5 citations · 10 across the 10 of their papers we have counts for
11 papers
From Statistical to Structural Synergy: A Predictability Framework to Quantify the Effects due to High-Order Mechanisms
Yuri Antonacci, Chiara Barà, Laura Sparacino +3
High-order interactions are increasingly recognized as a hallmark of collective dynamics in complex systems. The relationship between high-order behaviours (HOBs), observed as syne…
Redundant and synergistic interactions in a complex network of single-transistor electronic chaotic oscillators and in neurophysiological recordings
Chiara Barà, Yuri Antonacci, Laura Sparacino +8
Complex networks often exhibit emergent behaviors, where simple dyadic interactions yield collective dynamics that cannot be explained by examining the system's units individually…
Investigating High-Order Behaviors in Multivariate Cardiovascular Interactions via Nonlinear Prediction and Information-Theoretic Tools
Chiara Barà, Yuri Antonacci, Laura Sparacino +4
Assessing the synergistic high-order behaviors (HOBs) that emerge from underlying structural mechanisms is crucial to characterize complex systems. This work leverages the combined…
A Method for the Time-Frequency Analysis of High-Order Interactions in Non-Stationary Physiological Networks
Yuri Antonacci, Chiara Bara', Laura Sparacino +3
Several data-driven approaches based on information theory have been proposed for analyzing high-order interactions involving three or more components of a network system. Most of…
Predictive Information Decomposition as a Tool to Quantify Emergent Dynamical Behaviors In Physiological Networks
Luca Faes, Gorana Mijatovic, Laura Sparacino +1
Objective: This work introduces a framework for multivariate time series analysis aimed at detecting and quantifying collective emerging behaviors in the dynamics of physiological…
Decomposing Multivariate Information Rates in Networks of Random Processes
Laura Sparacino, Gorana Mijatovic, Yuri Antonacci +4
The Partial Information Decomposition (PID) framework has emerged as a powerful tool for analyzing high-order interdependencies in complex network systems. However, its application…
Partial Information Rate Decomposition
Luca Faes, Laura Sparacino, Gorana Mijatovic +4
Partial Information Decomposition (PID) is a principled and flexible method to unveil complex high-order interactions in multi-unit network systems. Though being defined exclusivel…
Measuring hierarchically-organized interactions in dynamic networks through spectral entropy rates: theory, estimation, and illustrative application to physiological networks
Laura Sparacino, Yuri Antonacci, Gorana Mijatovic +1
Recent advances in signal processing and information theory are boosting the development of new approaches for the data-driven modelling of complex network systems. In the fields o…
Assessing High-Order Links in Cardiovascular and Respiratory Networks via Static and Dynamic Information Measures
Gorana Mijatovic, Laura Sparacino, Yuri Antonacci +4
The network representation is becoming increasingly popular for the description of cardiovascular interactions based on the analysis of multiple simultaneously collected variables.…
A method to assess Granger causality, isolation and autonomy in the time and frequency domains: theory and application to cerebrovascular variability
Laura Sparacino, Yuri Antonacci, Chiara Barà +3
Concepts of Granger causality (GC) and Granger autonomy (GA) are central to assess the dynamics of coupled physiologic processes. While causality measures have been already propose…
A Framework for the Time- and Frequency-Domain Assessment of High-Order Interactions in Brain and Physiological Networks
Luca Faes, Gorana Mijatovic, Yuri Antonacci +7
While the standard network description of complex systems is based on quantifying links between pairs of system units, higher-order interactions (HOIs) involving three or more unit…