Major component analysis of dynamic networks of physiologic organ interactions

26th IUPAP Conference on Computational Physics

物理学;计算机科学

Liu, Kang K. L.^1,2 ; Bartsch, Ronny P.^3 ; Ma, Qianli D. Y.^1,4 ; Ivanov, Plamen Ch^1,5

Department of Physics, Boston University, 590 Commonwealth Ave, Boston

MA

02215, United States^1

Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston

MA

02115, United States^2

Department of Physics, Bar-Ilan University, Ramat-Gan

52900, Israel^3

College of Geographical and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing, China^4

Division of Sleep Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston

MA

02115, United States^5

关键词: Component analysis;    Dynamic interaction;    Dynamical networks;    Nonlinear characteristics;    Physiologic function;    Physiologic systems;    Relative contribution;    Robust association;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/640/1/012013/pdf
DOI  :  10.1088/1742-6596/640/1/012013

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.