Back

Dose-Response Relationships between Physical Exercises and Mental Health during Early Adolescence: an Investigation of the Underlying Neural and Genetic Mechanisms from the ABCD Study

Yu, G.; Wu, X.-R.; Liu, Z.; Shi, M.; Fan, H.; Liu, Y.; Kuang, N.; Peng, S.; Lian, Z.; Chen, J.; Yang, S.; Huang, C.; Wu, H.; Fan, B.; Feng, J.; Cheng, W.; Sahakian, B. J.; Robbins, T. W.; Becker, B.; Zhang, J.

2023-09-22 psychiatry and clinical psychology
10.1101/2023.09.20.23295830 medRxiv
Show abstract

Adolescence is a critical developmental with increased vulnerability to mental disorders. While the positive impact of physical exercise on adult mental health is well-established, dose-response relationships and the underlying neural and genetic mechanisms in adolescents remain elusive. Leveraging data from >11,000 pre-adolescents (9-10 years, ABCD Study) we examined associations between seven different measures of exercise dosage across 15 exercises and psychopathology, and the roles of brain function and structure and psychiatric genetic risks. Five specific exercises (basketball, baseball/softball, soccer, football, and skiing) were associated with better mental health while the beneficial effects varied with exercise types, dosage measures and dimensions of psychopathology. Interestingly, more exercise does not always translate to better mental health whilst earlier initiation was consistently advantageous. Communication between attention and default-mode brain networks mediated the beneficial effect of playing football. Crucially, exercise mitigates the detrimental effects of psychiatric genetic risks on mental health. We offer a nuanced understanding of exercise effects on adolescent mental health to promote personalized exercise-based interventions in youth.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.