Investigation of tissue level tolerance for cerebral contusion in a controlled cortical impact porcine model

Developing a porcine model of severe traumatic brain injury induced by high amplitude rotational acceleration

Introduction

It is unclear which pathophysiological processes initiate and drive dynamic cerebrovascular autoregulation (CA) impairment as seen in traumatic brain injury (TBI). This is not solely attributable to raised intracranial pressure (ICP), but also results from local tissue damage.

Research question

In order to investigate CA disturbing processes, a porcine model is needed that mimics severe TBI as seen in humans. This model requires high amplitude rotational acceleration.

Material and methods

A customized device was built to produce a rotational impulse with high amplitude and short pulse duration. Following preparatory tests on cadaver piglets, six piglets of six weeks old were sedated, ventilated and subjected to rotational impulses of different magnitudes. The impulse was immediately followed by installment of invasive monitoring of ICP, PbO₂, Laser Doppler Flowmetry and arterial blood pressure. TBI was further characterized by magnetic resonance brain imaging.

Results

The current setup enabled to reach sagittal head rotational maximal acceleration magnitudes up to 30 krad/s2. Half of the animals had an increase in ICP, measured shortly after the impulse. It has proved impossible so far to produce a sustained rise in ICP as seen in human severe TBI. MRI showed no anatomical abnormalities which would confirm severe TBI.

Discussion and conclusion

The challenge to build a porcine model in which severe TBI with ICP raise and MRI changes as seen in humans can be reliably reproduced is still ongoing. It might be that higher peak rotational accelerations are needed.

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.