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A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations
High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on mult...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515122/ https://www.ncbi.nlm.nih.gov/pubmed/33267342 http://dx.doi.org/10.3390/e21070629 |
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author | Duggento, Andrea Valenza, Gaetano Passamonti, Luca Nigro, Salvatore Bianco, Maria Giovanna Guerrisi, Maria Barbieri, Riccardo Toschi, Nicola |
author_facet | Duggento, Andrea Valenza, Gaetano Passamonti, Luca Nigro, Salvatore Bianco, Maria Giovanna Guerrisi, Maria Barbieri, Riccardo Toschi, Nicola |
author_sort | Duggento, Andrea |
collection | PubMed |
description | High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g., data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signals often present long-range dependencies which commonly require high autoregressive model orders/number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener–Volterra decompositions with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions. |
format | Online Article Text |
id | pubmed-7515122 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75151222020-11-09 A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations Duggento, Andrea Valenza, Gaetano Passamonti, Luca Nigro, Salvatore Bianco, Maria Giovanna Guerrisi, Maria Barbieri, Riccardo Toschi, Nicola Entropy (Basel) Article High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g., data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signals often present long-range dependencies which commonly require high autoregressive model orders/number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener–Volterra decompositions with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions. MDPI 2019-06-26 /pmc/articles/PMC7515122/ /pubmed/33267342 http://dx.doi.org/10.3390/e21070629 Text en © 2019 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 Duggento, Andrea Valenza, Gaetano Passamonti, Luca Nigro, Salvatore Bianco, Maria Giovanna Guerrisi, Maria Barbieri, Riccardo Toschi, Nicola A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations |
title | A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations |
title_full | A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations |
title_fullStr | A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations |
title_full_unstemmed | A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations |
title_short | A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations |
title_sort | parsimonious granger causality formulation for capturing arbitrarily long multivariate associations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515122/ https://www.ncbi.nlm.nih.gov/pubmed/33267342 http://dx.doi.org/10.3390/e21070629 |
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