Network Physiology: Mapping Interactions Between Networks of Physiologic Networks

Abstract

The human organism is an integrated network of interconnected and interacting organ systems, each representing a separate regulatory network. The behavior of one physiological system (network) may affect the dynamics of all other systems in the network of physiologic networks. Due to these interactions, failure of one system can trigger a cascade of failures throughout the entire network. We introduce a systematic method to identify a network of interactions between diverse physiologic organ systems, to quantify the hierarchical structure and dynamics of this network, and to track its evolution under different physiologic states. We find a robust relation between network structure and physiologic states: every state is characterized by specific network topology, node connectivity and links strength. Further, we find that transitions from one physiologic state to another trigger a markedly fast reorganization in the network of physiologic interactions on time scales of just a few minutes, indicating high network flexibility in response to perturbations. This reorganization in network topology occurs simultaneously and globally in the entire network as well as at the level of individual physiological systems, while preserving a hierarchical order in the strength of network links. Our findings highlight the need of an integrated network approach to understand physiologic function, since the framework we develop provides new information which can not be obtained by studying individual systems. The proposed system-wide integrative approach may facilitate the development of a new field, Network Physiology.

Nonlinear Directed Interactions Between HRV and EEG Activity in Children With TLE

“…suppression of respiratory centers in the brain stem), an ANS dysregulation, etc. Therefore, our results contribute to the development of the field of 'network physiology' [12] as well as 'nonlinear directed interactions', and may contribute to the elucidation of the mechanisms which cause SUDEP. Seizure prediction being another major clinical objective for epileptic patients was not the focus of our investigation.…”

“…HRV analysis in epilepsy is carried out with two major clinical objectives: to reveal the causes for the sudden unexpected death in epileptic patients (SUDEP) [10] and to detect and predict a seizure [11]. From the point of view of network physiology [12], the TLE-related epileptic neural network and the ANS can be considered as coupled networks. Thus, interactions between EEG and HRV signals before, during and after an epileptic seizure can be considered as indicators of time-varying couplings between the epileptic neural network and the ANS.…”

Advanced nonlinear approach to quantify directed interactions within EEG activity of children with temporal lobe epilepsy in their time course

“…Physiological networks are of great interest in many fields of neuroscience [41][42][43]. Our results contribute to the development of the field of network physiology [44] as well as nonlinear directed interactions. Seizure prediction being another major clinical objective for epileptic patients was not the focus of our investigation.…”

Multiscale network representation of physiological time series for early prediction of sepsis

“…Much of the effort have been concentrated on identification and quantification of the interactions between these physiological processes (Ivanov et al 2014). Bashan et al (Bashan et al 2012) proposed the concept of time delay stability (TDS) to quantify the dynamic interactions among physiological processes, such as sleep and cardio-respiratory coupling.…”