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Measuring the Coupling Direction between Neural Oscillations with Weighted Symbolic Transfer Entropy
Neural oscillations reflect rhythmic fluctuations in the synchronization of neuronal populations and play a significant role in neural processing. To further understand the dynamic interactions between different regions in the brain, it is necessary to estimate the coupling direction between neural...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767336/ https://www.ncbi.nlm.nih.gov/pubmed/33371251 http://dx.doi.org/10.3390/e22121442 |
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author | Li, Zhaohui Li, Shuaifei Yu, Tao Li, Xiaoli |
author_facet | Li, Zhaohui Li, Shuaifei Yu, Tao Li, Xiaoli |
author_sort | Li, Zhaohui |
collection | PubMed |
description | Neural oscillations reflect rhythmic fluctuations in the synchronization of neuronal populations and play a significant role in neural processing. To further understand the dynamic interactions between different regions in the brain, it is necessary to estimate the coupling direction between neural oscillations. Here, we developed a novel method, termed weighted symbolic transfer entropy (WSTE), that combines symbolic transfer entropy (STE) and weighted probability distribution to measure the directionality between two neuronal populations. The traditional STE ignores the degree of difference between the amplitude values of a time series. In our proposed WSTE method, this information is picked up by utilizing a weighted probability distribution. The simulation analysis shows that the WSTE method can effectively estimate the coupling direction between two neural oscillations. In comparison with STE, the new method is more sensitive to the coupling strength and is more robust against noise. When applied to epileptic electrocorticography data, a significant coupling direction from the anterior nucleus of thalamus (ANT) to the seizure onset zone (SOZ) was detected during seizures. Considering the superiorities of the WSTE method, it is greatly advantageous to measure the coupling direction between neural oscillations and consequently characterize the information flow between different brain regions. |
format | Online Article Text |
id | pubmed-7767336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77673362021-02-24 Measuring the Coupling Direction between Neural Oscillations with Weighted Symbolic Transfer Entropy Li, Zhaohui Li, Shuaifei Yu, Tao Li, Xiaoli Entropy (Basel) Article Neural oscillations reflect rhythmic fluctuations in the synchronization of neuronal populations and play a significant role in neural processing. To further understand the dynamic interactions between different regions in the brain, it is necessary to estimate the coupling direction between neural oscillations. Here, we developed a novel method, termed weighted symbolic transfer entropy (WSTE), that combines symbolic transfer entropy (STE) and weighted probability distribution to measure the directionality between two neuronal populations. The traditional STE ignores the degree of difference between the amplitude values of a time series. In our proposed WSTE method, this information is picked up by utilizing a weighted probability distribution. The simulation analysis shows that the WSTE method can effectively estimate the coupling direction between two neural oscillations. In comparison with STE, the new method is more sensitive to the coupling strength and is more robust against noise. When applied to epileptic electrocorticography data, a significant coupling direction from the anterior nucleus of thalamus (ANT) to the seizure onset zone (SOZ) was detected during seizures. Considering the superiorities of the WSTE method, it is greatly advantageous to measure the coupling direction between neural oscillations and consequently characterize the information flow between different brain regions. MDPI 2020-12-21 /pmc/articles/PMC7767336/ /pubmed/33371251 http://dx.doi.org/10.3390/e22121442 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Zhaohui Li, Shuaifei Yu, Tao Li, Xiaoli Measuring the Coupling Direction between Neural Oscillations with Weighted Symbolic Transfer Entropy |
title | Measuring the Coupling Direction between Neural Oscillations with Weighted Symbolic Transfer Entropy |
title_full | Measuring the Coupling Direction between Neural Oscillations with Weighted Symbolic Transfer Entropy |
title_fullStr | Measuring the Coupling Direction between Neural Oscillations with Weighted Symbolic Transfer Entropy |
title_full_unstemmed | Measuring the Coupling Direction between Neural Oscillations with Weighted Symbolic Transfer Entropy |
title_short | Measuring the Coupling Direction between Neural Oscillations with Weighted Symbolic Transfer Entropy |
title_sort | measuring the coupling direction between neural oscillations with weighted symbolic transfer entropy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767336/ https://www.ncbi.nlm.nih.gov/pubmed/33371251 http://dx.doi.org/10.3390/e22121442 |
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