Muscles transduce neural signals into the forces needed for movement. Every muscle is composed of a large population of motor units, each of which generates only a very small force. Virtually all previous work on motor unit properties has relied upon isometric conditions (constant muscle length) to facilitate measurement of these small forces. However, isometric conditions constitute only a small portion of normal motor behavior. Furthermore, data from whole muscles and single muscle fibers have shown that muscle tissue has a wide range of dynamic behaviors. The goal of this proposal is to obtain the first systematic measurements of dynamic motor unit properties. While there are many dynamic properties that could be studied in single units, Specific aim 1 proposes to determine which motor unit properties are actually important in normal movement conditions. For example, most muscle models rely only on the steady-state properties of muscle and thus assume dynamic properties play a minor role in force generation. A new decomposition technique has been developed to test this hypothesis. It has 2 phases: (1) techniques for accurately measuring single motor unit forces in dynamic conditions resembling those in normal movements; and (2) measurements in more controlled conditions that are designed to identify the effect of each mechanical property on the unit force output in those normal movement conditions.
Specific aims 2 &3 focus upon the behavior of motor units as a population of parallel mechanical elements. Since motor units form a heterogeneous population that is activated in order of increasing unit force, the population behavior cannot be predicted from that of any single unit. The hypothesis to be tested is that the population behavior increases the stability of muscle (i.e. its resistance to perturbations). The technique for testing this hypothesis also has 2 phases: (1) measurement of 2 basic motor unit properties that greatly influence stability, the force-velocity and force-length relations; and (20 prediction of population force-velocity-length behavior by use of realistic computer simulations based on these single unit data. These data should provide a foundation for understanding the underlying mechanisms of the functional deficits in diseases affecting both motor units and the control of motor units by the CNS.
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