Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Neural mass models as a tool to investigate neural dynamics during seizures

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

Abstract

Epilepsy is one of the most common neurological disorders and is characterized by recurrent seizures. We use theoretical neuroscience tools to study brain dynamics during seizures. We derive and simulate a computational model of a network of hippocampal neuronal populations. Each population within the network is based on a model that has been shown to replicate the electrophysiological dynamics observed during seizures. The results provide insights into possible mechanisms for seizure spread. We observe that epileptiform activity remains localized to a pathological region when a global connectivity parameter is less than a critical value. After establishing the critical value for seizure spread, we explored how to correct the effect by altering particular synaptic gains. The spreading of seizures is quantified using numerical methods for seizure detection. The results from this study provide a new avenue of exploration for seizure control.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Ahuja, AK, Dorn, JD, Caspi, A, McMahon, MJ, Dagnelie, G, da Cruz, L, Stanga, P, Humayun, MS, & Greenberg, RJ (2010). Blind subjects implanted with the Argus II retinal prosthesis are able to improve performance in a spatial-motor task. British Journal of Ophthalmology, 95(4), 539–543.

    Article  PubMed  PubMed Central  Google Scholar 

  • Australian Bureau of Statistics. http://www.abs.gov.au.

  • Bartolomei, F., Wendling, F., Bellanger, J. J., Regis, J., & Chauvel, P. (2001). Neural networks involving the medial temporal structures in temporal lobe epilepsy. Clinical Neurophysiology, 112, 1746–1760.

    Article  CAS  PubMed  Google Scholar 

  • Bertman, E H (2009). Temporal lobe epilepsy: Where do the seizures really begin? Epilepsy & Behavior, 14, 32–37.

    Google Scholar 

  • Clark, G. (2003). Cochlear Implants: Fundamentals and applications: Springer Vergas New York Inc.

  • Da Silva, D. F., Hoeks, A., Smits, H., & Zetterberg, L. H. (1974). Model of brain rhythmic activity. Kybernetik, 15(1), 27–37.

    Article  Google Scholar 

  • Deco, G., Jirka, V. K., Robinson, P. A., Breakspear, M., & Friston, K. (2008). The dynamic brain: From spiking neurons to neural masses and cortical fields. PLoS Computational Biology, 4(8), 1–35.

    Article  Google Scholar 

  • Esteller, R., Echauz, J., Tcheng, T., & Litt, B. (2001). Line length: An efficient feature for seizure onset detection. Proceedings of the IEEE EMBS Conference, 1707–1710.

  • Fisher, R. S., van Emde Boas, W., Blume, B., Elger, C., Genton, P., Lee, P., & Engel, Jr. J (2005). Epileptic seizures and epilepsy: Definitions proposed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE). Epilepsia, 46, 470–474.

  • FitzHugh, R. (1955). Mathematical models of threshold phenomena in the nerve membrane. Bulletin of Mathematical Biophysics, 17(4), 257–278.

    Article  Google Scholar 

  • Freestone, D. R., Aramd, P., Dewarc, M., Scerrif, K., Grayden, D. B., & Kadirkamanathand, V. (2011). A data-driven framework for neural field modeling. NeuroImage, 56(3), 1043–1058.

    Article  CAS  PubMed  Google Scholar 

  • Grimbert, F., & Faugeras, O. (2006). Bifurcation analysis of jansen’s neural mass model. Neural Computation, 18(12), 3052– 3068.

    Article  PubMed  Google Scholar 

  • Guo, L., Rivero, D., Dorado, J., Rabunal, J. R., & Pazos, A. (2010). Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. Journal of Neuroscience Methods, 191 (1), 101–109.

    Article  PubMed  Google Scholar 

  • Han, C. L., Hu, W., Stead, M., Zhang, T., Zhang, J. G., Worrell, G. A., & Meng, F. G. (2014). Electrical stimulation of hippocampus for the treatment of refractory temporal lobe epilepsy. Brain Research Bulletin, 109, 13–21.

