To allow for investigations of the effects of channel pathologies, channel blockers and ion concentrations on macroscopic activity, we formulate neural mass equations explicitly incorporating the single cell dynamics by using a bottom-up approach. The mean and variance of the firing rate and synaptic input distributions are modeled. The firing rate curve (F(I)-curve) is used as link between the single cell and macroscopic dynamics. We show that this model accurately reproduces the behavior of two populations of synaptically connected Hodgkin-Huxley neurons, also in non-steady state.

Vorheriger Artikel Cannabinoid-mediated short-term plasticity in hippocampus

Nächster Artikel Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

The simulation code is available from modelDB (Hines et al. 2004), accession nr. 155130 and Researchgate www.researchgate.net/profile/Bas-Jan_Zandt

Available from ModelDB, accession nr. 154739.

Allen, C., & Stevens, C.F. (1994). An evaluation of causes for unreliability of synaptic transmission. Proceedings National Academy Science USA, 383 (10), 380–10.

Amit, D., & Brunel, N. (1997). Dynamics of a recurrent network of spiking neurons before and following learning Network Computation in Neural Systems.

Baladron, J., Fasoli, D., Faugeras, O., Touboul, J. (2012). Mean-field description and propagation of chaos in networks of hodgkin-huxley and fitzhugh-nagumo neurons. Journal Mathematics Neuroscience, 2 (1), 10. doi:10.1186/2190-8567-2-10.CrossRef

Bazhenov, M., Timofeev, I., Frhlich, F., Sejnowski, T.J. (2008). Cellular and network mechanisms of electrographic seizures. Drug Discovery Today Dis Models, 5 (1), 45–57. doi:10.1016/j.ddmod.2008.07.005.CrossRef

Bhattacharya, B.S., Coyle, D., Maguire, L.P. (2011). A thalamo-cortico-thalamic neural mass model to study alpha rhythms in Alzheimer’s disease. Neural networks : the official journal of the International Neural Network Society, 24 (6), 631–45. doi:10.1016/j.neunet.2011.02.009.CrossRef

Chizhov, A., & Graham, L. (2007). Population model of hippocampal pyramidal neurons, linking a refractory density approach to conductance-based neurons. Physical Review E, 75(1) (011), 924. doi:10.1103/PhysRevE.75.011924.

De Schutter, E. (2010). Computational Modeling Methods for Neuroscientists. Mit Press, chap, 6. URL http://books.google.nl/books/about/Computational_Modeling_Methods_for_Neuro.html?id=20RvPgAACAAJ&pgis=1.

Deco, G., Jirsa, V.K., Pa, Robinson, Breakspear, M., Friston, K. (2008). The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS computational biology, 4(8) (e1000), 092. doi:10.1371/journal.pcbi.1000092.

Dreier, J.P. (2011). The role of spreading depression, spreading depolarization and spreading ischemia in neurological disease. Nature medicine, 17 (4), 439–47. doi:10.1038/nm.2333.PubMedCrossRef

Faugeras, O., Touboul, J., Cessac, B. (2009). A constructive mean-field analysis of multi-population neural networks with random synaptic weights and stochastic inputs. Frontiers in computational neuroscience, 3 (February), 1. doi:10.3389/neuro.10.001.2009.PubMedCentralPubMed

Fröhlich, F., Bazhenov, M., Iragui-Madoz, V., Sejnowski, T.J. (2008). Potassium dynamics in the epileptic cortex: new insights on an old topic. Neuroscientist, 14 (5), 422–433. doi:10.1177/1073858408317955.PubMedCentralPubMedCrossRef

Galtier, M.N., & Touboul, J. (2013). Macroscopic equations governing noisy spiking neuronal populations with linear synapses. PLoS One, 8(11) (e78), 917. doi:10.1371/journal.pone.0078917.

