Linkingin vivomuscle dynamics toin situforce-length and force-velocity reveals that guinea fowl lateral gastrocnemius operates at shorter than optimal lengths

Abstract

Force-length (F-L) and force-velocity (F-V) properties characterize skeletal muscles intrinsic properties under controlled conditions, and it is thought that these properties can inform and predict in vivo muscle function. Here, we map dynamic in vivo operating range and mechanical function during walking and running, to the measured in situ F-L and F-V characteristics of guinea fowl (Numida meleagris) lateral gastrocnemius (LG), a primary ankle extensor. We use in vivo patterns of muscle tendon force, fascicle length, and activation to test the hypothesis that muscle fascicles operate at optimal lengths and velocities to maximize force or power production during walking and running. Our findings only partly support our hypothesis: in vivo LG velocities are consistent with optimizing power during work production, and economy of force at higher loads. However, LG does not operate at lengths on the force plateau (+/-5% Fmax) during force production. LG length was near L0 at the time of EMG onset but shortened rapidly such that force development during stance occurred almost entirely on the ascending limb of the F-L curve, at shorter than optimal lengths. These data suggest that muscle fascicles shorten across optimal lengths in late swing, to optimize the potential for rapid force development near the swing-stance transition. This may provide resistance against unexpected perturbations that require rapid force development at foot contact. We also found evidence of passive force rise (in absence of EMG activity) in late swing, at lengths where passive force is zero in situ, suggesting that dynamic history dependent and viscoelastic effects may contribute to in vivo force development. Direct comparison of in vivo work loops and physiological operating ranges to traditional measures of F-L and F-V properties suggests the need for new approaches to characterize dynamic muscle properties in controlled conditions that more closely resemble in vivo dynamics.

Neural Networks Estimate Muscle Force in Dynamic Conditions Better than Hill-type Muscle Models

“…Second, we found that the change of the force-length relationship with activation was not correct, while for the force-velocity relationship, it was correct. For our network, the optimal fibre length increased with increasing activation, while previous experiments have shown that it decreases with increasing activation (e.g., [12,32]). However, the maximum shortening velocity decreased with decreasing activation, as was previously shown [13].…”

“…Therefore, this inaccuracy could be explained by a lack of training data in these regions, since most data was recorded at low and medium activation, and fibre lengths and velocities closer to optimal and isometric conditions. Another reason could be found in a recent study by Schwaner et al [32], who found that in-vivo force-length dynamics might not be represented well by in-situ measurements of the force-length relationship. Therefore, we need to find additional approaches to further validate the neural networks for application across species.…”

Viscoelastic materials are most energy efficient when loaded and unloaded at equal rates

“…These plots highlight the variation in observed strain patterns during different modes of locomotion. Data for (a) and (b) are recreated from [23,24], respectively.…”