Biomechanical simulation of traumatic brain injury in the rat

Abstract

Background: Traumatic brain injury poses an enormous clinical challenge. Rats are the animals most widely used in pre-clinical experiments. Biomechanical simulations of these experiments predict the distribution of mechanical stress and strain across key tissues. It is in theory possible to dramatically increase our understanding of traumatic brain injury pathophysiology by correlating stress and strain with histological and functional injury outcomes. This review summarizes the state of the art in biomechanical simulation of traumatic brain injury in the rat. It also places this body of knowledge in the context of the wider effort to understand traumatic brain injury in rats and in humans.

Methods: Peer-reviewed research articles on biomechanical simulation of traumatic brain injury in the rat were reviewed and summarized.

Findings: When mathematical models of traumatic brain injury in the rat first emerged, they relied on scant data regarding biomechanical properties. The data on relevant biomechanical properties has increased recently. However, experimental models of traumatic brain injury in the rat have also become less homogeneous. New and modified models have emerged that are biomechanically distinct from traditional models.

Interpretation: Important progress in mathematical modeling and measurement of biomechanical properties has led to credible, predictive simulations of traditional, experimental models of traumatic brain injury in the rat, such as controlled cortical impact. However, recent trends such as the increasing popularity of closed head models and blast models create new biomechanical challenges. Investigators studying rat brain biomechanics must continue to innovate to keep pace with these developments.

Keywords: Biomechanics; Finite element modeling; Rat; Traumatic brain injury.

Fig. 1

The ideal workflow for biomechanical simulation of TBI in the rat integrates experimental (green boxes) and computational (blue boxes) tasks. Starting from the bottom left, the material properties (e.g. the shear modulus, G, and the viscoelastic time constant, τ) are measured, the geometry is determined from MRI data or an atlas, and the boundary conditions are identified. This data informs creation of the finite element model during pre-processing. The model is solved and then output is extracted during post-processing. Then, some subset of the model output is compared to experimental data for validation.