Supraspinal Control of Human Locomotor Adaptation

标题 人体运动适应的脊椎上控制 摘要 脑电描记技术的发展使脑电动力学的研究成为可能 在人类的步态中。有源电极、新颖的信号处理方法和受试者特异性反向电学特性 头部模型让我们对人类大脑如何控制运动有了前所未有的了解。思进 在基于脑电图的移动的脑成像中， 在真实的世界情况下，改善运动障碍的诊断和治疗，并导致新的脑- 计算机接口。我们最近开发了一种新型的噪声消除EEG系统，可以大大改善 脑电信号信噪比我们建议使用我们的新型脑电系统来研究人类的运动 适应许多研究使用血氧水平依赖性成像（例如fMRI或fNIRS）来研究 上肢运动适应的脊髓上控制或想象的人类行走，但这些的时间尺度 成像模态不允许识别相对于步态周期的生物力学的脑活动。我们 建议使用我们的新型EEG系统来记录参与运动适应的大脑区域。具体地说， 我们将量化步态周期内的大脑活动频谱波动， 运动适应我们认为大脑的多个区域，包括前扣带回，小脑， 躯体感觉皮层和运动皮层可能参与行走的控制和适应。我们也 预计参与运动适应的区域将随着改善而减少光谱功率波动 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步行 不同的速度，在分裂带跑步机上行走，用单侧机器人踝关节外骨骼行走， 在平衡木上用视觉干扰。EEG的高时间分辨率提供了特别有价值的 了解步态周期内大脑活动的幅度和时间。我们的初步数据显示， 控制人类行走所涉及的皮层区域比文献中通常认识到的要多。 这些研究结果将增加我们对人类脊髓上控制的基础科学认识 运动适应，并应导致脑电图移动的脑成像技术的进一步发展。

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.