Name: 作者聚焦：使用 fNIRS 评估机器人辅助下肢康复中的大脑活动
Uploaded: 2024-06-07T12:40:00
Description: Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise ...

本研究的观点、假设、结论和建议是作者的观点、假设、结论和建议，并不一定代表资助机构的意见。JRS 感谢圣保罗研究基金会（FAPESP，资助号 2021/05332-8、2018/04654-9、2018/21934-5 和 2023/02538-0）和 Jackson Cionek 的技术支持。AMM 和 Vivax Ltda 感谢 FAPESP（圣保罗研究基金会）和 FIEP（巴西创新机构）。该项目由FAPESP（资助号2018/09559-4）和FINEP（资助号2019/09933-6）资助。

这项研究得到了联合国儿童保护计划（Centro Universitario Paulista）当地伦理审查委员会的批准。所有参与者都遵循有关人类科学研究的所有机构指南和联邦规范提供知情同意。他们没有按照巴西联邦法规的要求获得经济补偿。
1. fNIRS系统
<ol><li>使用 16 个光极准备盖子：8 个光源（760 nm 和 850 nm）和 8 个光检测器（见 图 2;光源位置：FC1、FC2、C3?...

在下肢机器人治疗期间评估 fNIRS 的大脑激活

在这项概念验证研究中，研究了使用健康受试者的 fNIRS 数据对大脑激活映射进行推断的可行性，同时他们使用机器人进行不同类型的运动进行下肢康复。成人的典型 fNIRS 记录会话超过 6 分钟54。然而，为了使记录在康复环境中可行，必须最大限度地减少实验的总持续时间，以避免受试者不必要的疲劳和努力。本研究还测试了具有 6 个任务重复块（会话持续时间为 6 分钟）的块设...

