title: Measuring neural representations with fMRI: practices and pitfalls.
abstract: Recently, there has been a dramatic increase in the number of functional magnetic resonance imaging studies seeking to answer questions about how the brain represents information. Representational questions are of particular importance in connecting neuroscientific and cognitive levels of analysis because it is at the representational level that many formal models of cognition make distinct predictions. This review discusses techniques for univariate, adaptation, and multivoxel analysis, and how they have been used to answer questions about content specificity in different regions of the brain, how this content is organized, and how representations are shaped by and contribute to cognitive processes. Each of the analysis techniques makes different assumptions about the underlying neural code and thus differ in how they can be applied to specific questions. We also discuss the many pitfalls of representational analysis, from the flexibility in data analysis pipelines to emergent nonrepresentational relationships that can arise between stimuli in a task.
authors.affiliation: Imaging Research Center, University of Texas at Austin, 1 University Station, Austin, TX 78712, USA. Thdavis@mail.utexas.edu
authors.forename: Tyler
authors.initials: T
authors.lastname: Davis
authors.forename: Russell A
authors.initials: RA
authors.lastname: Poldrack
mesh_terms: D049628:Body Size; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009420:Nervous System / Q000033:anatomy & histology*; D059906:Neuroimaging; D010775:Photic Stimulation
keywords: MVPA; adaptation; fMRI; representation