Since its inception, the statistical concept of Granger causality (GC) has grown in relevance in many applicative fields of time series analysis, especially thanks to its intimate link with the concept of information transfer. Applications of computational physiology and neuroscience are ubiquitous and involve the study of complex interactions between physiological systems in humans and animals under health and diseased conditions. Extensions of GC from pairwise to multivariate analysis involves the study of diverse subsystems and is used to elucidate, for example, the interaction between the central and autonomic nervous systems. GC-based approaches play a crucial role in the data-driven analyses grounded in the emerging and broader field of network physiology. Furthermore, the study of driver-response relationships has led methodological advances to provide reliable measurements of the causal effect or coupling strength, information flow, amount of predictive information and frequency band specificity, among others.
The number of research papers to determine causality and coupling, as well as the applications to different experimental protocols, has notably increased. Research papers have been directed towards the analysis of, but not limited to: (a) cardiovascular and respiratory systems control, (b) central and autonomic nervous systems interactions, (c) cerebrovascular regulation, (d) brain connectivity, (e) brain-heart interactions and (f) prediction of neural, cardiovascular and autonomic diseases. In light of recent methodological advances, most fundamental interactions, such as the baroreflex and respiratory sinus arrhythmia, have been revisited under different stress conditions and diseases to obtain a meaningful characterization of the physiological processes. Consequently, this Research Topic aims to assemble emerging and established approaches, applications to diverse physiological systems studied under different physiological states and pathological conditions, as well as tutorial and reviews in the field, to highlight advances in the field of Granger causality analysis and information transfer applied to multivariate physiological time series.
We welcome original articles, opinion and review papers, and multidisciplinary contributions in the field of causality analysis and information transfer in network physiology, with topics focused on, but not limited to:
• Emerging theoretical approaches to analyze causality and information transfer based on multivariate, parametric, nonlinear, multiscale, time-varying or spectral analysis
• Novel and refined methods to estimate Granger causality and Information Transfer in computational physiology and neuroscience
• Application of causality estimation to neural systems, cardiovascular and respiratory systems, cerebrovascular coupling, brain-body interactions, central versus autonomic nervous system
• Application of causality methods to diseases and clinical states such as sleep disorders, neurological diseases (e.g., depression, epilepsy), orthostatic intolerance, heart failure, cognitive dysfunctions, pulmonary diseases and respiratory failure.
Since its inception, the statistical concept of Granger causality (GC) has grown in relevance in many applicative fields of time series analysis, especially thanks to its intimate link with the concept of information transfer. Applications of computational physiology and neuroscience are ubiquitous and involve the study of complex interactions between physiological systems in humans and animals under health and diseased conditions. Extensions of GC from pairwise to multivariate analysis involves the study of diverse subsystems and is used to elucidate, for example, the interaction between the central and autonomic nervous systems. GC-based approaches play a crucial role in the data-driven analyses grounded in the emerging and broader field of network physiology. Furthermore, the study of driver-response relationships has led methodological advances to provide reliable measurements of the causal effect or coupling strength, information flow, amount of predictive information and frequency band specificity, among others.
The number of research papers to determine causality and coupling, as well as the applications to different experimental protocols, has notably increased. Research papers have been directed towards the analysis of, but not limited to: (a) cardiovascular and respiratory systems control, (b) central and autonomic nervous systems interactions, (c) cerebrovascular regulation, (d) brain connectivity, (e) brain-heart interactions and (f) prediction of neural, cardiovascular and autonomic diseases. In light of recent methodological advances, most fundamental interactions, such as the baroreflex and respiratory sinus arrhythmia, have been revisited under different stress conditions and diseases to obtain a meaningful characterization of the physiological processes. Consequently, this Research Topic aims to assemble emerging and established approaches, applications to diverse physiological systems studied under different physiological states and pathological conditions, as well as tutorial and reviews in the field, to highlight advances in the field of Granger causality analysis and information transfer applied to multivariate physiological time series.
We welcome original articles, opinion and review papers, and multidisciplinary contributions in the field of causality analysis and information transfer in network physiology, with topics focused on, but not limited to:
• Emerging theoretical approaches to analyze causality and information transfer based on multivariate, parametric, nonlinear, multiscale, time-varying or spectral analysis
• Novel and refined methods to estimate Granger causality and Information Transfer in computational physiology and neuroscience
• Application of causality estimation to neural systems, cardiovascular and respiratory systems, cerebrovascular coupling, brain-body interactions, central versus autonomic nervous system
• Application of causality methods to diseases and clinical states such as sleep disorders, neurological diseases (e.g., depression, epilepsy), orthostatic intolerance, heart failure, cognitive dysfunctions, pulmonary diseases and respiratory failure.
