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Reconstruction of Functional Connectivity from Multielectrode Recordings and Calcium Imaging

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In Vitro Neuronal Networks

Part of the book series: Advances in Neurobiology ((NEUROBIOL,volume 22))

Abstract

In the last two decades, increasing research efforts in neuroscience have been focused on determining both structural and functional connectivity of brain circuits, with the main goal of relating the wiring diagram of neuronal systems to their emerging properties, from the microscale to the macroscale. While combining multisite parallel recordings with structural circuits’ reconstruction in vivo is still very challenging, the reductionist in vitro approach based on neuronal cultures offers lower technical difficulties and is much more stable under control conditions. In this chapter, we present different approaches to infer the connectivity of cultured neuronal networks using multielectrode array or calcium imaging recordings. We first formally introduce the used methods, and then we will describe into details how those methods were applied in case studies. Since multielectrode array and calcium imaging recordings provide distinct and complementary spatiotemporal features of neuronal activity, in this chapter we present the strategies implemented with the two different methodologies in distinct sections.

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Notes

  1. 1.

    Modularity Index (MI) quantifies the degree to which a network can be clustered into small groups (modules). It measures the strength of the division of a network into modules. Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules.

References

  • Aertsen, A., & Gerstein, G. L. (1985, August). Evaluation of neuronal connectivity: Sensitivity of crosscorrelation. Brain Research, 340, 341–354.

    Article  CAS  Google Scholar 

  • Becchetti, A., Gullo, F., Bruno, G., Dossi, E., Lecchi, M., & Wanke, E. (2012). Exact distinction of excitatory and inhibitory neurons in neural networks: A study with GFP-GAD67 neurons optically and electrophysiologically recognized on multielectrode arrays. Frontiers in Neural Circuits, 6, 63.

    Article  Google Scholar 

  • Beckenbach, E. F. (1956). Modern mathematics for the engineer. New York: McGraw-Hill.

    Google Scholar 

  • Bedenbaugh, P., & Gerstein, G. L. (1997, Auguest 15). Multiunit normalized cross correlation differs from the average single-unit normalized correlation. Neural Computation, 9, 1265–1275.

    Google Scholar 

  • Berdondini, L., Imfeld, K., Maccione, A., Tedesco, M., Neukom, S., Koudelka-Hep, M., et al. (2009). Active pixel sensor array for high spatio-temporal resolution electrophysiological recordings from single cell to large scale neuronal networks. Lab on a Chip, 9, 2644–2651.

    Article  CAS  Google Scholar 

  • Bonifazi, P., Difato, F., Massobrio, P., Breschi, G. L., Pasquale, V., Levi, T., et al. (2013). In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses. Frontiers in Neural Circuits, 7, 40.

    Article  Google Scholar 

  • Bonifazi, P., Goldin, M., Picardo, M. A., Jorquera, I., Cattani, A., Bianconi, G., et al. (2009, December 4). GABAergic hub neurons orchestrate synchrony in developing hippocampal networks. Science, 326, 1419.

    Google Scholar 

  • Bonifazi, P., Ruaro, M. E., & Torre, V. (2005, December 1). Statistical properties of information processing in neuronal networks. European Journal of Neuroscience, 22, 2953–2964.

    Article  Google Scholar 

  • Bovetti, S., Moretti, C., Zucca, S., Dal Maschio, M., Bonifazi, P., & Fellin, T. (2017, January 5). Simultaneous high-speed imaging and optogenetic inhibition in the intact mouse brain. Scientific Reports, 7, 40041.

    Article  CAS  Google Scholar 

  • Brillinger, D. R., Bryant, H. L., & Segundo, J. P. (1976). Identification of synaptic interactions. Biological Cybernetics, 22, 213–228.

    Article  CAS  Google Scholar 

  • Bullmore, E., & Sporns, O. (2009, March). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186–198.

    Article  CAS  Google Scholar 

  • Buzsaki, G. (2006). Rhythms of the brain. New York: Oxford University Press.

    Book  Google Scholar 

  • Dahlhaus, R., Eichler, M., & Sandkuhler, J. (1997, November 7). Identification of synaptic connections in neural ensembles by graphical models. Journal of Neuroscience Methods, 77, 93–107.

