Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical, and environmental data, is performed to gain new insights into the phenotypic, genetic, and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.

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脑成像基因组学：综合分析和机器学习

脑成像基因组学是一个新兴的数据科学领域，对脑成像和基因组数据进行综合分析，通常与其他生物标志物、临床和环境数据相结合，以获得对大脑表型、遗传和分子特征的新见解以及它们对正常和紊乱的大脑功能和行为的影响。它具有为脑科学的生物医学发现做出重大贡献的巨大潜力。鉴于统计和机器学习在生物医学中日益重要的作用以及脑成像基因组学文献的快速增长，我们对脑成像基因组学的统计和机器学习方法进行了最新和全面的回顾，并对以下内容进行了实际讨论：各种生物医学应用的方法选择。