Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2012, Special Issue (2012), Article ID 472036, 14 pages.

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

Young-Jin Jung, Kyung Hwan Kim, and Chang-Hwan Im

Full-text: Open access

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.

Article information

Source
J. Appl. Math., Volume 2012, Special Issue (2012), Article ID 472036, 14 pages.

Dates
First available in Project Euclid: 17 October 2012

Permanent link to this document
https://projecteuclid.org/euclid.jam/1350492731

Digital Object Identifier
doi:10.1155/2012/472036

Mathematical Reviews number (MathSciNet)
MR2861938

Zentralblatt MATH identifier
1235.94033

Citation

Jung, Young-Jin; Kim, Kyung Hwan; Im, Chang-Hwan. Mathematical Issues in the Inference of Causal Interactions among Multichannel Neural Signals. J. Appl. Math. 2012, Special Issue (2012), Article ID 472036, 14 pages. doi:10.1155/2012/472036. https://projecteuclid.org/euclid.jam/1350492731


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