Adaptation of brain and body responses to perturbations during gait in young and older adults

年轻人和老年人的大脑和身体对步态扰动的反应的适应

• 批准号: 9219073
• 负责人: Helen J Huang
• 金额: $ 30.47万
• 依托单位: UNIVERSITY OF CENTRAL FLORIDA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9219073
• 关键词: Address; Age; Aging; Algorithms; Anterior; Area; Brain; Brain region; Characteristics; Clinic; Custom; Data; Elderly; Electroencephalography; Electromyography; Electrophysiology (science); Equilibrium; Frequencies; Gait; Goals; Human; Impairment; Intervention; Knowledge; Lateral; Left; Limb structure; Link; Location; Lower Extremity; Measures; Methods; Monitor; Morphologic artifacts; Motion; Motor; Movement; Muscle; Pattern; Periodicity; Persons; Process; Proprioception; Protocols documentation; Reaction; Rehabilitation therapy; Robotics; Scalp structure; Side; Signal Transduction; Source; Speed; Structure; System; Testing; Text; Time; Update; Upper Extremity; Walking; Work; base; early onset; experience; experimental study; falls; foot; gait rehabilitation; improved; independent component analysis; innovation; insight; kinematics; locomotor tasks; motor impairment; neuromuscular; relating to nervous system; response; tool; treadmill; young adult

There is a need to understand how the brain responds and adapts to losses of balance and missteps during walking as we age. This knowledge could help improve fall interventions and advance gait rehabilitation therapies. We propose to use electroencephalography (EEG) and independent components analysis (ICA) to identify and quantify brain responses to perturbations during walking and recumbent stepping, a locomotor task often used in clinics. We will test healthy young and older adults while we record their brain activity using EEG, muscle activity using electromyography (EMG), and body kinematics using motion capture as we perturb their stepping pattern. The perturbations will create stepping errors that will drive adaptation because people often update movements to minimize movement errors. We will use a typical motor adaptation protocol. For Aim 1, we will determine the electrocortical correlates of adapting to perturbations applied during rhythmic lower limb stepping on a recumbent stepper. We will use a robotic recumbent stepper to apply brief resistive force perturbations during specific instances in the stepping cycle. We hypothesize that A) a distributed network of brain regions is involved and includes the anterior cingulate, a brain structure associated with error monitoring; B) young and older adults will reduce stepping errors indicating that they adapted to the perturbations with repeated practice, and brain processes will have larger spectral fluctuations and shift to begin prior to the perturbation during perturbed stepping compared to unperturbed stepping; and C) older adults will use greater muscle coactivation, adapt less well, and have smaller and delayed spectral fluctuations of brain processes compared to young adults. For Aim 2, we will determine the electrocortical correlates of adapting to perturbations applied during walking. We will use a treadmill that can simulate slips and trips in the mediolateral (side-to-side) and anterior-posterior (forwards/backwards) directions to create perturbations during specific instances in the gait cycle. To address potential movement artifact concerns that may be created by the perturbations, we will first block the electrophysiological signals and record isolated movement artifact using the EEG system to characterize the movement artifact in our setup and protocol. This knowledge will help with the analysis and interpretation of the scalp EEG data and may help develop algorithms to remove the movement artifact from EEG signals. In addition to the hypotheses in Aim 1, we have specific hypotheses related to balance control during walking. We hypothesize that the left sensorimotor cortex will have larger spectral fluctuations during perturbed walking compared to unperturbed walking and will be more sensitive to mediolateral perturbations compared to anterior-posterior perturbations. The results of the proposed work will advance our knowledge of brain function in young and older adults by determining adaptation of electrocortical responses to perturbations during walking and a locomotor task. These findings could be applied to develop new fall interventions and gait rehabilitation therapies based on brain dynamics.

有必要了解大脑如何反应和适应失去平衡和失误， 随着年龄的增长而行走。这些知识可以帮助改善跌倒干预和促进步态康复 治疗我们建议使用脑电图（EEG）和独立分量分析（伊卡）， 识别和量化大脑对行走和横卧踏步（一种运动任务）期间扰动的反应 常用于诊所。我们将测试健康的年轻人和老年人，同时我们用脑电图记录他们的大脑活动， 肌肉活动使用肌电图（EMG），身体运动学使用运动捕捉，因为我们扰动他们的 步进模式扰动会产生步进误差，这将推动适应，因为人们经常 更新移动以最小化移动错误。我们将使用典型的运动适应协议。对于目标1， 我们将确定在节律性下肢运动中适应扰动的皮层电相关性， 踩着一个横卧的踏步机。我们将使用一个机器人横卧步进应用简短的阻力 在步进循环中的特定实例期间的扰动。我们假设A）一个分布式网络 涉及大脑区域，包括前扣带回，一种与错误监控相关的大脑结构; B）年轻人和老年人将减少步进误差，表明他们适应了扰动， 反复练习，大脑的过程会有较大的频谱波动，并转移到开始之前。 与未扰动的步进相比，扰动步进期间的扰动;以及C）老年人将使用更大的 肌肉共激活，适应性较差，大脑过程的频谱波动较小且延迟 与年轻人相比。对于目标2，我们将确定适应的皮层电相关性， 在行走过程中施加的扰动。我们将使用一个跑步机，可以模拟滑倒和绊倒在 中外侧（侧到侧）和前后（向前/向后）方向，以产生扰动 在步态周期的特定情况下。为了解决潜在的运动伪影问题， 我们将首先阻断电生理信号并记录孤立的运动 伪影使用EEG系统来表征我们的设置和协议中的运动伪影。这些知识 将有助于分析和解释头皮EEG数据，并可能有助于开发算法，以消除 脑电信号中的运动伪影除了目标1中的假设外，我们还有具体的假设 与行走时的平衡控制有关。我们假设左侧的感觉运动皮层 与未受干扰的行走相比，受干扰行走期间的光谱波动将对 与前-后扰动相比，中-外侧扰动。拟议工作的结果将 通过确定皮质电的适应性来提高我们对年轻人和老年人大脑功能的认识 在行走和运动任务中对扰动的反应。这些发现可以用于开发 新的跌倒干预和步态康复治疗的基础上，脑动力学。