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Recent developments in multivariate pattern analysis for functional MRI

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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.

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Authors and Affiliations

  1. Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China

    Zhi Yang

  2. Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, 310015, China

    Fang Fang & Xuchu Weng

  3. Department of Psychology, Peking University, Beijing, 100817, China

    Fang Fang

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  1. Zhi Yang
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  2. Fang Fang
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  3. Xuchu Weng
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Correspondence to Zhi Yang.

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Yang, Z., Fang, F. & Weng, X. Recent developments in multivariate pattern analysis for functional MRI. Neurosci. Bull. 28, 399–408 (2012). https://doi.org/10.1007/s12264-012-1253-3

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