Skeletal muscle function underpins muscle spindle abundance

Abstract

Muscle spindle abundance is highly variable within and across species, but we currently lack any clear picture of the mechanistic causes or consequences of this variation. Previous use of spindle abundance as a correlate for muscle function implies a mechanical underpinning to this variation, but these ideas have not been tested. Herein, we use integrated medical imaging and subject-specific musculoskeletal models to investigate the relationship between spindle abundance, muscle architecture and in vivo muscle behaviour in the human locomotor system. These analyses indicate that muscle spindle number is tightly correlated with muscle fascicle length, absolute fascicle length change, velocity of fibre lengthening and active muscle forces during walking. Novel correlations between functional indices and spindle abundance are also recovered, where muscles with a high abundance predominantly function as springs, compared to those with a lower abundance mostly functioning as brakes during walking. These data demonstrate that muscle fibre length, lengthening velocity and fibre force are key physiological signals to the central nervous system and its modulation of locomotion, and that muscle spindle abundance may be tightly correlated to how a muscle generates work. These insights may be combined with neuromechanics and robotic studies of motor control to help further tease apart the functional drivers of muscle spindle composition.

Keywords: MRI; biomechanics; muscle spindle; physics simulation; proprioception.

Figure 1.

Experimental workflow. 10 participants were MRI scanned to calculate muscle mass and measure median muscle fibre length. Using Banks' [11] equation to predict muscle spindle number, we predicted spindle number (S_pn) for 35 muscles across the lower leg muscles. Subsequently, subject-specific models and simulations were generated for ten participants walking overground, and we conducted correlative analysis of spindle composition with muscle-specific functional indices. (Online version in colour.)

Figure 2.

Relationship between spindle composition and morphometric derived specialization. Relationship between muscle fibre length and PCSA across the 10 participants with heat map colouring representing predicted spindle number (S_pn) (a) and spindle abundance (b). The relationship between log(S_pn) and log(L_f) (c) and muscle pennation angle (d). *p < 0.05. (Online version in colour.)

Figure 3.

Relationship between spindle composition and modelled muscle length and force characteristics during walking. Predicted spindle counts plotted against absolute strain amplitude (a), relative strain amplitude (b), maximum absolute lengthening velocity (c), maximum normalized lengthening velocity (d), maximum passive fibre force (e) and maximum active fibre force (f). *p < 0.05. (Online version in colour.)

Figure 4.

Relationship between spindle composition and functional indices during walking. Representative OpenSim modelled gait cycle (a). Relationship between muscle spindle abundance and the percentage of functional indexes for spring (b) and brake (c) behaviour. The average functional index for individual muscles from the 10 participants ranked in order of spindle abundance (d). *p < 0.05. (Online version in colour.)