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Physiology and Network Science

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Heart beats, stride intervals, EEGs and breathing intervals do not have normal statistics; these and many other physiologic phenomena manifest inverse power-law distributions. These long-tailed distributions imply that the underlying dynamics cannot be specified by a single scale such as a rate or frequency, ...

Heart beats, stride intervals, EEGs and breathing intervals do not have normal statistics; these and many other physiologic phenomena manifest inverse power-law distributions. These long-tailed distributions imply that the underlying dynamics cannot be specified by a single scale such as a rate or frequency, but span multiple scales that are interconnected through their nonlinear dynamics. Although many experiments have been done, data collected and analyzed and phenomena described in detail, the question of why has not been adequately addressed. Why should oscillations, bi-stability, criticality, chaos, scaling and fractals be so important in physiology and medicine? It seems unlikely that this importance is mechanism specific, although in particular cases such mechanisms can be identified. There is also evidence that the topological and temporal complexity in dynamic networks dovetail and so the question arises as to whether the generic properties of complex dynamic networks can provide the theoretical underpinning of living systems?
The concern of the present Research Topic is this overlap of physiology with networks and of particular interest is the degree to which the dynamics of physiologic processes can be described by complex networks through the existence of phase transitions, the importance of criticality in their healthy operation, their sensitivity to failure modes and ultimately how such networks can be controlled. We welcome research papers from all perspectives addressing this topic, including reviews that touch on dynamics systems theory and how it has morphed into network science; manuscripts that detail specific physiologic systems, for example the brain, and how networking and criticality entail the brain’s power-law statistical properties; as well as compilations of the most recent data on physiologic variability and its robust analysis.


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