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Recent developments in multivariate pattern analysis for functional MRI 被引量：5

Recent developments in multivariate pattern analysis for functional MRI
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摘要 Multivariate pattern analysis（MVPA） is a recently-developed approach for functional magnetic resonance imaging（fMRI） data analyses.Compared with the traditional univariate methods,MVPA is more sensitive to subtle changes in multivariate patterns in fMRI data.In this review,we introduce several significant advances in MVPA applications and summarize various combinations of algorithms and parameters in different problem settings.The limitations of MVPA and some critical questions that need to be addressed in future research are also discussed. Multivariate pattern analysis（MVPA） is a recently-developed approach for functional magnetic resonance imaging（fMRI） data analyses.Compared with the traditional univariate methods,MVPA is more sensitive to subtle changes in multivariate patterns in fMRI data.In this review,we introduce several significant advances in MVPA applications and summarize various combinations of algorithms and parameters in different problem settings.The limitations of MVPA and some critical questions that need to be addressed in future research are also discussed.
作者 Zhi Yang Fang Fang Xuchu Weng
机构地区 Key Laboratory of Behavioral Sciences Center for Cognition and Brain Disorders Department of Psychology
出处 《Neuroscience Bulletin》 SCIE CAS CSCD 2012年第4期399-408,共10页 神经科学通报（英文版）
基金 supported by grants from the National Natural Science Foundation of China (30900366,31070905)
关键词 multivariate analysis FMRI pattern recognition computational biology multivariate analysis fMRI pattern recognition computational biology
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论] Q426 [生物学—神经生物学]
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参考文献77

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同被引文献57

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引证文献5

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Neuroscience Bulletin
2012年 第4期

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