Partial Granger causality - Eliminating exogenous inputs and latent variables
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, Vol.172 (No.1). pp. 79-93. ISSN 0165-0270Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.jneumeth.2008.04.011
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 > QD Chemistry
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
|Divisions:||Faculty of Science > Centre for Scientific Computing
Faculty of Science > Computer Science
|Journal or Publication Title:||Journal of Neuroscience Methods|
|Date:||15 July 2008|
|Number of Pages:||15|
|Page Range:||pp. 79-93|
|Access rights to Published version:||Restricted or Subscription Access|
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