Abstract：
Contemporary biomechanical modeling of traumatic brain injury (TBI) focuses on either the global brain as an organ or a representative tiny section of a single axon. In addition, while it is common for a global brain model to employ real-world impacts as input, axonal injury models have largely been limited to inputs of either tension or compression with assumed peak strain and strain rate. These major gaps between global and microscale modeling preclude a systematic and mechanistic investigation of how tissue strain from impact leads to downstream axonal damage throughout the white matter. In this study, a unique subject-specific multimodality dataset from a male ice-hockey player sustaining a diagnosed concussion is used to establish an efficient and scalable computational pipeline. It is then employed to derive voxelized brain deformation, maximum principal strains and white matter fiber strains, and finally, to produce diverse fiber strain profiles of various shapes in temporal history necessary for the development and application of a deep learning axonal injury model in the future. The pipeline employs a structured, voxelized representation of brain deformation with adjustable spatial resolution independent of model mesh resolution. The method can be easily extended to other head impacts or individuals. The framework established in this work is critical for enabling large-scale (i.e., across the entire white matter region, head impacts, and individuals) and multiscale (i.e., from organ to cell length scales) modeling for the investigation of traumatic axonal injury (TAI) triggering mechanisms. Ultimately, these efforts could enhance the assessment of concussion risks and design of protective headgear. Therefore, this work contributes to improved strategies for concussion detection, mitigation, and prevention.
摘要：
当代创伤性脑损伤（TBI）的生物力学建模侧重于作为器官的全球大脑或单个轴突的代表性微小部分。此外,虽然全球大脑模型通常采用现实世界的影响作为输入，轴突损伤模型在很大程度上仅限于假定峰值应变和应变率的拉伸或压缩输入。全球和微观建模之间的这些主要差距排除了对撞击组织应变如何导致整个白质下游轴突损伤的系统和机械研究。在这项研究中,来自男性冰球运动员的独特的特定于主题的多模态数据集，用于维持诊断的脑震荡，用于建立有效且可扩展的计算管道。然后，它被用来推导体素化的大脑变形，最大主株和白质纤维株，最后，在未来的发展和应用深度学习轴索损伤模型所必需的时间历史中产生各种形状的不同纤维应变分布。管道采用了结构化的，具有可调空间分辨率的大脑变形的体素化表示，与模型网格分辨率无关。该方法可以容易地扩展到其他头部撞击或个人。在这项工作中建立的框架对于实现大规模(即，在整个白质区域，头部撞击,和个人)和多尺度(即，从器官到细胞的长度尺度）建模，用于研究创伤性轴突损伤（TAI）触发机制。最终,这些努力可以加强对脑震荡风险的评估和防护头盔的设计。因此,这项工作有助于改进脑震荡检测策略，缓解,和预防。
