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Wednesday, June 29 • 11:42am - 12:00pm
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.

avatar for Susan Holmes

Susan Holmes

Professor, Statistics, Stanford
I like teaching nonparametric multivariate analyses to biologists. | Reproducible research is really important to me and I make all my work available online, mostly as Rmd files. I still like to code, use Github and shiny as well as Bioconductor. I am trying to finish a book for biologists that includes code and a lot of fancy visualizations of `uncertainty'.


Bradley Russell Buchsbaum

Rotman Research Institute, Baycrest

Wednesday June 29, 2016 11:42am - 12:00pm
Lane & Lyons & Lodato 326 Galvez Street Stanford, CA 94305-6105

Attendees (27)