Metabolic cost underlies task-dependent variations in motor unit recruitment

Abstract

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.

Keywords: motor recruitment; motor units; muscles; musculoskeletal modelling.

Figure 1.

(a) Normalized muscle force, (b) mechanical power, (c) metabolic rate and (d) efficiency properties of the three muscle fibre types used in this study plotted against normalized fibre velocity. Fibre velocity was normalized by the maximum contraction velocity (V₀). Muscle force was normalized by the maximum isometric force (F₀). Mechanical power was calculated as the product of muscle force and muscle fibre velocity. Metabolic rate function was adapted from Minetti & Alexander [36]. Mechanical efficiency was the calculated as mechanical power over metabolic rate. (Online version in colour.)

Figure 2.

Mean experimental and simulation ankle angles (top) and net joint torques (bottom) for three cyclists across the six pedalling conditions. (Online version in colour.)

Figure 3.

(a) Muscle normalized fibre lengths and absolute fibre velocities of the ankle plantarflexors for three cyclists across the six pedalling conditions. (b) Mean normalized muscle fibre velocities of the fast (top) and slow (bottom) fibres of the plantarflexors. Fibre lengths and velocities were normalized to optimal fibre length (l₀) and maximum contraction velocity (V₀), respectively. Positive and negative fibre velocities represent lengthening and shortening, respectively. (Online version in colour.)

Figure 4.

Predicted time-varying muscle activations of the muscle fibre types of the plantarflexor during a simulated ramped isometric plantarflexion. Simulations were performed using the two muscle fibre combinations assigned to the plantarflexor fibres: (1) one purely slow fibre and one purely fast fibre (slow–fast), and (2) two mixed fibres (mixed–mixed) and using the two weighted cost functions: (a) min-metabolic-activation () and (b) min-activation (). (Online version in colour.)

Figure 5.

Predicted time-varying muscle activations (mean ± 1 s.d.) of the plantarflexor muscle fibre types for three cyclists across six pedalling conditions. Muscle activation was bounded between 0 (no activation) to 1 (fully activated). The model images above the plots depict the orientation of the lower limb and the crank. Two muscle fibre combinations (slow–fast and mixed–mixed) and two weighted cost functions (min-metabolic-activation function () (left) and min-activations () (right)) were used. (Online version in colour.)

Figure 6.

Mean activations of the muscle fibre types of the plantarflexor across six pedalling conditions and one simulated ramped isometric plantarflexion. Two muscle fibre combinations (slow–fast and mixed–mixed) and two weighted cost functions ((a) min-metabolic-activation function () and (b) min-activations ()) were used. Note that the mixed–mixed plantarflexor fibre combination (yellow and cyan bars) was predicted in separate simulations to the slow and fast (red and blue bars) plantarflexor fibre combination. (Online version in colour.)

Figure 7.

Total metabolic cost of the muscle fibre in the plantarflexor across six pedalling conditions and one simulated ramped isometric plantarflexion. Two muscle fibre combinations (slow–fast and mixed–mixed) and two weighted cost functions ((a) min-metabolic-activation function () and (b) min-activations ()) were used. Metabolic cost was normalized by the mass of the musculoskeletal model and the rotational distance travelled by the pedal around the crank centre over one crank cycle. (Online version in colour.)