流行病学数据表明，全世界每年有 ~1700 万新发卒中病例，低收入和中等收入国家的发病率有所增加1.据估计，到 2030 年，新病例数将增加到 7700 万例2.中风引起的运动障碍通常会影响患者的活动能力和参与日常生活活动，导致生活质量低下。传统的运动康复包括手法治疗，但在过去的几十年里，已经开发了用于康复的机器人系统。这些系统可以提供高强度、剂量、可量化、可靠性、可重复性和灵活性的治疗3，并已显示出作为急性和慢性卒中患者有效康复治疗的潜力4,5,6。除了提供治疗外，用于康复的机器人系统还可以用作评估工具，因为它们可以配备可以测量患者运动/动力学数据的传感器7,8。对于上肢运动康复，这些数据不仅被证明可用于评估机器人治疗引起的患者运动恢复水平，并作为传统临床评估的补充工具9,10，而且还有助于促进对中风运动恢复过程的理解11，12 以及健康受试者运动和运动学习的神经控制 3,13,14。因此，这些发现为加强康复治疗奠定了基础15.
在过去的二十年中，已经提出了许多用于下肢神经康复的机器人设备，从在步行过程中支持患者体重的外骨骼（例如，在跑步机上，例如 Lokomat16）到允许患者在不走路的情况下锻炼脚踝、膝盖或脚的固定机器人系统（例如罗格斯大学脚踝17， 高性能踝关节康复机器人18 和光州科学技术研究所 （GIST） 踝/足康复机器人19） 或主动足部矫形器，它们是患者佩戴在地面或跑步机上行走的驱动外骨骼（例如电动步态矫形器20 和 MIT 脚踝机器人21）。参见 22,23,4 关于机器人用于下肢康复的综述。
用于中风患者下肢康复的机器人设备的临床研究结果令人鼓舞，并表明这些系统可以改善关节的运动范围 （ROM）、肌肉力量或步态，具体取决于特定的设备和临床方案（参见 24,25 关于下肢机器人康复功效的综述）。虽然已经假设机器人辅助治疗促进神经可塑性变化，最终导致运动能力的提高26，但中风运动恢复过程究竟是如何发生的，以及哪些机器人训练方案优化了下肢运动能力的恢复过程，目前仍不清楚。事实上，康复机器人的不断发展（无论是学术研究人员还是商业实体）与对运动恢复背后的神经生理机制的有限理解之间存在着显着且日益扩大的差距4.使用嵌入式传感器对运动学或关节扭矩的测量有助于定量描述患者恢复下肢运动能力时发生的运动行为变化 27,28,29，部分填补了这一空白。然而，这种变化背后的神经相关性研究较少。这是由于几个原因。
在临床试验的背景下，脑功能成像非常耗时，有时难以完成，这通常需要将患者负担降至最低，以最大限度地提高患者依从性的可能性。这对于中风患者尤其如此，因为中风后疲劳和肌肉无力经常被观察到30.此外，基于磁场的成像模式，如功能性磁共振成像（fMRI），要求患者和机器人硬件都是磁铁安全的。
在非侵入性成像方式中，功能性近红外光谱 （fNIRS） 是一种成像技术，特别适用于评估接受机器人治疗的受试者的大脑激活区域。与功能磁共振成像类似，fNIRS测量大脑中的血氧/脱氧。然而，与fMRI不同的是，fNIRS与机器人硬件完全兼容，而且它通常是便携式的，甚至可以在床边使用。此外，fNIRS具有低成本和对运动伪影的敏感性较低31,32,33。
尽管自 70 年代末首次引入以来，fNIRS 具有明显的优势并在许多临床环境中得到广泛使用34，但只有少数研究使用 fNIRS 来量化与下肢运动和中风运动恢复相关的大脑激活。FNIRS 研究旨在阐明运动神经控制机制和/或中风运动恢复机制或评估运动恢复，主要研究单关节运动（例如，背屈、跖屈或膝关节伸展运动 35,36,37）、步行38、39、40、41、42、43 或骑自行车44。有关评论，请参见45。同样，关于机器人辅助治疗下肢的fNIRS研究主要集中在机器人辅助步态康复上;有关评论，请参见46。一些研究的重点是使用fNIRS作为脑机接口（BCI）系统的一部分来获取机器人设备的控制信号47,48;虽然该研究领域也依赖于fNIRS信号的处理，但其目标不同，主要集中在解码患者意图（例如，患有严重运动障碍的患者）。
本文介绍的试点研究是研究机器人系统对下肢康复影响的初步努力的一部分。该机器人可以提供以目标为导向的下肢康复，包括日常多关节运动的训练，以及对下肢的单个关节（例如膝盖或脚踝）进行治疗（即实施自下而上的康复计划）。
该研究旨在调查一种实验方案的可行性，该实验方案要求在执行下肢多关节指向运动期间获取 fNIRS 数据。本研究中数据采集期的持续时间限制为 6 分钟，比典型的 fNIRS 协议短。这是一个深思熟虑的选择，旨在提高这项研究的实用性和临床适用性，特别是在行动不便或力量有限的患者中。识别这种复杂的多关节运动的fNIRS相关性，并深入了解机器人辅助如何调节大脑激活也是感兴趣的点。为此，对相同的参与者进行了两次实验：一次没有机器人辅助，另一次有机器人辅助。最后，需要指出的是，本研究的重点是健康受试者，以便为未来在记录协议可行性和评估机器人治疗靶向运动期间大脑激活方面的研究奠定基础。
