Complexity Synchronization

TL;DR

This paper demonstrates that complex physiological networks like brain, heart, and lung exhibit synchronized complexity, revealing a generalized form of dynamic coordination across different organ systems through multifractal analysis.

Contribution

It introduces the concept of complexity synchronization (CS) as a new framework for understanding inter-network dynamics in physiological systems using modified diffusion entropy analysis.

Findings

High-order synchronization of complexity indices across EEG, ECG, and respiratory signals.

Complexity synchronization is governed by the interdependence of multifractal behaviors.

Results are independent of the coherence properties of the underlying time series.

Abstract

The observational ubiquity of inverse power law spectra (IPL) in complex phenomena entails theory for dynamic fractal phenomena capturing their fractal dimension, dynamics, and statistics. These and other properties are consequences of the complexity resulting from nonlinear dynamic networks collectively summarized for biomedical phenomena as the Network Effect (NE) or focused more narrowly as Network Physiology. Herein we address the measurable consequences of the NE on time series generated by different parts of the brain, heart, and lung organ networks, which are directly related to their inter-network and intra-network interactions. Moreover, these same physiologic organ networks have been shown to generate crucial event (CE) time series, and herein are shown, using modified diffusion entropy analysis (MDEA), to have scaling indices with quasiperiodic changes in complexity, as measured by scaling indices, over time. Such time series are generated by different parts of the brain, heart, and lung organ networks, and the results do not depend on the underlying coherence properties of the associated time series but demonstrate a generalized synchronization of complexity. This high order synchrony among the scaling indices of EEG (brain), ECG (heart), and respiratory time series is governed by the quantitative interdependence of the multifractal behavior of the various physiological organs' network dynamics. This consequence of the NE opens the door for an entirely general characterization of the dynamics of complex networks in terms of complexity synchronization (CS) independently of the scientific, engineering, or technological context.