Recent developments in multivariate pattern analysis for functional MRI  

作　　者：Zhi Yang Fang Fang Xuchu Weng 

机构地区：[1]Key Laboratory of Behavioral Sciences,Institute of Psychology,Chinese Academy of Sciences,Beijing 100101,China [2]Center for Cognition and Brain Disorders,Hangzhou Normal University,Hangzhou 310015,China [3]Department of Psychology,Peking University,Beijing 100817,China

出　　处：《Neuroscience Bulletin》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)

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.

关 键 词：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[自动化与计算机技术—计算机科学与技术]