The effect of scaling physiological cross-sectional area on musculoskeletal model predictions

Personalisation of model parameters is likely to improve biomechanical model predictions and could allow models to be used for subject- or patient-specific applications. This study evaluates the effect of personalising physiological cross-sectional areas (PCSA) in a large-scale musculoskeletal model of the upper extremity. Muscle volumes obtained from MRI were used to scale PCSAs of five subjects, for whom the maximum forces they could exert in six different directions on a handle held by the hand were also recorded. The effect of PCSA scaling was evaluated by calculating the lowest maximum muscle stress (sigma(max), a constant for human skeletal muscle) required by the model to reproduce these forces. When the original cadaver-based PCSA-values were used, strongly different between-subject sigma(max)-values were found (sigma(max)=106.1 +/- 39.9 N cm(-2)). A relatively simple, uniform scaling routine reduced this variation substantially (sigma(max)=69.4 +/- 9.4 N cm(-2)) and led to similar results to when a more detailed, muscle-specific scaling routine was used (sigma(max)=71.2 +/- + 10.8 N cm(-2)). Using subject-specific PCSA values to simulate an shoulder abduction task changed muscle force predictions for the subscapularis and the pectoralis major on average by 33% and 21%, respectively, but was <10% for all other muscles. The glenohumeral (GH) joint contact force changed less than 1.5% as a result of scaling. We conclude that individualisation of the model's strength can most easily be done by scaling PCSA with a single factor that can be derived from muscle volume data or, alternatively, from maximum force measurements. However, since PCSA scaling only marginally changed muscle and joint contact force predictions for submaximal tasks, the need for PCSA scaling remains debatable. (C) 2015 Elsevier Ltd. All rights reserved.