Identification of Traumatic axonal injury (TAI) is critical in clinical practice, particularly in terms of long-term prognosis, but also for medico-legal issues, to verify whether the death or the after-effects were attributable to trauma. Multidisciplinary approaches are an undeniable asset when it comes to solving these problems. The aim of this work is therefore to list the different techniques needed to identify axonal lesions and to understand the lesion mechanisms involved in their formation. Imaging can be used to assess the consequences of trauma, to identify indirect signs of TAI, to explain the patient's initial symptoms and even to assess the patient's prognosis. Three-dimensional reconstructions of the skull can highlight fractures suggestive of trauma. Microscopic and immunohistochemical techniques are currently considered as the most reliable tools for the early identification of TAI following trauma. Finite element models use mechanical equations to predict biomechanical parameters, such as tissue stresses and strains in the brain, when subjected to external forces, such as violent impacts to the head. These parameters, which are difficult to measure experimentally, are then used to predict the risk of injury. The integration of imaging data with finite element models allows researchers to create realistic and personalized computational models by incorporating actual geometry and properties obtained from imaging techniques. The personalization of these models makes their forensic approach particularly interesting.

Finite element modeling provides a digital representation of the human body. It is currently the most pertinent method to study the mechanisms of head injury, and is becoming a scientific reference in forensic expert reports. Improved biofidelity is a recurrent aim of research studies in biomechanics in order to improve earlier models whose mechanical properties conformed to simplified elastic behavior and mechanic laws. We aimed to study force transmission to the brain following impacts to the head, using a finite element head model with increased biofidelity. To the model developed by the Laboratory of Applied Biomechanics of Marseille, we added new brain structures (thalamus, central gray nuclei and ventricular systems) as well as three tracts involved in the symptoms of head injury: the corpus callosum, uncinate tracts and corticospinal tracts. Three head impact scenarios were simulated: an uppercut with the prior model and an uppercut with the improved model in order to compare the two models, and a lateral impact with an impact velocity of 6.5 m/s in the improved model. In these conditions, in uppercuts the maximum stress values did not exceed the injury risk threshold. On the other hand, the deep gray matter (thalamus and central gray nuclei) was the region at highest risk of injury during lateral impacts. Even if injury to the deep gray matter is not immediately life-threatening, it could explain the chronic disabling symptoms of even low-intensity head injury.

Reconstructing traumatic brain injury loading conditions remains challenging in forensic biomechanics. This study presents a proof-of-concept inverse-analysis framework for identifying plausible head impacts from post-traumatic brain imaging. It was applied to a pedestrian struck by a car who sustained severe brain injury and a malar fracture. Cerebral microbleeds on T1-weighted MRI and regions of abnormal fractional anisotropy on diffusion tensor imaging served as injury markers. A partially personalized finite element head model was subjected to 120 impacts compatible with the facial fracture, varying velocity, orientation, and contact surface. Predicted regions of elevated maximum principal strain (>0.2) were compared with observed injury locations using the Matthews correlation coefficient (MCC). With T1-weighted lesions alone, the best simulation achieved an MCC of 0.11, with high specificity (0.84) but low sensitivity (0.50), reflecting the difficulty of reproducing sparse, dispersed microbleeds. Including regions of abnormal fractional anisotropy improved performance, yielding an MCC of 0.34 and a sensitivity of 0.884. The best scenarios involved impacts to the lower posterior maxillary region, near the mandible and zygomatic bone, at 4–7 m/s. These findings support lesion-informed inverse finite element modeling for forensic impact reconstruction and suggest that fractional anisotropy alterations may provide complementary information on brain deformation.