6 papers
Dissecting Spectral Granger Causality through Partial Information Decomposition
Luca Faes, Gorana Mijatovic, Riccardo Pernice +3
Granger causality (GC), a popular statistical method for the inference of directional influences between time series measured from a complex network, is sensitive to high-order (no…
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…
Localizing synergies of hidden factors across complex systems: resting brain networks and HeLa gene expression profile as case studies
Marlis Ontivero-Ortega, Gorana Mijatovic, Luca Faes +2
Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Eve…
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…