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New Research Highlights Youth Football Players’ Brain Injury Risk

(SL) -A new study says young football players can have measurable changes in the brain after only one season of play. Researchers at the University of Texas Southwestern Medical Center in Dallas say that’s true even with youngsters who have not had concussions.

They found changes that can lead to traumatic brain injuries and mental disorders. Researchers say the vast majority of head impacts players experience don’t cause concussions. They say that’s something that needs a lot more study among youth football and high school players.shutterstock-68181940Science Daily reports, For the study, 26 youth football players (ages 9-13) were outfitted with the Head Impact Telemetry System (HITS) for an entire football season. HITS helmets are lined with accelerometers or sensors that measure the magnitude, location and direction of impacts to the head. Impact data from the helmets were used to calculate a risk of concussion exposure for each player. Players were equally divided into high and low concussion exposure groups. Players with a history of concussion were excluded.

A third group of 13 non-contact sport controls was established. Pre- and post-season resting functional MRI (fMRI) scans were performed on all players and controls, and connectivity within the DMN sub-components was analyzed.

Brain damage and sports injury concept with damaged brain model, american football helmet and a ball, illustrating CTE (Chronic traumatic encephalopathy) a syndrome caused by repeated concussion
The researchers used machine learning to analyze the fMRI data. Machine learning is a type of artificial intelligence that allows computers to perform analyses based on existing relationships of data.
“Machine learning has a lot to add to our research because it gives us a fresh perspective and an ability to analyze the complex relationships within the data,” said Murugesan. “Our results suggest an increasing functional change in the brain with increasing head impact exposure.”Five machine learning classification algorithms were used to predict whether players were in the high-exposure, low-exposure or non-contact groups based on the fMRI results.
The algorithm discriminated between high-impact exposure and non-contact with 82 percent accuracy, and low-impact exposure and non-contact with 70 percent accuracy. The results suggest an increasing functional change with increasing head-impact exposure.”The brains of these youth and adolescent athletes are undergoing rapid maturation in this age range.

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