DEVELOPING, TESTING AND OPTIMISING A MOUSE HINDLIMB MUSCULOSKELETAL MODEL

Charles, J P; Cappellari, O; Spence, A; Wells, D J; Hutchinson, J R

Abstract

Stability during locomotion arises from many complex interactions which are not yet fully understood. Studies into how sensory feedback from muscle spindles contributes to this have used neuromechanical simulations, however their accuracy is limited due to the lack of available physiological data. This presentation outlines the methods used to develop a musculoskeletal model of the mouse hindlimb, which will be used to predict responses to perturbations which occur with or without sensory feedback. The 3D musculoskeletal model, which represents bones, joints and 39 muscles of the mouse hindlimb, was created using SIMM (Musculographics, USA) through a combination of muscle architecture data and I2KI enhanced microCT scanning. Architecture data, such as muscle mass, pennation angle and fibre length were gathered from microdissections, while muscle geometry was determined using I2KI microCT scanning, a non-destructive method of determining muscle paths and attachment points. Muscle moment arms predicted by the model were found to agree with measured data from previous studies. In future work, in vivo optogenetics will be used to characterise changes in gait and muscle forces which occur in free running mice in response to induced neural and mechanical perturbations with or without sensory feedback from hindlimb muscles. These physiological data will be used to update and refine the musculoskeletal model.

Citation

Charles, J. P., Cappellari, O., Spence, A., Wells, D. J., & Hutchinson, J. R. (2015). DEVELOPING, TESTING AND OPTIMISING A MOUSE HINDLIMB MUSCULOSKELETAL MODEL. FASEB Journal, 29,

• Journal Article Type: Meeting Abstract
• Publication Date: Apr 1, 2015
• Deposit Date: Dec 17, 2015
• Journal: FASEB JOURNAL
• Print ISSN: 0892-6638
• Publisher: Federation of American Society of Experimental Biology (FASEB)
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Public URL: https://rvc-repository.worktribe.com/output/1401331