Biology subjects

Dick, T. J.

Publications and source records attributed to Dick, T. J..

2 recordsLinked to original sources

bioRxiv2025

The impact of mechanical requirements on the neural control of skeletal muscle and subsequent energetic rates

1The energetic cost of skeletal muscle contraction is a fundamental driver in the selection of locomotor strategies. Muscle energy consumption depends on muscle-fibre typology, neural drive, and mechanical state. While the influence of fibre-type and mechanical state on energy use has been extensively characterised in isolated muscle preparations, these experiments fail to capture the influence of realistic in vivo motor unit recruitment strategies. Hence, this study aims to capture neural drive in the tibialis anterior, in particular motor unit recruitment thresholds and rate coding, and the corresponding changes in energy use across a range of mechanical demands. Fixed ankle angle dorsiflexion contractions were performed at varying rates of torque development, while high density electromyography characterized motor unit spiking activity, B-mode ultrasound captured muscle fascicle dynamics, and indirect calorimetry measured energetic rates. Faster rates of torque development required earlier recruitment of motor units with increased motor unit firing rates which coincided with increased fascicle strain rates. At higher rates of torque development, these altered motor unit discharge patterns and muscle mechanics coincided with increased muscle energy use. Together, these findings highlight that in vivo muscle energetics emerge from the dynamic interplay between neural control and mechanical demands.

physiology↗

bioRxiv2024

Using physiologically-based models to predict in vivo skeletal muscle energetics

Understanding how muscles use energy is essential for elucidating the role of skeletal muscle in animal locomotion. Yet, experimental measures of in vivo muscle energetics are challenging to obtain, so physiologically-based muscle models are often used to estimate energy use. These predictions of individual muscle energy expenditure are not often compared to indirect whole body measures of energetic cost. Here, we examined and illustrated the capability of physiologically-based muscle models to predict in vivo measures of energy use. To improve model predictions and ensure a physiological basis for model parameters, we refined our model to include data from isolated muscle experiments. Simulations were performed to capture three different experimental protocols, which involved varying contraction frequency, duty cycle, and muscle fascicle length. Our results demonstrated that these models are able capture the general features of whole body energetics across contractile conditions, but tended to under predict the magnitude of energetic cost. Our analysis revealed that when predicting in vivo energetic rates across contractile conditions, the model was most sensitive to the force-velocity parameters and the data informing the energetic rates when predicting in vivo energetic rates across contractile conditions. This work highlights it is the mechanics of skeletal muscle contraction that govern muscle energy use.

bioengineering↗