Does Medical Imaging Show That Athletes Think Differently?

There are a lot of uses for medical imaging in research, but one of the most unusual recent studies has explored the neurological differences between elite-level athletes and those who play sports at an amateur or recreational level.

The study, published in the Journal of Exercise Science and Fitness, used MRI scans on 60 different people. This included 20 professional-level basketball players, 20 intermediate-skilled amateur players and 20 people of the same age who were not athletes and had no experience in basketball.

The team measured the voxel-mirrored homotopic connectivity (VMHC) of each person, as well as their grey matter density (GM) and amplitude of low-frequency fluctuations, which are quantitative indicators linked to the motor skills, coordination and executive function seen as vital to sporting success.

Compared to the 20 non-athletes, basketball players of both skill groups were shown to have a greater connection between areas that were responsible for control, motor function and decision-making ability, all factors vital for success in a team sport.

The key to a player doing well in a team sport is not just the ability to think quickly but to think clearly and coordinate with other players during a playing or training session.

This could have significant implications for athletes at all levels, as the difference between skill levels could be seen as much in the brain as it is in the body, in a way that can be seen through imaging and quantified.

This is not necessarily a new phenomenon; the National Football League has regularly used cognitive ability testing as part of its scouting and recruitment process, with certain playing positions seen as more mentally taxing than others.

There have historically been issues with cognitive testing, as whilst it can determine a form of basic general intelligence, it is not always suited for determining the types of intelligence needed in an elite player.

Using MRI or another imaging benchmark could potentially be a better alternative, but a lot more research needs to be done to determine this.