Event Abstract

Synchrony changes on different time-scales during in vitro neuronal network development

  • 1 Aschaffenburg University of Applied Sciences, Faculty of Engeneering, Germany

Motivation Continuous long-term recordings of developing in vitro neuronal networks have shown distinct intrinsic activity fluctuations over time (Gal et al., 2010; Mukai et al., 2003; van Pelt et al., 2004; Saalfrank et al., 2015). Those fluctuations are highly relevant as they potentially influence the results of in vitro experiments. While those studies focused on spike rate or burst and network burst parameter, the level of spike train synchrony has not been addressed yet. Synchrony, however, is thought to play a major role in neuroscience, e. g. cognitive processes (Ward, 2003). It has also been shown that synchrony is an appropriate parameter to quantify the effect of active components in in vitro experiments (Selinger et al., 2004; Eisenman et al., 2015; Ciba et al., 2017). In this work we applied a recently proposed synchrony measure called Spike-contrast to a long-term recording of an in vitro cortical network. Spike-contrast measures the synchrony in between multiple spike trains. Unlike known algorithms, it provides not only one absolute maximal value, but also displays synchrony as a function of time scale (Ciba et al., 2018). Material and Methods Microelectrode array (MEA) chips (60MEA200/30iR-Ti, Multichannel Systems, Reutlingen) were coated with 0.1% Polyethylenimine (Sigma-Aldrich, St. Louis, USA) and 20 g/mL Laminin (Thermo Fisher Scientific Corporation, 2014). Cryopreserved primary rat cortex neurons (E18) (Thermo Fisher Scientific, Waltham, USA) were seeded with a number of 125,000 per chip. MEA chips with cells were incubated at 37 °C and 5% CO2. Half of the medium was exchanged every third day and PDMS lids were used to reduce evaporation (Blau et al., 2009). Activity was recorded from 20 div to 23 div (around 4000 minutes) in a “dry” incubator at a sampling frequency of 10 kHz (MEA 1060, Multichannel Systems MCS GmbH, Reutlingen). Note that the temperature inside the “dry” incubator was set to 30 °C, while the heater of the MEA 1060 system was adjusted at 37 °C to avoid overheating. Half of the media was changed shortly before recording at 20 div. During recording the incubator was not opened as mechanical vibration or shock may affect the network activity (Wagenaar et al., 2006). Analysis (e.g. 50 Hz high pass filtering, threshold based spike detection) was performed offline using a custom written software in MATLAB® (MATLAB 2017a, The MathWorks, Inc., Natick, Massachusetts, USA) based on ”DrCell” (Nick et al., 2013). Spike trains were split into 20 minute windows and a synchrony curve was generated for each window using the synchrony measure called Spike-contrast (Ciba et al., 2018). Results In the first 6 hours of the long-term recording almost no activity was observed (see Fig. 1a and 1b), possibly related to stress induced during the preceding media exchange. Subsequently, the mean spike rate (averaged over all active electrodes) showed fluctuation with a tendency to rise towards the end of recording time (Fig. 1b). Figure 1c and 1d show the results obtained by the synchrony measure. In a first step, the Spike-contrast calculated the synchrony as a function of the bin size (“synchrony curve”) for each time window, resulting in a synchrony spectrum (Fig. 1c). The bin size is the time period in which spikes are considered synchronous. In a second step, the maximum of each synchrony curve was used to obtain an overall maximum synchrony value (Fig. 1d). Interestingly, the maximum synchrony value generally remained more stable than the spike rate after the system had settled (Fig. 1d). Additional information is provided by the analysis of the synchrony spectrum (Fig. 1c). During the first 36 hours synchronization of the spike trains was distinct at large time scales (bin sizes of around 10 seconds) indicating the occurrence of synchronized bursts formed by unsynchronized spikes. Throughout the following 18 hours, synchronization shifted to smaller time scales (bin sizes of seconds to milliseconds). Finally synchronization at large and small time scales was observed, representing the increasing synchronization of the burst-forming spikes. Discussion and Conclusion In this study, we aimed to investigate synchrony alterations during network development. Therefore we established a protocol that allowed us to continuously record the activity of a cortical neuronal network in a “dry” incubator for three days and longer. In agreement with previous studies (Gal et al., 2010; Mukai et al., 2003; van Pelt et al., 2004; Saalfrank et al., 2015), we observed an intrinsically fluctuating spike rate with a clear tendency towards increasing network activity during maturation. Further and of more relevance for this work, the level of synchrony was determined as a function of maturation time and time scale. To this end we applied the synchrony measure Spike-contrast for the first time to long-term recorded data. Summarizing, we found that the maximum synchrony value is - other than spike rate - generally stable over time. Therefore, we suggest that the maximum synchrony value is an adequate parameter for in vitro experiments as it is widely independent of intrinsic activity fluctuations. Further, we found that the time scale of synchrony is not constant but shifts from large to small values with increasing culture age. This indicates that spikes become more and more synchronized within smaller time periods. Therefore, we believe that the synchrony spectrum is an interesting new parameter that helps to understand the character of the synchrony more deeply. It may also be applied to visualize even longer recordings like the 70 days recordings of Saalfrank et al. (2015) to obtain new information about developing neuronal networks. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Figure 1: Continuous extracellular recording of a cultured cortical neuronal network from 20 days in vitro (div) to almost 23 div. (a) Raster plots of simultaneously recorded spike trains. Absolute spike amplitudes are color-coded. (b) Mean firing rate averaged over all active electrodes calculated for each window (window size = 20 minutes). (c) Synchrony spectrum over time calculated by the synchrony measure Spike-contrast (synchrony = 0 for no synchrony, synchrony = 1 for full synchrony). For each window, synchrony is calculated for different time-scales. (d) Maximum synchrony value taken from (c) for each window.

Figure 1


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Keywords: synchrony, Time-scales, spike-contrast, developing neuronal networks, spike train analysis

Conference: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays, Reutlingen, Germany, 4 Jul - 6 Jul, 2018.

Presentation Type: Poster Presentation

Topic: Neural Networks

Citation: Ciba M and Thielemann C (2019). Synchrony changes on different time-scales during in vitro neuronal network development. Conference Abstract: MEA Meeting 2018 | 11th International Meeting on Substrate Integrated Microelectrode Arrays. doi: 10.3389/conf.fncel.2018.38.00081

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Received: 18 Mar 2018; Published Online: 17 Jan 2019.

* Correspondence: Mr. Manuel Ciba, Aschaffenburg University of Applied Sciences, Faculty of Engeneering, Aschaffenburg, Bavaria, 63743, Germany, manuel.ciba@h-ab.de

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