According to the Centers for Disease Control, more than 214,000 individuals were hospitalized for traumatic brain injury (TBI) in 2020. In addition, over 69,000 TBI-related deaths were reported in 2021.
TBI may lead to short or long-term health problems. But Biomedical Engineering Assistant Professor Ahmed Alshareef recently started a research project that combines two novel approaches to better understand the biomechanics of the human brain during high-severity impacts.
Alshareef’s five-year, $2.6 million project is funded by the National Institute of Neurological Disorders and Stroke and the National Institutes of Health (NIH). The multi-institution project is in collaboration with the University of Virginia (UVA) and Uniformed Services University (USU). Alshareef’s successful grant proposal was accepted after participating in the Propel Research Mentorship Program, an initiative through the University of South Carolina Office of the Vice President for Research, which provides training and mentorship on NIH proposal writing.
“This project is directly related to my graduate and postdoctoral training in TBI biomechanics, a field where we try to understand how the brain deforms from external impact to the head,” Alshareef says. “This is relevant to automotive crashes, concussions, and military and sports-related blunt impacts.”
According to Alshareef, TBIs can range in severity, with most classified as mild TBI, commonly known as concussion. These generally do not result in loss of consciousness or present any visible findings on brain scans. To better predict and prevent these injuries, Alshareef’s project is aimed at measuring brain deformation at concussive loading conditions.
“If we can measure the relationship between how the head moves and how the brain deforms during an impact, we can develop tools to mitigate those injuries,” Alshareef says. “This includes tools that account for different sized heads or brains, and other subject-specific factors that can lead to different mechanical responses or risk of injury.”
During his graduate studies at UVA, Alshareef used a pioneering sonomicrometry (measurement of distance using sound) technique by embedding sensors inside the brains of deceased subjects to measure how the brain moved within the skull. As a postdoctoral fellow at Johns Hopkins University, he worked on a team that used specialized magnetic resonance imaging (MRI) techniques to measure in humans how the brain moves inside their skull.
“We use sonomicrometry to precisely measure how the brain deforms during head impact, from mild cases up to the most severe that cause injury,” says Matthew Panzer, professor of mechanical and aerospace engineering at UVA. “Ultimately the data we collect using sonomicrometry, along with the other imaging data collected in this study, will be used to improve and validate computational models of the human brain that are critical for advancing TBI research and developing effective injury prevention strategies.”
Alshareef’s experiences with both techniques place the University of South Carolina in a unique position, while bringing together a multi-institutional team to develop multimodal experimental data. While UVA’s sonomicrometry technique can only be performed in people who donate their bodies for research and measures brain motion at high impact severities representative of concussions, USU specializes in the dynamic tagged MRI technique that measures brain motion at low-level, non-injurious head impacts. The experiments will be used to guide the development of next generation computational brain models as tools for improved risk prediction and safety design.
“The multimodal part of the project is bringing together these independently developed techniques to measure across the spectrum of injury severity,” Alshareef says. “The novelty lies in using information from each experiment to gain a comprehensive understanding of how the brain moves within the skull.”
Alshareef’s team will focus on using the new data to integrate the information about brain deformation and biomechanics. They will also combine the multimodal data from both approaches to build computational models. Alshareef believes that computational modeling is a key approach to better understanding TBI. With this project, he wants to ensure that these models are grounded in science, accurately simulating the biomechanics of a human brain and provide a tool for designing at the highest safety standards.
“Computational resources are the gold standard for how people design safety equipment. Most of the testing done by engineers is to take a design and repeatedly test it using computational models before testing it in the real world and making it publicly available,” Alshareef says.
Alshareef added that his work will involve many computational methods development in addition to the experimental acquisition of multimodal data.
“It will also require planning and coordination within the sites, learning from the two techniques, and integrating experiments and simulations,” Alshareef says. “We've also proposed various machine learning big data approaches to integrate the multimodal data.”
Alshareef describes his research project as a unique approach since the techniques are more specialized, even in the field of brain biomechanics.
“The work will be complex since the availability of experimental data to guide the computational models is scarce,” Alshareef says. “As a team, we’ve generated much of the current data that is used to guide computational model development, and we want to keep generating more. The data will be publicly shared to other researchers to use and integrate in their research.”
At the end of the project, Alshareef aims to answer a simple question: If someone is hit with impact to the head, how does the brain deform within the skull? He also intends to share the latest developments and improvements on his computational models for TBI prediction and prevention.
“We hope to not only answer that question but have the data to back it up,” Alshareef says. “We want to share the data so other researchers can use it to develop their own models, tools or techniques.”