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J Comput Neurosci. 2011 Feb;30(1):45-67. doi: 10.1007/s10827-010-0262-3. Epub 2010 Aug 13.

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

Author information

1
Max Planck Institute for Brain Research, Frankfurt, Germany. vicente@fias.uni-frankfurt.de

Abstract

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.

PMID:
20706781
PMCID:
PMC3040354
DOI:
10.1007/s10827-010-0262-3
[Indexed for MEDLINE]
Free PMC Article

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