装置 一个设计用于提供下肢康复的便携式机器人（见 图1）用于进行我们的实验。该机器人具有 3D 可触及的工作空间，结构紧凑，重量轻，重约 35 磅，便于运输和安装。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66004/66004fig01.jpg"> 图 1：实验设置。 （A） 专为下肢设计的机器人系统（安装在地板上）。图中显示一名志愿者用右脚使用界面。（B） 受试者脚部的支撑结构，能够连接到机器人系统。（C） 野餐游戏的屏幕截图。游戏的目标是将脚（绿色和白色的鞋子）移动到目标（黄色圆圈）。 <a href="https://www.jove.com/files/ftp_upload/66004/66004fig01large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
机器人系统旨在帮助患者执行类似于日常任务（例如指向或踢腿）的下肢运动。它使用交互式虚拟现实游戏，这些游戏显示在计算机显示器或放置在机器人设备前面的电视屏幕上（见 图1）。机器人末端执行器连接到患者的下肢（例如脚踝），其位置映射到屏幕上光标的位置。典型的游戏显示患者的运动目标（例如，要指向的物体或踢球的位置）。
为了完成运动任务，机器人可以协助患者提供一定程度的帮助，范围从完全协助到无协助。机器人辅助的水平是在每次康复课程开始时根据患者的运动障碍程度来选择的。游戏使用受试者执行的动作来对患者的表现进行评分，并为他们提供有关其表现的反馈（例如，ROM、动作次数和机器人辅助水平）。这些游戏旨在具有互动性和娱乐性，以保持患者的兴趣和注意力。在这项研究中，参与者玩了“野餐游戏”，玩家必须阻止昆虫到达毛巾并偷走食物（屏幕截图见 图1底部面板）。
使用便携式fNIRS采集系统进行数据采集，该系统具有两个不同的连续波光电极（760 nm和850 nm），8个双尖LED光源和8个双尖有源探测器。信号是使用 10.17 Hz 的采样率采集的。使用笔记本电脑使用fNIRS系统创建的Wi-Fi网络进行校准优化和信号记录。
使用盖子将视光管固定在预定位置。源和探测器根据 10-10 国际脑电图系统放置在网格空间分布中。每个fNIRS通道由光极间距离约为30 mm的源-检测器对定义。光电极放置在辅助电机、前电机和电机区域的 图 2 所示位置。通道总数为 28 个，其中 8 个是短距离通道，使用光纤适配器耦合到单个探测器的每个源。考虑到硬件的多路复用设置，只需使用一个探测器就可以从所有来源获取短距离信息。
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66004/66004fig02.jpg"> 图 2：使用 10-10 EEG 系统的蒙太奇布局。 字母和数字表示源/探测器位置。红点和蓝点分别代表光源和检测器光电极。绿线表示fNIRS通道，由源和检测器对组成。 <a href="https://www.jove.com/files/ftp_upload/66004/66004fig02large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
试验设计 该实验是在两种不同的实验条件下进行的，机器人为受试者的运动提供的帮助水平不同。在第一种情况下，机器人被编程为不为受试者的运动提供任何帮助，而在第二种情况下，机器人控制受试者的脚和腿运动（机器人辅助运动）。
每个实验都遵循一个模块设计范式，包括运动任务（玩游戏 - 30 秒）和休息（30 秒）的交替循环，如 图 3 所示。每个阶段（游戏/游戏或休息）的开始和结束都通过计算机屏幕向受试者发出视觉信号。在休息阶段，显示一条指示暂停的消息。每个循环（游戏/游戏 + 休息）的持续时间为 60 秒，重复 6 次，总运行时间为 360 秒（6 分钟）。
参与者玩了“野餐游戏”，目的是防止昆虫到达毛巾并偷走食物。该游戏涉及一系列下肢运动，从指定的本垒目标（初始位置）开始，延伸到三个外围目标之一，然后返回本垒目标。在屏幕上，外部目标在视觉上表现为动画移动的昆虫，参与者必须伸手踩到它们。有三个向外延伸的目标，每个目标随机出现的次数相等，每个动作都有一个共同的本垒目标。脚从本目标到外部目标位置所需的距离形成一个弧形，大约为 26 厘米。运动任务需要执行多关节运动，要求膝关节屈曲/伸展、跖屈和背屈运动之间的协调。
fNIRS数据记录与游戏通过机器人产生的晶体管-晶体管-逻辑（TTL）脉冲呈现给受试者的视觉刺激同步。脉冲在每个阶段（游戏/游戏和休息）开始时产生。因此，所有计时控制均由游戏执行，游戏向参与者提供视觉提示（目标）以开始每个动作，向fNIRS系统发送TTL信号以标记大脑活动记录，并在实验需要时向机器人控制系统发送信号以启动运动辅助。

