Pattern classification using functional magnetic resonance imaging

Authors

  • Dietrich Samuel Schwarzkopf,

    1. Institute of Cognitive Neuroscience & Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
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  • Geraint Rees

    Corresponding author
    1. Institute of Cognitive Neuroscience & Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
    • Institute of Cognitive Neuroscience & Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
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Abstract

Over the past decade, pattern classification methods have become widespread in functional magnetic resonance imaging (fMRI). These methods, typically referred to as multivoxel pattern analysis (MVPA) or multivariate pattern decoding, are now applied to a wide range of neuroscientific questions. There has been particular interest in applying these approaches, e.g., in detecting deception or for diagnostic purposes. In this review, we will focus on what can be achieved by pattern classification analyses of fMRI data; the strengths and weaknesses of this approach; and the biological processes giving rise to the signals measured by this method. Finally, we will discuss how these multivariate approaches are starting to be applied to the analysis of anatomical magnetic resonance imaging (MRI) and magnetoencephalographic (MEG) data. WIREs Cogni Sci 2011 2 568–579 DOI: 10.1002/wcs.141

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