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  • Open Access

Investigating intrinsic and evoked activities in cultured neuronal networks by dimensional reduction techniques and high-density MEAs

  • 1Email author,
  • 1,
  • 1,
  • 1 and
  • 1
BMC Neuroscience201516 (Suppl 1) :P13

https://doi.org/10.1186/1471-2202-16-S1-P13

  • Published:

Keywords

  • Network Event
  • Stimulate Electrode
  • Lower Dimensional Space
  • Microelectrode Array
  • Burst Event

High density microelectrode arrays (MEAs) provide extracellular recordings from thousands of closely spaced electrodes and with sub-millisecond resolution. These devices offer thus novel capabilities to investigate the interplay between ongoing and evoked electrophysiological signaling within networks in-vitro. However, to effectively take advantage from the spatiotemporal resolution of these MEAs, adapted analysis tools are needed. Here we report on our recent advancements toward this goal. A novel high density MEA with on-chip stimulating electrodes was used to record from 4096 electrodes and electrical stimulation was delivered alternatively through one of the 16 equally spaced electrodes on hippocampal primary cultures derived from mouse. The evoked activities propagated reliably across the network and were specific to the stimulating electrode [1]. Moreover, from the second week in vitro cell cultures also displayed synchronous like events (called synchronous burst events, SBEs) that propagated similarly to the evoked activities across the entire network. These propagations might be informative of the underlying network connectivity and their classification based on the spatiotemporal patterns might elucidate the network's organization and its ongoing dynamic. Former attempts, in classifying SBEs, considered simplified descriptors of neural activity (e.g. center activity trajectory [2]). Here, we have adopted a more rigorous approach by applying dimensional reduction techniques (PCA) that take advantage of the redundancy and of the sparseness of multi-unit recordings. We found that a large fraction (i.e. >50%) of the variance of the network events (either evoked or spontaneous) was explained by as few as 3 principal components (PCs). By increasing the number of PCs (i.e. ~10) up to 80% of the total variance of the network events could be explained. Thus, the PCA approach constitutes an effective methodology to represent the spontaneous/evoked events in a lower dimensional space. As a consequence the principled PCA methodology we developed improved the clustering of the network events respect to existing methodologies [2] in different experimental settings (e.g. treatment with chemical compounds). Finally, the PCA clustering approach also allowed to infer on state dependent processing phenomena occurring in these networks.

Declarations

Acknowledgements

We acknowledge the financial support of the SI-CODE project of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of The European Commission, under FET-Open grant number: FP7-284553.

Authors’ Affiliations

(1)
Neuroscience Brain Technology Istituto Italiano di Tecnologia, via Morego 30, 16163 Genoa, Italy

References

  1. Maccione A, Nieus T, Simi A, Amin H, Gandolfo M, Berdondini L: Multi-site electrical stimulation integrated on 4'096 high density micro electrode arrays (MEAs) reveals the effective connectivity of dissociated neuronal cultures SFN. 2012, , October 13th-17th, New Orleans, USAGoogle Scholar
  2. Gandolfo M, Maccione A, Tedesco M, Martinoia S, Berdondini L: Tracking burst patterns in hippocampal cultures with high-density CMOS-MEAs. J Neural Eng. 2010, 7 (5): 056001-PubMedView ArticleGoogle Scholar

Copyright

© Nieus et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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