International Conference on Neural Information Processing

Neural Information Processing pp 324-331 | Cite as

Multivariate Autoregressive-based Neuronal Network Flow Analysis for In-vitro Recorded Bursts

  • Imali T. Hettiarachchi
  • Asim Bhatti
  • Paul A. Adlard
  • Saeid Nahavandi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9492)


Neuroscientific studies of in vitro neuron cell cultures has attracted paramount attention to investigate the behaviour of neuronal networks in response to different environmental conditions and external stimuli such as drugs, optical and electrical stimulations. Microelectrode array (MEA) technology has been widely adopted as a tool for this investigation. In this work, we present a new approach to estimate interconnectivity of neural spikes using multivariate autoregressive (MVAR) analysis and Partial Directed Coherence (PDC). The proposed approach has the potential to discover hidden intra-burst causal connectivity patterns and to help understand the spatiotemporal communication patterns within bursts, pre and post stimulations.


Multi electrode array Bursts Partial directed coherence Multivariate autoregressive modelling 


  1. 1.
    Johnstone, A.F.M., Gross, G.W., Weiss, D.G., Schroeder, O.-H., Gramowski, A., Shafer, T.J.: Microelectrode arrays: a physiologically based neurotoxicity testing platform for the 21st century. Neurotoxicology 31(4), 331–350 (2010)CrossRefGoogle Scholar
  2. 2.
    Zhou, H., Mohamed, S., Bhatti, A., Lim, C.P., Gu, N., Haggag, S., Nahavandi, S.: Spike sorting using hidden markov models. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013. LNCS, vol. 8226, pp. 553–560. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  3. 3.
    Haggag, S., Mohamed, S., Bhatti, A., Gu, N., Zhou, H., Nahavandi, S.: Cepstrum based unsupervised spike classification. In: Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, pp. 3716–3720 (2013)Google Scholar
  4. 4.
    Wagenaar, D.A., Madhavan, R., Pine, J., Potter, S.M.: Controlling bursting in cortical cultures with closed-loop multi-electrode stimulation. J. Neurosci. 25(3), 680–688 (2005)CrossRefGoogle Scholar
  5. 5.
    Segev, R., Baruchi, I., Hulata, E., Ben-Jacob, E.: Hidden neuronal correlations in cultured networks. Phys. Rev. Lett. 92(11), Article no. 118102 (2004)Google Scholar
  6. 6.
    Baruchi, I., Ben-Jacob, E.: Functional holography of recorded neuronal networks activity. Neuroinformatics 2(3), 333–351 (2004)CrossRefGoogle Scholar
  7. 7.
    Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Pereda, E., Quiroga, R.Q., Bhattacharya, J.: Nonlinear multivariate analysis of neurophysiological signals. Prog. Neurobiol. 77(1–2), 1–37 (2005)CrossRefGoogle Scholar
  9. 9.
    Sameshima, K., Baccalá, L.A.: Using partial directed coherence to describe neuronal ensemble interactions. J. Neurosci. Methods 94(1), 93–103 (1999)CrossRefGoogle Scholar
  10. 10.
    Kamiński, M.J., Blinowska, K.J.: A new method of the description of the information flow in the brain structures. Biol. Cybern. 65(3), 203–210 (1991)CrossRefMATHGoogle Scholar
  11. 11.
    Baccalá, L.A., Sameshima, K.: Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 84(6), 463–474 (2001)CrossRefMATHGoogle Scholar
  12. 12.
    Cocatre-Zilgien, J.H., Delcomyn, F.: Identification of bursts in spike trains. J. Neurosci. Methods 41(1), 19–30 (1992)CrossRefGoogle Scholar
  13. 13.
    Kim, S., Putrino, D., Ghosh, S., Brown, E.N.: A granger causality measure for point process models of ensemble neural spiking activity. PLoS Comput. Biol. 7(3), e1001110 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Nedungadi, A.G., Rangarajan, G., Jain, N., Ding, M.: Analyzing multiple spike trains with nonparametric granger causality. J. Comput. Neurosci. 27(1), 55–64 (2009)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Cadotte, A.J., DeMarse, T.B., He, P., Ding, M.: Causal measures of structure and plasticity in simulated and living neural networks. PLoS One 3(10), e3355 (2008)CrossRefGoogle Scholar
  16. 16.
    Lamanna, J., Esposti, F., Signorini, M.G.: Study of neuronal networks development from in-vitro recordings: a granger causality based approach. In: Conference Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 4842–4845 (2010)Google Scholar
  17. 17.
    Fanselow, E.E., Sameshima, K., Baccalá, L.A., Nicolelis, M.A.L.: Thalamic bursting in rats during different awake behavioral states. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 98(26), pp. 15330–15335 (2001)Google Scholar
  18. 18.
    Rodriguez, M.Z., Pedrino, E.C., Saito, J.H., Destro Filho, J.B.: Evolutionary dynamics of in vitro cultures of neurons in Multi Electrode Array - MEA. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 78–83 (2012)Google Scholar
  19. 19.
    Zhu, L., Lai, Y., Hoppensteadt, F.C., He, J.: Probing changes in neural interaction during adaptation. Neural Comput. 15(10), 2359–2377 (2003)CrossRefMATHGoogle Scholar
  20. 20.
    Kamiński, M., Ding, M., Truccolo, W.A., Bressler, S.L.: Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance. Biol. Cybern. 85(2), 145–157 (2001)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Imali T. Hettiarachchi
    • 1
  • Asim Bhatti
    • 1
  • Paul A. Adlard
    • 2
  • Saeid Nahavandi
    • 1
  1. 1.Centre for Intelligent Systems ResearchDeakin UniversityGeelongAustralia
  2. 2.Synaptic Neurobiology LaboratoryThe Florey Institute of Neuroscience and Mental HealthMelbourneAustralia

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