Neuroinformatics

, Volume 2, Issue 3, pp 333–351 | Cite as

Functional holography of recorded neuronal networks activity

Original Article

Abstract

We present a new approach for analyzing multi-channel recordings, such as ECoG (electrocorticograph) recordings of cortical brain activity and of individual neuron dynamics, in cultured networks. The latter are used here to illustrate the method and its ability to discover hidden functional connectivity motifs in the recorded activity.

The cultured networks are formed from dissociated mixtures of cortical neurons and glia-cells that are homogeneously spread over multi-electrode array for recording of the neuronal activity. Rich, spontaneous dynamical behavior is detected, marked by the formation of temporal sequences of synchronized bursting events (SBEs), partitioned into statistically distinguishable subgroups, each with its own characteristic spatio-temporal pattern of activity.

In analogy with coherence connectivity networks for multi-location cortical recordings, we evaluated the inter-neuron correlation-matrix for each subgroup. Ordinarily such matrices are mapped onto a connectivity network between neuron positions in real space. In our functional holography, the correlations are normalized by the correlation distances—Euclidian distances between the matrix columns. Then, we project the N-dimensional (for N channels) space spanned by the matrix of the normalized correlations, or correlation affinities, onto a corresponding 3D manifold (3D Cartesian space constructed by the three leading principal vectors of the principal component algorithm). The neurons are located by their principal eigenvalues and linked by their original (not normalized) correlations. By looking at these holograms, hidden causal motifs are revealed: each SBEs subgroup generates its characteristic connectivity diagram (network) in the 3D manifold, where the neuron locations and their links form simple structures. Moreover, the computed temporal ordering of neuron activity, when projected onto the connectivity diagrams, also exhibits simple patterns of causal propagation. We show that the method can expose functional connectivity motifs like the co-existence of subneuronal functional networks in the space of affinities.

The method can be directly utilized to construct similar causal holograms for recorded brain activity. We expect that by doing so, hidden functional connectivity motifs with relevance to the understanding of brain activity might be discovered.

