Biomechanics of the Human Brain During High-Severity Impacts: A Multimodal Approach

Project Summary Traumatic brain injury (TBI) is a significant health care problem, affecting over 2.5 million people in the United States annually. Most cases are classified as mild or concussive TBI, caused by a rapid acceleration to the skull that may lead to short-term loss of consciousness and memory, as well as long-term disability. Prevention of TBI is therefore a critical consideration in contact sports, military operations, motor vehicle use and other activities. Accurate quantification of the locations, magnitudes, and orientations of brain deformation (strain) during TBI is a precursor to a better understanding of the onset and secondary cascades of injury. A key approach to understanding the risk of TBI and assessing preventative measures is computational modeling, which simulates the external forces applied to the head and the associated biomechanical response of the brain. The development of accurate computational models of TBI is challenging, however, requiring the collection of experimental data to calibrate and validate the models. Recently, our team has developed two complementary approaches to acquiring measurements of brain biomechanics under loading. One approach employs dynamic magnetic resonance imaging (MRI) in living human subjects undergoing both mild head accelerations. The other approach employs sonomicrometry to track the motion of markers in cadaveric brain specimens at concussion- level accelerations. The output of these experimental datasets has been limited to either 1) dense 3D brain strain data at non-injurious head motion in vivo, or 2) spatially sparse brain displacement data at injurious loading in cadavers. As a result, there remains two unknowns in our understanding of brain deformation during head impact: 1) the actual magnitude of strain at concussive accelerations, and 2) the accuracy of local strain distributions and if the strain patterns change with increases in impact severity. To address these fundamental deficiencies in our knowledge of brain biomechanics, we propose to integrate these two previously disparate but complementary ends of the experimental TBI spectrum to better enable prediction of the biomechanical response of the living brain during concussion. We will perform three aims: 1) Optimize dynamic MRI techniques for increased loading in phantoms and cadavers; 2) Perform multiscale biomechanical characterization of cadaveric specimens using both MRI and sonomicrometry techniques; 3) Demonstrate application of combined MRI and sonomicrometry data for improved injury prediction by evaluating publicly available computational models and applying them to sports impact databases. This work will provide a novel and complete characterization of the intracranial biomechanics of TBI. The combined use of cadavers and phantoms will enable a cross-validation of our ability to predict high acceleration response based on low acceleration measurements. By the end of the project, we will have fully characterized phantom and cadaveric data that will be made publicly available for other researchers to use and, by combining the acquired data with existing in vivo data, developed new tools to predict the response of the living human brain during concussion-level loading and improve injury prediction models.