Flexible Control of Motor Units: Is the Multidimensionality of Motor Unit Manifolds a Sufficient Condition?

Abstract

AbstractThe level of flexibility in the neural control of motor units remains a topic of debate. Understanding this flexibility would require the identification of the distribution of common inputs to the motor units. In this study, we identified large samples of motor units from two lower limb muscles: the vastus lateralis (VL; up to 60 motor units/participant) and gastrocnemius medialis (GM; up to 67 motor units/participant). First, we applied a linear dimensionality reduction method to assess the dimensionality of the manifolds underlying the motor unit activity. We subsequently investigated the flexibility in motor unit control under two conditions: sinusoidal contractions with torque feedback, and online control with visual feedback on motor unit firing rates. Overall, we found that the activity of GM motor units was effectively captured by a single latent factor defining a unidimensional manifold, whereas the VL motor units were better represented by three latent factors defining a multidimensional manifold. Despite this difference in dimensionality, the recruitment of motor units in the two muscles exhibited similar but limited levels of flexibility. Using a spiking network model, we proposed that VL motor unit behaviors can be explained by a combination of a single common input of cortical origin with recurrent circuits involving connections with other motor unit pools. This study clarifies an important debated issue in motor unit control by showing that motor unit firings can lie in a multidimensional manifold; however, it may still be impossible for the central nervous system to flexibly control these motor units.Significant statementTo generate movement, the central nervous system distributes both excitatory and inhibitory inputs to the motor units. The level of flexibility in the neural control of these motor units remains a topic of debate with significant implications for understanding the smallest level of movement control. In this study, by combining experimental data and in silico models, we demonstrated that the activity of a large sample of motor units from a single muscle can be represented by a multidimensional linear manifold; however, these units show very limited flexibility in their recruitment. This is compatible with the presence of a single common input of cortical origin along with recurrent circuits involving connections with other pools, potentially including synergist muscles.