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	<tbody>
		<tr>
			<td>32 inch Smart TV</td>
			<td>Samsung</td>
			<td>N/A</td>
			<td>TV connected to robot via HDMI cable</td>
		</tr>
		<tr>
			<td>8-detector silicon photodiode (SiPD) optodes for optical detection with dual tip</td>
			<td>NIRx Medical Technologies (Glen Head, NY, USA)</td>
			<td>https://nirx.net/nirsport</td>
			<td></td>
		</tr>
		<tr>
			<td>8-source optodes bundle for optical illumination with dual tip</td>
			<td>NIRx Medical Technologies (Glen Head, NY, USA)</td>
			<td>https://nirx.net/nirsport</td>
			<td></td>
		</tr>
		<tr>
			<td>Aurora acquisition software</td>
			<td>NIRx Medical Technologies (Glen Head, NY, USA)</td>
			<td>https://nirx.net/nirsport</td>
			<td></td>
		</tr>
		<tr>
			<td>Laptop Precision XPS 13</td>
			<td>Dell Technologies (Round Rock, TX, USA)</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>nirsLAB fNIRS Analysis software</td>
			<td>NIRx Medical Technologies (Glen Head, NY, USA)</td>
			<td>https://nirx.net/nirsport</td>
			<td></td>
		</tr>
		<tr>
			<td>NIRSports2 fNIRS acquisition system</td>
			<td>NIRx Medical Technologies (Glen Head, NY, USA)</td>
			<td>https://nirx.net/nirsport</td>
			<td>It has two different continuous wave optics (760 and 850 nm), 8 dual-ended LED sources and 8 dual-ended active detectors.</td>
		</tr>
		<tr>
			<td>R</td>
			<td>R-project.org (open source software)</td>
			<td>https://www.r-project.org/</td>
			<td></td>
		</tr>
		<tr>
			<td>Standard cut cap, black color for up to 128 holders.</td>
			<td>Easycap GmbH (W&#246;rthsee, Germany)</td>
			<td>https://www.easycap.de/</td>
			<td></td>
		</tr>
		<tr>
			<td>Vivax Assistive Rehabilitation Machine (ARM)&#160;</td>
			<td>Vivax Ltda (S&#227;o Paulo, Brazil)</td>
			<td>https://vivaxbr.com/home/</td>
			<td>It is a portable robot designed to deliver lower limb rehabilitation. It has a 3D reachable workspace and is compact and light, weighing about 35 lb., which makes it easy to transport and to install.&#160;</td>
		</tr>
	</tbody>
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an experiment using functional near-infrared spectroscopy and robot-assisted multi-joint pointing movements of the lower limb

