Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks

Abstract

Abstract:The continuously growing framework of information dynamics encompasses a set of tools, rooted in information theory and statistical physics, which allow to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of complex networks. Building on the most recent developments in this field, this work designs a complete approach to dissect the information carried by the target of a network of multiple interacting systems into the new information produced by the system, the information stored in the system, and the information transferred to it from the other systems; information storage and transfer are then further decomposed into amounts eliciting the specific contribution of assigned source systems to the target dynamics, and amounts reflecting information modification through the balance between redundant and synergetic interaction between systems. These decompositions are formulated quantifying information either as the variance or as the entropy of the investigated processes, and their exact computation for the case of linear Gaussian processes is presented. The theoretical properties of the resulting measures are first investigated in simulations of vector autoregressive processes. Then, the measures are applied to assess information dynamics in cardiovascular networks from the variability series of heart period, systolic arterial pressure and respiratory activity measured in healthy subjects during supine rest, orthostatic stress, and mental stress. Our results document the importance of combining the assessment of information storage, transfer and modification to investigate common and complementary aspects of network dynamics; suggest the higher specificity to alterations in the network properties of the measures derived from the decompositions; and indicate that measures of information transfer and information modification are better assessed, respectively, through entropy-based and variance-based implementations of the framework.

Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models

“…The lower complexity during HUT is detected up to = 0.225 Hz using RMSE (Figure 3(b)) and up to = 0.1 Hz using LMSE (Figure 3(a)). At scale 1 ( = 0.5 Hz), these results confirm a number of previous investigations documenting the decreased complexity of heart rate variability during postural stress and its unaltered complexity during mental stress [36,[38][39][40]. Here we extend these findings showing that during MA the complexity of HP is left unchanged at any time scale and during HUT it is kept at lower levels until both the high-frequency (HF, >0.15 Hz) and the low-frequency (LF, ∼0.1 Hz) oscillations are removed by filtering.…”

“…This result, which is again more evident using LMSE than RMSE, has been previously observed at scale 1 [36] and is possibly related to complex patterns of autonomic activation following cognitive load [41,42] 0.15 Hz). The emergence of a significantly higher complexity of SAP during HUT when HF fluctuations are filtered out is a novel finding, suggesting that the postural stress decreases the regularity of LF arterial pressure oscillations, while HF oscillations display similar or even increased regularity.…”

“…The analyzed time series belong to a database collected to assess the dynamics of HP, SAP, and RESP during two types of physiological stress commonly studied in cardiovascular variability, that is, postural stress induced by head-up tilt (HUT) and mental stress induced by mental arithmetics (MA); we refer to [35,36] for a detailed description of the population and the experimental setup. Briefly, a group of 61 young healthy subjects (37 females, 17.5 ± 2.4 years old) were monitored in a relaxed state in the resting supine position (SU), in the upright position during HUT, and in the supine position during MA, measuring the surface electrocardiogram (ECG), the finger arterial blood pressure collected noninvasively by the photoplethysmographic method, and the respiration signal measured through respiratory inductive plethysmography.…”

Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

“…These elements essentially reflect the new information produced at each moment in time about a target system in the network [2], the information stored in the target system [3,4], the information transferred to it from the other connected systems [5,6] and the modification of the information flowing from multiple source systems to the target [7,8]. The measures of information dynamics have gained more and more importance in both theoretical and applicative studies in several fields of science [9][10][11][12][13][14][15][16][17][18]. While the information-theoretic approaches to the definition and quantification of new information, information storage and information transfer are well understood and widely accepted, the problem of defining, interpreting and using measures of information modification has not been fully addressed in the literature.…”

Information Theoretical Study of Cross-Talk Mediated Signal Transduction in MAPK Pathways

“…In the context of dynamical systems obeying Gaussian noise processes the formalism has been further extended by Barrett [55]. We note here that the measure of interaction information is the negative of the measure of net synergy [56]. Considering the interactions between two parallel MAPK pathway, we identify signal integration and signal bifurcation motif.…”