Origins of low frequency components of physiological tremor

生理性震颤（PT）是一种不自主的运动，需要考虑到人体运动方案，特别是那些需要高度精确的手指运动。虽然PT高频振荡的起源有很好的记录；低频分量的起源还不是很清楚。这个PT的“未知”分量是最重要的，因为它的振幅占PT振幅的90%以上。因此，目前的建议力求通过四项研究来确定低频成分PT的起源。具体来说，这些研究将确定肌电图活动、运动单元活动、肌肉纺锤体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完善的文献和初步结果。了解PT的组成将澄清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认识，使这些新发现可以纳入运动控制的神经模型。此外，了解PT低频振荡的起源，大部分PT振幅所在的地方，可以导致新的和改进的策略，以抵消这些振荡在精确运动期间的麻烦，如在显微外科手术过程中。我们的长期目标是转移这些新知识，例如，在手术机器人中实现更好的过滤技术，以减少PT和提高准确性。最后，我们可以利用这些知识来开发简单的设备，可以区分正常和异常的震动，比如我们目前正在使用智能手机。

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.