Finding the needle in a high -dimensional haystack: Canonical correlation analysis for neuroscientists
Finding the needle in a high -dimensional haystack: Canonical correlation analysis for neuroscientists
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DOI：
10.1016/j.neuroimage.2020.116745
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发表时间：
2020-08-01
期刊：
影响因子：
5.7
通讯作者：
Bzdok, Danilo
中科院分区：
文献类型：
--
作者：
Wang, Hao-Ting;Smallwood, Jonathan;Bzdok, Danilo
世纪标志着“大数据”的出现，具有多种测量的数据集的可用性迅速增加。在神经科学中，脑成像数据集通常伴随着行为、神经和基因组水平上的数十或数百个表型主题描述符。这种“大数据”存储库的复杂性为系统神经科学提供了新的机遇，也提出了新的挑战。典型相关分析（CCA）是一个典型的家庭的方法，是有用的，在识别变量集之间的联系，从不同的模态。重要的是，CCA非常适合描述多个数据集之间的关系，例如最近可用的大型生物医学数据集。我们的入门讨论了CCA的基本原理、承诺和陷阱。
The 21st century marks the emergence of “big data” with a rapid increase in the availability of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such “big data” repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data, such as in recently available big biomedical datasets. Our primer discusses the rationale, promises, and pitfalls of CCA.