Authors of the article address the measurable consequences of the network effect (NE) on time series generated by different parts of the brain, heart, and lung organ-networks (ONs), which are directly related to their inter-network and intra-network interactions. The authors assert that these same physiologic ONs 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.
The College of Science provides students with the high-demand skills and knowledge to succeed as researchers and professionals. The College includes four departments: Biology, Chemistry, Math, and Physics, and is also home to a number of interdisciplinary programs, centers, institutes, intercollegiate programs, labs, and services.
Authors of the article address the measurable consequences of the network effect (NE) on time series generated by different parts of the brain, heart, and lung organ-networks (ONs), which are directly related to their inter-network and intra-network interactions. The authors assert that these same physiologic ONs 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.
Physical Description
10 p.
Notes
Abstract: Herein we address the measurable consequences of the network effect (NE) on time series generated by different parts of the brain, heart, and lung organ-networks (ONs), which are directly related to their inter-network and intra-network interactions. Moreover, these same physiologic ONs 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 ONs, 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 ONs’ 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. CS is truly a transdisciplinary effect.
This article is part of the following collection of related materials.
UNT Scholarly Works
Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.
Mahmoodi, Korosh; Kerick, Scott E.; Grigolini, Paolo; Franaszczuk, Piotr J. & West, Bruce J.Complexity synchronization: a measure of interaction between the brain, heart and lungs,
article,
July 15, 2023;
(https://digital.library.unt.edu/ark:/67531/metadc2288944/:
accessed May 2, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Science.
