NEUROPLASTICITY AND NEURAL SELF-REGULATION IN HIGH-PERFORMANCE ATHLETES: AN INTEGRATED NEUROBEHAVIORAL MODEL

Resumo

The optimization of elite athletic performance transcends mere physical conditioning, entering the domain of cognition and neural self-regulation. This systematic review investigates the hypothesis that an integrated neurobehavioral model, combining Artificial Intelligence (AI)-driven visual projection, adaptive continuous improvement cycles (PDCA), and a structured regenerative protocol, can promote significant changes in neuroplasticity and neural self-regulation in high-performance athletes. The objective is to analyze the literature from the last five years to substantiate a model that connects cognitive-visual training, procedural metacognition, and physiological recovery as a unified system for enhancing performance. The proposed methodology for future longitudinal investigations includes the assessment of heart rate variability (HRV) to monitor autonomic state, executive function tests (e.g., inhibitory control, cognitive flexibility), behavioral scales, and analysis of motor execution consistency. The literature analysis suggests that elite athletes exhibit superior executive functions and that neuroscience-based interventions, such as real-time visual feedback, can accelerate motor learning and decision-making. It is concluded that the integration of these three facets—AI-enhanced visualization, iterative planning with PDCA, and optimized recovery—offers a promising framework for inducing lasting neuroplastic adaptations, improving the athlete’s ability to regulate their neural state and, consequently, their physical and mental performance under pressure.