A data-driven optimization framework for improving the adaptation of the neuromuscular system in brain pathology

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This projects aims to establish a novel in silico framework that can be used to explain adaptation mechanisms in the neuro-musculoskeletal system in response to brain pathology such as stroke, cerebral palsy or multiple sclerosis. These pathologies significantly limit motor abilities in affected subjects, however, satisfactory treatments do not exist unfortunately. The ultimate goal is to support the development of novel and the improvement of existing therapeutic applications. Based on the concept that the body aims to adapt in such a way as to optimally deal with the given conditions, we intend to use mathematical techniques from constrained optimization to tackle this goal. By employing a systemic multi-scale model of the neuro-musculoskeletal system, we expect that such an approach can make meaningful predictions for the real physiological system. However, given the high complexity that such a framework demands with respect to modeling, computation and mathematics, such an approach has never been attempted. To achieve this vision we aim to unite our multi-scale neuromuscular model and our 3D continuum-mechanical musculoskeletal model, for which the following contributions are foreseen:We will integrate new mathematical models of motor control and brain lesions. In addition, the existing neuromuscular modeling toolbox need to be enriched by heteronymous feedback circuits, remodeling processes and muscle metabolism. To provide a flexible simulation and optimization framework with exchangeable components, we intend to set up a partitioned simulation framework. This requires new technical and numerical coupling methods as well as concepts for handling multi-scale properties between short-term and long-term reactions to brain pathology.Optimization based on these new models again requires model-mathematics-HPC co-design: (i) objective functions that reflect the high-level goals of the neuromuscular system and optimization parameters, \ie, the degrees of freedom in the neuro-musculoskeletal model that represent the permissible short- or long-term adaptation to a given perturbation; (ii) further components of the optimization framework need to be developed, in particular surrogate models to reduce the computational cost, adjoints if we follow a Lagrangian approach, and the implementation of the outer optimization framework itself. For potential future clinical applications (beyond the scope of this project), further data handling challenges to our composable optimization framework need to be considered. All tasks require a close interaction between the expertise gathered in the groups of the PIs in the sense of co-design between models, numerics, HPC and data.