The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Transfer entropy—a model-free measure of effective connectivity for the neurosciences
Open accessAbstract
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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Cites
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Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
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Investigating Causal Relations by Econometric Models and Cross-Spectral Methods
Granger, C. W. J. (2010) · Essays in Econometrics · doi:10.1017/cbo9780511753978.002 · Cited by 4,762
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“Coherent activity in the beta band has also been demonstrated between bilateral motor cortices (Mima et al. 2000; Murthy and Fetz 1996).”
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Cited by
Communication dynamics in complex brain networks
Avena-Koenigsberger, A., Misic, B., & Sporns, O. (2018) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.149 · Cited by 856
Untangling cross-frequency coupling in neuroscience
Aru, J., Aru, J., Priesemann, V., Wibral, M., Lana, L., Pipa, G., Singer, W., & Vicente, R. (2015) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.08.002 · Cited by 608
Self-citationDisruption of frontal-parietal communication by ketamine, propofol, and sevoflurane
Lee, U., Ku, S., Noh, G., Baek, S., Choi, B., & Mashour, G. A. (2013) · Anesthesiology · doi:10.1097/aln.0b013e31829103f5 · Cited by 426
A critical assessment of connectivity measures for EEG data: A simulation study
Haufe, S., Nikulin, V. V., Müller, K.-R., & Nolte, G. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.036 · Cited by 334
Measuring Integrated Information from the Decoding Perspective
Oizumi, M., Amari, S.-I., Yanagawa, T., Fujii, N., & Tsuchiya, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004654 · Cited by 184
Metadata
- Authors
- Vicente, R., Wibral, M., Lindner, M., & Pipa, G.
- Venue
- Journal of Computational Neuroscience
- Published
- 2010-08-13
- Volume
- 30
- Issue
- 1
- Pages
- 45-67
- ISSN
- 0929-5313 (ISSN-L)
- 1573-6873
- OpenAlex
- W2142858796
- Cited by
- 1,014
- References
- 5
- FWCI
- 13.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
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