The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
How Local Is the Local Field Potential?
Open accessAbstract
Summary Local field potentials (LFPs) are of growing importance in neurophysiological investigations. LFPs supplement action potential recordings by indexing activity relevant to EEG, MEG and hemodynamic (fMRI) signals. Recent reports suggest that LFPs reflect activity within very small domains of several hundred microns. We examined this conclusion by comparing LFP, current source density (CSD), and multiunit activity (MUA) signals in macaque auditory cortex. Estimated by frequency tuning bandwidths, these signals' “listening areas” differ systematically with an order of: MUA < CSD < LFP. Computational analyses confirm that observed LFPs receive non-local contributions. Direct measurements indicate passive spread of LFPs to sites more than a centimeter from their origins. These findings appear to be independent of the frequency content of the LFP. Our results challenge the idea that LFP recordings typically integrate over extremely circumscribed local domains. Rather, LFPs appear as a mixture of local potentials with “volume conducted” potentials from distant sites.
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Cites
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
(2005) · Cited by 5,977
Electric Fields of the Brain: The Neurophysics of EEG
Nunez, P. L., & Harth, E. (1982) · Physics Today · doi:10.1063/1.2915137 · Cited by 2,232
Mitzdorf, U. (1985) · Physiological Reviews · doi:10.1152/physrev.1985.65.1.37 · Cited by 1,673
Electric Fields of the Brain: The Neurophysics of EEG
Fermaglich, J. (1982) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1982.03320380071046 · Cited by 1,543
Neurobiology of cerebellar evolution and development
Snider, R. S. (1971) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(71)90269-0 · Cited by 1,512
Lakatos, P., Shah, A. S., Knuth, K. H., Ulbert, I., Karmos, G., & Schroeder, C. E. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00263.2005 · Cited by 1,237
Self-citationCoupling Between Neuronal Firing, Field Potentials, and fMRI in Human Auditory Cortex
Mukamel, R., Gelbard, H., Arieli, A., Hasson, U., Fried, I., & Malach, R. (2005) · Science · doi:10.1126/science.1110913 · Cited by 1,035
Cole, K. S. (1968) · University of California Press eBooks · doi:10.1525/9780520326514 · Cited by 992
Neuronal oscillations and multisensory interaction in primary auditory cortex
Lakatos, P., Chen, C.-M., O'Connell, M. N., Mills, A., & Schroeder, C. E. (2007) · Neuron · doi:10.1016/j.neuron.2006.12.011 · Cited by 961
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Cited by
The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes
Buzsáki, G., Anastassiou, C. A., & Koch, C. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3241 · Cited by 4,225
A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls
Bastos, A. M., & Schoffelen, J.-M. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00175 · Cited by 1,313
Modelling and analysis of local field potentials for studying the function of cortical circuits
Einevoll, G. T., Kayser, C., Logothetis, N. K., & Panzeri, S. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3599 · Cited by 857
Neuromodulation of Brain States
Lee, S.-H., & Dan, Y. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.012 · Cited by 675
Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models
Mazzoni, A., Lindén, H., Cuntz, H., Lansner, A., Panzeri, S., & Einevoll, G. T. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004584 · Cited by 461
Metadata
- Authors
- Kajikawa, Y., & Schroeder, C. E.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2011-12
- Volume
- 72
- Issue
- 5
- Pages
- 847-858
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W1989700973
- Cited by
- 590
- References
- 88
- FWCI
- 14.77
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
