A dynamic Bayesian network analysis of functional connectivity during a language listening comprehension task

K. Shiba, T. Kaburagi, Y. Kurihara
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引用次数: 1

Abstract

We aim to characterize functional connectivity during a listening comprehension task in terms of fit to common network topology models. The functional connectivity is expressed as a network structure which is reconstructed from cerebral blood volume measurements. The cerebral blood volume in the frontal lobe is measured using functional near-infrared spectroscopy (NIRS). Based on the reconstructed functional network structure, we discuss whether the functional connectivity has a scale-free or random graph structure. The feasibility of the reconstructed network is evaluated based on the distribution of the number of edges at nodes. In order to validate our proposed model, two language listening comprehension tasks were presented to subjects and the feasibility of the model structure is discussed. The experimental results suggest that the reconstructed functional connectivity network is more likely to be a scale-free network with an “ultra-small” world than a random network.
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语言听力过程中功能连通性的动态贝叶斯网络分析
我们的目标是描述听力理解任务中的功能连通性,以适应常见的网络拓扑模型。功能连通性表示为一个网络结构,由脑血容量测量重建。使用功能近红外光谱(NIRS)测量额叶的脑血容量。在重构功能网络结构的基础上,讨论了功能连通性是否具有无标度结构或随机图结构。基于节点边数的分布,对重构网络的可行性进行了评价。为了验证我们提出的模型,我们提出了两个语言听力理解任务,并讨论了模型结构的可行性。实验结果表明,重构的功能连接网络比随机网络更有可能是一个具有“超小”世界的无标度网络。
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