Index Entries

Correlation matrix epilepsy glia cells information processing manifolds 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bishop, C.B. (1999) Pulsed neural networks, MIT Press, Cambridge, MA.Google Scholar
  2. Daniels, K. E. and Bodenschatz, E. (2002) Defects Turbulence in Inclined Layers Correction. Phys. Rev. Lett. 88:034501.CrossRefGoogle Scholar
  3. Dayan, P. and Abbot, L. F. (2001) Theoretical Neuroscience, MIT Press, Boston, MA.Google Scholar
  4. Fields, R. D. and Steven-Graham, B. (2002) New Insights into Neuron-Glia Communication. Science 298, 556–562.CrossRefGoogle Scholar
  5. Gluckman, B.J., Nguyen, H., Weinstein, L., and Schiff, S. J. (2001) Adaptive Electric Field of Control of Epileptic Seizures. J. Neurosci. 21, 590–600.Google Scholar
  6. Hilgetag C.-C., O Neill M.A., and Young M.P. (1996) Indeterminate organization of the visual system. Science 271, 776–777.CrossRefGoogle Scholar
  7. Hilgetag, C.-C., Burns, G. A., O’Neill, M. A., Scannell, J.W., and Young, M.P. (2000) Anatomical connectivity defines the organization of clusters of Cortical areas in Macaque monkey and cat Phil. Trans. R. Soc. Lond. B355, 91–110.CrossRefGoogle Scholar
  8. Hulata, E., Segev, R., Shapira, Y., Benveniste, M., and Ben-Jacob, E. (2000) Detection and sorting of neural spikes using wavelet packets. Phys. Rev. Lett. 85, 4637–4640.CrossRefGoogle Scholar
  9. Hulata, E., Segev, R., and Ben-Jacob E. (2002) A method for spike sorting and detection based on wavelet packets and Shannon’s mutual information J. Neurosc. Meth. 117,1–12.CrossRefGoogle Scholar
  10. Hulata, E. Baruchi, I. Segev, R. Shapira, Y., and Ben Jacob, E. (2003) Self Regulated Complexity in Cultured Networks Phys. Rev. Lett. in press.Google Scholar
  11. Hunter, J. D., Wu, J., and Milton, J. G. Clustering Neuronal Spike Trains with Transient Response (preprint).Google Scholar
  12. Kandel, E. R., Schaertz, J. H. and Jessell, T. M. (2000) Principles of Neural Science, 4th ed., McGraw-Hill.Google Scholar
  13. Kreiman, G., Koch, C., and Fried I. (2000) Imagery neurons in the human brain. Nature 408, 357–361.CrossRefGoogle Scholar
  14. Laming, P. R. Kimeberg, H. Robinson, S., et al. (2000) Neuronal-glial Interactions and Behavior. Neuroscience and Biobehavioral Reviews 24, 295–340.CrossRefGoogle Scholar
  15. Levine, H. and Ben Jacob, E. (2004) Physical schemata underlying biological pattern formation—examples, issues and strategies. J. Phys. Biol. 1, 14–22.CrossRefGoogle Scholar
  16. Luscher, C., Malenka, R., Nicoll, R., and Muller, D., (2000) Synaptic plasticity and dynamic modulation of the postsynaptic membrane. Nat. Neurosci. 3, 545–550.CrossRefGoogle Scholar
  17. Milton, J. and Jung P., eds. (2002) Epilepsy as a Dynamic Disease. The Biological and Medical Physics series Springer Verlag, Berlin.Google Scholar
  18. Netoff, T. I. and Schiff, S. J. (2002) Decreased neuronal synchronization during experimental seizures. J. Neurosci. 22, 7297–7307.Google Scholar
  19. Press, W. H., Teukolsky, S. A., Vetterling W. T., and Flannery, B. P. (1992) Numerical Recipes in C. Cambridge University Press, UK.Google Scholar
  20. Rieke, F., Warland, D., de Ruyter van Stevenink, R., and Bialek, W. (1997) Spikes: exploring the neural code. MIT Press, Cambridge, MA.Google Scholar
  21. Segev, R. and Ben Jacob, E., (2001) Spontaneous synchronized bursting in 2D neural networks. Physica A. 302, 64–69.CrossRefGoogle Scholar
  22. Segev, R., Benveniste, M., Shapira, Y., and Ben-Jacob, E. (2001) Observations and modeling of synchronized bursting in 2D Neural Networks Phys. Rev. E. 64, 011920.CrossRefGoogle Scholar
  23. Segev, R., Benveniste, M., Shapira, Y., et al. (2002) Long term behavior of lithographically prepared in vitro neuronal networks. Phys. Rev. Lett. 64, 118102.CrossRefGoogle Scholar
  24. Segev, R., Benveniste, M., Shapira, Y., Hulata, E., and Ben-Jacob, E. (2003) Formation of electrically active clusterized neuronal networks. Phys. Rev. Lett. 90, 168101.CrossRefGoogle Scholar
  25. Segev, R., Baruchi, I., Hulata, E., and Ben-Jacob, E. (2004) Hidden neuronal correlations in cultured networks. Phys. Rev. Lett. 92, 118102.CrossRefGoogle Scholar
  26. Shuai, J. W. and Jung, P. (2003) Selection of Intracellular Calcium Patterns in a Model with Clustered Ca release Channels. Phys. Rev. E. 67, 031905 and Ion Channel Clustering for Intracellular Calcium Signaling. PNAS 100, 506–510.CrossRefGoogle Scholar
  27. Stephan, K. E., Burns, G. A., O’Neill, M. A., Young, M. P., and Kotter, R., (2000) Computational Analysis of Functional Connectivity Between Areas of Primate Cerebral Cortex. Phil. Trans. R. Soc. Lond. B. 355 111–126.CrossRefGoogle Scholar
  28. Stevens, B., Porta, S., Haak, L. L., Gallo, V., Fields, R. D. (2002) Adenosine: A Neuron-Glial Transmitter Promoting Myelination in the CNS in Response to Action Potentials. Neuron 36, 855–868.CrossRefGoogle Scholar
  29. Stout, C. E., Constantin, J. L., Naus, C. G., and Charles, A.C. (2002) Intercellular calcium signaling in astrocytes via ATP release through connexin hemichannels. J. Biol. Chem. 277, 10482–10488.CrossRefGoogle Scholar
  30. Towle, V.L., Carder, R.K., Khorasanil, L., and Lindberg, D. (1999) Electrocurticographic Coherence Patterns J. Clin. Neurophysiol. 16, 528–547.CrossRefGoogle Scholar
  31. Volman, V., Baruchi, I., Persi, E., and Ben-Jacob, E. (2004) Generative Modeling of Regulated Dynamical Behavior in Cultured Neuronal Networks. Physica A. 335, 249–278.CrossRefGoogle Scholar

Copyright information

© Humana Press Inc 2004

Authors and Affiliations

  1. 1.School of Physics and Astronomy, Beverly and Raymond Sackler Faculty of Exact SciencesTel Aviv UniversityTel AvivIsrael
  2. 2.Kavli Institute for Theoretical PhysicsUniversity of California at Santa BarbaraSanta Barbara

Personalised recommendations