Origins of low frequency components of physiological tremor

生理性震颤(PT)是一种非自愿运动，需要在人类运动方案中考虑到，特别是那些需要高度准确的手指运动的运动。虽然PT高频振荡的起源已经有了很好的文献记载，但对低频分量的起源却没有很好的理解。PT的这个“未知”分量是最重要的，因为它的幅度占PT幅度的90%以上。因此，目前的建议试图通过四项研究来识别PT的低频成分的来源。具体地说，这些研究将确定肌电活动、运动单位活动、肌梭和反射弧活动以及感觉模式对低频PT振荡特征的作用。这些范式的选择是基于成熟的文献和初步结果。了解PT的组成将阐明产生这些肢体振荡的基本神经机制和机械因素，从而有助于确定PT在神经运动方案中的重要性。这项研究将使我们能够提高我们对运动系统的了解，以便将这些新发现纳入运动控制的神经模型中。此外，了解PT低频振荡的来源，也就是PT幅度的主要来源，可能会导致新的和改进的策略，以抵消这些振荡在精确运动中的滋扰，例如在显微手术过程中。我们的长期目标是传递这一新知识，例如，在外科机器人中实施更好的过滤技术，以减少PT并提高准确性。最后，我们可以利用这一知识来开发简单的设备，可以区分正常和异常震动，就像我们目前使用智能手机所做的那样。

Physiological tremor (PT) is an involuntary movement that needs to be taken into account within the human motor scheme, especially those needed for highly accurate finger movements. Although the origins of the high frequency oscillations of PT are well documented; the origin of low frequency component is not as well understood. This "unknown" component of PT is of first importance since its amplitude represents more than 90% of PT amplitude. Thus, the current proposal strives to identify the origins of low frequency component of PT using four studies. Specifically, these studies will identify the role of EMG activity, motor unit activity, muscle spindle and reflex arc activity, and sensory modalities on the characteristics of low frequency PT oscillations. These paradigms were chosen based on well-establish literature and preliminary results. Understanding the components of PT will clarify the basic neural mechanisms and mechanical factors involved in the generation of these limb oscillations, thus helping determine the importance of PT within the neural motor scheme. This research will allow us to improve our knowledge of the motor system such that these new discoveries could be incorporated into neural models of motor control. Also, knowing the origin of low frequency oscillations of PT, where the majority of PT amplitude lies, could lead to new and improved strategies to counteract the nuisance of these oscillations during precise movements such as during microsurgical procedures. Our long-term goals is to transfer this new knowledge, for example, for the implementation of better filtering techniques in surgical robots to reduce PT and improve accuracy. Finally, we can use this knowledge to develop simple devices that can differentiate normal from abnormal tremors such as we are currently doing using smart phones.