Understanding how different areas of the human brain communicate with each other is a
crucial issue in neuroscience. The concepts of structural, functional and effective …

In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

Identifying causal relationships and quantifying their strength from observational time series
data are key problems in disciplines dealing with complex dynamical systems such as the …

Causal network reconstruction from time series is an emerging topic in many fields of
science. Beyond inferring directionality between two time series, the goal of causal network …

The availability of time series of the evolution of the properties of physical systems is
increasing, stimulating the development of many novel methods for the extraction of …

Background Sleep and circadian rhythm disruptions are common in patients with mood
disorders. The intricate relationship between these disruptions and mood has been …

Conditional independence testing is a fundamental problem underlying causal discovery
and a particularly challenging task in the presence of nonlinear dependencies. Here a fully …

Multivariate transfer entropy (TE) is a model-free approach to detect causalities in
multivariate time series. It is able to distinguish direct from indirect causality and common …

The broad abundance of time series data, which is in sharp contrast to limited knowledge of
the underlying network dynamic processes that produce such observations, calls for a …

Inference of causality is central in nonlinear time series analysis and science in general. A
popular approach to infer causality between two processes is to measure the information …