The family of neuroimaging analytical techniques known as multivoxel pattern analysis (MVPA) has dramatically increased in popularity over the past decade, particularly in social and affective neuroscience research using functional magnetic resonance imaging (fMRI). MVPA examines patterns of neural responses, rather than analyzing single voxel- or region-based values, as is customary in conventional univariate analyses. Here, we provide a practical introduction to MVPA and its most popular variants (namely, representational similarity analysis (RSA) and decoding analyses, such as classification using machine learning) for social and affective neuroscientists of all levels, particularly those new to such methods. We discuss how MVPA differs from traditional mass-univariate analyses, the benefits MVPA offers to social neuroscientists, experimental design and analysis considerations, step-by-step instructions for how to implement specific analyses in one’s own dataset and issues that are currently facing research using MVPA methods.

中文翻译：

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功能磁共振成像中的贸易多体素模式分析工具：面向社会和情感神经科学家的实用介绍。

在过去的十年中，被称为多体素模式分析（MVPA）的神经影像分析技术家族迅速普及，特别是在使用功能磁共振成像（fMRI）的社会和情感神经科学研究中。MVPA会检查神经反应的模式，而不是像常规单变量分析中惯常的那样分析单个基于体素或区域的值。在这里，我们为所有级别的社交和情感神经科学家（尤其是新接触这种方法的人）提供了MVPA及其最流行的变体（即，代表相似性分析（RSA）和解码分析，例如使用机器学习进行分类）的实用介绍。我们讨论了MVPA与传统的质量单变量分析有何不同，MVPA为社会神经科学家带来的好处，