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.