中风每年影响全球约1700万人，是导致长期残疾的主要原因。机器人疗法在帮助中风患者恢复失去的运动功能方面显示出希望。增加对运动恢复如何发生的理解的一个潜在途径是研究健康个体治疗所针对的运动期间的大脑激活。功能性近红外光谱 （fNIRS） 已成为一种很有前途的神经成像技术，用于检查运动功能的神经基础。本研究旨在调查健康受试者复杂下肢运动的 fNIRS 神经相关性。参与者被要求使用机器人设备进行 6 分钟的休息和运动循环，以进行运动康复。这项任务需要协调膝关节和踝关节运动，以指向计算机屏幕上显示的目标。探索了机器人提供不同运动辅助水平的两种实验条件。结果表明，fNIRS协议在任务期间有效地检测了与运动控制相关的大脑区域。值得注意的是，与辅助条件相比，在无辅助条件下，所有受试者在对侧前运动区域表现出更大的激活。总之，fNIRS似乎是一种有价值的方法，用于检测与下肢多关节指向运动相关的氧合血红蛋白浓度变化。这项研究可能有助于理解中风运动恢复机制，并可能为改善中风患者的康复治疗铺平道路。然而，需要进一步的研究来充分阐明fNIRS在研究运动功能及其在临床环境中的应用方面的潜力。

