A systems approach to understanding sensory-motor control of aimed limb movements

我们自己的肢体动作--甚至是昆虫的肢体动作--在灵巧性和健壮性上远远超过任何机器人。损害或阻止受控肢体运动的事故或医疗疾病，如大纤维感觉神经病，对受影响者的生活质量有深远影响。我们试图了解大脑是如何控制肢体运动的，以便更好地理解疾病过程中的问题，并开发更好的医疗干预措施。人类的大脑功能异常复杂，很难或不可能进行研究它所需的许多类型的实验。因此，我们使用了一种简单得多的动物--蝗虫--在这种动物中，我们可以记录、分析和操纵单个神经细胞在进行有针对性的运动时的活动。使用基于视频的运动跟踪来分析运动，这些数据被用来测试我们的计算机模型。我们方法的一个巨大优势是，我们对蝗虫体内特定神经细胞的作用比对人类或任何其他哺乳动物的作用有了更全面的了解。蝗虫在进行有目的的运动时必须解决的问题与人类面临的问题是一样的，所以我们寻找支撑所有这些运动的一般组织原则。在这项提案中，我们开始分析神经细胞组如何共同运作以产生有针对性的运动，并确定信号如何在神经系统的不同部分之间传输。我们研究的第二条线索是开发软件，使我们和与我们合作的临床医生能够分析从大脑记录的神经细胞信号。我们已经开发出强大的方法来检测清醒患者大脑记录中单个神经细胞的活动，并用这些方法来揭示这些细胞如何对复杂刺激做出反应的重要方面。我们现在希望开发这些方法，以允许使用来自多个电极的记录来检测许多神经细胞，并且工作得更快。信号处理方面的这些进展将对提高我们对人脑功能的理解非常重要，对于开发由患者大脑活动交互控制的假肢将是至关重要的。开发这种方法需要处理来自真实记录的大量数据，这是从人类患者那里获得的非常困难和昂贵的数据。取而代之的是，我们将使用蝗虫记录的信号进行开发工作。我们识别蝗虫个体识别细胞的能力为我们提供了极其强大的方法来验证我们的方法。为了实现我们的目标，我们有以下主要目标：1.开发、验证、使用并向其他用户提供我们用于处理神经数据的软件的强大改进版本。神经细胞信号的哪些方面使我们能够在复杂的记录中最好地识别它们的活动？我们如何才能最准确、最快速地对这些细胞的放电进行分类？2.描述在有针对性的肢体运动中驱动腿部屈曲的运动神经细胞的不同角色。每个细胞的信号是如何不同的，不同细胞中所看到的模式之间有什么关系？我们将开发同时记录多个单个细胞的新技术。3.分析神经系统不同部分之间传递信息的神经细胞的活动模式。他们的投入和产出是什么？我们的软件能否在复杂的多细胞记录中自动区分不同类型的细胞？4.描述发出腿部位置信号的感觉神经细胞的反应。这些因素是如何影响目标运动的？在感觉器官受损后，它们又是如何变化的？

Our own limb movements - and even those of insects - far exceed in dexterity and robustness those of any robot. Accidents or medical disorders such as large fibre sensory neuropathy that impair or prevent controlled limb movements have profound effects on the quality of life of those affected. We seek to understand how the brain controls aimed limb movements so that it is possible to better understand what goes wrong in disease processes, and to develop better medical interventions. Brain function in humans is exceptionally complex, and it is difficult or impossible to carry out many of the sorts of experiments that are required to investigate it. We therefore work with a much simpler animal - a locust - in which we can record, analyse and manipulate the activity of individual nerve cells while it makes aimed movements. The movements are analysed using video-based movement tracking and such data are used to test our computer models. A great advantage of our approach is that we have a much more complete understanding of the roles of particular nerve cells in a locust than we do in humans or any other mammals. The problems that a locust must solve in making an aimed movement are the same as those faced by humans, so we seek out the general principles of organisation that underpin all such movements. In this proposal we set out to analyse how groups of nerve cells operate together to generate aimed movements, and to determine how signals are transferred between different parts of the nervous system. A second strand of our research develops software that enables both us and clinicians with whom we collaborate to analyse nerve cell signals recorded from the brain. We have developed powerful methods to permit detection of the activity of single nerve cells in recordings made from the brains of awake patients, and have used these to reveal important aspects of how these cells respond to complex stimuli. We now wish to develop these methods to permit the detection of many nerve cells using recordings from multiple electrodes, and to work much more quickly. Such advances in signal processing will be very important for improving our understanding of human brain function and will be crucial in the development of prosthetic limbs that are controlled interactively by the activity of a patient's brain. Developing such methods requires the processing of large amounts of data from real recordings, which is very difficult and costly to obtain from human patients. We will instead carry out our development work using signals recorded from locusts. Our ability to recognise individual identified cells in a locust provides us with extremely powerful ways of validating our approach. To achieve our aims we have the following main objectives: 1. Develop, validate, use and make available to other users a powerful improved version of our software for processing neural data. What aspects of nerve cell signals permit us to best identify their activity in a complex recording? How can we classify the firing of these cells most accurately and most rapidly? 2. Characterise the separate roles of motor nerve cells that drive leg flexion during an aimed limb movement. How do the signals of each cell differ, and what are the relationships between the patterns seen in the different cells? We will develop new techniques for recording many single cells simultaneously. 3. Analyse the patterns of activity in nerve cells that carry information between different parts of the nervous system. What are their inputs and outputs? Can our software automatically distinguish between different types of cells in complex multiple-cell recordings? 4. Characterise the responses of sensory nerve cells that signal leg position. How do these influence an aimed movement, and how do they change after damage to the sense organ?