arXiv subjects

Riccardo Pernice

Publications and source records attributed to Riccardo Pernice.

5 recordsLinked to original sources

arXiv2026

Dissecting Spectral Granger Causality through Partial Information Decomposition

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 (non-pairwise) interactions which fundamentally shape the collective network dynamics. This work introduces Partial Decomposition of Granger Causality (PDGC), a tool eliciting redundant and synergistic causal interactions in the pattern of information flow between the subsystems of physiological networks. The tool exploits the framework of partial information decomposition to dissect the multivariate GC from a set of driver random processes to a target process into unique effects carried exclusively by each driver, redundant effects carried identically by more drivers, and synergistic effects carried jointly by some drivers but not by any of them individually. Computation is based on multivariate state-space models expanded in the frequency domain to assess PDGC both in specific bands of physiological interest and in the time domain after whole-band integration. The validation on benchmark simulations demonstrates that the measures of unique, redundant, and synergistic GC reflect the underlying causal mechanisms and are computationally reliable. The application to arterial pressure, respiration, cerebral blood velocity and heart period variability reveals striking differences in the response to postural stress of patients prone to neurally-mediated syncope compared to healthy controls. The extraction of high-order causality patterns from the spectral GC favors dissecting the mechanisms of causal influence underlying multivariate interactions among oscillatory processes in many data-driven applications of network science.

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arXiv2022

Multiscale Partial Information Decomposition of Dynamic Processes with Short and Long-range correlations: Theory and Application to Cardiovascular Control

Heart rate variability results from the combined activity of several physiological systems, including the cardiac, vascular, and respiratory systems which have their own internal regulation, but also interact with each other to preserve the homeostatic function. These control mechanisms operate across multiple temporal scales, resulting in the simultaneous presence of short-term dynamics and long-range correlations. The Network Physiology framework provides statistical tools based on information theory able to quantify structural aspects of multivariate and multiscale interconnected mechanisms driving the dynamics of complex physiological networks. In this work, the multiscale representation of Transfer Entropy from Systolic Arterial Pressure (S) and Respiration (R) to Heart Period (H) and of its decomposition into unique, redundant and synergistic contributions is obtained using a Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This novel approach allows to quantify the directed information flow accounting for the simultaneous presence of short-term dynamics and long-range correlations among the analyzed processes. The approach is first illustrated in simulated VARFI processes and then applied to H, S and R time series measured in healthy subjects monitored at rest and during mental and postural stress. Our results highlight the dependence of the information transfer on the balance between short-term and long-range correlations in coupled dynamical systems, which cannot be observed using standard methods that do not consider long-range correlations. The proposed methodology shows that postural stress induces larger redundant effects at short time scales and mental stress induces larger cardiovascular information transfer at longer time scales.

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arXiv2022

A Framework for the Time- and Frequency-Domain Assessment of High-Order Interactions in Brain and Physiological Networks

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 units play a major role in governing the collective network behavior. This work introduces an approach to quantify pairwise and HOIs for multivariate rhythmic processes interacting across multiple time scales. We define the so-called O-information rate (OIR) as a new metric to assess HOIs for multivariate time series, and propose a framework to decompose it into measures quantifying Granger-causal and instantaneous influences, as well as to expand it in the frequency domain. The framework exploits the spectral representation of vector autoregressive and state-space models to assess synergistic and redundant interactions among groups of processes, both in specific bands and in the time domain after whole-band integration. Validation on simulated networks illustrates how the spectral OIR can highlight redundant and synergistic HOIs emerging at specific frequencies but not using time-domain measures. The application to physiological networks described by heart period, arterial pressure and respiration measured in healthy subjects during paced breathing, and to brain networks described by ECoG signals acquired in an animal experiment during anesthesia, document the capability of our approach to identify informational circuits relevant to well-defined cardiovascular oscillations and brain rhythms and related to specific physiological mechanisms of autonomic control and altered consciousness. The proposed framework allows a hierarchically-organized evaluation of time- and frequency-domain interactions in networks mapped by multivariate time series, and its high flexibility and scalability make it suitable to investigate networks beyond pairwise interactions in neuroscience, physiology and other fields.

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arXiv2019

Comparison of Methods for the Assessment of Nonlinearity in Short-Term Heart Rate Variability under different Physiopathological States

Despite the widespread diffusion of nonlinear methods for heart rate variability (HRV) analysis, the presence and the extent to which nonlinear dynamics contribute to short-term HRV is still controversial. This work aims at testing the hypothesis that different types of nonlinearity can be observed in HRV depending on the method adopted and on the physiopathological state. Two entropy-based measures of time series complexity (normalized complexity index, NCI) and regularity (information storage, IS), and a measure quantifying deviations from linear correlations in a time series (Gaussian linear contrast, GLC), are applied to short HRV recordings obtained in young (Y) and old (O) healthy subjects and in myocardial infarction (MI) patients monitored in the resting supine position and in the upright position reached through head-up tilt. The method of surrogate data is employed to detect the presence of and quantify the contribution of nonlinear dynamics to HRV. We find that the three measures differ both in their variations across groups and conditions and in the number and strength of nonlinear HRV dynamics detected: at rest, IS reveals a significantly lower number of nonlinear dynamics in Y, whereas during tilt GLC reveals significantly stronger nonlinear HRV dynamics in MI; in the transition from rest to tilt, all measures detect a significant weakening of nonlinear HRV dynamics in Y, while only GLC detects a significant strengthening of such dynamics in MI. These results suggest that distinct dynamic structures, detected with different sensitivity by nonlinear measures, lie beneath short-term HRV in different physiological states and pathological conditions.

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arXiv2018

Multiscale Information Storage of Linear Long-Range Correlated Stochastic Processes

Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric framework which allows to compute information storage across multiple time scales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). The framework exploits the theory of state space models to provide the multiscale representation of linear fractionally integrated autoregressive (ARFI) processes, from which information storage is computed at any given time scale relating the process variance to the prediction error variance. This enables the theoretical assessment and a computationally reliable quantification of a complexity measure which incorporates the effects of LRC together with that of short-term dynamics. The proposed measure is first assessed in simulated ARFI processes reproducing different types of autoregressive (AR) dynamics and different degrees of LRC, studying both the theoretical values and the finite sample performance. We find that LRC alter substantially the complexity of ARFI processes even at short time scales, and that reliable estimation of complexity can be achieved at longer time scales only when LRC are properly modeled. Then, we assess multiscale information storage in physiological time series measured in humans during resting state and postural stress, revealing unprecedented responses to stress of the complexity of heart period and systolic arterial pressure variability, which are related to the different role played by LRC in the two conditions.

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