Abstract
Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introduced an adaptive burst analysis method which enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive burst detection. The main emphasis is, if network-wide adaptive burst detection can provide new insights into the assessment of network activity. Therefore, we propose a modification to the previously introduced inter-spike interval (ISI) histogram based cumulative moving average (CMA) algorithm to analyze multiple spike trains simultaneously. The network size can be freely defined, e.g., to include all the electrodes in a microelectrode array (MEA) recording. Additionally, the method can be applied on a series of measurements on the same network to pool the data for statistical analysis. Firstly, we apply both the original CMA-algorithm and our proposed network-wide CMA-algorithm on artificial spike trains to investigate how the modification changes the burst detection. Thereafter, we use the algorithms on MEA data of spontaneously active chemically manipulated in vitro rat cortical networks. Moreover, we compare the synchrony of the detected bursts introducing a new burst synchrony measure. Finally, we demonstrate how the bursting statistics can be used to classify networks by applying k-means clustering to the bursting statistics. The results show that the proposed network wide adaptive burst detection provides a method to unify the burst definition in the whole network and thus improves the assessment and classification of the neuronal activity, e.g., the effects of different pharmaceuticals. The results indicate that the novel method is adaptive enough to be usable on networks with different dynamics, and it is especially feasible when comparing the behavior of differently spiking networks, for example in developing networks.
Highlights
Neuronal networks are often studied using microelectrode arrays (MEAs)
Artificial Data We firstly demonstrated the four different burst detection algorithms with artificial data resembling MEA measurements
The MEA-cumulative moving average (CMA) gives only one threshold, which is in the same range as the thresholds of the network-CMA
Summary
Neuronal networks are often studied using microelectrode arrays (MEAs). MEAs are especially useful in studies considering neuronal networks instead of single cells (Heikkilä et al, 2009; Johnstone et al, 2010). The neuronal networks are typically characterized by their spiking activity, of which an elemental part is bursting (Wagenaar et al, 2006). These thresholds are fixed (Chiappalone et al, 2005), but they can be determined based on the properties of the spike train based on e.g., the mean ISI, spike rate, or the distribution of the ISIs (Wagenaar et al, 2006; Mazzoni et al, 2007)
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