Traumatic axonal injury: Clinic, forensic and biomechanics perspectives

• Published in: Legal medicine (Tokyo, Japan) Japan), 2024-09, Vol.70, p.102465, Article 102465
• Main Authors: Delteil, Clémence, Manlius, Thais, Bailly, Nicolas, Godio-Raboutet, Yves, Piercecchi-Marti, Marie-Dominique, Tuchtan, Lucile, Hak, Jean-Francois, Velly, Lionel, Simeone, Pierre, Thollon, Lionel
• Format: Article
• Language: English
• Subjects: Finite element model; Imagery; Pathology; Traumatic axonal injury
• ISSN: 1344-6223; 1873-4162; 1873-4162
• DOI: 10.1016/j.legalmed.2024.102465

•FEM is one of the most pertinent methods to study the mechanisms of head injury.•Imaging give us indirect sign of traumatic axonal injury (TAI).•Neuropathology show direct or indirect changes associated with trauma TAI.•Integration of imaging data with FEM allowed personalized computationnal model. 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.