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Detecting effective connectivity in networks of coupled neuronal oscillators

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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.

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Acknowledgements

This work was partially funded from the start-up funding to SST through the Dept of Pediatrics at UF and the startup funding to WOO and alumina fellowship to ERB through the Dept of Biomedical Eng at UF. SST and PRC were partially funded through the Children’s Miracle Network Funds and Eve and B. Wilder Center of Excellence for Epilepsy Research. PPK was partially funded through the Eckis Professor Endowment at the University of Florida. We appreciate constructive feedback from Dr. Alex Cadotte. We also acknowledge the generosity of Dr CJ Frazier for allowing access to his electrophysiology rig set up and Ms. Aishwarya Parthasarthy for assistance in setting up the dynamic clamp and collection of experimental datasets. We finally acknowledge assistance from Mr. Kyungpyo Hong in generating Fig. 2 of the manuscript.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA

    Erin R. Boykin & Pramod P. Khargonekar

  2. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA

    William O. Ogle

  3. Department of Pediatrics, Neurology, Neuroscience, and Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA

    Paul R. Carney

  4. Department of Pediatrics, Neuroscience, and Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA

    Sachin S. Talathi

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  1. Erin R. Boykin
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  5. Sachin S. Talathi
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Correspondence to Sachin S. Talathi.

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Boykin, E.R., Khargonekar, P.P., Carney, P.R. et al. Detecting effective connectivity in networks of coupled neuronal oscillators. J Comput Neurosci 32, 521–538 (2012). https://doi.org/10.1007/s10827-011-0367-3

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  • DOI: https://doi.org/10.1007/s10827-011-0367-3

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