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Mathematical Issues in the Inference of Causal Interactions among Multichannel Neural Signals

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  • Young-Jin Jung
  • Kyung Hwan Kim
  • Chang-Hwan Im

Abstract

Within the last few decades, attempts have been made to characterize the underlying mechanisms of brain activity by analyzing neural signals recorded, directly or indirectly, from the human brain. Accordingly, inference of functional connectivity among neural signals has become an indispensable research tool in modern neuroscience studies aiming to explore how different brain areas are interacting with each other. Indeed, remarkable advances in computational sciences and applied mathematics even allow the estimation of causal interactions among multichannel neural signals. Here, we introduce the brief mathematical background of the use of causality inference in neuroscience and discuss the relevant mathematical issues, with the ultimate goal of providing applied mathematicians with the current state‐of‐the‐art knowledge on this promising multidisciplinary topic.

Suggested Citation

  • Young-Jin Jung & Kyung Hwan Kim & Chang-Hwan Im, 2012. "Mathematical Issues in the Inference of Causal Interactions among Multichannel Neural Signals," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:472036
    DOI: 10.1155/2012/472036
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    References listed on IDEAS

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    1. Sargan, John Denis & Bhargava, Alok, 1983. "Testing Residuals from Least Squares Regression for Being Generated by the Gaussian Random Walk," Econometrica, Econometric Society, vol. 51(1), pages 153-174, January.
    2. Baeck, E.G. & Brock, W.A., 1992. "A Nonparametric Test for Independence of a Multivariate Time Series," Working papers 9204, Wisconsin Madison - Social Systems.
    3. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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