CRCNS: Dimensionality Reduction in Cortico-Muscular Control of the Hand

描述（由申请人提供）：当你抓住你的咖啡杯时，你运动皮层中的数千个神经元控制着大约40块肌肉的活动，这些肌肉使你的手的22个骨骼自由度运动。控制这样一个日常行为的复杂性似乎令人生畏。但最近的研究表明，由于手部许多骨骼自由度的运动是高度相关的，因此22个自由度的运动中多达90%可以被捕获在仅2至7个主成分中。换句话说，描述手的大部分运动所需的维数可以从22减少到7或更少。类似地，其他同时记录19块肌肉的肌电活动的研究表明，高达80%的同时记录的肌电活动可以表示为3至5个随时间变化的肌肉协同作用。因此，描述肌肉活动所需的维度数量可以从19个减少到5个或更少。这样的降维可能会简化控制这些日常运动的复杂性吗？在这里，我们建议测试的一般假设，皮质肌肉控制的手和手指，利用降维。通过减少同时记录的数据神经元，肌肉和运动的三个不同层次的尺寸，我们将采取新颖的，全面的方法比较减少空间之间的对应关系在所有三个层次。通过这些比较，我们将探索以前未检验的假设：1）特定手指肌肉的生物力学结构产生手和手指运动学的某些主成分; 2）时变肌肉协同作用对应于手和手指运动学的主成分; 3）时变神经元协同作用代表手和手指运动学的主成分;以及4）时变神经元协同作用代表时变肌肉协同作用。为了验证我们的假设，我们将同时采集数据从128个单神经元微电极植入在初级运动皮层的手表示，从16个肌电图电极植入在各种肌肉，并从23个标记跟踪手指运动学，在把握运动的16至48个不同的对象。使用这些数据，我们将提取时变神经元协同作用，时变肌肉协同作用，以及手和手指运动学的主要组成部分。我们将确定是否个别肌肉，随时间变化的肌肉协同作用，和/或神经元的协同作用对应的手运动学的主要组成部分，以及随时间变化的神经元协同作用是否对应于肌肉协同作用。我们的假设将被拒绝，如果在不同层次的神经元，肌肉和运动的降维空间未能对应。相比之下，不同简化空间中元素之间的强关系将支持手部和手指的皮质肌肉控制实际上利用降维的概念。 除了对大脑如何控制运动的进一步理解对社会的长期利益之外，拟议的项目将在不断发展的神经修复学领域产生影响。皮质-肌肉系统中的视神经性降低将提供一种使控制神经驱动假体装置的机载计算机所承载的在线计算负荷最小化的手段。更广泛地说，我们的方法可以提供一个模型，用于计算减少和解释大型，复杂的，行为和认知神经科学数据集。我们的建议建立在罗切斯特大学的Schieber和约翰霍普金斯大学的Thakor之间相对较新的合作基础上，Schieber带来了运动系统生理学方面的专业知识，Thakor带来了生物医学工程方法的专业知识。通过频繁的视频电话会议和2-3个月的交流访问，这两个实验室将为两个机构的合作PI，研究生和本科生（包括代表性不足的少数民族）提供跨学科的培训。来自霍普金斯的生物医学工程师将在罗切斯特学习记录生理数据。来自罗切斯特的运动生理学家将在霍普金斯学习先进的数学分析技术。来自两个小组的学生将在神经科学和工程会议上展示他们的工作，在那里，共同PI将组织实践研讨会，以进一步传播研究结果本身，并将该项目作为跨学科研究的典范。共同PI还将协调一个创新的机构间研究生课程。

DESCRIPTION (provided by applicant): As you grasp your coffee cup, thousands of neurons in your motor cortex control the activity of some 40 muscles that move your hand's 22 skeletal degrees of freedom. The complexity of controlling such an everyday action seems daunting. But recent studies have shown that because the movements of many skeletal degrees of freedom in the hand are highly correlated, as much as 90% of the motion of the 22 degrees of freedom can be captured in only 2 to 7 principal components. In other words, the number of dimensions needed to describe most of the motion of the hand can be reduced from 22 down to 7 or fewer. Similarly, other studies in which electromyographic activity has been recorded simultaneously from 19 muscles have shown that up to 80% of the simultaneously recorded electromyographic activity can be expressed as 3 to 5 time-varying muscle synergies. The number of dimensions needed to describe muscle activity thereby can be reduced from 19 down to 5 or fewer. Might such dimensionality reduction simplify the complexity of controlling such everyday movements? Here we propose to test the general hypothesis that cortico-muscular control of the hand and fingers makes use of dimensionality reduction. By reducing dimensions at three different levels of simultaneously recorded data-neuronal, muscular and kinematic-we will take the novel, comprehensive approach of comparing the correspondence between the reduced spaces at all three levels. Through these comparisons, we will explore the previously unexamined hypotheses that: 1) the biomechanical structure of particular finger muscles produces certain principal components of hand and finger kinematics; 2) time-varying muscle synergies correspond to principal components of hand and finger kinematics; 3) time-varying neuron synergies represent principal components of hand and finger kinematics; and 4) time-varying neuron synergies represent time-varying muscle synergies. To test our hypotheses, we will acquire data simultaneously from 128 single neuron microelectrodes implanted in the primary motor cortex hand representation, from 16 electromyographic electrodes implanted in various muscles, and from 23 markers tracking finger kinematics, during grasping movements of 16 to 48 different objects. Using these data, we will extract time-varying neuron synergies, time-varying muscle synergies, and principle components of hand and finger kinematics. We will determine whether individual muscles, time-varying muscle synergies, and/or neuron synergies correspond to principal components of hand kinematics, and whether time-varying neuron synergies correspond to muscle synergies. Our hypotheses will be rejected if the spaces of reduced dimensionality at different levels-neuronal, muscular and kinematic-fail to correspond. In contrast, strong relationships between elements in the different reduced spaces would support the notion that cortico-muscular control of the hand and fingers actually utilizes dimensionality reduction. In addition to the long term benefit to society of an improved understanding of how the brain controls movement, the proposed project will have ramifications in the growing field of neuroprosthetics. Dimensionality reduction in the cortico-muscular system would provide a means of minimizing the on-line computational load carried by on-board computers that will control neurally driven prosthetic devices. More broadly, our approach may provide a model for computational reduction and interpretation of large, complex, behavioral and cognitive neuroscience datasets. Our proposal builds upon a relatively new collaboration between Schieber at the University of Rochester, who brings expertise in motor systems physiology, and Thakor at Johns Hopkins University, who brings expertise in biomedical engineering approaches to computation. Through frequent videoteleconferencing and 2-3 month exchange visits, these two labs will provide cross-disciplinary training for the co-PIs, graduate students, and undergraduates (including under-represented minorities) at both institutions. Biomedical engineers from Hopkins will learn to record physiological data while at Rochester. Motor physiologists from Rochester will learn advanced mathematical techniques for analysis while at Hopkins. The students from both groups will present their work at both neuroscience and engineering conferences, where the co-PIs will organize hands-on workshops for further dissemination of the findings per se, and of the project as a model for inter-disciplinary research. The co-PIs also will coordinate an innovative inter-institutional graduate level course.