Pyrcca：Python中的正则化核典型相关分析及其在神经成像中的应用
Pyrcca: regularized kernel canonical correlation analysis in Python and its applications to neuroimaging
摘要：
典型相关分析（CCA）是一种解释不同维度相关数据集间互协方差的有效方法。CCA在神经影像数据分析中有许多潜在的应用。例如，CCA可以用于在从多个受试者收集的fMRI数据集之间找到功能相似性，而无需将单个数据集重新定位到模板解剖结构。在本文中，我们介绍了Pyrcca，这是一个开源的Python模块，用于在两个或多个数据集之间执行CCA。Pyrcca可用于在有或没有正则化的情况下，以及在有或没有数据集的线性或高斯核化的情况下实现CCA。我们展示了一个应用CCA实现Pyrcca神经影像数据分析。我们使用CCA来找到一组数据驱动的功能反应模式，这些模式在自然电影实验中的个体受试者之间是相似的。然后，我们展示了如何这组CCA发现的反应模式可以用来准确地预测主题的反应，以新颖的自然电影刺激。
英文摘要：
Canonical correlation analysis (CCA) is a valuable method for interpreting cross-covariance across related datasets of different dimensionality. There are many potential applications of CCA to neuroimaging data analysis. For instance, CCA can be used for finding functional similarities across fMRI datasets collected from multiple subjects without resampling individual datasets to a template anatomy. In this paper, we introduce Pyrcca, an open-source Python module for executing CCA between two or more datasets. Pyrcca can be used to implement CCA with or without regularization, and with or without linear or a Gaussian kernelization of the datasets. We demonstrate an application of CCA implemented with Pyrcca to neuroimaging data analysis. We use CCA to find a data-driven set of functional response patterns that are similar across individual subjects in a natural movie experiment. We then demonstrate how this set of response patterns discovered by CCA can be used to accurately predict subject responses to novel natural movie stimuli.
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