While the standard network description of complex systems is based on quantifying link between pairs system units, higher-order interactions (HOIs) involving three or more units often play a major role in governing collective behavior.This work introduces new approach to quantify pairwise and HOIs for multivariate rhythmic processes interacting across multiple time scales.We define so-called O-information rate (OIR) as metric assess series, present framework decompose OIR into measures...

Objective: This work introduces a framework for multivariate time series analysis aimed at detecting and quantifying collective emerging behaviors in the dynamics of physiological networks. Methods: Given network system mapped by vector random process, we compute predictive information (PI) between present past states dissect it into amounts unique, redundant synergistic shared each unit. Emergence is then quantified as prevalence over contribution. The implemented practice using...

The Partial Information Decomposition (PID) framework has emerged as a powerful tool for analyzing high-order interdependencies in complex network systems. However, its application to dynamic processes remains challenging due the implicit assumption of memorylessness, which often falls real-world scenarios. In this work, we introduce Rate (PIRD) that extends PID random with temporal correlations. By leveraging mutual information rate (MIR) instead (MI), our approach decomposes shared by...

Partial Information Decomposition (PID) is a principled and flexible method to unveil complex high-order interactions in multi-unit network systems. Though being defined exclusively for random variables, PID ubiquitously applied multivariate time series taken as realizations of processes with temporal statistical structure. Here, overcome the incorrect depiction effects by schemes dynamic networks, we introduce framework Rate (PIRD). PIRD formalized applying lattice theory decompose...

This study provides a comprehensive investigation of the spontaneous short-term regulatory mechanisms affecting cardiovascular and cardiorespiratory interactions during supine rest in response to postural stress. The direct causality measure conditional transfer entropy was applied beat-to-beat heart period, arterial pressure, respiration, compliance variability series assessed thirty-nine healthy subjects resting state orthostatic challenge. inferred physiological networks behind these two...

The study of high order dependencies in complex systems has recently led to the introduction statistical synergy, a novel quantity corresponding form emergence which patterns at large scales are not traceable from lower scales. As consequence, several works last years dealt with synergy and its counterpart, redundancy. In particular, O-information is signed metric that measures balance between redundant synergistic dependencies. spite growing use, this does provide insight about role played...

Recent advances in signal processing and information theory are boosting the development of new approaches for data-driven modelling complex network systems. In fields Network Physiology Neuroscience where signals interest often rich oscillatory content, spectral representation systems is essential to ascribe analyzed interactions specific oscillations with physiological meaning. this context, present work formalizes a coherent framework which integrates several dynamics quantify...

This work presents a comparison between different approaches for the model-free estimation of information-theoretic measures dynamic coupling short realizations random processes. The considered are mutual information rate (MIR) two processes X and Y terms its decomposition evidencing either individual entropy rates their joint rate, or transfer entropies from to instantaneous shared by Y. All estimated through discretization variables forming processes, performed via uniform quantization...

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Goal:</i> The network representation is becoming increasingly popular for the description of cardiovascular interactions based on analysis multiple simultaneously collected variables. However, traditional methods to assess links pairwise interaction measures cannot reveal high-order effects involving more than two nodes, and are not appropriate infer underlying topology. To address these...

The recovery of motor functions after stroke is fostered by the functional integration large-scale brain networks, including network (MN) and high-order cognitive controls such as default mode (DMN) executive control (ECN) networks. In this paper, electroencephalography signals are used to investigate interactions among these three resting state networks (RSNs) in subacute patients rehabilitation. A novel metric, O-information rate (OIR), quantify balance between redundancy synergy complex...

Keeping up with the shift towards personalized neuroscience essentially requires derivation of meaningful insights from individual brain signal recordings by analyzing descriptive indexes physio-pathological states through statistical methods that prioritize subject-specific differences under varying experimental conditions. Within this framework, current study presents a methodology for assessing value single-subject fingerprints functional connectivity, assessed both standard pairwise and...

The concept of self-predictability plays a key role for the analysis self-driven dynamics physiological processes displaying richness oscillatory rhythms. While time domain measures self-predictability, as well time-varying and local extensions, have already been proposed largely applied in different contexts, they still lack clear spectral description, which would be significantly useful interpretation frequency-specific content investigated processes. Herein, we propose novel approach to...

Understanding brain dynamics during motor tasks is a significant challenge in neuroscience, often limited to studying pairwise interactions. This study provides comprehensive hierarchical characterization of node-specific, and higher-order interactions within the human brain's network handgrip task execution. The source activity was reconstructed from scalp EEG signals ten healthy subjects performing task, identifying five regions contralateral ipsilateral networks. Using spectral entropy...

Concepts of Granger causality (GC) and autonomy (GA) are central to assess the dynamics coupled physiologic processes. While measures have been already proposed applied in time frequency domains, quantifying self-dependencies still limited time-domain formulation lack clear spectral representation.

While the standard network description of complex systems is based on quantifying links between pairs system units, higher-order interactions (HOIs) involving three or more units play a major role in governing collective behavior. This work introduces an approach to quantify pairwise and HOIs for multivariate rhythmic processes interacting across multiple time scales. We define so-called O-information rate (OIR) as new metric assess series, propose framework decompose it into measures...

Nowadays, the ever-growing interest to health and quality of life individuals advancements in electronic devices technology are pushing development portable wearable biomedical able pursue a minimally invasive monitoring physiological parameters daily-life conditions. Such can now carry out real-time assessment subjects' overall status possibly even detect ongoing diseases. In this context, we have designed implemented multisensor system perform synchronous acquisitions electrocardiographic...

The network representation is becoming increasingly popular for the description of cardiovascular interactions based on analysis multiple simultaneously collected variables. However, traditional methods to assess links pairwise interaction measures cannot reveal high-order effects involving more than two nodes, and are not appropriate infer underlying topology. To address these limitations, here we introduce a framework which combines assessment with statistical inference characterization...