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

用于改善脑病理学中神经肌肉系统适应性的数据驱动优化框架

• 批准号: 465243391
• 负责人: Professor Dr. Dominik Göddeke
• 金额: --
• 依托单位: Lehrstuhl Mathematische Methoden für komplexe Simulation der Naturwissenschaft und Technik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/465243391?language=en
• 关键词: data; driven; optimization; framework; improving

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.

该项目旨在建立一个新的计算机框架，可以用来解释神经-肌肉骨骼系统对脑病理（如中风、脑瘫或多发性硬化症）的适应机制。这些病理明显限制了受影响受试者的运动能力，然而，不幸的是，不存在令人满意的治疗方法。最终目标是支持新的发展和现有治疗应用的改进。基于身体旨在以最佳方式处理给定条件的概念，我们打算使用约束优化的数学技术来解决这一目标。通过采用神经-肌肉-骨骼系统的系统多尺度模型，我们期望这种方法可以对真实的生理系统做出有意义的预测。然而，考虑到这种框架在建模、计算和数学方面要求的高度复杂性，这种方法从未被尝试过。为了实现这一愿景，我们的目标是将我们的多尺度神经肌肉模型和我们的3D连续机械肌肉骨骼模型结合起来，可以预见以下贡献：我们将整合运动控制和大脑病变的新数学模型。此外，现有的神经肌肉建模工具箱需要通过异源反馈回路、重塑过程和肌肉代谢来丰富。为了提供一个具有可交换组件的灵活的仿真和优化框架，我们打算建立一个分区的仿真框架。这需要新的技术和数值耦合方法，以及处理脑病理短期和长期反应之间的多尺度特性的概念。基于这些新模型的优化再次需要模型-数学- hpc协同设计：(i)反映神经肌肉系统高级目标的目标函数和优化参数，即神经肌肉骨骼模型中的自由度，代表对给定扰动的允许的短期或长期适应；（ii）优化框架的进一步组成部分需要开发，特别是代理模型，以减少计算成本，如果我们遵循拉格朗日方法的伴随，以及外部优化框架本身的实现。对于潜在的未来临床应用（超出本项目范围），需要考虑我们的可组合优化框架的进一步数据处理挑战。在模型、数值、高性能计算和数据协同设计的意义上，所有任务都需要在pi组中收集的专业知识之间进行密切的互动。