Cross-Sectional Analysis of the Relationship Between High-Level Running and Cortical Thickness in Elite and Masters Level Runners
Description: The thickness of the brain is reflective of development and aging, where thickness naturally decreases with age. This thinning is linked to poorer walking metrics, increased fall risk, and an increased risk of Alzheimer's disease. The natural decline in brain thickness can be reduced through lifestyle factors such as exercise. While short-term exercise is associated with increases in brain thickness, the relationship between long-term, elite-level exercise and the natural decline in brain thickness remains poorly understood. This study will examine differences in brain thickness between young elite runners, older elite runners, and age-matched controls. The young runners will consist of elite runners aged 18-34, while older runners will be represented by world-class master athletes aged 35+. It is predicted that runners will display thicker areas of the brain in regions involved in movement, sensation, memory, and executive function in comparison to controls. It is also predicted that older adults will display a thinner brain than younger adults of similar fitness levels. This study will use magnetic resonance imaging scans of 36 young runners, 15 masters athletes, and 41 age- and sex-matched controls to examine brain thickness. The thicknesses across the entire brain and in specific areas of the brain will be compared between groups, adjusting for age, fitness level, and sex. These findings could support evidence of a relationship between elite-level running and preserved brain structure. Future work could examine if running training could lead to increases in brain thickness to maintain motor functions in older adults. Finally, these findings could provide insight on prevention methods for conditions related to changes in brain structure, such as Alzheimer's disease.
Amount: CA$23,830
Funding type: fellowship
Funder scheme: Master's Award: Canada Graduate Scholarships
Start date: 2024-05-01
End date: 2025-04-30
Investigator: Sara Perfetto
Institution: McGill University
Funded outputs count: 0
Funder award ID: 194927_1
OpenAlex ID: G5777892570