A review of brain injury at multiple time scales and its clinicopathological correlation through in silico modeling

Understanding the correlation between pathological changes and the type of brain injury is pivotal in mitigating the damage and planning reliable and improved treatment strategies. Swift identification of the underlying mechanisms behind brain injury is essential for early diagnosis, surgical planning, and post-operative therapies. Brain injury may stem from various sources, including trauma (resulting in traumatic brain injury), treatment (leading to surgical brain injury), and neurodegenerative mechanisms. These injuries can manifest spatially, affecting individual neurons to the entire organ and temporally, ranging from immediate to long-term degeneration. However, direct evidence linking injury mechanisms to short and long-term tissue damage in the human population is limited, posing challenges in establishing a clear clinicopathological connection. Recently, in silico modeling has emerged as a cost-effective approach that can assist clinicians in gaining deeper insights and uncover new injury pathways. Physics and machine learning-based in silico modeling offers valuable contributions to injury prevention, diagnosis, prognosis, treatment planning, and patient monitoring, especially given the complexities of acquiring patient-specific clinical data related to brain injuries. Considering the spatiotemporal complexity of brain tissue damage, developing a comprehensive, multiscale, and multiphysics model is imperative for a better understanding. This study aims to categorize and explore strategies for modeling brain injuries across three distinct time scales, review damage mechanisms at various length scales, and recommend the development of a comprehensive biomechanical model that integrates multimodal data and multiphysics. Such an integrated approach will provide personalized diagnosis and treatment strategies tailored to individual patients.

Statement of Significance: The connection between clinical observations and brain pathology is crucial for managing brain injuries. Brain injuries result in brain damage via diverse factors across scales, from neurons to organs, from initial trauma to neurodegeneration. However, limited direct evidence linking injury mechanisms to long-term human tissue damage hinders clinicopathological connections. In silico modeling, a cost-effective approach utilizing physics and machine learning-based principles, can aid clinicians in uncovering injury pathways. A comprehensive, multimodal, and multiphysics model is vital for understanding complex brain tissue damage. This study categorizes modeling strategies, reviews damage mechanisms across scales, and recommends comprehensive biomechanical models for personalized treatment.