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. Gran...
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| Format: | Article |
| Language: | en_US |
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Public Library of Science
2011
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| Online Access: | http://hdl.handle.net/1721.1/66108 https://orcid.org/0000-0003-2668-7819 |
| _version_ | 1826209239583948800 |
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| author | Kim, Sanggyun Putrino, David Ghosh, Soumya Brown, Emery N. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Kim, Sanggyun Putrino, David Ghosh, Soumya Brown, Emery N. |
| author_sort | Kim, Sanggyun |
| collection | MIT |
| 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. |
| first_indexed | 2024-09-23T14:19:42Z |
| format | Article |
| id | mit-1721.1/66108 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:19:42Z |
| publishDate | 2011 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/661082022-10-01T20:36:35Z A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity Kim, Sanggyun Putrino, David Ghosh, Soumya Brown, Emery N. Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Brown, Emery N. Kim, Sanggyun Brown, Emery N. 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. National Institutes of Health (U.S.) (Grant DP1-OD003646) National Institutes of Health (U.S.) (Grant R01-EB006385) 2011-09-28T20:19:25Z 2011-09-28T20:19:25Z 2011-03 2010-06 Article http://purl.org/eprint/type/JournalArticle 1553-7358 1553-734X http://hdl.handle.net/1721.1/66108 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. https://orcid.org/0000-0003-2668-7819 en_US http://dx.doi.org/10.1371/journal.pcbi.1001110 PLoS Computational Biology Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | Kim, Sanggyun Putrino, David Ghosh, Soumya Brown, Emery N. A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity |
| title | 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_short | 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 |
| url | http://hdl.handle.net/1721.1/66108 https://orcid.org/0000-0003-2668-7819 |
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