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

有必要了解大脑如何对失去平衡和失误做出反应和适应 随着年龄的增长而行走。这一知识有助于改进跌倒干预措施和推进步态康复 治疗。我们建议使用脑电(EEG)和独立分量分析(ICA)来 识别和量化大脑在行走和卧步时对扰动的反应，这是一项运动任务 通常在临床上使用。我们将测试健康的年轻人和老年人，同时使用EEG记录他们的大脑活动， 使用肌电(EMG)的肌肉活动，以及在我们干扰他们的身体时使用运动捕捉的身体运动学 步进模式。扰动将产生步进误差，这将驱动适应，因为人们通常 更新移动以最大限度地减少移动错误。我们将使用典型的运动适应方案。对于目标1， 我们将确定在节奏性下肢运动中适应扰动的大脑皮层相关因素。 踩在卧式踏板上。我们将使用机器人卧式踏板施加短暂的阻力 步进周期中特定情况下的扰动。我们假设A)是一个分布式网络， 大脑区域受累，包括前扣带回，这是一个与错误监测相关的大脑结构； B)年轻人和老年人会减少步进误差，表明他们适应了 反复练习，大脑过程将有更大的光谱波动和转变，开始于 与未受干扰的踏步相比，受扰踏步期间的扰动；以及C)老年人将使用更多 肌肉协同激活，适应能力较差，大脑过程的光谱波动较小且延迟 与年轻人相比。对于目标2，我们将确定适应的皮层相关因素 在行走过程中应用的扰动。我们将使用一台跑步机，它可以模拟滑行和绊倒 内侧(侧面到侧面)和前后(向前/向后)方向，以制造扰动 在步态周期的特定情况下。要解决潜在的移动伪影问题，可能会 由扰动产生的，我们将首先阻断电生理信号并记录孤立的运动 在我们的设置和方案中，使用脑电系统来描述运动伪像。这一知识 将有助于分析和解释头皮EEG数据，并可能有助于开发算法来去除 脑电信号中的运动伪影。除了目标1中的假设之外，我们还有具体的假设 与行走时的平衡控制有关。我们假设左侧感觉运动皮质会有更大的 与非扰动步行相比，扰动步行过程中的光谱波动对 内侧摄动与前后摄动的比较。拟议工作的结果将是 通过测定大脑皮质的适应性来增进我们对年轻人和老年人大脑功能的了解 在行走和运动任务中对干扰的反应。这些发现可以应用于开发 基于脑动力学的新跌倒干预和步态康复疗法。

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.