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Non-parametric directionality analysis: extension for removal of a single common predictor and application to time series

Halliday, David M.; Senik, Mohd Harizal; Stevenson, Carl W.; Mason, Robert

Non-parametric directionality analysis: extension for removal of a single common predictor and application to time series Thumbnail


Authors

David M. Halliday

Mohd Harizal Senik

Robert Mason



Abstract

BACKGROUND: The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity.

NEW METHOD: We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor.

RESULTS: The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats.

COMPARISON WITH EXISTING METHOD(S): The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data.

CONCLUSIONS: The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data.

Citation

Halliday, D. M., Senik, M. H., Stevenson, C. W., & Mason, R. (2016). Non-parametric directionality analysis: extension for removal of a single common predictor and application to time series. Journal of Neuroscience Methods, 268, https://doi.org/10.1016/j.jneumeth.2016.05.008

Journal Article Type Article
Acceptance Date May 4, 2016
Online Publication Date May 7, 2016
Publication Date Aug 1, 2016
Deposit Date Nov 4, 2016
Publicly Available Date Nov 4, 2016
Journal Journal of Neuroscience Methods
Print ISSN 0165-0270
Electronic ISSN 1872-678X
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 268
DOI https://doi.org/10.1016/j.jneumeth.2016.05.008
Keywords Directionality, Partial Coherence, Non parametric, Time series, Point process, Conditional independence,
Granger causality
Public URL https://nottingham-repository.worktribe.com/output/797539
Publisher URL http://www.sciencedirect.com/science/article/pii/S0165027016300863
Contract Date Nov 4, 2016

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