TY  - JOUR
A1  - Vicente, Raul
A1  - Wibral, Michael
A1  - Lindner, Michael
A1  - Pipa, Gordon
T1  - Transfer entropy - a model-free measure of effective connectivity for the neurosciences
T2  - Journal of computational neuroscience
N2  - 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.
KW  - Information theory
KW  - Effective connectivity
KW  - Causality
KW  - Information transfer
KW  - Electroencephalography
KW  - Magnetoencephalography
Y1  - 2010
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/29499
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-294998
SN  - 1573-6873
SN  - 0929-5313
N1  - 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
VL  - 30.2011
IS  - 1
SP  - 45
EP  - 67
PB  - Springer Science + Business Media B.V
CY  - Dordrecht [u.a.]
ER  -