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Journal of Applied Mathematics
Volume 2012, Article ID 472036, 14 pages
Review Article

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

1Department of Biomedical Engineering, Hanyang University, Seoul 133-791, Republic of Korea
2Research Institute of Industrial Science, Hanyang University, Seoul 133-791, Republic of Korea
3Department of Biomedical Engineering, Yonsei University, Wonju 220-710, Republic of Korea

Received 28 October 2011; Accepted 16 November 2011

Academic Editor: Kiwoon Kwon

Copyright © 2012 Young-Jin Jung et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.