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Characterizing Oscillatory Cortical Networks with Granger Causality

  • Anil Bollimunta
  • Yonghong Chen
  • Charles E. Schroeder
  • Mingzhou Ding
Chapter
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 3)

Abstract

Multivariate neural recordings are becoming commonplace. Statistical techniques such as Granger causality promise to reveal the patterns of neural interactions and their functional significance in these data. In this chapter, we start by reviewing the essential mathematical elements of Granger causality with special emphasis on its spectral representation. Practical issues concerning the estimation of such measures from time series data via autoregressive models are discussed. Simulation examples are used to illustrate the technique. Finally, we analyze local field potential recordings from the visual cortex of behaving monkeys to address the neuronal mechanisms of the alpha oscillation.

Keywords

Granger Causality Causal Power Local Field Potential Causal Influence Alpha Rhythm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by NIH grants MH070498, MH079388, and MH060358.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Anil Bollimunta
  • Yonghong Chen
  • Charles E. Schroeder
  • Mingzhou Ding
    • 1
  1. 1.J. Crayton Pruitt Family Department of Biomedical EngineeringUniversity of FloridaGainesvilleUSA

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