title: Revealing representational content with pattern-information fMRI--an introductory guide.
abstract: 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.
authors.affiliation: Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA. mariekemur@gmail.com
authors.forename: Marieke
authors.initials: M
authors.lastname: Mur
authors.forename: Peter A
authors.initials: PA
authors.lastname: Bandettini
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Kriegeskorte
mesh_terms: D001921:Brain / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D008606:Mental Processes / Q000502:physiology; D010363:Pattern Recognition, Automated; D012107:Research Design