Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy
Välkki, I. A., Lenk, K., Mikkonen, J., Kapucu, F. E., & Hyttinen, J. A. K. (2017). Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy. Frontiers in Computational Neuroscience, 11, Article 40. https://doi.org/10.3389/fncom.2017.00040
Julkaistu sarjassa
Frontiers in Computational NeuroscienceTekijät
Päivämäärä
2017Tekijänoikeudet
© 2017 Välkki, Lenk, Mikkonen, Kapucu and Hyttinen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
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.
...
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Frontiers Research FoundationISSN Hae Julkaisufoorumista
1662-5188Asiasanat
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https://converis.jyu.fi/converis/portal/detail/Publication/27070501
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