CHICAGO, Nov. 27, 2017 /PRNewswire-USNewswire/ — School-age football players with a history of concussion and high impact exposure undergo brain changes after one season of play, according to two new studies conducted at UT Southwestern Medical Center in Dallas and Wake Forest University in Winston-Salem and presented today at the annual meeting of the Radiological Society of North America (RSNA).
Both studies analyzed the default mode network (DMN), a network of brain regions that is active during wakeful rest. Changes in the DMN are observed in patients with mental disorders. Decreased connectivity within the network is also associated with traumatic brain injury.
“The DMN exists in the deep gray matter areas of the brain,” explained Elizabeth M. Davenport, Ph.D., a postdoctoral researcher in the Advanced NeuroScience Imaging Research (ANSIR) lab at UT Southwestern’s O’Donnell Brain Institute. “It includes structures that activate when we are awake and engaging in introspection or processing emotions, which are activities that are important for brain health.”
In the first study, researchers studied youth football players without history of concussion to identify the effect of repeated subconcussive impacts on the DMN.
“Over a season of football, players are exposed to numerous head impacts. The vast majority of these do not result in concussion,” said Gowtham Krishnan Murugesan, a Ph.D. student in biomedical engineering and member of the ANSIR lab. “This work adds to a growing body of literature indicating that subconcussive head impacts can have an effect on the brain. This is a highly understudied area at the youth and high school level.”
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.
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