Network Physiology: Mapping interactions between complex physiological systems

The human organism is an integrated network where multi-component organ systems, each with its own regulatory mechanisms, continuously interact to optimize and coordinate their function. Organ-to-organ interactions occur at multiple levels and spatiotemporal time scales to produce distinct physiologic states: wake and sleep; light and deep sleep; consciousness and unconsciousness. Disrupting organ communications can lead to dysfunction of individual systems or to collapse of the entire organism. Yet, we know almost nothing about the nature of the interactions between diverse organ systems and their collective role in maintaining health. We propose a framework to probe dynamical interactions among physiological systems, and we identify a physiological network. We find that each physiological state is characterized by a specific network structure, demonstrating a robust interplay between network topology and physiologic function. Across physiological states, the network undergoes topological transitions associated with fast reorganization of physiological interactions on time scales of a few minutes, indicating high network flexibility in response to perturbations. The proposed system-wide integrative approach facilitates the development of a new field, Network PhysiologyUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Fractal scale-invariant and nonlinear properties of cardiac dynamics remain stable with advanced age: A new mechanistic picture of cardiac control in healthy elderly

We analyze heartbeat interval recordings from two independent databases: (a) 19 healthy young (avg. age 25.7 years) and 16 healthy elderly subjects (avg. age 73.8 years) during 2h under resting conditions from the Fantasia database; and (b) 29 healthy elderly subjects (avg. age 75.9 years) during $\approx{}8$h of sleep from the SHHS database, and the same subjects recorded 5 years later. We quantify: (1) The average heart rate ; (2) the SD $\sigma_{RR}$ and $\sigma_{\Delta{}RR}$ of the heartbeat intervals RR and their increments $\Delta{}RR$; (3) the long-range correlations in RR as measured by the scaling exponent $\alpha_{RR}$ using the Detrended Fluctuation Analysis; (4) fractal linear and nonlinear properties as represented by the scaling exponents $\alpha^{sign}$ and $\alpha^{mag}$ for the time series of the sign and magnitude of $\Delta{}RR$; (5) the nonlinear fractal dimension $D(k)$ of $RR$ using the Fractal Dimension Analysis. We find: (1) No significant difference in \left (P>0.05); (2) a significant difference in $\sigma_{RR}$ and $\sigma_{\Delta{}RR}$ for the Fantasia groups (P<10^{-4}) but no significant change with age between the elderly SHHS groups (P>0.5); (3) no significant change in the fractal measures $\alpha_{RR}$ (P>0.15), $\alpha^{sign}$ (P>0.2), $\alpha^{mag}$ (P>0.3), and D(k) with age. Our findings do not support the hypothesis that fractal linear and nonlinear characteristics of heartbeat dynamics break down with advanced age in healthy subjects. While our results indeed show a reduced SD of heartbeat fluctuations with advanced age, the inherent temporal fractal and nonlinear organization of these fluctuations remains stable.Comment: 19 pages, 14 figure

Impact of Stock Market Structure on Intertrade Time and Price Dynamics

We analyse times between consecutive transactions for a diverse group of stocks registered on the NYSE and NASDAQ markets, and we relate the dynamical properties of the intertrade times with those of the corresponding price fluctuations. We report that market structure strongly impacts the scale-invariant temporal organisation in the transaction timing of stocks, which we have observed to have long-range power-law correlations. Specifically, we find that, compared to NYSE stocks, stocks registered on the NASDAQ exhibit significantly stronger correlations in their transaction timing on scales within a trading day. Further, we find that companies that transfer from the NASDAQ to the NYSE show a reduction in the correlation strength of transaction timing on scales within a trading day, indicating influences of market structure. We also report a persistent decrease in correlation strength of intertrade times with increasing average intertrade time and with corresponding decrease in companies' market capitalization–a trend which is less pronounced for NASDAQ stocks. Surprisingly, we observe that stronger power-law correlations in intertrade times are coupled with stronger power-law correlations in absolute price returns and higher price volatility, suggesting a strong link between the dynamical properties of intertrade times and the corresponding price fluctuations over a broad range of time scales. Comparing the NYSE and NASDAQ markets, we demonstrate that the stronger correlations we find in intertrade times for NASDAQ stocks are associated with stronger correlations in absolute price returns and with higher volatility, suggesting that market structure may affect price behavior through information contained in transaction timing. These findings do not support the hypothesis of universal scaling behavior in stock dynamics that is independent of company characteristics and stock market structure. Further, our results have implications for utilising transaction timing patterns in price prediction and risk management optimization on different stock markets

Systems with Correlations in the Variance: Generating Power-Law Tails in Probability Distributions

We study how the presence of correlations in physical variables contributes to the form of probability distributions. We investigate a process with correlations in the variance generated by (i) a Gaussian or (ii) a truncated L\'{e}vy distribution. For both (i) and (ii), we find that due to the correlations in the variance, the process ``dynamically'' generates power-law tails in the distributions, whose exponents can be controlled through the way the correlations in the variance are introduced. For (ii), we find that the process can extend a truncated distribution {\it beyond the truncation cutoff}, which leads to a crossover between a L\'{e}vy stable power law and the present ``dynamically-generated'' power law. We show that the process can explain the crossover behavior recently observed in the S&P500 stock index.Comment: 7 pages, five figures. To appear in Europhysics Letters (2000