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Biology subjects

Wallnoefer, E.

Publications and source records attributed to Wallnoefer, E..

2 recordsLinked to original sources

Femoral bone growth predictions based on personalized multi-scale simulations: Validation and sensitivity analysis of a mechanobiological model

Musculoskeletal function is pivotal to long-term health. However, various patient groups develop torsional deformities, leading to clinical, functional problems. Understanding the interplay between movement pattern, bone loading and growth is crucial for improving the functional mobility of these patients and preserving long-term health. Multi-scale simulations in combination with a mechanobiological bone growth model have been used to estimate bone loads and predict femoral growth trends based on cross-sectional data. The lack of longitudinal data in previous studies hindered refinements of the mechanobiological model and validation of subject-specific growth predictions, thereby limiting clinical applications. This study aimed to validate the growth predictions using magnetic resonance images and motion capture data - collected longitudinally - from ten growing children. Additionally, a sensitivity analysis was conducted to refine model parameters. A linear regression model based on physical activity information, anthropometric data, and predictions from the refined mechanobiological model explained 70% of femoral anteversion development. Notably, the direction of femoral development was accurately predicted in 18 out of 20 femurs, suggesting that growth predictions could help to revolutionize treatment strategies for torsional deformities. Statements and DeclarationsThe authors have no relevant financial or non-financial interests to disclose.

pathology↗

Trial-to-trial similarity and distinctness of muscle synergy activation coefficients increases during learning and with a higher level of movement proficiency

Muscle synergy analyses are used to increase our understanding of motor control. Spatially fixed synergy vectors coordinate multiple co-active muscles through activation commands, known as activation coefficients. To better understand motor learning, it is crucial to know how synergy recruitment varies during a learning task and different levels of movement proficiency. Within one session participants walked on a line, a beam, and learned to walk on a tightrope - tasks that represent different levels of proficiency. Muscle synergies were extracted over all conditions and the number of synergies was determined through the knee-point of the total variance accounted for (tVAF) curve. We found that the tVAF of one synergy decreased with task proficiency (line < beam < tightrope). Additionally, trial-to-trial similarity and distinctness of synergy activation coefficients increased with proficiency and after a learning process. We conclude that precise adjustment and refinement of synergy activation coefficients play a crucial role in motor learning.

neuroscience↗