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Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series

The detection of causal effects among simultaneous observations provides knowledge about the underlying network, and is a topic of interests in many scientific areas. Over the years different causality measures have been developed, each with their own advantages and disadvantages. However, an extens...

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Autores principales: Heyse, Jolan, Sheybani, Laurent, Vulliémoz, Serge, van Mierlo, Pieter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8569855/
https://www.ncbi.nlm.nih.gov/pubmed/34744643
http://dx.doi.org/10.3389/fnsys.2021.620338
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author Heyse, Jolan
Sheybani, Laurent
Vulliémoz, Serge
van Mierlo, Pieter
author_facet Heyse, Jolan
Sheybani, Laurent
Vulliémoz, Serge
van Mierlo, Pieter
author_sort Heyse, Jolan
collection PubMed
description The detection of causal effects among simultaneous observations provides knowledge about the underlying network, and is a topic of interests in many scientific areas. Over the years different causality measures have been developed, each with their own advantages and disadvantages. However, an extensive evaluation study is missing. In this work we consider some of the best-known causality measures i.e., cross-correlation, (conditional) Granger causality index (CGCI), partial directed coherence (PDC), directed transfer function (DTF), and partial mutual information on mixed embedding (PMIME). To correct for noise-related spurious connections, each measure (except PMIME) is tested for statistical significance based on surrogate data. The performance of the causality metrics is evaluated on a set of simulation models with distinct characteristics, to assess how well they work in- as well as outside of their “comfort zone.” PDC and DTF perform best on systems with frequency-specific connections, while PMIME is the only one able to detect non-linear interactions. The varying performance depending on the system characteristics warrants the use of multiple measures and comparing their results to avoid errors. Furthermore, lags between coupled variables are inherent to real-world systems and could hold essential information on the network dynamics. They are however often not taken into account and we lack proper tools to estimate them. We propose three new methods for lag estimation in multivariate time series, based on autoregressive modelling and information theory. One of the autoregressive methods and the one based on information theory were able to reliably identify the correct lag value in different simulated systems. However, only the latter was able to maintain its performance in the case of non-linear interactions. As a clinical application, the same methods are also applied on an intracranial recording of an epileptic seizure. The combined knowledge from the causality measures and insights from the simulations, on how these measures perform under different circumstances and when to use which one, allow us to recreate a plausible network of the seizure propagation that supports previous observations of desynchronisation and synchronisation during seizure progression. The lag estimation results show absence of a relationship between connectivity strength and estimated lag values, which contradicts the line of thinking in connectivity shaped by the neuron doctrine.
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spelling pubmed-85698552021-11-06 Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series Heyse, Jolan Sheybani, Laurent Vulliémoz, Serge van Mierlo, Pieter Front Syst Neurosci Neuroscience The detection of causal effects among simultaneous observations provides knowledge about the underlying network, and is a topic of interests in many scientific areas. Over the years different causality measures have been developed, each with their own advantages and disadvantages. However, an extensive evaluation study is missing. In this work we consider some of the best-known causality measures i.e., cross-correlation, (conditional) Granger causality index (CGCI), partial directed coherence (PDC), directed transfer function (DTF), and partial mutual information on mixed embedding (PMIME). To correct for noise-related spurious connections, each measure (except PMIME) is tested for statistical significance based on surrogate data. The performance of the causality metrics is evaluated on a set of simulation models with distinct characteristics, to assess how well they work in- as well as outside of their “comfort zone.” PDC and DTF perform best on systems with frequency-specific connections, while PMIME is the only one able to detect non-linear interactions. The varying performance depending on the system characteristics warrants the use of multiple measures and comparing their results to avoid errors. Furthermore, lags between coupled variables are inherent to real-world systems and could hold essential information on the network dynamics. They are however often not taken into account and we lack proper tools to estimate them. We propose three new methods for lag estimation in multivariate time series, based on autoregressive modelling and information theory. One of the autoregressive methods and the one based on information theory were able to reliably identify the correct lag value in different simulated systems. However, only the latter was able to maintain its performance in the case of non-linear interactions. As a clinical application, the same methods are also applied on an intracranial recording of an epileptic seizure. The combined knowledge from the causality measures and insights from the simulations, on how these measures perform under different circumstances and when to use which one, allow us to recreate a plausible network of the seizure propagation that supports previous observations of desynchronisation and synchronisation during seizure progression. The lag estimation results show absence of a relationship between connectivity strength and estimated lag values, which contradicts the line of thinking in connectivity shaped by the neuron doctrine. Frontiers Media S.A. 2021-10-22 /pmc/articles/PMC8569855/ /pubmed/34744643 http://dx.doi.org/10.3389/fnsys.2021.620338 Text en Copyright © 2021 Heyse, Sheybani, Vulliémoz and van Mierlo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Heyse, Jolan
Sheybani, Laurent
Vulliémoz, Serge
van Mierlo, Pieter
Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series
title Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series
title_full Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series
title_fullStr Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series
title_full_unstemmed Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series
title_short Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series
title_sort evaluation of directed causality measures and lag estimations in multivariate time-series
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8569855/
https://www.ncbi.nlm.nih.gov/pubmed/34744643
http://dx.doi.org/10.3389/fnsys.2021.620338
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