Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI

Luca Faes, Jesus M. Cortes, Daniele Marinazzo, Luca Faes, Leonardo Angelini, Guorong Wu, Sebastiano Stramaglia

Risultato della ricerca: Articlepeer review

26 Citazioni (Scopus)


Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.
Lingua originaleEnglish
pagine (da-a)2518-2524
Numero di pagine7
RivistaIEEE Transactions on Biomedical Engineering
Stato di pubblicazionePublished - 2016

All Science Journal Classification (ASJC) codes

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