中国科学院心理研究所机构知识库

多体素模式分析(multi-voxel pattern analysis,MVPA)是一种基于机器学习理论发展出来的新的功能磁共振数据分析技术.MVPA技术通过训练分类器,对由不同认知状态引起的多体素信号模式进行分类.与传统的基于单个体素的分析方法相比,该技术可更敏感地检测脑对认知状态的表征,并使得从神经信号解码认知状态成为可能.文章介绍MVPA技术的基本原理,分析步骤以及可以用MVPA来解决的科学问题,并对应用中可能面临的问题提供了参考建议

Multi-voxel pattern analysis（MVPA）,which is based on machine learning theories,has gained great popularity over the past years as a new approach for fMRI data analysis.By training a classifier,MVPA categorizes multi-voxel patterns tuned by different cognitive states.Compared to conventional voxel-wise methods,this new approach provide higher sensitivity for detecting cognitive representations in the brain.It opens up the possibility for ＂reading out＂ mental states of human beings from the non-invasive recordings of brain activities.This paper introduce the fundamental principles of MVPA and the basic realization procedures.Scientific questions that may be properly addressed with this new approach and potential problems in its applications are also discussed.