Department of Psychology, University of California, Los Angeles, CA 90095, USA.
Brain Research Institute, University of California, Los Angeles, CA 90095, USA.
Soc Cogn Affect Neurosci. 2020 Jun 23;15(4):487-509. doi: 10.1093/scan/nsaa057.
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.
多体素模式分析（MVPA）是一种神经影像学分析技术，在过去十年中，其应用范围显著扩大，尤其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 检查的是神经反应模式，而不是像传统的单变量分析那样，分析单个体素或区域的值。在这里，我们为各级社会和情感神经科学家，特别是那些新接触此类方法的科学家，提供了一个关于 MVPA 及其最流行变体（即代表性相似性分析（RSA）和解码分析，如使用机器学习进行分类）的实用介绍。我们讨论了 MVPA 与传统的大规模单变量分析的区别，MVPA 为社会神经科学家提供的优势，实验设计和分析注意事项，在自己的数据集上实施特定分析的逐步说明，以及当前使用 MVPA 方法面临的问题。
