A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity
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Kim-2011-A Granger Causality.pdf
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Author(s)
Kim, Sanggyun
Putrino, David
Ghosh, Soumya
Brown, Emery N.
Date Issued
March 2011
Journal
PLoS Computational Biology
Publisher
Public Library of Science
Citation
Kim, Sanggyun et al. “A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity.” Ed. Karl J. Friston. PLoS Computational Biology 7 (3) (2011): e1001110. © 2011 Kim et al.
Version
Final published version
Abstract
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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1001110
