Predictive Neuromusculoskeletal Simulations of Aged Human Spines

• Applicant: Dr.-Ing. Maria Hammer
• Subject Area: Orthopaedics, Traumatology, Reconstructive Surgery ; Bioinformatics and Theoretical Biology ; Medical Informatics and Medical Bioinformatics ; Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 564597055

The human spine is a complex and vital structure that provides mechanical support to the torso while protecting the spinal cord. Age-related changes, such as degeneration of intervertebral discs and facet joints, are significant contributors to common spinal disorders, including back pain and adult spinal deformity. These conditions often lead to chronic pain and disability. Despite the increasing prevalence because of an ageing society and the high impact of these disorders on the quality of life, the biomechanical consequences of age-related spinal degeneration remain poorly understood. This gap in knowledge limits the development of effective prevention strategies and hinders accurate clinical assessments and treatment planning. This research project aims to address these challenges by improving our understanding of the biomechanical changes in the ageing spine and enhancing the predictive capabilities of neuromusculoskeletal models. The first objective is to develop a population-based model of an aged spine, incorporating age-related changes in soft tissue properties and their interrelation with spinopelvic parameters. Using muscle-driven predictive simulations, the project aims to quantify internal forces during daily activities, assess the risk of tissue damage due to mechanical loads, and explore cause-effect relationships in typical degeneration patterns. The second objective is to evaluate the extent to which muscle-driven simulations using personalised models can predict the individual mechanical behaviour of elderly spines. This involves a detailed analysis of the necessary individualisation steps in the modelling process, aiming to enhance prediction accuracy while minimising computational and modelling effort. Within the project, these patient-specific models will be validated directly using data from clinical imaging and motion capture studies conducted on elderly subjects. By focusing on these main objectives, the project seeks to deepen our understanding of spinal biomechanics in ageing populations. This work will provide a foundation to improve the accuracy of personalised treatment plans and reduce the need for repeat surgeries, enabling more effective clinical decision-making and personalised spine care in the future.

• DFG Programme: WBP Position