Biomechanics of the Human Brain during Dynamic Rotation of the Head

Abstract

Traumatic brain injuries (TBI) are a substantial societal burden. The development of better technologies and systems to prevent and/or mitigate the severity of brain injury requires an improved understanding of the mechanisms of brain injury, and more specifically, how head impact exposure relates to brain deformation. Biomechanical investigations have used computational models to identify these relations, but more experimental brain deformation data are needed to validate these models and support their conclusions. The objective of this study was to generate a dataset describing in situ human brain motion under rotational loading at impact conditions considered injurious. Six head-neck human post-mortem specimens, unembalmed and never frozen, were instrumented with 24 sonomicrometry crystals embedded throughout the parenchyma that can directly measure dynamic brain motion. Dynamic brain displacement, relative to the skull, was measured for each specimen with four loading severities in the three directions of controlled rotation, for a total of 12 tests per specimen. All testing was completed 42-72 h post-mortem for each specimen. The final dataset contains approximately 5,000 individual point displacement time-histories that can be used to validate computational brain models. Brain motion was direction-dependent, with axial rotation resulting in the largest magnitude of displacement. Displacements were largest in the mid-cerebrum, and the inferior regions of the brain-the cerebellum and brainstem-experienced relatively lower peak displacements. Brain motion was also found to be positively correlated to peak angular velocity, and negatively correlated with angular velocity duration, a finding that has implications related to brain injury risk-assessment methods. This dataset of dynamic human brain motion will form the foundation for the continued development and refinement of computational models of the human brain for predicting TBI.

Keywords: FE model validation; brain biomechanics; sonomicrometry; traumatic brain injury.

FIG. 1.

Representative computed tomography images following the specimen preparation and crystal insertion procedure. Specimen 903 (left) includes the mounting plates and instrumentation plate. Specimen 904 (right) shows the transmitters affixed to the skull, the receivers in the brain (note that slack is intentionally introduced in the wires during insertion), and the perfusion ports in the carotid arteries and occipital skull. Color image is available online.

FIG. 2.

An example of the repeatability of a single loading case (axial – 40 rad/sec – 30 msec) for the six specimens in controlled dynamic rotation (CDR) using the rotational test device (RTD) (left: angular velocity; right: angular acceleration). Color image is available online.

FIG. 3.

Trajectory plot for the 20 rad/sec – 30 msec (A,B) and 40 rad/sec – 30 msec (C,D) sagittal and axial tests for specimen 900. The red dots symbolize the initial position of each receiver. The black dot represents the CG of the head, about which the rotation was applied. Blue dots represent the transmitter crystals in the skull. Color image is available online.

FIG. 4.

Maximum displacement (${\delta }_i$) of all crystals for all specimens for the axial – 40 rad/sec – 30 msec tests in the sagittal (A), coronal (B), and axial (C) directions. Color image is available online.

FIG. 5.

Box plots of maximum displacement (${\delta }_i$) for all specimens for each test. The blue boxes represent the 25th and 75th quartile values, the red line represents the median, and the dashed lines represent the maximum and minimum values. Color image is available online.

FIG. 6.

Surface plots depicting the results of the linear regression model for maximum brain displacement (${\delta }_{max}$) using the maximum angular velocity and duration for the sagittal (A), coronal (B), and axial (C) tests. The black dots correspond to the data points (${\delta }_{max}$) used in the regression fit. The sagittal regression model had an R² of 0.67. The coronal regression model had an R² of 0.69. The axial regression model had an R² of 0.79. All estimated coefficients of the regression model were statistically significant (p < 0.05). Color image is available online.