    Article  CAS  Google Scholar 

  • Eichler, M., Dahlhaus, R., & Sandkuhler, J. (2003). Partial correlation analysis for the identification of synaptic connections. Biological Cybernetics, 89, 289–302.

    Article  Google Scholar 

  • Eversmann, B., Jenkner, M., Paulus, C., Hofmann, F., Brederlow, R., Holzapfl, B., et al. (2003). A 128/spl times/128 CMOS bio-sensor array for extracellular recording of neural activity. In 2003 IEEE international solid-state circuits conference, 2003. Digest of technical papers. ISSCC (Vol. 1, pp. 222–489).

    Chapter  Google Scholar 

  • Fejtl, M., Stett, A., Nisch, W., Boven, K. H., & Möller, A. (2006). Advances in network electrophysiology: Using multi-electrode arrays. New York: Springer.

    Google Scholar 

  • Feldt, S., Bonifazi, P., & Cossart, R. (2011, May). Dissecting functional connectivity of neuronal microcircuits: Experimental and theoretical insights. Trends in Neuroscience, 34, 225–236.

    Article  CAS  Google Scholar 

  • Friston, K. J. (2011, January 1). Functional and effective connectivity: A review. Brain Connectivity, 1, 13–36.

    Article  Google Scholar 

  • Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37, 424–438.

    Article  Google Scholar 

  • Grun, S., & Rotter, S. (Eds.) (2010). Analysis of parallel spike trains (Series in computational neuroscience). New York: Springer.

    Google Scholar 

  • Herzog, N., Shein-Idelson, M., & Hanein, Y. (2011, October). Optical validation of in vitro extra-cellular neuronal recordings. Journal of Neural Engineering, 8, 056008.

    Article  Google Scholar 

  • Kanagasabapathi, T. T., Massobrio, P., Barone, R. A., Tedesco, M., Martinoia, S., Wadman, W. J., et al. (2012). Functional connectivity and dynamics of cortical-thalamic networks co-cultured in a dual compartment device. Journal of Neural Engineering, 9, 036010.

    Article  Google Scholar 

  • Kanner, S., Goldin, M., Galron, R., Ben Jacob, E., Bonifazi, P., & Barzilai, A. (2018, July 31). Astrocytes restore connectivity and synchronization in dysfunctional cerebellar networks. Proceedings of the National Academy of Sciences of the United States of America, 115, 8025–8030.

    Article  CAS  Google Scholar 

  • Lefebvre, B., Yger, P., & Marre, O. (2016, November). Recent progress in multi-electrode spike sorting methods. Journal of Physiology, Paris, 110, 327–335.

    Article  Google Scholar 

  • Lichtman, J. W., & Denk, W. (2011, November 4). The big and the small: Challenges of imaging the brain’s circuits. Science, 334, 618–623.

    Article  CAS  Google Scholar 

  • Marissal, T., Bonifazi, P., Picardo, M. A., Nardou, R., Petit, L. F., Baude, A., et al. (2012). Pioneer glutamatergic cells develop into a morpho-functionally distinct population in the juvenile CA3 hippocampus. Nature Communications, 3, 1316.

    Article  Google Scholar 

  • Marom, A., Shor, E., Levenberg, S., & Shoham, S. (2017). Spontaneous activity characteristics of 3D “optonets”. Frontiers in Neuroscience, 10, 602.

    Article  Google Scholar 

  • Marom, S., & Shahaf, G. (2002, February). Development, learning and memory in large random networks of cortical neurons: Lessons beyond anatomy. Quarterly Reviews of Biophysics, 35, 63–87.

    Article  Google Scholar 

  • Massobrio, P., Pasquale, V., & Martinoia, S. (2015). Self-organized criticality in cortical assemblies occurs in concurrent scale-free and small-world networks. Scientific Reports, 5, 10578.

    Article  CAS  Google Scholar 

  • Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103, 8577–8582.

    Article  CAS  Google Scholar 

  • Orlandi, J. G., Stetter, O., Soriano, J., Geisel, T., & Battaglia, D. (2014). Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging. PLoS One, 9, e98842.

    Article  Google Scholar 

  • Pastore, V. P., Massobrio, P., Godjoski, A., & Martinoia, S. (2018). Identification of excitatory-inhibitory links and network topology in large scale neuronal assemblies from multi-electrode recordings. PLoS Computational Biology, 14, e1006381.