Epidemiological data indicates that worldwide there are ~17 million new cases of stroke each year, with an increase in incidence in low- and middle-income countries1. The number of new cases is estimated to increase to 77 million by 20302. Motor impairment due to stroke often affects patient&#39;s mobility and participation in daily life activities, contributing to a low quality of life. Traditional motor rehabilitation includes manual therapy, but over the past few decades, robotic systems for rehabilitation have been developed. These systems can deliver therapy at high intensity, dose, quantifiability, reliability, repeatability, and flexibility3 and have shown potential as effective rehabilitation treatments for both acute and chronic stroke patients4,5,6. In addition to delivering therapy, robotic systems for rehabilitation can be used as evaluation tools as they can be equipped with sensors that can measure patient movement kinematic/kinetic data7,8. For upper extremity motor rehabilitation, such data has not only been proven to be useful for assessing the level of patient&#39;s motor recovery elicited by robotic therapy and served as a supplementary tool to traditional clinical assessments9,10, but it has also contributed to advancing the understanding of the process of motor recovery from stroke11,12 as well as neural control of movement and motor learning in healthy subjects3,13,14. As a result, these findings have provided a foundation for enhancing rehabilitation treatments15.
Over the last two decades, many robotic devices for lower limb neurorehabilitation have been proposed, spanning from exoskeletons that support the patient body weight during walking (e.g., over a treadmill, such as Lokomat16) to stationary robotic systems that allow the patient to exercise the ankle, knee or foot without walking (such as the Rutgers Ankle17, the High-Performance Ankle Rehabilitation Robot18, and the Gwangju Institute of Science and Technology (GIST) ankle/foot rehabilitation robot19) or active foot orthoses that are actuated exoskeletons worn by the patient to walk overground or over a treadmill (such as the Powered Gait Orthosis20&#160;and the MIT Anklebot21).&#160;See22,23,4 for a review on robots for lower limb rehabilitation.
Results of clinical studies of robotic devices for lower limb rehabilitation on stroke patients have been encouraging and have shown that these systems may improve joints&#39; Range of Motion (ROM), muscle strength, or gait, depending on the specific device and clinical protocol (see&#160;24,25&#160;for a review on the efficacy of lower limb robots for rehabilitation). While it has been postulated that robot-assisted therapy promotes neuroplastic changes, which ultimately result in improved motor abilities26, how the process of motor recovery from stroke exactly occurs and which robotic training protocols optimize the process of recovery of lower limb motor abilities, remain mostly unclear. In fact, there is a significant, growing disparity between the increasing development of rehabilitation robots (either by academic researchers or commercial entities) and the limited understanding of the neurophysiological mechanisms that underlie motor recovery4. Measurements of movement kinematics or joint torques taken with embedded sensors have contributed to quantitatively describing motor behavioral changes that occur as patients recover lower limb motor abilities27,28,29, partially filling this gap. However, the neural correlates underlying such changes have been less investigated. This is due to several reasons.
Brain functional imaging is time-consuming and sometimes difficult to complete in the context of clinical trials, which often require keeping patient burden minimal to maximize the likelihood of patient adherence to the study. This holds particularly true for individuals who have suffered a stroke, given the fact that post-stroke fatigue and muscle weakness are frequently observed30. Also, imaging modalities that are based on magnetic fields, such as functional Magnetic Resonance Imaging (fMRI), require both patient and robotic hardware to be magnet-safe.
Among non-invasive imaging modalities, functional Near-Infrared Spectroscopy (fNIRS) is an imaging technique particularly suitable for assessing areas of brain activation in subjects undergoing robotic therapy. Similarly to fMRI, fNIRS measures blood oxygenation/deoxygenation in the brain. However, unlike fMRI, fNIRS is fully compatible with robotic hardware, and it is often portable, even being usable at the bedside. Also, fNIRS has low cost and less sensitivity to motion artifacts31,32,33.
Despite its clear advantages and widespread use in many clinical settings since its first introduction in the late 70s34, only a few studies have used fNIRS to quantify brain activation associated with lower limb movements and stroke motor recovery. FNIRS studies aimed at elucidating mechanisms of neural control of movement and/or mechanisms or evaluation of motor recovery from stroke have mostly investigated single-joint movements (e.g., dorsiflexion, plantar flexion or knee extension movements35,36,37), walking38,39,40,41,42,43, or cycling44. See45&#160;for a review. Similarly, fNIRS studies on robot-assisted therapy for the lower limb have mostly focused on robot-assisted gait rehabilitation; see46&#160;for a review. A few studies have focused on using fNIRS as part of a Brain-Computer Interface (BCI) system to derive control signals for robotic devices47,48; while this research area also relies on the processing of fNIRS signals, its goal is different and mainly&#160;focused on decoding patient intentions (e.g., patients with severe motor disabilities).
The pilot study presented herein is part of an initial effort to investigate the effects of a robotic system for lower limb rehabilitation. The robot can deliver target-oriented lower limb rehabilitation that involves training in everyday multi-joint movements as well as deliver therapy to single joints (e.g., knee or ankle) of the lower limb (i.e., implement a bottom-up rehabilitation program).
The study aimed to investigate the feasibility of an experimental protocol that required the acquisition of fNIRS data during the performance of lower limb, multi-joint pointing movements. The duration of the data acquisition period in this study, which was limited to 6 min, is shorter than typical fNIRS protocols. This was a deliberate choice made with the aim of enhancing the practicality and clinical applicability of this research, particularly in patients with limited mobility or strength. Identifying fNIRS correlates of such complex multi-joint movements and gaining insights into how brain activation was modulated by robot assistance were also points of interest. For this purpose, two sessions of experiments were conducted with the same participants: one with no robot assistance and one with robot assistance. Finally, it is important to remark that this study focused on healthy subjects in order to establish a foundation for future research in terms of recording protocol feasibility and evaluation of brain activation during movements targeted by robotic therapy.
