Using transfer entropy to measure the patterns of information flow though cortex : application to MEG recordings from a visual Simon task
- Poster presentation: Functional connectivity of the brain describes the network of correlated activities of different brain areas. However, correlation does not imply causality and most synchronization measures do not distinguish causal and non-causal interactions among remote brain areas, i.e. determine the effective connectivity [1]. Identification of causal interactions in brain networks is fundamental to understanding the processing of information. Attempts at unveiling signs of functional or effective connectivity from non-invasive Magneto-/Electroencephalographic (M/EEG) recordings at the sensor level are hampered by volume conduction leading to correlated sensor signals without the presence of effective connectivity. Here, we make use of the transfer entropy (TE) concept to establish effective connectivity. The formalism of TE has been proposed as a rigorous quantification of the information flow among systems in interaction and is a natural generalization of mutual information [2]. In contrast to Granger causality, TE is a non-linear measure and not influenced by volume conduction. ...
Author: | Michael WibralORCiDGND, Raul VicenteORCiD, Jochen TrieschORCiD, Gordon PipaORCiDGND |
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URN: | urn:nbn:de:hebis:30-70795 |
DOI: | https://doi.org/10.1186/1471-2202-10-S1-P232 |
Parent Title (English): | BMC neuroscience |
Document Type: | Article |
Language: | English |
Year of Completion: | 2009 |
Year of first Publication: | 2009 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2009/09/20 |
Volume: | 10(Suppl 1) |
Issue: | P232 |
Note: | © 2009 Wibral et al; licensee BioMed Central Ltd. |
Source: | from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009 |
HeBIS-PPN: | 21898491X |
Institutes: | Medizin / Medizin |
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS) | |
Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
Sammlungen: | Sammlung Biologie / Sondersammelgebiets-Volltexte |
Licence (German): | Deutsches Urheberrecht |