Phase Synchronization Analysis for Reconstructing Physiologic Networks

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Organs in the human body can be regarded as complex systems under neural regulation. Their behavior is monitored by recording long multivariate time series. The complexity in these signals is increased by various couplings and feedback interactions between the organs. Non-stationary, intermittent, and non-linear fluctuations and oscillations occur, which require tools from statistical physics for a full description. The characterization of the dynamics and quantification of the interactions to improve physiological models and to identify diagnostically relevant parameters are major challenges to the methods of time series analysis.A relevant interaction between the cardiac and respiratory systems is described by phase synchronization. Advances in nonlinear dynamics have led to several phase-synchronization analysis methods. Since no systematic comparison was done, we will compare these methods using data from more than 1400 subjects and study systematically detrimental effects of noise and outliers. The goal is to identify advantages and disadvantages for each method. Phase synchronization with cardiovascular low-frequency oscillations will also be considered. In addition, we want to develop and establish novel auto-synchronization and time-delayed synchronization approaches to characterize oscillations in time series and to quantify the inter-relations between two time series. The goal is to identify parameters based on synchronization measures that can be used as predictors of mortality after myocardial infarction and early diagnostic tools for Parkinson's disease, Alzheimer's disease, and depression.In the second part of the project we will study relations between well-established auto-correlation scaling behavior, recently introduced cross-modulation properties, and novel auto-synchronization properties of time series. The goal is to clarify the causes of frequently observed, but hardly explained scaling laws by investigating transitions in the scaling of brain-wave amplitudes, body motion, heartbeat, and respiration. Besides this general approach, we will focus on baroreflex physiology as an example of a physiologically relevant interaction in a limited but still complex system. The main goals are developing and applying novel methods for a reliable quantification of baroreflex sensitivity and distinguishing baroreflex interactions from other physiologic interactions.The third part of the project deals with the reconstruction, characterization, and application of multi-organ interaction networks. Based on our very recent works, we will determine physiologic networks from cross-correlated fluctuations and oscillations in multivariate data. We plan to monitor and characterize the dynamics and evolution of these networks during different states and diseases. Particular goals are identifying indicators for transitions between physiologic states like sleep stages and developing early diagnostic tools for the diseases mentioned above.