Supraspinal Control of Human Locomotor Adaptation

标题 人类运动适应的棘上控制 摘要 脑电(EEG)技术的进步使研究脑电动力学成为可能 在人类的步态中。有源电极、新的信号处理方法和特定对象的逆电学 头部模型使人们能够前所未有地深入了解人类大脑是如何控制运动的。进一步的进展 基于EEG的移动脑成像将增加我们对人脑工作原理的基本了解 在现实世界中，改善运动障碍的诊断和治疗，并产生新的大脑- 计算机接口。我们最近开发了一种新型的噪声消除脑电系统，它可以极大地提高 脑电的信噪比。我们计划使用我们的新型脑电系统来研究人类的运动 适应。许多研究已经使用血氧水平依赖成像(例如，fMRI或fNIRS)来研究 上肢运动适应或想象的人类行走的脊柱上控制，但这些 成像方式不允许识别与步态周期的生物力学相关的大脑活动。我们 建议使用我们的新型脑电系统来记录与运动适应有关的大脑区域。具体来说， 我们将量化步态周期内的大脑活动光谱波动，这表明与 运动适应。我们预计多个脑区，包括前扣带回，小脑， 躯体感觉皮质和运动皮质可能参与了行走的控制和适应。我们也 预计涉及运动适应的区域将通过改进减少频谱功率波动 在具有挑战性的步态任务中的运动表现。我们将调查的具体任务是 不同的速度，在分带跑步机上行走，使用单侧机器人脚踝外骨骼行走，以及行走 在有视觉干扰的平衡木上。脑电的高时间分辨率提供了特别有价值的 洞察步态周期中大脑活动的幅度和时间。我们的初步数据表明 控制人类行走的大脑皮层区域比文献中通常所认识的要多。 这些研究的结果将增加我们对人类脊椎上控制的基本科学理解 运动适应，并应导致脑电移动脑成像技术的进一步进步。

Title Supraspinal Control of Human Locomotor Adaptation Abstract Advances in electroencephalography (EEG) technology have made it feasible to study electrical brain dynamics during human gait. Active electrodes, novel signal processing approaches, and subject-specific inverse electrical head models allow for unprecedented insight into how the human brain controls locomotion. Further advances in EEG based mobile brain imaging will increase our fundamental understanding of how the human brain works in real world situations, improve diagnosis and treatment of movement disorders, and result in new brain- computer interfaces. We recently developed a novel noise-cancelling EEG system that can greatly improve the signal to noise ratio for EEG. We propose to use our novel EEG system to investigate human locomotor adaptation. Many studies have used blood-oxygen-level dependent imaging (e.g. fMRI or fNIRS) to study supraspinal control of upper limb motor adaptation or imagined human walking, but the timescale of those imaging modalities do not allow for identifying brain activity relative to the biomechanics of the gait cycle. We propose to use our novel EEG system to document the brain areas involved in locomotor adaptation. Specifically, we will quantify brain activity spectral fluctuations within the gait cycle that demonstrate correlations with locomotor adaptation. We expect that multiple brain areas, including the anterior cingulate, cerebellum, somatosensory cortex, and motor cortex are likely involved in the control and adaptation of walking. We also expect that areas involved in locomotor adaptation will decrease spectral power fluctuations with improvements in locomotor performance during challenging gait tasks. The specific tasks that we will investigate are walking at different speeds, walking on a split-belt treadmill, walking with a unilateral robotic ankle exoskeleton, and walking on a balance beam with visual perturbations. The high temporal resolution of EEG provides particularly valuable insight into both amplitude and timing of brain activity within the gait cycle. Our preliminary data suggest that there are more cortical areas involved in controlling human walking than are generally recognized in the literature. The results from these studies will increase our basic science understanding of the supraspinal control of human locomotor adaptation and should lead to further advances in EEG mobile brain imaging technology.