Abstract
Recent advances in information theory have provided several tools to characterize high-order interactions (HOIs) in complex systems. Among them, the so-called O-information is emerging as particularly useful in practical analysis thanks to its ability to capture the overall balance between redundant and synergistic HOIs. While the O-information is computed for random variables, its extension to random processes studied in the frequency domain is very important to widen the applicability of this tool to networks whose node exhibit rich oscillatory content, such as brain and physiological networks. This work presents the O-information rate (OIR), a measure based on the vector autoregressive and state space modelling of multivariate time series devised to assess the synergistic and redundant HOIs among groups of series in specific bands of biological interest. The new measure is illustrated in two paradigmatic examples of physiological networks characterized by coupled oscillations across a wide range of temporal scales, i.e. the network of cardiovascular and cerebrovascular interactions where redundant synchronized activity emerges around the frequencies of vasomotor and respiratory rhythms, and the network of scalp electroencephalographic signals where synergetic HOIs are detected among the alpha and beta waves recorded over the primary sensorimotor cortex.
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Sparacino, L., Antonacci, Y., Marinazzo, D., Stramaglia, S., Faes, L. (2023). Quantifying High-Order Interactions in Complex Physiological Networks: A Frequency-Specific Approach. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_25
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