Competitive sport experience is associated with reduced off-field aggression and distinct functional brain
Yujing Huang1, Zhuofei Lin2, Chenglin Zhou3
1School of Psychology, Shanghai University of Sport, Shanghai, China.
Background:
While aggression has been widely studied in clinical and forensic contexts, its expression in healthy individuals, particularly athletes, is less well understood. Competitive sport experience may be linked to trait aggression through experience-related changes in large-scale brain networks, but the underlying neural mechanisms remain unclear.
Objective:
This study aimed to examine whether long-term engagement in competitive athletics is associated with intrinsic functional connectivity related to trait aggression, focusing on off-field behaviors.
Methods:
We combined group- and individual-level analyses to examine neural correlates of trait aggression in 84 athletes and 106 non-athletes. Resting-state functional connectivity (RSFC) was assessed using Network-Based Statistics (NBS) for group differences and Connectome-Based Predictive Modeling (CPM) to predict aggression traits from AQ-CV scores.
Results:
Athletes scored lower on total aggression and four of the five subscales. NBS identified a widespread subnetwork of stronger connectivity in athletes, spanning nine brain networks. CPM revealed that total and physical aggression were predicted by distributed RSFC patterns, primarily negative associations across prefrontal, motor, and subcortical regions. Self-directed aggression was predicted by a smaller, more selective network. Notably, four overlapping edges linked NBS and CPM findings, connecting group-level differences to individual aggression variability.
Conclusion:
Our findings suggest that competitive sport experience is associated with distinct functional integration across brain systems involved in emotion regulation and motor control, which may underlie athletes' reduced off-field aggression. This work provides novel insights into the neural basis of aggression in non-clinical populations and highlights the value of combining multilevel connectivity analyses.
