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Partial Granger causality - eliminating exogenous inputs and latent variables

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Guo, Shuixia, Seth, Anil K., Kendrick, Keith M., Zhou, Cong and Feng, Jianfeng (2008) Partial Granger causality - eliminating exogenous inputs and latent variables. Journal of Neuroscience Methods, Volume 172 (Number 1). pp. 79-93. doi:10.1016/j.jneumeth.2008.04.011 ISSN 0165-0270.

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Official URL: http://dx.doi.org/10.1016/j.jneumeth.2008.04.011

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Abstract

Attempts to identify causal interactions in multivariable biological time series (e.g., gene data, protein data, physiological data) can be undermined by the confounding influence of environmental (exogenous) inputs. Compounding this problem, we are commonly only able to record a subset of all related variables in a system. These recorded variables are likely to be influenced by unrecorded (latent) variables. To address this problem, we introduce a novel variant of a widely used statistical measure of causality - Granger causality - that is inspired by the definition of partial correlation. Our 'partial Granger causality' measure is extensively tested with toy models, both linear and nonlinear, and is applied to experimental data: in vivo multielectrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of sheep. Our results demonstrate that partial Granger causality can reveal the underlying interactions among elements in a network in the presence of exogenous inputs and latent variables in many cases where the existing conditional Granger causality fails. (C) 2008 Elsevier B.V. All rights reserved.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science, Engineering and Medicine > Science > Centre for Scientific Computing
Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Latent variables, Computational neuroscience, Time-series analysis
Journal or Publication Title: Journal of Neuroscience Methods
Publisher: Elsevier BV
ISSN: 0165-0270
Official Date: 15 July 2008
Dates:
DateEvent
15 July 2008Published
Volume: Volume 172
Number: Number 1
Number of Pages: 15
Page Range: pp. 79-93
DOI: 10.1016/j.jneumeth.2008.04.011
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

Data sourced from Thomson Reuters' Web of Knowledge

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