Working Paper Article Version 2 This version is not peer-reviewed

A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations

Version 1 : Received: 3 May 2019 / Approved: 6 May 2019 / Online: 6 May 2019 (11:27:51 CEST)
Version 2 : Received: 24 June 2019 / Approved: 25 June 2019 / Online: 25 June 2019 (10:24:23 CEST)

A peer-reviewed article of this Preprint also exists.

Duggento, A.; Valenza, G.; Passamonti, L.; Nigro, S.; Bianco, M.G.; Guerrisi, M.; Barbieri, R.; Toschi, N. A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations. Entropy 2019, 21, 629. Duggento, A.; Valenza, G.; Passamonti, L.; Nigro, S.; Bianco, M.G.; Guerrisi, M.; Barbieri, R.; Toschi, N. A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations. Entropy 2019, 21, 629.

Journal reference: Entropy 2019, 21, 629
DOI: 10.3390/e21070629

Abstract

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.

Subject Areas

Granger causality; directed brain connectivity; MEG connectivity; Laguerre polynomials

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