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Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony

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Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony. / Kapucu, Fikret E.; Mikkonen, Jarno E.; Tanskanen, Jarno M.A.; Hyttinen, Jari A.K.

2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC). IEEE, 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Kapucu, FE, Mikkonen, JE, Tanskanen, JMA & Hyttinen, JAK 2016, Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony. in 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC). IEEE, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1/01/00. https://doi.org/10.1109/EMBC.2016.7591017

APA

Kapucu, F. E., Mikkonen, J. E., Tanskanen, J. M. A., & Hyttinen, J. A. K. (2016). Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony. In 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) IEEE. https://doi.org/10.1109/EMBC.2016.7591017

Vancouver

Kapucu FE, Mikkonen JE, Tanskanen JMA, Hyttinen JAK. Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony. In 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC). IEEE. 2016 https://doi.org/10.1109/EMBC.2016.7591017

Author

Kapucu, Fikret E. ; Mikkonen, Jarno E. ; Tanskanen, Jarno M.A. ; Hyttinen, Jari A.K. / Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony. 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC). IEEE, 2016.

Bibtex - Download

@inproceedings{96cb60e2528e449da1064ef1a02928fb,
title = "Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony",
abstract = "In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relations with in vivo rat hippocampal recordings, and observe the time courses of the correlations between different regions of hippocampus in three sequential recordings. Additionally, we evaluate the results with a commonly employed causality analysis method to assess the possible correlated findings. Results show that time correlated spectral entropy reveals different levels of interrelations in neuronal networks, which can be interpreted as different levels of neuronal network synchrony.",
author = "Kapucu, {Fikret E.} and Mikkonen, {Jarno E.} and Tanskanen, {Jarno M.A.} and Hyttinen, {Jari A.K.}",
year = "2016",
doi = "10.1109/EMBC.2016.7591017",
language = "English",
isbn = "978-1-4577-0219-8",
publisher = "IEEE",
booktitle = "2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony

AU - Kapucu, Fikret E.

AU - Mikkonen, Jarno E.

AU - Tanskanen, Jarno M.A.

AU - Hyttinen, Jari A.K.

PY - 2016

Y1 - 2016

N2 - In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relations with in vivo rat hippocampal recordings, and observe the time courses of the correlations between different regions of hippocampus in three sequential recordings. Additionally, we evaluate the results with a commonly employed causality analysis method to assess the possible correlated findings. Results show that time correlated spectral entropy reveals different levels of interrelations in neuronal networks, which can be interpreted as different levels of neuronal network synchrony.

AB - In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relations with in vivo rat hippocampal recordings, and observe the time courses of the correlations between different regions of hippocampus in three sequential recordings. Additionally, we evaluate the results with a commonly employed causality analysis method to assess the possible correlated findings. Results show that time correlated spectral entropy reveals different levels of interrelations in neuronal networks, which can be interpreted as different levels of neuronal network synchrony.

U2 - 10.1109/EMBC.2016.7591017

DO - 10.1109/EMBC.2016.7591017

M3 - Conference contribution

SN - 978-1-4577-0219-8

BT - 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)

PB - IEEE

ER -