Abstract
Functional networks, i.e. networks representing the interactions between the elements of a complex system and reconstructed from the observed elements’ dynamics, are becoming a fundamental tool to unravel the structures created by the movement of information in systems like the human brain. They also present drawbacks, one of the most important being the inherent difficulty in representing and interpreting the resulting structures for large number of nodes and links. I here propose a causality clustering approach, based on grouping nodes into clusters according to their similarity in the overall information dynamics, the latter one being measured by a causality metric. The whole system can then arbitrarily be simplified, with nodes being grouped in e.g. sources, brokers and sinks of information. The advantages and limitations of the proposed approach are discussed using a set of synthetic and real-world data sets, the latter ones representing two neuroscience and technological problems.
Highlights
Functional networks, i.e. networks representing the interactions between the elements of a complex system and reconstructed from the observed elements’ dynamics, are becoming a fundamental tool to unravel the structures created by the movement of information in systems like the human brain
Introduced in n euroscience[4], functional networks are based on the hypothesis that relationships between the elements composing a system affect their respective dynamics, such that the dynamics is a function of the structure; the latter, and time series representing such dynamics, can be used to infer the underlying connectivity
Starting from neuroimaging data, for instance recorded through electroencephalography (EEG), magnetoencephalography (MEG) or functional magnetic resonance imaging, the resulting networks represent how information is distributed across different brain regions
Summary
Functional networks, i.e. networks representing the interactions between the elements of a complex system and reconstructed from the observed elements’ dynamics, are becoming a fundamental tool to unravel the structures created by the movement of information in systems like the human brain. Starting from neuroimaging data, for instance recorded through electroencephalography (EEG), magnetoencephalography (MEG) or functional magnetic resonance imaging (fMRI), the resulting networks represent how information is distributed across different brain regions. This analysis can both be performed for an unguided dynamics (what is known as resting state) or during specific cognitive tasks; and can be used to compare healthy and pathological dynamics. Note that these secondary flows may be inherent to the activity of the system, but may be the result of observational noise and statistical
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