ArticleScientific reports2026
Neuroimaging-driven recommendation systems for personalized sports training and injury prevention.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Neuroimaging has become an essential tool in sports science, offering profound insights into brain function, cognitive-motor interactions, and injury mechanisms. Traditional neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and diffusion tensor imaging (DTI), have significantly contributed to our understanding of brain plasticity and its role in athletic performance. However, these conventional approaches often face challenges, including low temporal resolution, sensitivity to motion artifacts, and difficulties in translating controlled laboratory findings into real-world sports applications. These limitations hinder the ability to fully harness neuroimaging for optimizing training regimens and preventing sports-related injuries.To overcome these challenges, we propose an advanced neuroimaging-driven recommendation system for personalized sports training and injury prevention. Our novel model, NeuroAthleteNet, leverages cutting-edge spatiotemporal neural feature extraction alongside graph-based connectivity analysis to establish precise mappings between neurophysiological markers and athletic performance metrics. we introduce NeuroSportSync, a multimodal strategy that synchronizes neuroimaging data with real-time biomechanical and physiological signals. This integration enables a comprehensive, holistic framework for performance enhancement and injury risk assessment.Experimental validation demonstrates that our approach significantly improves predictive accuracy, interpretability, and practical applicability, outperforming traditional neuroimaging analysis methods. By bridging the gap between neuroscience and sports training, our framework support the development of neuroadaptive athletic programs and lay a foundation for future exploration of cognitive-motor feedback mechanisms and concussion risk modeling, potentially contributing to the advancement of personalized sports training and injury prevention in neuroscience-informed athletic care.
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