Metabolic cost underlies task-dependent variations in motor unit recruitment

Abstract

<jats:p>Mammalian skeletal muscles are comprised of many motor units, each containing a group of muscle fibres that have common contractile properties: these can be broadly categorized as slow and fast twitch muscle fibres. Motor units are typically recruited in an orderly fashion following the ‘size principle’, in which slower motor units would be recruited for low intensity contraction; a metabolically cheap and fatigue-resistant strategy. However, this recruitment strategy poses a mechanical paradox for fast, low intensity contractions, in which the recruitment of slower fibres, as predicted by the size principle, would be metabolically more costly than the recruitment of faster fibres that are more efficient at higher contraction speeds. Hence, it would be mechanically and metabolically more effective for recruitment strategies to vary in response to contraction speed so that the intrinsic efficiencies and contraction speeds of the recruited muscle fibres are matched to the mechanical demands of the task. In this study, we evaluated the effectiveness of a novel, mixed cost function within a musculoskeletal simulation, which includes the metabolic cost of contraction, to predict the recruitment of different muscle fibre types across a range of loads and speeds. Our results show that a metabolically informed cost function predicts favoured recruitment of slower muscle fibres for slower and isometric tasks versus recruitment that favours faster muscles fibres for higher velocity contractions. This cost function predicts a change in recruitment patterns consistent with experimental observations, and also predicts a less expensive metabolic cost for these muscle contractions regardless of speed of the movement. Hence, our findings support the premise that varying motor recruitment strategies to match the mechanical demands of a movement task results in a mechanically and metabolically sensible way to deploy the different types of motor unit.</jats:p>

Task-dependent recruitment across ankle extensor muscles and between mechanical demands is driven by the metabolic cost of muscle contraction

“…In this study, we tested the theory that a strategy of prioritizing the minimization of the metabolic cost of muscle contractions will explain both recruitment between MUs within muscles and the relative coordination of muscles within synergistic groups in the limb. This study builds on our previous work where we demonstrated that recruitment between MUs within a muscle can be explained by the minimization of metabolic cost [8], but extends these concepts to a multi-muscle system in a whole limb. We focused on the synergistic ankle plantarflexor muscles, which face the additional challenges of varying muscle lengths [13] (owing to differences in one-or two-joint muscles) and muscle sizes.…”

“…Indeed, a variety of experimental studies have shown that MU recruitment can vary depending on the mechanical demands of the task [4][5][6][7]. This is further supported by a recent modelling study [8] that suggested a strategy of minimizing the metabolic cost of muscle contractions can result in a more flexible set of principles for MU recruitment, for which orderly recruitment is present during slow contractions but favours the recruitment of faster MUs at faster contraction speeds.…”

Lower-limb muscle function is influenced by changing mechanical demands in cycling

“…Yet, in our musculoskeletal simulations, we represented the SO with mixed properties, which most likely influenced the predicted activation in the SO at faster cadences where differences in maximum shortening velocity are significant. Second, the use of a single-element Hill-type muscle model has been shown to be unable to fully explain the different recruitment strategies that occur across mechanical demands (Lai et al, 2018). Third, other muscle-specific factors such as inertial properties and history-dependent effects were excluded from the muscle models (Ross et al, 2018).…”

OpenSim Moco: Musculoskeletal optimal control

“…The advantages of direct collocation have led biomechanists to use the method for prescribing motions [26,27], tracking motions [16,[28][29][30][31], predicting motions [32][33][34][35][36][37][38][39][40][41][42][43][44], fitting muscle properties [45], and optimizing model parameters [46]. Researchers have made key methodological advances, including efficiently handling multibody and muscle dynamics via implicit formulations [26,47], minimizing energy consumption [48,49], and employing algorithmic differentiation to simulate complex models more rapidly compared to using finite differences [50].…”