Apparatus 
A portable robot designed to deliver lower limb rehabilitation (see Figure 1) was used to conduct our experiments. The robot has a 3D reachable workspace and is compact and light, weighing about 35 lb., which makes it easy to transport and install.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66004/66004fig01.jpg" /> 
Figure 1: Experimental setup. (A) The robotic system (installed on the floor) designed for the lower limb. A volunteer is shown utilizing the interface with their right foot. (B) Support structure for the subject&#39;s foot that enables attachment to the robotic system. (C) A screenshot of the Picnic game. The objective of the game is to move the foot (green and white shoe) to the target (yellow circle). <a href="https://www.jove.com/files/ftp_upload/66004/66004fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The robotic system is designed to assist a patient in performing lower limb movements similar to those performed in everyday tasks, such as pointing or kicking. It uses interactive virtual reality games, which are displayed on a computer monitor or a television screen placed in front of the robotic device (see Figure 1). The robot-end effector is attached to the patient&#39;s lower limb (e.g., ankle), and its position is mapped to the position of a cursor on the screen. A typical game shows the patient movement targets (e.g., the object to point to or where to kick the ball).
To complete the movement task, the robot may assist the patient with a level of assistance that can range from full assistance to none. The level of robotic assistance is chosen at the beginning of each rehabilitation session based on the patient&#39;s level of motor impairment. The movements performed by the subject are used by the game to score the patient&#39;s performance and provide them feedback on their performance (e.g., ROM, number of movements, and level of robotic assistance). The games are designed to be interactive and entertaining to sustain patient interest and attention. In this study, participants played the &#34;Picnic game&#34;, in which the player had to stop the insects from reaching the towel and stealing the food (see Figure 1, bottom panel, for a screenshot).
Data acquisition was performed with a portable fNIRS acquisition system with two different continuous-wave optodes (760 nm and 850 nm), 8 dual-tip LED sources, and 8 dual-tip active detectors. The signals were acquired using a sampling rate of 10.17 Hz. A laptop was used for the calibration optimization and signal recording using a Wi-Fi network created by the fNIRS system.
A cap was used to hold the optodes in the predetermined locations. The sources and detectors were placed according to the 10-10 international EEG system in a grid spatial distribution. Each fNIRS channel was defined by a source-detector pair with inter-optode distances of approximately 30 mm. The optodes were placed over the supplementary motor, premotor, and motor areas at the locations shown in Figure 2. The total number of channels was 28, where 8 were short-distance channels that were coupled to each source using a fiber optics adapter to a single detector. Given the multiplexing setup of the hardware, it is possible to acquire short-distance information from all sources using only one detector.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66004/66004fig02.jpg" /> 
Figure 2: Montage layout using the 10-10 EEG system. The letters and numbers indicate the source/detector locations. The red and blue dots represent the source and detector optodes, respectively. The green lines represent the fNIRS channels which consist of source and detector pairs. <a href="https://www.jove.com/files/ftp_upload/66004/66004fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Experimental design 
The experiment was conducted under two distinct experimental conditions, differing in the level of assistance provided by the robot for the subject&#39;s movements. In the first condition, the robot was programmed not to provide any assistance to the subject&#39;s movements, while in the second condition, the robot controlled the subject&#39;s foot and leg movements (robot-assisted movement).
Each experiment followed a block design paradigm involving alternating cycles of a motor task (playing the game - 30 s) and resting (30 s), as illustrated in Figure 3. The starting and conclusion of each phase (play/game or rest) were visually signaled to the subject through the computer screen. During the rest phase, a message indicating a pause was displayed. Each cycle (play/game + rest) had a duration of 60 s and was repeated six times, resulting in a total runtime of 360 s (6 min).
The participants played the &#34;Picnic game&#34;, wherein the objective was to prevent insects from reaching the towel and stealing food. This game involved a sequence of lower limb movements, starting from a designated home target (initial position) and extending towards one of three outer targets before returning to the home target. On the screen, the outer targets were visually represented as animated moving insects, which the participants had to reach and step onto. There were three outer-reaching targets, each randomly presented an equal number of times, alongside a common home target for every movement. The distance that the foot needed to travel from the home target to the position of the outer targets formed an arc, approximately measuring 26 cm. The motor task required the execution of multi-joint movements, demanding coordination between knee flexion/extension, plantar flexion, and dorsiflexion movements.
The fNIRS data recordings were synchronized with the visual stimuli presented by the game to the subject through a transistor-transistor-logic (TTL) pulse generated by the robot. Pulses were generated at the outset of each phase (play/game and rest). Thus, all timing control was performed by the game, which provided visual cues (targets) to the participant to start each movement, sent TTL signals to the fNIRS system to mark the brain activity recordings, and, if required by the experiment, sent signals to the robot control system to initiate movement assistance.

