Analysis of neuroimaging biomarkers from the elite athlete

精英运动员的神经影像生物标志物分析

• 批准号: 531080-2018
• 负责人: Khademi, April
• 金额: $ 1.82万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=655387
• 关键词: Analysis; neuroimaging; biomarkers; elite; athlete

Due to the availability and advancements of image analysis and machine learning algorithms, quantitative**analysis of data has begun to penetrate many markets. One area that data analytics has great potential to disrupt**is in sports performance and analysis. Imaging and other sports-related data can measure the performance of**athletes and quantitative analysis can give feedback for training programs to improve and optimize the athlete's**performance. This project focuses on quantifying performance-based biomarkers from diffusion weighted**magnetic resonance images of the brain, in order to offer managers, coaches and athletes deeper, scientific**insight into the athlete's performance abilities, potential and risk for injury. The focus is on robust design by**considering novel preprocessing and analysis methodologies to normalize data variability prior to biomarker**extraction and performance-based quantification. The result will be a series of image analysis and machine**learning tools that can reliably extract neuroimaging biomarkers from different scanning devices. The**deliverables of this project will enhance the partner's existing portfolio of sports-performance analytics by**ensuring that the diffusion-weighted analysis biomarkers are robust, quantitative, reliable and efficient to**obtain. Each assessment will be packaged in a thorough, easy-to-read Performance Scorecard so that managers**can make strategic decisions on behalf of their players and team. It will translate into products that are offered**in a wide, global market, ultimately increasing revenues and creating more opportunities for employment of**high-quality personnel. This project has a direct benefit to Canada, in terms of contribution to the artificial**intelligence eco-system and establishing Canada's leadership in this field.

由于图像分析和机器学习算法的可用性和进步，数据的定量分析已经开始渗透到许多市场。数据分析有很大潜力颠覆 ** 的一个领域是运动表现和分析。成像和其他运动相关数据可以测量运动员的表现，定量分析可以为训练计划提供反馈，以改善和优化运动员的表现。该项目的重点是量化基于性能的生物标志物从弥散加权 ** 磁共振图像的大脑，以提供管理人员，教练和运动员更深入，科学 ** 洞察运动员的表现能力，潜力和风险的伤害。重点是通过 ** 考虑新的预处理和分析方法，在生物标志物 ** 提取和基于性能的定量之前标准化数据变异性，进行稳健的设计。其结果将是一系列图像分析和机器学习工具，可以从不同的扫描设备中可靠地提取神经成像生物标志物。该项目的 ** 交付成果将通过 ** 确保扩散加权分析生物标志物的稳健性、定量性、可靠性和有效性 ** 来增强合作伙伴现有的运动表现分析产品组合。每项评估都将打包在一个全面的、易于阅读的绩效记分卡中，以便经理 ** 代表他们的球员和球队做出战略决策。它将转化为在广泛的全球市场上提供的产品，最终增加收入并为高素质人员创造更多的就业机会。这个项目对加拿大有直接的好处，因为它对人工智能生态系统做出了贡献，并确立了加拿大在这一领域的领导地位。