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Network-wide adaptive burst detection depicts neuronal activity with improved accuracy

Tutkimustuotosvertaisarvioitu

Standard

Network-wide adaptive burst detection depicts neuronal activity with improved accuracy. / Välkki, Inkeri A.; Lenk, Kerstin; Mikkonen, Jarno E.; Kapucu, Fikret E.; Hyttinen, Jari A.K.

julkaisussa: Frontiers in Computational Neuroscience, Vuosikerta 11, 40, 31.05.2017.

Tutkimustuotosvertaisarvioitu

Harvard

Välkki, IA, Lenk, K, Mikkonen, JE, Kapucu, FE & Hyttinen, JAK 2017, 'Network-wide adaptive burst detection depicts neuronal activity with improved accuracy', Frontiers in Computational Neuroscience, Vuosikerta. 11, 40. https://doi.org/10.3389/fncom.2017.00040

APA

Välkki, I. A., Lenk, K., Mikkonen, J. E., Kapucu, F. E., & Hyttinen, J. A. K. (2017). Network-wide adaptive burst detection depicts neuronal activity with improved accuracy. Frontiers in Computational Neuroscience, 11, [40]. https://doi.org/10.3389/fncom.2017.00040

Vancouver

Välkki IA, Lenk K, Mikkonen JE, Kapucu FE, Hyttinen JAK. Network-wide adaptive burst detection depicts neuronal activity with improved accuracy. Frontiers in Computational Neuroscience. 2017 touko 31;11. 40. https://doi.org/10.3389/fncom.2017.00040

Author

Välkki, Inkeri A. ; Lenk, Kerstin ; Mikkonen, Jarno E. ; Kapucu, Fikret E. ; Hyttinen, Jari A.K. / Network-wide adaptive burst detection depicts neuronal activity with improved accuracy. Julkaisussa: Frontiers in Computational Neuroscience. 2017 ; Vuosikerta 11.

Bibtex - Lataa

@article{d21c7e3f4d514f3393a4e27f7ba5ef72,
title = "Network-wide adaptive burst detection depicts neuronal activity with improved accuracy",
abstract = "Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introducedan adaptive burst analysis methodwhich 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 howthe bursting statistics can be used to classify networks by applying k-means clustering to the bursting statistics. The results showthat 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.",
keywords = "Burst detection, Burst synchrony, Microelectrode arrays, Network classification, Neuronal networks",
author = "V{\"a}lkki, {Inkeri A.} and Kerstin Lenk and Mikkonen, {Jarno E.} and Kapucu, {Fikret E.} and Hyttinen, {Jari A.K.}",
note = "EXT={"}Mikkonen, Jarno E.{"}",
year = "2017",
month = "5",
day = "31",
doi = "10.3389/fncom.2017.00040",
language = "English",
volume = "11",
journal = "Frontiers in Computational Neuroscience",
issn = "1662-5188",
publisher = "Frontiers",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Network-wide adaptive burst detection depicts neuronal activity with improved accuracy

AU - Välkki, Inkeri A.

AU - Lenk, Kerstin

AU - Mikkonen, Jarno E.

AU - Kapucu, Fikret E.

AU - Hyttinen, Jari A.K.

N1 - EXT="Mikkonen, Jarno E."

PY - 2017/5/31

Y1 - 2017/5/31

N2 - Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introducedan adaptive burst analysis methodwhich 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 howthe bursting statistics can be used to classify networks by applying k-means clustering to the bursting statistics. The results showthat 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.

AB - Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introducedan adaptive burst analysis methodwhich 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 howthe bursting statistics can be used to classify networks by applying k-means clustering to the bursting statistics. The results showthat 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.

KW - Burst detection

KW - Burst synchrony

KW - Microelectrode arrays

KW - Network classification

KW - Neuronal networks

U2 - 10.3389/fncom.2017.00040

DO - 10.3389/fncom.2017.00040

M3 - Article

VL - 11

JO - Frontiers in Computational Neuroscience

JF - Frontiers in Computational Neuroscience

SN - 1662-5188

M1 - 40

ER -