Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada, USA.
University of Colorado, Boulder, Colorado, USA.
Hum Brain Mapp. 2020 Sep;41(13):3807-3833. doi: 10.1002/hbm.25090. Epub 2020 Jun 27.
Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA-variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well-known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA-related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA-related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.
收集同一主题的综合数据集已成为神经科学研究的标准，近年来，揭示这些数据集之间的多元关系受到了广泛关注。典型相关分析（CCA）是联合研究多个数据集之间关系的有力多元工具之一，它可以同时揭示不同模态中的疾病或环境效应，并全面描述发育、衰老和疾病进展过程中的变化。在过去的 10 年中，尽管越来越多的研究在多元分析中利用了 CCA，但简单的传统 CCA 仍然占据主导地位。已经提出了多种 CCA 变体技术来提高模型性能；然而，复杂的多元公式和不太为人知的功能阻碍了它们的广泛应用。因此，在本研究中，对 CCA 及其变体技术进行了全面综述。详细的技术公式、解析和数值解、当前在神经科学研究中的应用以及每种与 CCA 相关的技术的优缺点进行了讨论。最后，根据可用数据集的特性和特定的神经科学问题，提供了如何选择最合适的 CCA 相关技术的一般准则。
