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

描述(申请人提供)：当你握住咖啡杯时，你的运动皮质中数以千计的神经元控制着大约40块肌肉的活动，这些肌肉移动你的手的22个骨骼自由度。控制这种日常行为的复杂性似乎令人望而生畏。但最近的研究表明，由于手中许多骨骼自由度的运动是高度相关的，高达90%的22个自由度的运动仅用2到7个主成分就能捕捉到。换句话说，描述手的大部分运动所需的维度数量可以从22个减少到7个或更少。同样，其他研究同时记录了19个肌肉的肌电活动，表明高达80%的同时记录的肌电活动可以表示为3到5个时变的肌肉协同作用。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。这样的降维是否会简化控制这种日常运动的复杂性？在这里，我们建议检验一般假设，即手和手指的皮质肌肉控制利用降维。通过同时记录数据的三个不同级别的降维-神经、肌肉和运动学-我们将采用新颖的综合方法来比较所有三个级别的降维空间之间的对应关系。通过这些比较，我们将探索以前未被检验的假设：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)时变肌肉协同对应于手和手指运动学的主成分；3)时变神经元协同代表手和手指运动学的主成分；以及4)时变神经元协同代表时变肌肉协同。为了验证我们的假设，我们将同时从128个植入初级运动皮质手部表征的单个神经元微电极、16个植入不同肌肉的肌电电极和23个跟踪手指运动学的标记中获取数据，在16到48个不同对象的抓取过程中。利用这些数据，我们将提取时变神经元协同效应、时变肌肉协同效应以及手和手指运动学的主成分。我们将确定单个肌肉、时变肌肉协同效应和/或神经元协同效应是否对应于手部运动学的主成分，以及时变神经元协同效应是否对应于肌肉协同效应。如果不同层次的降维空间--神经元、肌肉和运动学的降维空间--不一致，我们的假设将被推翻。相比之下，不同简化空间中的元素之间的强烈关系将支持这样的概念，即手和手指的皮质肌肉控制实际上利用了降维。 除了改善对大脑如何控制运动的理解对社会的长期好处外，拟议中的项目还将在不断增长的神经假体领域产生影响。皮质-肌肉系统的降维将提供一种手段，最大限度地减少控制神经驱动假肢设备的机载计算机所承载的在线计算负荷。更广泛地说，我们的方法可能为计算减少和解释大型、复杂的、行为和认知神经科学数据集提供一个模型。我们的建议建立在罗切斯特大学的Schieber和Johns Hopkins大学的Thakor之间相对较新的合作之上，前者带来了运动系统生理学方面的专业知识，后者将生物医学工程方法的专业知识带到了计算中。通过频繁的视频会议和2-3个月的互访，这两个实验室将为两个机构的合作个人、研究生和本科生(包括代表不足的少数群体)提供跨学科培训。来自霍普金斯大学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大学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学会议上介绍他们的工作，在那里，联合PIS将组织实践研讨会，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。联合PIS还将协调一个创新的跨院校研究生水平课程。

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.