Grefkes, C., & Fink, G.R. (2011). Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain : a journal of neurology, 134 (Pt 5), 1264–76. doi:10.1093/brain/awr033.CrossRef

Hindriks, R., & Putten, van MJaM (2012). Meanfield modeling of propofol-induced changes in spontaneous EEG rhythms. NeuroImage, 60 (4), 2323–34. doi:10.1016/j.neuroimage.2012.02.042.PubMedCrossRef

Hines, M.L., Morse, T., Migliore, M., Carnevale, N.T., Shepherd, G.M. (2004). Modeldb: A database to support computational neuroscience. Journal Computational Neuroscience, 17 (1), 7–11. doi:10.1023/B:JCNS.0000023869.22017.2e.CrossRef

Hocepied, G., Legros, B., Van Bogaert, P., Grenez, F., Nonclercq, A. (2013). Early detection of epileptic seizures based on parameter identification of neural mass model. Computer Biology Medicine, 43 (11), 1773–1782. doi:10.1016/j.compbiomed.2013.08.022.CrossRef

Holt, G. (1997). A critical reexamination of some assumptions and implications of cable theory in neurobiology. PhD thesis: California Institute of Technology. URL http://lnc.usc.edu/holt/papers/thesis/.

Hutt, A. (2012). The population firing rate in the presence of GABAergic tonic inhibition in single neurons and application to general anaesthesia. Cognitive neurodynamics, 6 (3), 227–37. doi:10.1007/s11571-011-9182-9.PubMedCentralPubMedCrossRef

Hutt, A. (2013). The anesthetic propofol shifts the frequency of maximum spectral power in eeg during general anesthesia: analytical insights from a linear model. Front Computational Neuroscience, 7, 2. doi:10.3389/fncom.2013.00002.CrossRef

Izhikevich, E.M. (2007). Dynamical Systems in Neuroscience The Geometry of Excitability and Bursting: Computational neuroscience, vol First. MIT Press. URL http://www.amazon.com/dp/0262514206.

Jansen, B.H., & Rit, V.G. (1995). Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biology Cybernetics, 73 (4), 357–366.CrossRef

Liley, D.T.J., Cadusch, P.J., Dafilis, M.P. (2002). A spatially continuous mean field theory of electrocortical activity. Network (Bristol England), 13 (1), 67–113.CrossRef

Manwani, A., & Koch, C. (1999). Detecting and estimating signals in noisy cable structure, i: neuronal noise sources. Neural Computation, 11 (8), 1797–1829.PubMedCrossRef

Marreiros, A.C., Daunizeau, J., Kiebel, S.J., Friston, K.J. (2008). Population dynamics: variance and the sigmoid activation function. NeuroImage, 42 (1), 147–57. doi:10.1016/j.neuroimage.2008.04.239.PubMedCrossRef

Meisler, M.H., & Kearney, J.A. (2005). Sodium channel mutations in epilepsy and other neurological disorders. Journal Clinical Investigation, 115 (8), 2010–2017. doi:10.1172/JCI25466.CrossRef

Moran, R., Pinotsis, D.A., Friston, K. (2013). Neural masses and fields in dynamic causal modeling. Front Computational Neuroscience, 7, 57. doi:10.3389/fncom.2013.00057.CrossRef

Ostojic, S., & Brunel, N. (2011). From spiking neuron models to linear-nonlinear models. PLoS computational biology, 7(1) (e1001), 056. doi:10.1371/journal.pcbi.1001056.

van Putten M.J., & Zandt B.J. (2013). Neural Mass modeling for predicting seizures. Clinical Neurophysiology. doi:10.1016/j.clinph.2013.11.013.

Robinson, P.A., Rennie, C.J., Wright, J.J., Bahramali, H., Gordon, E., Rowe, D.L. (2001). Prediction of electroencephalographic spectra from neurophysiology. Physics Review E Statistics Nonlinear Soft Matter Physics, 63(2 Pt 1) (021), 903.

