Assessing Redundancy and Synergy in Brain-Heart Interactions: A Conditioning Approach*

Mutual Information Rate (MIR) is the primary method for quantifying the dynamical coupling of two processes in a network. However, it can vary substantially in its conditioned calculation due to high-order dependencies among the system processes. In this work, we develop a methodology that seeks multiplets of variables that maximize or minimize the dynamic coupling. This allows to decompose the maximal MIR into unique, redundant, and synergistic components, quantifying high-order effects relative importance compared to dyadic effects. The proposed approach is employed to analyze the high order interactions of brain-heart crosstalk in healthy individuals.

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Publication Details

Published
2024-10-23
DOI
https://doi.org/10.1109/esgco63003.2024.10766979
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
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article

Assessing Redundancy and Synergy in Brain-Heart Interactions: A Conditioning Approach*

Yuri Antonacci, Luca Faes, Hélder Pinto, Ana Paula Rocha et al.
EEG and Brain-Computer Interfaces
article

Assessing Redundancy and Synergy in Brain-Heart Interactions: A Conditioning Approach*

Yuri Antonacci, Luca Faes, Hélder Pinto, Ana Paula Rocha, Valeria R. Vergara
article en

Abstract

Mutual Information Rate (MIR) is the primary method for quantifying the dynamical coupling of two processes in a network. However, it can vary substantially in its conditioned calculation due to high-order dependencies among the system processes. In this work, we develop a methodology that seeks multiplets of variables that maximize or minimize the dynamic coupling. This allows to decompose the maximal MIR into unique, redundant, and synergistic components, quantifying high-order effects relative importance compared to dyadic effects. The proposed approach is employed to analyze the high order interactions of brain-heart crosstalk in healthy individuals.

Universidade do Porto (PT), University of Palermo (IT)
Openalex Percentile: Top 78%
EEG and Brain-Computer Interfaces
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