What makes a pattern? Matching decoding methods to data in multivariate pattern analysis.

Published

Journal Article

Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique's introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.

Full Text

Duke Authors

Cited Authors

  • Kragel, PA; Carter, RM; Huettel, SA

Published Date

  • January 2012

Published In

Volume / Issue

  • 6 /

Start / End Page

  • 162 -

PubMed ID

  • 23189035

Pubmed Central ID

  • 23189035

Electronic International Standard Serial Number (EISSN)

  • 1662-453X

International Standard Serial Number (ISSN)

  • 1662-4548

Digital Object Identifier (DOI)

  • 10.3389/fnins.2012.00162

Language

  • eng