Journal of Computational Neuroscience

, Volume 32, Issue 3, pp 521–538 | Cite as

Detecting effective connectivity in networks of coupled neuronal oscillators

  • Erin R. Boykin
  • Pramod P. Khargonekar
  • Paul R. Carney
  • William O. Ogle
  • Sachin S. Talathi
Article

Abstract

The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.

Keywords

Granger causality Partial directed coherence Phase dynamics modeling Effective connectivity Neuronal oscillators Decision tree classifiers 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Erin R. Boykin
    • 1
  • Pramod P. Khargonekar
    • 1
  • Paul R. Carney
    • 3
  • William O. Ogle
    • 2
  • Sachin S. Talathi
    • 4
  1. 1.Department of Electrical and Computer EngineeringUniversity of FloridaGainesvilleUSA
  2. 2.J. Crayton Pruitt Family Department of Biomedical EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Department of Pediatrics, Neurology, Neuroscience, and Biomedical EngineeringUniversity of FloridaGainesvilleUSA
  4. 4.Department of Pediatrics, Neuroscience, and Biomedical EngineeringUniversity of FloridaGainesvilleUSA

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