Over the last decades, scaling analysis of time series has been used in a wide range of disciplines. Using either monofractal or multifractal approaches, researchers have estimated global scaling exponents to characterize scale invariance or long-range temporal correlations.
It is now believed that such fractal or multifractal dynamics, possibly characterized by multiscale behaviors, may arise from network interactions across scales within and across physiological systems. Thus, fractality may be an emerging property of systems with underlying network dynamics. However, our understanding of its functional relevance from both physiological and clinical viewpoints remains quite limited. Advances are needed at both conceptual and technical level to gain knowledge about the functional relevance of physiological systems showing scale invariance.
Accordingly, this Research Topic invites contributors to address the importance of obtaining robust estimations of scale invariance and to concentrate on how fractal-based techniques can be related to other methods used to characterize the dynamics of time series, such as transfer entropy and multiscale entropy. We also call for manuscripts focusing on procedures for tracking any variation of the scaling regime(s) and on the functional meaning of these variations, as these may offer much needed insights regarding transitions and fluctuations in physiology.
We welcome original articles, opinions, review papers and multidisciplinary contributions providing novel insights into the fractal analysis of physiological time series and its association with the framework of information theory. Areas of interest may include, but not be limited to:
- Phase transitions, synchronization, criticality and reverberation in the brain.
- Cognition, attention, learning and sleep phases.
- Cardiovascular and respiratory complexity.
- Ageing and disease.
- Neurodevelopment.
- Inflammation and neuroimmune interactions.
- Cellular activity and metabolic networks.
Over the last decades, scaling analysis of time series has been used in a wide range of disciplines. Using either monofractal or multifractal approaches, researchers have estimated global scaling exponents to characterize scale invariance or long-range temporal correlations.
It is now believed that such fractal or multifractal dynamics, possibly characterized by multiscale behaviors, may arise from network interactions across scales within and across physiological systems. Thus, fractality may be an emerging property of systems with underlying network dynamics. However, our understanding of its functional relevance from both physiological and clinical viewpoints remains quite limited. Advances are needed at both conceptual and technical level to gain knowledge about the functional relevance of physiological systems showing scale invariance.
Accordingly, this Research Topic invites contributors to address the importance of obtaining robust estimations of scale invariance and to concentrate on how fractal-based techniques can be related to other methods used to characterize the dynamics of time series, such as transfer entropy and multiscale entropy. We also call for manuscripts focusing on procedures for tracking any variation of the scaling regime(s) and on the functional meaning of these variations, as these may offer much needed insights regarding transitions and fluctuations in physiology.
We welcome original articles, opinions, review papers and multidisciplinary contributions providing novel insights into the fractal analysis of physiological time series and its association with the framework of information theory. Areas of interest may include, but not be limited to:
- Phase transitions, synchronization, criticality and reverberation in the brain.
- Cognition, attention, learning and sleep phases.
- Cardiovascular and respiratory complexity.
- Ageing and disease.
- Neurodevelopment.
- Inflammation and neuroimmune interactions.
- Cellular activity and metabolic networks.