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Pattern classification using functional magnetic resonance imaging
Author(s) -
Samuel Schwarzkopf Dietrich,
Rees Geraint
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.526
H-Index - 49
eISSN - 1939-5086
pISSN - 1939-5078
DOI - 10.1002/wcs.141
Subject(s) - functional magnetic resonance imaging , magnetic resonance imaging , magnetoencephalography , multivariate statistics , focus (optics) , computer science , artificial intelligence , cognition , neuroimaging , pattern recognition (psychology) , psychology , neuroscience , machine learning , physics , medicine , electroencephalography , optics , radiology
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 This article is categorized under: Neuroscience > Cognition