Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy

Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillati...

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Autores principales:	Iván De La Pava Panche, Viviana Gómez-Orozco, Andrés Álvarez-Meza, David Cárdenas-Peña, Álvaro Orozco-Gutiérrez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	EEG data phase-amplitude interactions cross-frequency interactions transfer entropy kernel methods Renyi’s entropy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/38dbc7cf3e92435c9a740a98999787df
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spelling	oai:doaj.org-article:38dbc7cf3e92435c9a740a98999787df2021-11-11T14:57:21ZEstimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy10.3390/app112198032076-3417https://doaj.org/article/38dbc7cf3e92435c9a740a98999787df2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9803https://doaj.org/toc/2076-3417Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillating at low frequencies, to local, rapidly oscillating neural assemblies. A promising approach to estimating such interactions is the use of transfer entropy (TE), a non-linear, information-theory-based effective connectivity measure. The conventional method involves feeding instantaneous phase and amplitude time series, extracted at the target frequencies, to a TE estimator. In this work, we propose that the problem of directed phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels. We implement our proposal using a kernel-based TE estimator, defined in terms of Renyi’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> entropy, which has successfully been used to compute single-trial phase TE. We tested our approach on the synthetic data generated through a simulation model capable of producing a time series with directed phase-amplitude interactions at two given frequencies, and on EEG data from a cognitive task designed to activate working memory, a memory system whose underpinning mechanisms are thought to include phase–amplitude couplings. Our proposal detected statistically significant interactions between the simulated signals at the desired frequencies for the synthetic data, identifying the correct direction of the interaction. It also displayed higher robustness to noise than the alternative methods. The results attained for the working memory data showed that the proposed approach codes connectivity patterns based on directed phase–amplitude interactions, that allow for the different cognitive load levels of the working memory task to be differentiated.Iván De La Pava PancheViviana Gómez-OrozcoAndrés Álvarez-MezaDavid Cárdenas-PeñaÁlvaro Orozco-GutiérrezMDPI AGarticleEEG dataphase-amplitude interactionscross-frequency interactionstransfer entropykernel methodsRenyi’s entropyTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9803, p 9803 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EEG data phase-amplitude interactions cross-frequency interactions transfer entropy kernel methods Renyi’s entropy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	EEG data phase-amplitude interactions cross-frequency interactions transfer entropy kernel methods Renyi’s entropy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Iván De La Pava Panche Viviana Gómez-Orozco Andrés Álvarez-Meza David Cárdenas-Peña Álvaro Orozco-Gutiérrez Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy
description	Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillating at low frequencies, to local, rapidly oscillating neural assemblies. A promising approach to estimating such interactions is the use of transfer entropy (TE), a non-linear, information-theory-based effective connectivity measure. The conventional method involves feeding instantaneous phase and amplitude time series, extracted at the target frequencies, to a TE estimator. In this work, we propose that the problem of directed phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels. We implement our proposal using a kernel-based TE estimator, defined in terms of Renyi’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> entropy, which has successfully been used to compute single-trial phase TE. We tested our approach on the synthetic data generated through a simulation model capable of producing a time series with directed phase-amplitude interactions at two given frequencies, and on EEG data from a cognitive task designed to activate working memory, a memory system whose underpinning mechanisms are thought to include phase–amplitude couplings. Our proposal detected statistically significant interactions between the simulated signals at the desired frequencies for the synthetic data, identifying the correct direction of the interaction. It also displayed higher robustness to noise than the alternative methods. The results attained for the working memory data showed that the proposed approach codes connectivity patterns based on directed phase–amplitude interactions, that allow for the different cognitive load levels of the working memory task to be differentiated.
format	article
author	Iván De La Pava Panche Viviana Gómez-Orozco Andrés Álvarez-Meza David Cárdenas-Peña Álvaro Orozco-Gutiérrez
author_facet	Iván De La Pava Panche Viviana Gómez-Orozco Andrés Álvarez-Meza David Cárdenas-Peña Álvaro Orozco-Gutiérrez
author_sort	Iván De La Pava Panche
title	Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy
title_short	Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy
title_full	Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy
title_fullStr	Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy
title_full_unstemmed	Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy
title_sort	estimating directed phase-amplitude interactions from eeg data through kernel-based phase transfer entropy
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/38dbc7cf3e92435c9a740a98999787df
work_keys_str_mv	AT ivandelapavapanche estimatingdirectedphaseamplitudeinteractionsfromeegdatathroughkernelbasedphasetransferentropy AT vivianagomezorozco estimatingdirectedphaseamplitudeinteractionsfromeegdatathroughkernelbasedphasetransferentropy AT andresalvarezmeza estimatingdirectedphaseamplitudeinteractionsfromeegdatathroughkernelbasedphasetransferentropy AT davidcardenaspena estimatingdirectedphaseamplitudeinteractionsfromeegdatathroughkernelbasedphasetransferentropy AT alvaroorozcogutierrez estimatingdirectedphaseamplitudeinteractionsfromeegdatathroughkernelbasedphasetransferentropy
_version_	1718437876284260352

Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy

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