Process / pipelineMachine learning decoding
多元模式分析
多元模式分析（Multivariate Pattern Analysis, MVPA）是一种用于功能性磁共振成像（fMRI）的机器学习方法，它通过解码全脑神经活动的空间模式来识别认知状态、刺激或行为。MVPA由Haxby及其同事于2001年开创，将fMRI视为一个分类问题：一个训练好的解码器能否仅凭大脑活动模式预测一个人正在感知或思考的内容？
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来源
- Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430. DOI: 10.1016/j.tics.2006.07.005 ↗
- Haxby, J. V., Gobbini, M. I., Furey, M. L., et al. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539), 2425–2430. DOI: 10.1126/science.1063736 ↗
如何引用本页
ScholarGate. (2026, June 3). Multivariate Pattern Analysis (MVPA). ScholarGate. https://scholargate.app/zh/neuroimaging/multivariate-pattern-analysis
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