Differences in Brain Functional Network Topology in High and Low Working Memory Performance

Ilia M. Ernston, Timofei V. Adamovich
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引用次数: 0

Abstract

Nowadays the network approach in neuroscience provides a promising way of analyzing neurophysiological mechanisms that underlie psychological functions and is widely used to study working memory. To date, data obtained in neuroimaging studies links working memory with topological features of brain networks, such as increased connectivity between frontal, parietal, and temporal regions, as well as increased integration in brain networks as a whole. The present study examines the relationship between the topological characteristics of functional brain networks with the performance in the Sternberg item recognition paradigm based on electroencephalographic data. It is shown that the higher performance in Sternberg paradigm, implying a higher efficiency of the processes of encoding, storage, and retrieval of information from working memory, is associated with an increase in the integration of functional networks, expressed in differences in the clustering coefficient, participation coefficient, Wiener index and eigenvector centrality between the groups of high and low working memory task performance (p < .01). In addition, our data suggest the variability in the topological pattern of connectivity, which can be traced through changes in the magnitude of the standard deviation of the values of topological metrics during the task.
高、低工作记忆表现的脑功能网络拓扑差异
目前,神经科学中的网络方法为分析心理功能背后的神经生理机制提供了一种很有前途的方法,并被广泛用于研究工作记忆。迄今为止,从神经成像研究中获得的数据将工作记忆与大脑网络的拓扑特征联系起来,例如额叶、顶叶和颞叶区域之间的连接增加,以及整个大脑网络的整合增加。本研究基于脑电图数据,探讨了脑功能网络拓扑特征与Sternberg项目识别范式的关系。研究表明,在Sternberg范式下,工作记忆任务的编码、存储和检索过程效率越高,功能网络的整合程度越高,表现在工作记忆任务绩效高组和低组的聚类系数、参与系数、Wiener指数和特征向量中心性的差异上(p <. 01)。此外,我们的数据表明了连通性拓扑模式的可变性,这可以通过任务期间拓扑度量值的标准偏差大小的变化来追踪。
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