| Title |
Transfer entropy—a model-free measure of effective connectivity for the neurosciences
|
|---|---|
| Published in |
Journal of Computational Neuroscience, August 2010
|
| DOI | 10.1007/s10827-010-0262-3 |
| Pubmed ID | |
| Authors |
Raul Vicente, Michael Wibral, Michael Lindner, Gordon Pipa |
| 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. |
Mendeley readers
The data shown below were compiled from readership statistics for 804 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 15 | 2% |
| United States | 11 | 1% |
| United Kingdom | 7 | <1% |
| Spain | 4 | <1% |
| Canada | 4 | <1% |
| Netherlands | 3 | <1% |
| Brazil | 2 | <1% |
| Italy | 2 | <1% |
| Poland | 2 | <1% |
| Other | 19 | 2% |
| Unknown | 735 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 224 | 28% |
| Researcher | 166 | 21% |
| Student > Master | 106 | 13% |
| Student > Bachelor | 42 | 5% |
| Student > Doctoral Student | 41 | 5% |
| Other | 123 | 15% |
| Unknown | 102 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 127 | 16% |
| Neuroscience | 119 | 15% |
| Agricultural and Biological Sciences | 100 | 12% |
| Computer Science | 91 | 11% |
| Physics and Astronomy | 62 | 8% |
| Other | 162 | 20% |
| Unknown | 143 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 November 2023.
All research outputs
#2,347,772
of 25,610,986 outputs
Outputs from Journal of Computational Neuroscience
#11
of 331 outputs
Outputs of similar age
#8,452
of 104,718 outputs
Outputs of similar age from Journal of Computational Neuroscience
#2
of 7 outputs
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So far Altmetric has tracked 331 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 96% of its peers.
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We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.