Average Responses of Brain Displacement Under Rotational Loading for Computational Model Validation

Computational models of the brain are typically validated using individual subjects from datasets of brain motion, but a comparison to an individual subject does not consider the biomechanical variation that naturally exists in the population. When data from multiple subjects is available, biomechanical corridors are constructed for the assessment of model biofidelity. However, a robust set of corridors for brain's biomechanical response due to applied head kinematics does not exist for model validation. The aim of this study was to create corridors based on a dataset of in situ brain displacement that included six specimens tested under a set of twelve loading conditions.

There were three main factors that complicated this task, including variation in head kinematics, differences in the initial position of the sensors, and the clustering of spatially scattered data. We employed various numerical and statistical methods to account for these experimental variations, with optimization and validation of the techniques conducted using the existing in situ dataset and a computational brain model.

Corridors were constructed using average and standard deviation of the specimen responses in the dataset for 24 discrete locations within the brain. Peak displacement showed a variance of less than 30% for most brain sensor locations.

The corridors will serve as a better validation tool for assessing the biofidelity of computational brain models and will help understand inter-subject variability in brain biomechanics.