University of Palermo
Signal processing and Analysis tools
The Bioengineering Group at the University of Palermo develops signal processing and analysis tools, mainly rooted in the framework of information dynamics, to uncover the organization of complex networks and explain how complex dynamical behaviors arise from the interplay between the activity of single network units and the connectivity between different units.
The framework of information dynamics is rapidly emerging at the forefront between the theoretical fields of information theory and statistical physics and many applicative fields such as neuroscience, physiology, climatology and econometrics, as a versatile and unifying set of tools that incorporate temporal information within standard information-theoretic measures to quantify different aspects of the dynamics of complex networks. The tools of information dynamics allow to dissect the general concept of “information processing” in a network of multiple interacting dynamical systems into basic elements of computation which reflect different aspects of the functional organization of the network, i.e. the new information produced at each moment in time about a target system in the network, the information stored in the target system, the information transferred to it from the other connected systems and the redundant/synergetic modification of the information flowing from multiple source systems to the target.
Focusing on the multivariate, nonlinear, multi-scale and time-variant aspects of dynamical networks, we apply the tools of information dynamics to different contexts:
- Physiological networks, studied within a paradigm that interprets the human body as an integrated network composed by several organ systems which have their own internal dynamics but are also functionally connected to preserve the physiological function. We aim at providing new insight on the functional structure of the human physiological networks and on their evolution across different physiological states and pathological conditions. A prime example of application of this multi-system approach is the study of sleep state transitions and sleep disorders.
- Human brain networks, where we study the temporal dynamics of brain networks reconstructed from EEG or fMRI measurements in order to improve the understanding of how the brain function emerges from the coordinated behavior of spatially separated cerebral regions. Information-theoretic measures are complemented with frequency-domain measures of brain connectivity to provide a complete analysis framework that is exploited to assess resting-state and brain networks and their modifications induced by stimulation or cognitive elicitation.
- Muscle networks, i.e. functional networks of muscular activity probed recording the EMG from multiple muscles distributed across the body. Under the hypothesis that neural synchrony provides a mechanism for the formation of muscle synergies, we study the information dynamics of muscle networks to find consistent patterns of information processing across the multiple muscles that need to be coordinated to perform specific tasks such as pointing towards a target or maintaining posture.
L Faes, A Porta, G Nollo, M Javorka, 'Information decomposition in multivariate systems: definitions, implementation and application to cardiovascular networks', Entropy, special issue on Multivariate entropy measures and their applications, 2017, 19(1), 5.
L Faes, S Stramaglia, G Nollo, D Marinazzo, 'Multiscale Granger causality', Phys. Rev. E, 2017, 96:042150 (7 pages).
A Porta, L Faes, 'Wiener-Granger Causality in Network Physiology with Applications to Cardiovascular Control and Neuroscience', Proceedings of the IEEE 2016; 104(2): 282-309.
L Faes, D Marinazzo, G Nollo, A Porta 'An information-theoretic framework to map the spatio-temporal dynamics of the scalp electroencephalogram', IEEE Trans. Biomed. Eng., special issue on Brain Connectivity, 2016; 63(12):2488-2496.
L Faes, G Nollo, F Jurysta, D Marinazzo, 'Information dynamics of brain-heart physiological networks during sleep', New J Phys 2014; 16:105005.