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

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
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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Metadaten
Author:Raúl Vicente, Michael Wibral, Michael Lindner, Gordon Pipa
URN:urn:nbn:de:hebis:30:3-294998
DOI:http://dx.doi.org/10.1007/s10827-010-0262-3
ISSN:1573-6873
ISSN:0929-5313
Parent Title (English):Journal of computational neuroscience
Publisher:Springer Science + Business Media B.V
Place of publication:Dordrecht [u.a.]
Document Type:Article
Language:English
Date of Publication (online):2013/05/08
Date of first Publication:2010/08/13
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2013/05/08
Tag:Causality; Effective connectivity; Electroencephalography; Information theory; Information transfer; Magnetoencephalography
Volume:30.2011
Issue:1
Pagenumber:23
First Page:45
Last Page:67
Note:
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited
HeBIS PPN:338744770
Institutes:Medizin
Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell 3.0

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