what makes a pattern? matching decoding methods to data in multivariate pattern analysis

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ID: 174867
2012
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Abstract
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 nonlinear 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.
Reference Key
kragel2012frontierswhat Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Philip A Kragel;Philip A Kragel;R. McKell eCarter;R. McKell eCarter;Scott A Huettel;Scott A Huettel;Scott A Huettel
Journal Journal of enzyme inhibition and medicinal chemistry
Year 2012
DOI
10.3389/fnins.2012.00162
URL
http://journal.frontiersin.org/Journal/10.3389/fnins.2012.00162/full
Keywords
Machine learning fmri classification mvpa nonlinear neuropsychiatry biological psychiatry multivariateneurosciences

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