Characterizing Neuronal Network Burst Synchrony by Using Burst Signal
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1
Tampere University of Technology, Department of Electronics and Communications Engineering, Finland
Motivation
Neuronal cultures on microelectrode arrays (MEAs) show varying types of spiking and bursting behavior. Network bursts can be defined using different criteria, e.g., number of spikes on multiple electrodes in a very short time window. However, each electrode of the MEA can show very different spiking behavior, so setting the thresholds for the bursts may become complex. Here, we show how network burst dynamics can be characterized by using the extent of network-bursts from a signal deduced from the number of the electrodes showing bursts simultaneously. This signal we call here burst signal.
Material and Methods
Commercially available embryonic mouse cortical neurons (Thermo Fisher Scientific, A15586) were cultured on poly-L-lysine and laminin coated 60-channel MEAs (Multi Channel Systems MCS) for 4 weeks. Bursts in spontaneous activity were detected individually on each electrode using the cumulative moving average -algorithm [1]. The algorithm determines the the start and end times of the bursts, based on which the number of bursting electrodes was calculated for each time point. This gives a temporal signal, here called the burst signal, of which an example is shown in the Figure. Panel A shows the spikes and the detected bursts on all the channels along with the corresponding burst signal. The burst signal is mostly close to zero as no or only a few electrodes show bursts. However, when there are bursts on multiple channels simultaneously, the signal shows high peaks, which can be interpreted as network bursts. To analyze the bursting behavior of the network, the peaks of the burst signal were detected using different thresholds for peak prominence, i.e., the number of simultaneously bursting channels. Based on these peaks, we characterized the synchronized bursting in the network during its development.
Results and Discussion
The results of the burst analysis are shown in the Figure. The spike (Panel A) and burst rates (Panel B) on individual electrodes show, how the activity of the network increases as the network develops. Panel D-G show the characteristics of the network bursts with different thresholds for peak prominence. The number of peaks (Panel D) corresponds to the network-burst rate in the network, which tends to increase as the network develops, as can be expected. The peak height (Panel E) describes the number of simultaneously bursting electrodes. It can be seen, how from DIV 11 to DIV 18 the number of simultaneously bursting channels increases greatly, but after that the number of simultaneously bursting channels remains relatively stable, even if the burst rate on the MEA increases further. This is related to the increasing network-burst count: The network-bursts become more frequent, but the number of the participating channels does not increase further.
The peak width (Panel F) corresponds to the burst duration. The peaks in the early DIVs are very wide, as the spiking in the detected bursts is sparse and burst durations are long. However, as the bursts become more simultaneous, they also become shorter, and from DIV 15 onwards the burst duration remains the same. The peak distance (G) can be used to characterize the temporal peak-to-peak distance between bursts. In the early DIVs, the bursts are close to each other, as the network is not so synchronized, and smaller amount of channels is participating in the bursts at different times. As the network becomes more synchronized, the bursts are further away from each other, as all the channels express bursts at the same times as seen in the burst signal.
Conclusion
We showed that the burst signal is a useful way of characterizing synchronized bursting on MEAs. By calculating properties of the peaks of the burst signal, one can inspect for example network-burst lengths or number of the participating channels. The burst signal can provide a new way to look into network synchrony as many parameters such as the rise time of the network-bursts can be computed.
Figure: (A) An example spike raster plot from DIV 27 (top, black dots) with detected bursts (top, red lines), and burst signal (bottom, black line) with detected peaks (bottom, triangles). (B)-(G) Bursting statistics on different DIVs: (B) Mean spike rate and (C) burst rate on electrodes, (D) mean peak number, (E) mean peak height, (F) mean peak width and (G) mean peak distance. The different colors correspond to different minimum prominences of the detected peaks.
Keywords:
network,
spontaneous activity,
synchrony,
burst
Conference:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016.
Presentation Type:
Poster Presentation
Topic:
MEA Meeting 2016
Citation:
Vornanen
I,
Kapucu
FE,
Johansson
J,
Lenk
K and
Hyttinen
J
(2016). Characterizing Neuronal Network Burst Synchrony by Using Burst Signal.
Front. Neurosci.
Conference Abstract:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays.
doi: 10.3389/conf.fnins.2016.93.00079
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Received:
22 Jun 2016;
Published Online:
24 Jun 2016.
*
Correspondence:
Dr. Jari Hyttinen, Tampere University of Technology, Department of Electronics and Communications Engineering, Tampere, Finland, jari.hyttinen@tuni.fi