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Social Cognitive and Affective Neuroscience Advance Access originally published online on January 17, 2009
Social Cognitive and Affective Neuroscience 2009 4(1):101-109; doi:10.1093/scan/nsn044
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© The Author (2009). Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

Revealing representational content with pattern-information fMRI—an introductory guide

Marieke Mur1,2, Peter A. Bandettini1,3 and Nikolaus Kriegeskorte1

1Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA, 2Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands, and 3Functional Magnetic Resonance Imaging Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA

Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually taken to indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patterns of activity within a brain region. These patterns of activity, which are thought to reflect neuronal population codes, can be investigated by pattern-information analysis. In this framework, a region's multivariate pattern information is taken to indicate representational content. This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.

Correspondence should be addressed to Marieke Mur, Faculty of Psychology and Neuroscience, Universiteitssingel 40, 6229 ER Maastricht, Netherlands. E-mail: mariekemur{at}gmail.com

Received November 6, 2008. Accepted November 8, 2008.


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