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利用高密度脑电图和运动捕捉技术来描述执行复杂动作时的感觉运动整合。

Utilizing High-Density Electroencephalography and Motion Capture Technology to Characterize Sensorimotor Integration While Performing Complex Actions.

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2020 Jan;28(1):287-296. doi: 10.1109/TNSRE.2019.2941574. Epub 2019 Sep 27.

DOI:10.1109/TNSRE.2019.2941574
PMID:31567095
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7021210/
Abstract

Studies of sensorimotor integration often use sensory stimuli that require a simple motor response, such as a reach or a grasp. Recent advances in neural recording techniques, motion capture technologies, and time-synchronization methods enable studying sensorimotor integration using more complex sensory stimuli and performed actions. Here, we demonstrate that prehensile actions that require using complex sensory instructions for manipulating different objects can be characterized using high-density electroencephalography and motion capture systems. In 20 participants, we presented stimuli in different sensory modalities (visual, auditory) containing different contextual information about the object with which to interact. Neural signals recorded near motor cortex and posterior parietal cortex discharged based on both the instruction delivered and object manipulated. Additionally, kinematics of the wrist movements could be discriminated between participants. These findings demonstrate a proof-of-concept behavioral paradigm for studying sensorimotor integration of multidimensional sensory stimuli to perform complex movements. The designed framework will prove vital for studying neural control of movements in clinical populations in which sensorimotor integration is impaired due to information no longer being communicated correctly between brain regions (e.g. stroke). Such a framework is the first step towards developing a neural rehabilitative system for restoring function more effectively.

摘要

研究感觉运动整合通常使用需要简单运动反应的感觉刺激,例如伸手或抓握。最近神经记录技术、运动捕捉技术和时间同步方法的进步,使得使用更复杂的感觉刺激和执行动作来研究感觉运动整合成为可能。在这里,我们证明了需要使用复杂的感觉指令来操作不同物体的抓握动作可以使用高密度脑电图和运动捕捉系统来描述。在 20 名参与者中,我们呈现了不同感觉模态(视觉、听觉)中的刺激,这些刺激包含有关要交互的物体的不同上下文信息。记录在运动皮层和顶后皮层附近的神经信号根据提供的指令和操作的物体放电。此外,还可以在参与者之间区分手腕运动的运动学。这些发现证明了一种用于研究多维感觉刺激的感觉运动整合以执行复杂运动的行为范例。该设计框架对于研究由于大脑区域之间信息不再正确通信而导致感觉运动整合受损的临床人群(例如中风)的运动神经控制至关重要。这样的框架是开发神经康复系统以更有效地恢复功能的第一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1140/7021210/2297a8ae2d1a/nihms-1552582-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1140/7021210/81151ed7ac8a/nihms-1552582-f0001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1140/7021210/f46858a7fdd2/nihms-1552582-f0003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1140/7021210/2297a8ae2d1a/nihms-1552582-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1140/7021210/81151ed7ac8a/nihms-1552582-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1140/7021210/6aacd8d5304a/nihms-1552582-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1140/7021210/f46858a7fdd2/nihms-1552582-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1140/7021210/a1aad4a867d8/nihms-1552582-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1140/7021210/2297a8ae2d1a/nihms-1552582-f0005.jpg

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