Pa, Robinson, Wu, H., Kim, J.W. (2008). Neural rate equations for bursting dynamics derived from conductance-based equations. Journal of theoretical biology, 250 (4), 663–72. doi:10.1016/j.jtbi.2007.10.020.CrossRef

Schevon, C.A., Ng, S.K., Cappell, J., Goodman, R.R., McKhann, G Jr, Waziri, A., Branner, A., Sosunov, A., Schroeder, C.E., Emerson, R.G. (2008). Microphysiology of epileptiform activity in human neocortex. Journal Clinical Neurophysiol, 25 (6), 321–330. doi:10.1097/WNP.0b013e31818e8010.CrossRef

Shriki, O., Hansel, D., Sompolinsky, H. (2003). Rate models for conductance-based cortical neuronal networks. Neural computation, 15 (8), 1809–41. doi:10.1162/08997660360675053.PubMedCrossRef

Somjen, G. (2004). Ions in the Brain: Normal Function, Seizures and Stroke. USA: Oxford University Press. http://books.google.nl/books?id=yaNrAAAAMAAJ.

Somjen, G.G. (2001). Mechanisms of spreading depression and hypoxic spreading depression-like depolarization. Physiology Reviews, 81 (3), 1065–1096.

Stead, M., Bower, M., Brinkmann, B.H., Lee, K., Marsh, W.R., Meyer, F.B., Litt, B., Van Gompel, J., Worrell, G.A. (2010). Microseizures and the spatiotemporal scales of human partial epilepsy. Brain, 133 (9), 2789–2797. doi:10.1093/brain/awq190.PubMedCentralPubMedCrossRef

Tjepkema-Cloostermans, M.C., Hindriks, R., Hofmeijer, J., van Putten MJAM (2013). Generalized periodic discharges after acute cerebral ischemia: Reflection of selective synaptic failure?. Clinical Neurophysiol. doi:10.1016/j.clinph.2013.08.005.

Touboul, J., Hermann, G., Faugeras, O. (2012). Noise-induced behaviors in neural mean field dynamics. SIAM Journal on Applied Dynamical Systems, 11 (1), 49–81. doi:10.1137/110832392. http://epubs.siam.org/doi/pdf/10.1137/110832392.CrossRef

Victor, J.D., Drover, J.D., Conte, M.M., Schiff, N.D. (2011). Mean-field modeling of thalamocortical dynamics and a model-driven approach to EEG analysis. Proceedings of the National Academy of Sciences of the United States of America 108 Suppl, 15, 631–8. doi:10.1073/pnas.1012168108.

Visser, S., & Van Gils, S. (2014). Lumping Izhikevich neurons. EPJ Nonlinear Biomedical Physics, 2 (1), 6. doi:10.1140/epjnbp19. URL http://www.epjnonlinearbiomedphys.com/content/2/1/6.

Wilson, H.R., & Cowan, J.D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical journal, 12 (1), 1–24. doi:10.1016/S0006-3495(72)86068-5.PubMedCentralPubMedCrossRef

Zandt, B.J., ten Haken, B., van Dijk, J.G., van Putten, M J.A.M. (2011). Neural dynamics during anoxia and the ”wave of death”. PLoS One, 6(7) (e22), 127. doi:10.1371/journal.pone.0022127.

Ziburkus, J., Cressman, J.R., Barreto, E., Schiff, S.J. (2006). Interneuron and pyramidal cell interplay during in vitro seizure-like events. Journal Neurophysiology, 95 (6), 3948–3954. doi:10.1152/jn.01378.2005.CrossRef

Titel: A neural mass model based on single cell dynamics to model pathophysiology
verfasst von: Bas-Jan Zandt
Sid Visser
Michel J. A. M. van Putten
Bennie ten Haken
Publikationsdatum: 01.12.2014
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 3/2014
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-014-0517-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 3/2014

Cannabinoid-mediated short-term plasticity in hippocampus

Modelling fast forms of visual neural plasticity using a modified second-order motion energy model

Stimulation-induced ectopicity and propagation windows in model damaged axons

Encoding whisker deflection velocity within the rodent barrel cortex using phase-delayed inhibition

Dimensional reduction of a V1 ring model with simple and complex cells

Context-dependent coding in single neurons

Premium Partner