作者聚焦：使用 fNIRS 评估机器人辅助下肢康复中的大脑活动

使用功能性近红外光谱和机器人辅助下肢多关节指向运动的实验

In this study, we aim to investigate the feasibility of an experimental protocol involving movements of the lower limb attached to a robotic device and simultaneous acquisition of functional near-infrared spectroscopy, fNIRS, data. Few studies have employed fNIRS to study brain activation in stroke, motor recovery, and lower limb movements, focusing mainly on single joint actions, walking, or cycling. Our study pioneers in exploring multi-joints lower limb training for target rehabilitation.Functional near-infrared spectroscopy, fNIRS, effectively measures temporal changes in brain blood oxygenation, serving as an indirect brain activity indicator, to functional MRI signals. Unlike fMRI, fNIRS is compatible with robotic hardware, portable, and more robust to motion artifacts. There are two main challenge.The first is to synchronize the robotic device movements with fNIRS data. The second is to obtain enough data to achieve the statistical power, to detect hemodynamic changes in only six minutes. Our study confirmed the feasibility of the protocol and yielded anticipated results.In the assisted condition, only two subject exhibited significant brain activation. The unassisted exercise, requiring more effort, showed a greater HbO concentration change than assisted ones, with notable difference in the contralateral PMC.

所有六名受试者都完成了两个实验。在无援助条件下，每个受试者平均完成了76.67次试验（标准10.73）（注意，对于每个受试者，试验次数取决于成功达到的次数，因为只有在达到前一个目标时才会显示新目标）。在辅助条件下，受试者的运动完全由机器人辅助，所有受试者都完成了 70 次试验。成功记录了所有受试者的fNIRS数据。
图 4 显示了比较 HbO 和 H...

据估计，全世界每 6 个人中就有 1 个人在其一生中会中风，导致长期残疾，其康复机制仍然知之甚少。本研究提出了一种在下肢机器人治疗期间通过功能性近红外光谱 （fNIRS） 评估大脑激活的方案。

Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise ...

在这项研究中，我们旨在研究一种实验方案的可行性，该协议涉及连接到机器人设备的下肢运动和同时获取功能性近红外光谱 fNIRS 数据。很少有研究使用fNIRS来研究卒中、运动恢复和下肢运动中的大脑激活，主要关注单个关节动作、步行或骑自行车。我们的研究率先探索了用于目标康复的多关节下肢训练。功能性近红外光谱 （fNIRS） 可有效测量脑血氧的时间变化，作为功能性 MRI 信号的间接脑活动指标。与功能磁共振成像不同，fNIRS与机器人硬件兼容，便携，并且对运动伪影更可靠。有两个主要挑战。第一种是将机器人设备的运动与fNIRS数据同步。二是要获得足够的数据来达到统计功效，只需六分钟即可检测出血流动力学变化。我们的研究证实了该协议的可行性并产生了预期的结果。在辅助条件下，只有两名受试者表现出显着的大脑激活。与辅助运动相比，需要更多努力的无辅助运动显示出更大的 HbO 浓度变化，对侧 PMC 存在显着差异。

an-experiment-using-functional-near-infrared-spectroscopy-robot

Research

JoVE Journal

Neuroscience

An Experiment Using Functional Near-Infrared Spectroscopy and Robot-Assisted Multi-Joint Pointing Movements of the Lower Limb

Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise in helping stroke patients regain lost motor functions. One potential avenue for increasing the understanding of how motor recovery occurs is to study brain activation during the movements that are targeted by therapy in healthy individuals. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising neuroimaging technique for examining neural underpinnings of motor function. This study aimed to investigate fNIRS neural correlates of complex lower limb movements in healthy subjects. Participants were asked to perform cycles of rest and movement for 6 min using a robotic device for motor rehabilitation. The task required coordinated knee and ankle joint movements to point to targets displayed on a computer screen. Two experimental conditions with different levels of movement assistance provided by the robot were explored. The results showed that the fNIRS protocol effectively detected brain regions associated with motor control during the task. Notably, all subjects exhibited greater activation in the contralateral premotor area during the no-assistance condition compared to the assisted condition. In conclusion, fNIRS appears to be a valuable approach for detecting changes in oxyhemoglobin concentration associated with multi-joint pointing movements of the lower limb. This research might contribute to the understanding of stroke motor recovery mechanisms and might pave the way for improved rehabilitation treatments for stroke patients. However, further research is needed to fully elucidate the potential of fNIRS in studying motor function and its applications in clinical settings.

It is estimated that 1 in 6 individuals worldwide will have a stroke in their lifetime, causing long-term disability, whose rehabilitation mechanisms are still poorly understood. This study proposes a protocol to evaluate brain activation by functional near-infrared spectroscopy (fNIRS) during a lower limb robotic therapy session.