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

TY - JOUR

T1 - Non parametric directionality analysis - extension for removal of a single common predictor and application to time series

AU - Halliday, David M

AU - Senik, Mohd Harizal

AU - Stevenson, Carl W

AU - Mason, Rob

N1 - © Elsevier 2016. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

PY - 2016/8/1

Y1 - 2016/8/1

N2 - 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.

AB - 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.

KW - Action Potentials

KW - Algorithms

KW - Animals

KW - Cerebral Cortex/physiology

KW - Computer Simulation

KW - Data Interpretation, Statistical

KW - Disease Models, Animal

KW - Epilepsy, Temporal Lobe/physiopathology

KW - Hippocampus/physiology

KW - Kainic Acid

KW - Models, Neurological

KW - Multivariate Analysis

KW - Neurons/physiology

KW - Rats

KW - Signal Processing, Computer-Assisted

KW - Software

KW - Time Factors

U2 - 10.1016/j.jneumeth.2016.05.008

DO - 10.1016/j.jneumeth.2016.05.008

M3 - Article

C2 - 27168497

SN - 0165-0270

VL - 268

SP - 87

EP - 97

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

ER -