    Article  Google Scholar 

  • Pereda, E., Quiroga, R. Q., & Bhattacharya, J. (2005, September 1). Nonlinear multivariate analysis of neurophysiological signals. Progress in Neurobiology, 77, 1–37.

    Article  Google Scholar 

  • Perkel, D. H., Gerstein, G. L., & Moore, G. P. (1967). Neuronal spike train and stochastic point processes I. the single spike train. Biophysical Journal, 7, 391–418.

    Article  CAS  Google Scholar 

  • Poli, D., Pastore, V. P., Martinoia, S., & Massobrio, P. (2016). From functional to structural connectivity using partial correlation in neuronal assemblies. Journal of Neural Engineering, 13, 026023.

    Article  Google Scholar 

  • Poli, D., Pastore, V. P., & Massobrio, P. (2015). Functional connectivity in in vitro neuronal assemblies. Frontiers in Neural Circuits, 9, 57.

    Article  Google Scholar 

  • Rey, H. G., Pedreira, C., & Quian Quiroga, R. (2015, October 1). Past, present and future of spike sorting techniques. Brain Research Bulletin, 119, 106–117.

    Article  Google Scholar 

  • Salinas, E., & Sejnowski, T. J. (2001). Correlated neuronal activity and the flow of neural information. Nature Reviews Neuroscience, 2, 539–550.

    Article  CAS  Google Scholar 

  • Schreiber, T. (2000, July 10). Measuring information transfer. Physical Review Letters, 85, 461–464.

    Article  CAS  Google Scholar 

  • Schroeter, M. S., Charlesworth, P., Kitzbichler, M. G., Paulsen, O., & Bullmore, E. T. (2015). Emergence of Rich-Club topology and coordinated dynamics in development of hippocampal functional networks in vitro. The Journal of Neuroscience, 35, 5459–5470.

    Article  CAS  Google Scholar 

  • Spanne, A., & Jörntell, H. (2015, July 1). Questioning the role of sparse coding in the brain. Trends in Neurosciences, 38, 417–427.

    Article  CAS  Google Scholar 

  • Sporns, O. (2013). Structure and function of complex brain networks. Dialogues in Clinical Neuroscience, 15, 247–262.

    PubMed  PubMed Central  Google Scholar 

  • Stetter, O., Battaglia, D., Soriano, J., & Geisel, T. (2012). Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals. PLoS Computational Biology, 8, e1002653.

    Article  CAS  Google Scholar 

  • Tibau, E., Valencia, M., & Soriano, J. (2013). Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures. Frontiers in Neural Circuits, 7, 199.

    Article  Google Scholar 

  • Van Bussel, F., Kriener, B., & Timme, M. (2011, February 1). Inferring synaptic connectivity from spatio-temporal spike patterns. Frontiers in Computational Neuroscience, 5, 3.

    PubMed  PubMed Central  Google Scholar 

  • Ventura, V., Cai, C., & Kass, R. E. (2005). Statistical assessment of time-varying dependency between two neurons. Journal of Neurophysiology, 94, 2940.

    Article  Google Scholar 

  • Weisenburger, S., & Vaziri, A. (2018). A guide to emerging technologies for large-scale and whole-brain optical imaging of neuronal activity. Annual Review of Neuroscience, 41, 431–452.

    Article  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Biocruces Health Research Institute, Barakaldo, Spain

    Paolo Bonifazi

  2. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

    Paolo Bonifazi

  3. Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Genoa, Italy

    Paolo Massobrio

Authors
  1. Paolo Bonifazi
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  2. Paolo Massobrio
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Editor information

Editors and Affiliations

  1. Rehab Technologies IIT-INAIL Joint Lab, Istituto Italiano di Tecnologia, Genova, Italy

    Michela Chiappalone

  2. Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy

    Valentina Pasquale

  3. Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands

    Monica Frega

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Bonifazi, P., Massobrio, P. (2019). Reconstruction of Functional Connectivity from Multielectrode Recordings and Calcium Imaging. In: Chiappalone, M., Pasquale, V., Frega, M. (eds) In Vitro Neuronal Networks. Advances in Neurobiology, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-11135-9_9

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