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手指运动过程中大脑功能小世界网络的重组。

Reorganization of brain functional small-world networks during finger movements.

机构信息

Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland 20892, USA.

出版信息

Hum Brain Mapp. 2012 Apr;33(4):861-72. doi: 10.1002/hbm.21253. Epub 2011 Apr 11.

DOI:10.1002/hbm.21253
PMID:21484955
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6870111/
Abstract

A functional measure of brain organization is the efficiency of functional connectivity. The degree of functional connectivity can differ during a task compared to the rest, and to study this issue, we investigated the functional connectivity networks in healthy subjects during a simple, right-handed, sequential finger-tapping task using graph theoretic measures. EEGs were recorded from 58 channels in 15 healthy subjects at rest and during a motor task. We estimated mutual information values of wavelet coefficients to create an association matrix between EEG electrodes and produced a series of adjacency matrices or graphs, A, by thresholding with network cost. These graphs are called small-world networks, and we assessed their efficiency measures. We found economical small-world properties in brain functional connectivity networks in the alpha and beta band networks. The efficiency of the brain networks was enhanced during the task in the beta band networks, but not in the alpha band networks. A regional efficiency analysis during the task showed that the bilateral primary motor and left sensory areas showed increased nodal efficiency, Enodal, whereas decreased Enodal was found over the posterior parietal areas. The present study provides evidence for the reorganization of brain functional connectivity networks in a motor task with the greatest increase in Enodal in motor executive areas.

摘要

大脑组织的功能测量是功能连接的效率。与休息时相比,任务期间的功能连接程度可能会有所不同,为了研究这个问题,我们使用图论测量方法研究了健康受试者在简单的、惯用右手的、顺序手指敲击任务期间的功能连接网络。EEG 从 15 名健康受试者的 58 个通道在休息和运动任务期间进行记录。我们估计了小波系数的互信息值,以在 EEG 电极之间创建一个关联矩阵,并通过网络成本进行阈值处理生成一系列邻接矩阵或图,A。这些图称为小世界网络,我们评估了它们的效率度量。我们发现,在 alpha 和 beta 波段网络中,大脑功能连接网络具有经济的小世界属性。在 beta 波段网络中,任务期间大脑网络的效率得到了提高,但在 alpha 波段网络中则没有。任务期间的区域效率分析表明,双侧初级运动和左侧感觉区域的节点效率 Enodal 增加,而枕后区域的 Enodal 则减少。本研究为运动任务中大脑功能连接网络的重组提供了证据,运动执行区域的 Enodal 增加最大。

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