Abstract
Introduction
: Age-related cognitive decline and mental health problems are accompanied by changes in resting-state functional connectivity (rsFC) indices, such as reduced brain network segregation. Meanwhile, exercise can improve cognition, mood, and neural network function in older adults. Studies on effects of exercise on rsFC outcomes in older adults have chiefly focused on changes after exercise training and suggest improved network segregation through enhanced within-network connectivity. However, effects of acute exercise on rsFC measures of neural network integrity in older adults, which presumably underlie changes observed after exercise training, have received less attention. In this study, we hypothesized that acute exercise in older adults would improve functional segregation of major cognition and affect-related brain networks.
Methods:
To test this, we analyzed rsFC data from 37 healthy and physically active older adults after they completed 30 min of moderate-to-vigorous intensity cycling and after they completed a seated rest control condition. Conditions were performed in a counterbalanced order across separate days in a within-subject crossover design. We considered large-scale brain networks associated with cognition and affect, including the frontoparietal network (FPN), salience network (SAL), default mode network (DMN), and affect-reward network (ARN).
Results:
We observed that after acute exercise, there was greater segregation between SAL and DMN, as well as greater segregation between SAL and ARN.
Conclusion:
These findings indicate that acute exercise in active older adults alters rsFC measures in key cognition and affect-related networks in a manner that opposes age-related dedifferentiation of neural networks that may be detrimental to cognition and mental health.
Impact Statement
Our findings contribute novel insight on changes in large-scale functional brain network organization after acute exercise in healthy, active older adults. We argue that these effects of acute exercise may benefit cognition and mental health during older age by countering age-related rsFC changes in major functional brain networks (e.g., salience, default mode, and affect-reward networks). Our multilayered analysis approach of large-scale network rsFC (e.g., between-network segregation; within- and between-network connectivity) and novel between-network segregation index formula can feasibly be employed by the field in the future.
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