Time-varying information measures: an adaptive estimation of information storage with application to brain-heart interactions

Abstract

Network Physiology is a rapidly growing field of study that aims to understand how physiological systems interact to maintain health. Within the information theory framework the information storage (IS) allows to measure the regularity and predictability of a dynamic process under stationarity assumption. However, this assumption does not allow to track over time the transient pathways occurring in the dynamical activity of a physiological system. To address this limitation, we propose a time-varying approach based on the recursive least squares algorithm (RLS) for estimating IS at each time instant, in non-stationary conditions. We tested this approach in simulated time-varying dynamics and in the analysis of electroencephalographic (EEG) signals recorded from healthy volunteers and timed with the heartbeat to investigate brain-heart interactions. In simulations, we show that the proposed approach allows to track both abrupt and slow changes in the information stored in a physiological system. These changes are reflected in its evolution and variability over time. The analysis of brain-heart interactions reveals marked differences across the cardiac cycle phases of the variability of the time-varying IS. On the other hand, the average IS values exhibit a weak modulation over parieto-occiptal areas of the scalp. Our study highlights the importance of developing more advanced methods for measuring IS that account for non-stationarity in physiological systems. The proposed time-varying approach based on RLS represents a useful tool for identifying spatio-temporal dynamics within the neurocardiac system and can contribute to the understanding of brain-heart interactions.

Testing dynamic correlations and nonlinearity in bivariate time series through information measures and surrogate data analysis

“…Future developments will aim to test the surrogate methods discussed herein on various biosignals in the context of network physiology ( Ivanov, 2021 ), particularly focusing on the analysis of brain-heart interactions ( Koutlis et al, 2021 ; Antonacci et al, 2023 ; Varley et al, 2023 ). The proposed integrated approach for evaluating dynamic dependencies and nonlinearities in univariate and bivariate time series, implemented with the provided toolbox, will foster the assessment of the statistical temporal structure in coupled processes within Network Physiology and its related fields.…”

Physiological network approach to prognosis in cirrhosis: A shifting paradigm

“…Indeed, the use of causal indices based on mathematically more sophisticated methods may provide further insights and should be the focus of future research in the field. Most importantly, because the field of network physiology is still developing with application in various diseases (Berner et al., 2022 ; dos Santos et al., 2022 ; Hall et al., 2024 ; Legault et al., 2024 ; Lehnertz et al., 2023 ; Rizzo et al., 2023 ) and scenarios including in sports and sleep analysis among others (Antonacci et al., 2023 ; Difrancesco et al., 2023 ; Ganglberger et al., 2023 ; Mangalam et al., 2024 ; Marsh et al., 2023 ; Sides et al., 2023 ), the methods available for detecting physiological connectivity as well as for quantifying such connectivity will depend on the nature of the research question being assessed, the type, quality, and quantity of data available as well as the technical repertoire at the disposal of researchers.…”

Intracortical brain‐heart interplay: An EEG model source study of sympathovagal changes

“…Indeed, several methodologies have emerged for estimating the BHI, addressing different aspects, such as directionality, synchronization, and complexity. These include formulation of an ad hoc synthetic data‐generation model (Candia‐Rivera, 2023; Candia‐Rivera, Catrambone, Barbieri, & Valenza, 2022; Catrambone et al, 2019; Catrambone, Talebi, et al, 2021), the application of information theory to disentangle linear and nonlinear components (Faes et al, 2015) and to quantify information storage (Antonacci et al, 2023; Barà et al, 2023), nervous system—wise functional estimation through microstate occurrences (Catrambone & Valenza, 2023b), or methods exploiting state‐space reconstruction to investigate nonlinear and directed interactions (Schiecke et al, 2016).…”