传递熵
癫痫
癫痫发作
神经科学
计算机科学
熵(时间箭头)
信息传递
脑电图
心理学
物理
人工智能
最大熵原理
电信
量子力学
作者
S. Wing,Kristin M. Gunnarsdottir,Jorge González-Martínez,Sridevi V. Sarma
标识
DOI:10.1109/embc46164.2021.9629793
摘要
Transfer entropy (TE) is used to examine the connectivity between nodes and the roles of nodes in epileptic neural networks during rest, moments before seizure, during seizure, and moments after seizure. There is a set of nodes that dominate information flow to epileptogenic zone (EZ) nodes, regions that trigger seizure, and non-EZ nodes during rest. The TE from the dominant to the EZ nodes decreases shortly before a seizure event and reaches a minimum during seizure. During the seizure, the dominant nodes cease or only weakly interact with the EZ nodes. This supports the hypothesis that seizure occurs when some nodes stop inhibiting the EZ nodes. The TE from the dominant to the EZ nodes peaks immediately after seizure, suggesting that seizure may stop when the brain exerts the highest level of information flow/activation/communication to the EZ nodes. The information flow from the dominant to EZ nodes is different from that to non-EZ nodes. This TE dynamics entering and exiting seizures may identify more accurately the EZ nodes, which may improve surgical planning.
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