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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 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.
Author: | Raul VicenteORCiD, Michael WibralORCiDGND, Michael Lindner, Gordon PipaORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-294998 |
DOI: | https://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 |
Page Number: | 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 / Medizin |
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS) | |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Sammlungen: | Universitätspublikationen |
Licence (German): | Creative Commons - Namensnennung-Nicht kommerziell 3.0 |