Empirical and theoretical aspects of generation and transfer of information in a neuromagnetic source network.

Abstract

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Variability in the complexity of the source dynamics across the sources in an activated network may be indicative of how the information is processed within the network. Information-theoretic tools allow one not only to characterize local brain dynamics but also to describe interactions between distributed brain activity. This study follows such a framework and explores the relations between signal complexity and asymmetry in mutual interdependencies in a data-driven pipeline of nonlinear analysis of neuromagnetic sources reconstructed from human magnetoencephalographic (MEG) datacollected during a face recognition task. Asymmetry in nonlinear interdependencies in the network was analyzed using transfer entropy. Complexity of the time series was estimated as multi-scaleentropy. The empirical results are supported by an analysis of synthetic data based on the dynamics of coupled systems with time delay in coupling. We found that the amount of information transferred from one source to another was correlated with the difference in complexity between the dynamics of these two sources, with the directionality of dominant information flow depending on the time scale at which the complexity was computed. The results based on synthetic data suggest that both time delay and strength of coupling can contribute to the relations between complexity of brain signals and information flow between them. Our findings support the previous attempts to characterize functional organization of the activated brain, based on a combination of non-linear dynamics andtemporal features of brain connectivity, such as time delay.

Keywords

• MEG
• Complexity
• transfer entropy
• conditional mutual information
• multi-scale entropy
• sample entropy