Transfer entropy—a model-free measure of effective connectivity for the neurosciences

Max Planck Institute for Brain Research, Frankfurt, Germany.
Journal of Computational Neuroscience (Impact Factor: 1.74). 02/2011; 30(1):45-67. DOI: 10.1007/s10827-010-0262-3
Source: PubMed


Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain's activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction.

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    • "More recently, a novel concept of measuring causality has been proposed: transfer entropy (TE), by Schreiber [33]. TE has since found its place in many areas , including again neurosciences [36], chemistry [3] and others [23]. To our knowledge, it has not been previously used in computer vision. "
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    International Conference on Computer Vision, Santiago, Chile; 12/2015
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    • "In contrast to GC, TE is a model-free measure based on information theory that does not require a model of the interaction. TE has demonstrated its robustness against volume conduction as well as its effectiveness in revealing non-linear interactions between brain regions (Lindner et al., 2011; Vicente et al., 2011). "
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    • "Network reconstruction was carried out by identifying causal influences between neurons through TE (Schreiber, 2000; Stetter et al., 2012; Vicente et al., 2011). TE is an information-theoretic measure that identifies the flow of information between two time traces. "
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