The Library
Partial Granger causality - eliminating exogenous inputs and latent variables
Tools
Tools
Tools
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
Research output not available from this repository, contact author.
Official URL: http://dx.doi.org/10.1016/j.jneumeth.2008.04.011
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 > Centre for Scientific Computing Faculty of 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: |
|
||||
| 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
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
