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Effect of neural connectivity on autocovariance and cross covariance estimates
BACKGROUND: Measurements of auto and cross covariance functions are frequently used to investigate neural systems. In interpreting this data, it is commonly assumed that the largest contribution to the recordings comes from sources near the electrode. However, the potential recorded at an electrode...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1802087/ https://www.ncbi.nlm.nih.gov/pubmed/17227577 http://dx.doi.org/10.1186/1475-925X-6-3 |
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author | Stecker, Mark M |
author_facet | Stecker, Mark M |
author_sort | Stecker, Mark M |
collection | PubMed |
description | BACKGROUND: Measurements of auto and cross covariance functions are frequently used to investigate neural systems. In interpreting this data, it is commonly assumed that the largest contribution to the recordings comes from sources near the electrode. However, the potential recorded at an electrode represents the superimposition of the potentials generated by large numbers of active neural structures. This creates situations under which the measured auto and cross covariance functions are dominated by the activity in structures far from the electrode and in which the distance dependence of the cross-covariance function differs significantly from that describing the activity in the actual neural structures. METHODS: Direct application of electrostatics to calculate the theoretical auto and cross covariance functions that would be recorded from electrodes immersed in a large volume filled with active neural structures with specific statistical properties. RESULTS: It is demonstrated that the potentials recorded from a monopolar electrode surrounded by dipole sources in a uniform medium are predominantly due to activity in neural structures far from the electrode when neuronal correlations drop more slowly than 1/r(3 )or when the size of the neural system is much smaller than a known correlation distance. Recordings from quadrupolar sources are strongly dependent on distant neurons when correlations drop more slowly than 1/r or the size of the system is much smaller than the correlation distance. Differences between bipolar and monopolar recordings are discussed. It is also demonstrated that the cross covariance of the recorded in two spatially separated electrodes declines as a power-law function of the distance between them even when the electrical activity from different neuronal structures is uncorrelated. CONCLUSION: When extracellular electrophysiologic recordings are made from systems containing large numbers of neural structures, it is important to interpret measured auto and cross covariance functions cautiously in light of the long range nature of the electric fields. Using recording electrodes that are bipolar or quadrupolar minimizes or eliminates these effects and hence these electrodes are preferred when electrical recordings are made for the purpose of auto and cross correlation analysis of local electrical activity. |
format | Text |
id | pubmed-1802087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-18020872007-02-23 Effect of neural connectivity on autocovariance and cross covariance estimates Stecker, Mark M Biomed Eng Online Research BACKGROUND: Measurements of auto and cross covariance functions are frequently used to investigate neural systems. In interpreting this data, it is commonly assumed that the largest contribution to the recordings comes from sources near the electrode. However, the potential recorded at an electrode represents the superimposition of the potentials generated by large numbers of active neural structures. This creates situations under which the measured auto and cross covariance functions are dominated by the activity in structures far from the electrode and in which the distance dependence of the cross-covariance function differs significantly from that describing the activity in the actual neural structures. METHODS: Direct application of electrostatics to calculate the theoretical auto and cross covariance functions that would be recorded from electrodes immersed in a large volume filled with active neural structures with specific statistical properties. RESULTS: It is demonstrated that the potentials recorded from a monopolar electrode surrounded by dipole sources in a uniform medium are predominantly due to activity in neural structures far from the electrode when neuronal correlations drop more slowly than 1/r(3 )or when the size of the neural system is much smaller than a known correlation distance. Recordings from quadrupolar sources are strongly dependent on distant neurons when correlations drop more slowly than 1/r or the size of the system is much smaller than the correlation distance. Differences between bipolar and monopolar recordings are discussed. It is also demonstrated that the cross covariance of the recorded in two spatially separated electrodes declines as a power-law function of the distance between them even when the electrical activity from different neuronal structures is uncorrelated. CONCLUSION: When extracellular electrophysiologic recordings are made from systems containing large numbers of neural structures, it is important to interpret measured auto and cross covariance functions cautiously in light of the long range nature of the electric fields. Using recording electrodes that are bipolar or quadrupolar minimizes or eliminates these effects and hence these electrodes are preferred when electrical recordings are made for the purpose of auto and cross correlation analysis of local electrical activity. BioMed Central 2007-01-16 /pmc/articles/PMC1802087/ /pubmed/17227577 http://dx.doi.org/10.1186/1475-925X-6-3 Text en Copyright © 2007 Stecker; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Stecker, Mark M Effect of neural connectivity on autocovariance and cross covariance estimates |
title | Effect of neural connectivity on autocovariance and cross covariance estimates |
title_full | Effect of neural connectivity on autocovariance and cross covariance estimates |
title_fullStr | Effect of neural connectivity on autocovariance and cross covariance estimates |
title_full_unstemmed | Effect of neural connectivity on autocovariance and cross covariance estimates |
title_short | Effect of neural connectivity on autocovariance and cross covariance estimates |
title_sort | effect of neural connectivity on autocovariance and cross covariance estimates |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1802087/ https://www.ncbi.nlm.nih.gov/pubmed/17227577 http://dx.doi.org/10.1186/1475-925X-6-3 |
work_keys_str_mv | AT steckermarkm effectofneuralconnectivityonautocovarianceandcrosscovarianceestimates |