When college athletes are diagnosed with a concussion caused by a blow to the head, neck or body that affects the brain, they want to know when they will recover so they can return to playing sports again. While the typical recovery time for college athletes with concussions is within four weeks, approx 15 to 20 percent of them take longer to recover due to persistent post-concussion symptoms (PPCS). These symptoms include headache, dizziness, irritability, and loss of concentration and memory.
A prognostic model that could predict the risk of late recovery with greater accuracy than current clinical models that rely on symptom data would help clinicians start treatment and other interventions earlier.
A collaborative team of NIH-funded researchers developed a new approach based on their analysis of an advanced type of brain imaging data and retrospective clinical records. The results of their study published in Neuroimaging: Clinical found that imaging data related to specific white matter brain regions had a significant correlation with return to play and that their model could correctly identify early versus late recovery 90% of the time.
“This model can improve the prognostic accuracy of assessing a person’s outlook after a concussion. These advances, including the new automated platform, will serve as part of the infrastructure that supports neuroscience research and may lead to new discoveries,” said Qi Duan, Ph.D., program director in NIBIB’s Department of Health Information Technology.
How they built the concussion recovery model
The researchers wanted to determine whether there were differences in the brain’s microstructural properties white substance was associated with return to play. Previous studies have shown that concussions typically affect white matter, a deep brain region that facilitates communication between different areas of the brain.
Their analysis focused on return-to-play recordings and an advanced type of magnetic resonance imaging (diffusion MRI), which allows the movement of water molecules in tissue to be measured, providing detailed images of the brain.
“After a concussion, both patients and families are truly affected by the patient’s inability to return to normal activities. Currently, clinicians lack tools to predict when a concussed patient can return to work and resume normal activities. To address this gap in clinical practice, we have developed a tool that uses machine learning and diffusion MRI, a widely available technology, to predict that patients can return to life with concussion.” Pestilli, Ph.D., co-corresponding author and professor of psychology and neuroscience at the University of Texas at Austin.
The researchers analyzed diffusion MRI (dMRI) data for 51 injuries in 45 student athletes, who were a subset of National Collegiate Athletic Association and Department of Defense Concussion Assessment, Research and Education Consortium (CARE) study. They designed one automated cloud computing platform with NIBIB and NIMH funding to process and analyze neuroscience data. The platform (brainlife.io) is available to other neuroscience researchers with dMRI data obtained by scanning different concussion patient populations.
The return-to-play data were divided into two groups: athletes who returned in less than 28 days and athletes who returned after 28 days or more. The late return-to-play group met the definition of PPCS.
The researchers developed a data analysis approach that used the automated platform to extract the microstructural properties of various brain white matter structures. For each region, they calculated two established measures of the movement of water molecules in white brain tissue: fractional anisotropy (FA), which indicates how freely water molecules can move in multiple directions, and mean diffusivity (MD), which measures the speed of water movement.
The researchers found that only the FA measure for all white matter areas correlated significantly with the return-to-play data.
Subsequent analyzes focused on whether FA data for specific white matter tracts (bundles of fiber that connect different parts of the brain) could predict early versus late recovery for athletes.
Focusing on the 16 channels with the strongest data, the researchers developed a model to predict which athletes would take longer than 28 days to recover. Using this model, the researchers were able to correctly categorize the athletes 90% of the time.
“Overall, this preliminary study demonstrates the feasibility of using the microstructural properties of white matter tracts to develop a prognostic model for PPCS that outperforms current predictive models based on clinical data,” said co-corresponding author Nicholas Port, Ph.D., professor of optometry and adjunct professor of psychological and brain sciences at Indiana University in Bloomington.
These results describe preliminary research. A major limitation was the small number of injuries (51) included in the study, which highlights the need for larger dMRI concussion studies focusing on recovery. Another limitation is that the subjects were all college athletes.
The researchers plan to address these limitations in a large clinical trial involving a broader set of concussion patients.
“Once we collect the data, we want to use it to retrain the machine learning model to make it more robust and ultimately clinically impactful,” Pestilli said.
This study, particularly the cloud computing platform (brainlife.io), was supported in part by NIBIB grants R01EB030896 and R01EB029272 and grants from the National Institute of Mental Health (R01MH126699, R01MH13370), the National Institute of Neurological Disorders (201NS,132ke) U24NS140384) and the National Science Foundation (OAC-1916518, IIS-1912270, IIS-1636893 and BCS-1734853).
Additional research was supported by the Department of Defense (W81XWH-20-1-0717, W81XWH-14-2-0151).
This scientific highlight describes a fundamental research result. Basic research increases our understanding of human behavior and biology, which is the basis for advancing new and better ways to prevent, diagnose and treat disease. Science is an unpredictable and incremental process – each research advance builds on previous discoveries, often in unexpected ways. Most clinical advances would not be possible without knowledge of basic basic research.
Study reference: G Berto et al. Diffusion tensor analysis of white matter tracts is prognostic of persistent symptoms after concussion in collegiate athletes. Neuroimaging: Clin. 2024. doi: 10.1016/j.nicl.2024.103646.
