Abstract
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.
P.A. Adlard—PAA is supported by an ARC Future Fellowship. In addition, the Florey Institute of Neuroscience and Mental Health acknowledge the strong support from the Victorian Government and in particular the funding from the Operational Infrastructure Support Grant.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
Segev, R., Baruchi, I., Hulata, E., Ben-Jacob, E.: Hidden neuronal correlations in cultured networks. Phys. Rev. Lett. 92(11), Article no. 118102 (2004)
Baruchi, I., Ben-Jacob, E.: Functional holography of recorded neuronal networks activity. Neuroinformatics 2(3), 333–351 (2004)
Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969)
Pereda, E., Quiroga, R.Q., Bhattacharya, J.: Nonlinear multivariate analysis of neurophysiological signals. Prog. Neurobiol. 77(1–2), 1–37 (2005)
Sameshima, K., Baccalá, L.A.: Using partial directed coherence to describe neuronal ensemble interactions. J. Neurosci. Methods 94(1), 93–103 (1999)
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)
Baccalá, L.A., Sameshima, K.: Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 84(6), 463–474 (2001)
Cocatre-Zilgien, J.H., Delcomyn, F.: Identification of bursts in spike trains. J. Neurosci. Methods 41(1), 19–30 (1992)
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)
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)
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)
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)
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)
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)
Zhu, L., Lai, Y., Hoppensteadt, F.C., He, J.: Probing changes in neural interaction during adaptation. Neural Comput. 15(10), 2359–2377 (2003)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hettiarachchi, I.T., Bhatti, A., Adlard, P.A., Nahavandi, S. (2015). Multivariate Autoregressive-based Neuronal Network Flow Analysis for In-vitro Recorded Bursts. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-26561-2_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26560-5
Online ISBN: 978-3-319-26561-2
eBook Packages: Computer ScienceComputer Science (R0)