    Article  PubMed  Google Scholar 

  • Hirtz, D., Thurman, D., Gwinn-Hardy, K., Mohammad, M., Chaudhuri, A. R., & Zalutsky, R. (2007). How common are the “common” neurologic disorders? Neurology, 68(5), 326–337.

    Article  CAS  PubMed  Google Scholar 

  • Holtzheimer, P. E., & Mayberg, H. S. (2011). Deep brain stimulation for psychiatric disorders. Annual Reviews in the Neurosciences, 34, 289–307.

    Article  CAS  Google Scholar 

  • Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500–544.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  • Nunez, P. L., & Srinivasan, R. (2006). Electric fields of the brain, 2nd Edn. Oxford: Oxford University Press.

  • Rowe, D. L., Robinson, P. A., Rennie, C. J., Harris, A. W., Felmingham, K. L., Lazzaro, I. L., & Gordon, E. (2004). Neurophysiologically-based mean-field modelling of tonic cortical activity in post-traumatic stress disorder (PTSD), schizophrenia, first episode schizophrenia and attention deficit hyperactivity disorder (ADHD). Journal of Integrative Neuroscience, 3(4), 453–487.

    Article  PubMed  Google Scholar 

  • Schelter, B., Mader, M., Mader, W., Sommerlade, L., Platt, B., Lai, Y. C., Gregory, C., & Tiel, M. (2014). Overarching framework for data-based modelling. Frontiers of Physics, 105, 30004.

    Google Scholar 

  • Schevron, C. A., Weiss, S. A., McKhann, Jr G., Goodman, R. R., Yuste, R., Emerson, R. G., Trevelyan, A.J, & Evidence of an inhibitory restraint of seizure activity in humans (2012). Nature Communications. doi:10.1038/ncomms2056.

  • Sobayo, T., Fine, A. S., Gunnar, E., Kazlauskas, C., Nicholls, D., & Mogul, D. J. (2013). Synchrony dynamics across brain structures in limbic epilepsy vary between initiation and termination phases of seizures. IEEE Transactions on Biomedical Engineering, 60(3), 821–829.

    Article  PubMed  Google Scholar 

  • 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: A Journal of Neurology, 133, 2789–2797.

  • Touboul, J., Wendling, F., Chauvel, P., & Faugeras, O. (2011). Neural mass activity, bifurcations, and epilepsy. Neural Computation, 23(12), 3232–3286.

    Article  PubMed  Google Scholar 

  • Wang, Y., Goodfellow, M., Taylor, P. N., & Baier, G. (2014). Dynamic Mechanisms of Neocortical Focal Seizure Onset. PLoS Computational Biology, 10(8), e1003787.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wendling, F., Bellanger, J. J., Bartolomei, F., & Chauvel, P. (2000). Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. Biological Cybernetics, 4, 367–378.

    Article  Google Scholar 

  • Wendling, F., Bartolomei, F., Bellanger, J. J., & Chauvel, P. (2002). Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. European Journal of Neuroscience, 9, 1499–1508.

    Article  Google Scholar 

  • Wilson, H. R., & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12(1), 1–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This research was supported by the Australian Research Council through the Discovery Early Career Researcher Award (DECRA) DE120102210. Dr Freestone acknowledges the support of the Australian-American Fulbright Association. Dr Freestone would also like to thank Prof Liam Paninski at Columbia University for support and guidance. The authors would also like to thank and acknowledge important input from Prof Mark Cook, Dr Andre Peterson, Prof David Grayden, Prof Anthony Burkitt and all the members of the NeuroEngineering lab at The University of Melbourne.

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Australia

    Tatiana Kameneva, Tianlin Ying & Ben Guo

  2. Department of Medicine, St. Vincent’s Hospital, The University of Melbourne, Melbourne, Australia

    Dean R. Freestone

Authors
  1. Tatiana Kameneva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianlin Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ben Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dean R. Freestone
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tatiana Kameneva.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Additional information

Action Editor: Steven J. Schiff

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kameneva, T., Ying, T., Guo, B. et al. Neural mass models as a tool to investigate neural dynamics during seizures. J Comput Neurosci 42, 203–215 (2017). https://doi.org/10.1007/s10827-017-0636-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-017-0636-x

Keywords

Search

Navigation