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

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.

Keywords

multivariate analysis; fMRI; pattern recognition; computational biology

[SpringerLink]