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

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초록

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.

키워드

DIRECTED TRANSFER-FUNCTIONCORTICAL FUNCTIONAL CONNECTIVITYEVENT-RELATED CAUSALITYINFORMATION-FLOWGRANGER CAUSALITYSPEECH-PERCEPTIONSEIZURE ONSETTEMPORAL-LOBETIME-SERIESEEG
제목
Mathematical Issues in the Inference of Causal Interactions among Multichannel Neural Signals
저자
Jung, Young-JinKim, Kyung HwanIm, Chang-Hwan
DOI
10.1155/2012/472036
발행일
2012-00
유형
Review
저널명
Journal of Applied Mathematics
2012
페이지
1 ~ 14

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