A computational pipeline towards large-scale and multiscale modeling of traumatic axonal injury

• Published in: Computers in biology and medicine 2024-03, Vol.171, p.108109-108109, Article 108109
• Main Authors: Zhang, Chaokai, Bartels, Lara, Clansey, Adam, Kloiber, Julian, Bondi, Daniel, van Donkelaar, Paul, Wu, Lyndia, Rauscher, Alexander, Ji, Songbai
• Format: Article
• Language: English
• Subjects: Accuracy; Alzheimer's disease; Axons; Biomechanics; Brain; Brain - diagnostic imaging; Brain Concussion - diagnostic imaging; Brain Injuries, Traumatic - diagnostic imaging; Computational neuroscience; Concussion; Datasets; Deep learning; Deformation; Displacement voxelization; Efficiency; Fiber strain; Finite element method; Head; Head injuries; Humans; Impact damage; Injury prevention; Investigations; Kinematics; Male; Medical imaging; Multimodal analysis; Multiscale modeling; Neuroimaging; Risk assessment; Simulation; Spatial discrimination; Spatial resolution; Strain rate; Substantia alba; Traumatic axonal injury; Traumatic brain injury
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108109

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. •A scalable computational pipeline is developed to unify a global, organ-level brain injury model and a microscale axonal injury model.•A displacement voxelization scheme is employed to generate structured data representation for brain deformation from head impact.•White matter fiber strain profiles are identified in specific anatomical regions such as gray-white matter interface and the corpus callosum.•Diverse fiber strain profiles are established across the white matter as model input necessary to develop and apply a deep learning axonal injury surrogate model in the future.