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Dragon boat exercise reshapes the temporal-spatial dynamics of the brain
Hongke Jiang1, Shanguang Zhao1, Qianqian Wu2
1Department of Physical Education, Shanghai Maritime University, Shanghai, China.
Peerj
|July 2, 2024
Summary
Dragon boat training enhances brain network efficiency by strengthening attentional networks and reducing overall brain complexity. Professional racers showed altered microstate D dynamics and lower brain complexity compared to amateurs.
Area of Science:
- Neuroscience
- Exercise Physiology
- Sports Science
Background:
- Exercise training is known to improve neurological function.
- Research on exercise's impact on functional brain network spatiotemporal synchronization is limited.
- Understanding these dynamics is crucial for neurological health.
Purpose of the Study:
- To investigate how dragon boat exercise training affects the spatiotemporal synchronization properties of functional brain networks.
- To compare brain activity and complexity between professional and amateur dragon boat racers.
- To provide evidence for exercise-induced improvements in cerebral functional network efficiency.
Main Methods:
- Recruited 23 professional and 24 amateur dragon boat racers.
- Recorded electroencephalography (EEG) during simulated paddling on ergometers.
- Analyzed spatiotemporal brain dynamics using microstates and omega complexity.
Main Results:
- Professional racers exhibited significantly altered temporal dynamics in microstate D (attentional networks), with increased duration, occurrence, and coverage.
- Transition probabilities of microstate D were also significantly different between groups.
- Professional racers showed lower brain complexity, indicated by a significant decrease in omega complexity in the alpha and beta frequency bands.
Conclusions:
- Dragon boat training appears to strengthen the brain's attentional networks.
- This training may lead to reduced overall brain complexity, suggesting increased efficiency.
- The study provides evidence that dragon boat exercise enhances cerebral functional network efficiency on a spatiotemporal scale.

