Multivoxel Pattern Analysis of fMRI Data

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Analysis of functional magnetic resonance imaging (fMRI) data has traditionally been carried out by analyzing each voxel's time-series independently with a linear model. While this approach has been effective for creating statistical maps of brain activity, recent work has show that greater sensitivity to distributed neural signals can be achieved with multivariate approaches that analyze patterns of activity rather than methods that work only on voxel at a time. This has led to an explosion of interest in so-called "multivoxel pattern analysis" (MVPA) which is essentially the application of machine learning algorithms to neuroimaging data. The R programming environment is well-suited for MVPA analyses due to its large and varied support for statistical learning methods available on CRAN. Many of these methods are can be conveniently accessed using a standard interface provided by the 'caret' library. Here we present a new library (rMVPA) that makes MVPA analyses of fMRI data available to R users by leveraging the 'caret' and 'neuroim' packages. The rMVPA analyses implements multiple methods for multivariate analysis of fMRI data including the spherical searchlight method, region of interest analyses, and a new hierarchical ensemble approach to MVPA.





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