Mathematical issues in the inference of causal interactions among multichannel neural signals

Young Jin Jung, Kyung Hwan Kim, Chang Hwan Im

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Article number472036
JournalJournal of Applied Mathematics
Publication statusPublished - 2012

All Science Journal Classification (ASJC) codes

  • Applied Mathematics


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