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. 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.Author Summary: Recent advances in multiple-electrode recording have made it possible to record the activities of multiple neurons simultaneously. This provides an opportunity to study how groups of neurons form functional ensembles as different brain areas perform their various functions. However, most of the methods that attempt to identify associations between neurons provide little insight into the directional nature of the interactions that they detect. Recently, Granger causality has proven to be an efficient method to infer causal relationships between sets of continuous-valued data, but cannot be directly applied to point process data such as neural spike trains. Here, we propose a novel and successful attempt to expand the application of Granger causality to point process data. The proposed method performed well with simulated data, and was then applied to real experimental data recorded from sets of simultaneously recorded neurons from the primary motor cortex. The results of the real data analysis suggest that the proposed method has the potential to provide unique neurophysiological insights about network properties in the cortex that have not been possible with other contemporary methods of functional interaction detection.