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并适应平衡和失误的损失 随着年龄的增长而行。这些知识可以帮助改善秋季干预措施并提高步态康复 疗法。我们建议使用脑电图（EEG）和独立组件分析（ICA）进行 识别并量化大脑对行走和卧式踏脚过程中对扰动的反应，这是一项运动任务 经常用于诊所。我们将在使用脑电图记录大脑活动时测试健康的年轻人和老年人， 使用肌电图（EMG）和身体运动学的肌肉活性在我们扰动时使用运动捕获 步进模式。扰动将造成阶梯错误，因为人们经常 更新动作以最大程度地减少运动错误。我们将使用典型的电机适应协议。对于目标1， 我们将确定适应节奏下肢施加的对扰动的皮质相关性 踩到卧式的步进。我们将使用机器人卧式步进措施施加短暂的电阻力 在阶梯周期中的特定实例中的扰动。我们假设a） 大脑区域涉及并包括前扣带回，这是与错误监测有关的大脑结构； b）年轻人和老年人会减少垫脚错误，表明他们适应了与扰动的适应 重复练习，大脑过程将具有较大的光谱波动，并在开始之前开始 与不受干扰的步进相比，在干扰阶梯的过程中扰动； c）老年人将使用更大的 肌肉共激活，适应不善，脑部过程的光谱波动较小且延迟 与年轻人相比。对于AIM 2，我们将确定适应到适应的皮质相关性 步行期间施加扰动。我们将使用一个跑步机，可以模拟滑移和旅行 中外侧（左右）和前后（前向/向后）方向以产生扰动 在步态周期的特定情况下。解决可能存在的潜在移动工件关注 通过扰动创建，我们将首先阻止电生理信号并记录孤立运动 使用脑电图系统来表征我们的设置和协议中的运动伪像。这个知识 将有助于对头皮脑电图数据的分析和解释，并可能有助于开发算法以删除 脑电图信号的动作工件。除了AIM 1中的假设外，我们还有特定的假设 与步行过程中的平衡控制有关。我们假设左感觉运动皮层将具有更大的 与不受干扰的行走相比 与前后扰动相比，中外侧扰动。拟议工作的结果将 通过确定皮质的适应来提高我们对年轻人和老年人的大脑功能知识 步行过程中对扰动的响应和运动任务。这些发现可以用于发展 基于大脑动态的新秋季干预和步态康复疗法。