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熵可量化机械阻抗约束主动手臂运动诱发的脑激活:一项功能性近红外光谱研究。

Entropy Could Quantify Brain Activation Induced by Mechanical Impedance-Restrained Active Arm Motion: A Functional NIRS Study.

作者信息

Yu Byeonggi, Jang Sung-Ho, Chang Pyung-Hun

机构信息

Department of Robotics Engineering, Graduate School, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea.

Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu 42415, Korea.

出版信息

Entropy (Basel). 2022 Apr 15;24(4):556. doi: 10.3390/e24040556.

DOI:10.3390/e24040556
PMID:35455219
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9024511/
Abstract

Brain activation has been used to understand brain-level events associated with cognitive tasks or physical tasks. As a quantitative measure for brain activation, we propose entropy in place of signal amplitude and beta value, which are widely used, but sometimes criticized for their limitations and shortcomings as such measures. To investigate the relevance of our proposition, we provided 22 subjects with physical stimuli through elbow extension-flexion motions by using our exoskeleton robot, measured brain activation in terms of entropy, signal amplitude, and beta value; and compared entropy with the other two. The results show that entropy is superior, in that its change appeared in limited, well established, motor areas, while signal amplitude and beta value changes appeared in a widespread fashion, contradicting the modularity theory. Entropy can predict increase in brain activation with task duration, while the other two cannot. When stimuli shifted from the rest state to the task state, entropy exhibited a similar increase as the other two did. Although entropy showed only a part of the phenomenon induced by task strength, it showed superiority by showing a decrease in brain activation that the other two did not show. Moreover, entropy was capable of identifying the physiologically important location.

摘要

大脑激活已被用于理解与认知任务或身体任务相关的大脑层面的事件。作为大脑激活的一种定量测量方法,我们提出用熵来代替信号幅度和β值,信号幅度和β值虽被广泛使用,但有时因其作为此类测量方法的局限性和缺点而受到批评。为了研究我们这一主张的相关性,我们通过使用外骨骼机器人让22名受试者进行肘部屈伸运动来提供身体刺激,从熵、信号幅度和β值方面测量大脑激活情况,并将熵与其他两者进行比较。结果表明,熵更具优势,因为其变化出现在有限的、已明确的运动区域,而信号幅度和β值的变化则广泛出现,这与模块化理论相矛盾。熵可以预测大脑激活随任务持续时间的增加,而其他两者则不能。当刺激从静止状态转变为任务状态时,熵与其他两者一样呈现出类似的增加。尽管熵仅显示了任务强度所引发现象的一部分,但它通过显示出其他两者未显示出的大脑激活减少而表现出优势。此外,熵能够识别生理上重要的位置。

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本文引用的文献

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Neural Correlates of Knee Extension and Flexion Force Control: A Kinetically-Instrumented Neuroimaging Study.膝关节伸展和屈曲力量控制的神经关联:一项动力学仪器神经影像学研究。
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Design of a clinically relevant upper-limb exoskeleton robot for stroke patients with spasticity.针对患有痉挛的中风患者设计一款具有临床相关性的上肢外骨骼机器人。
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