title: Recent developments in multivariate pattern analysis for functional MRI.
abstract: 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.
authors: Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China. yangz@psych.ac.cn
authors: Zhi
authors: Z
authors: Yang
authors: Fang
authors: F
authors: Fang
authors: Xuchu
authors: X
authors: Weng
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D019295:Computational Biology / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015999:Multivariate Analysis