A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity

The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Grange...

Full description

Bibliographic Details
Main Authors:	Kim, Sanggyun, Putrino, David, Ghosh, Soumya, Brown, Emery N.
Format:	Online
Language:	English
Published:	Public Library of Science 2011
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3063721/

id	pubmed-3063721
recordtype	oai_dc
spelling	pubmed-30637212011-03-31 A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity Kim, Sanggyun Putrino, David Ghosh, Soumya Brown, Emery N. Research Article The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains. Public Library of Science 2011-03-24 /pmc/articles/PMC3063721/ /pubmed/21455283 http://dx.doi.org/10.1371/journal.pcbi.1001110 Text en Kim et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Kim, Sanggyun Putrino, David Ghosh, Soumya Brown, Emery N.
spellingShingle	Kim, Sanggyun Putrino, David Ghosh, Soumya Brown, Emery N. A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity
author_facet	Kim, Sanggyun Putrino, David Ghosh, Soumya Brown, Emery N.
author_sort	Kim, Sanggyun
title	A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity
title_short	A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity
title_full	A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity
title_fullStr	A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity
title_full_unstemmed	A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity
title_sort	granger causality measure for point process models of ensemble neural spiking activity
description	The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.
publisher	Public Library of Science
publishDate	2011
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3063721/
_version_	1611446852572413952

A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity

Similar Items