Skip to Main content Skip to Navigation
New interface
Journal articles

Multiclass Sparse Bayesian Regression for fMRI-Based Prediction

Abstract : Inverse inference has recently become a popular approach for analyzing neuroimaging data, by quantifying the amount of information contained in brain images on perceptual, cognitive, and behavioral parameters. As it outlines brain regions that convey information for an accurate prediction of the parameter of interest, it allows to understand how the corresponding information is encoded in the brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, as there are far more features (voxels) than samples (images), and dimension reduction is thus a mandatory step. We introduce in this paper a new model, called Multiclass Sparse Bayesian Regression (MCBR), that, unlike classical alternatives, automatically adapts the amount of regularization to the available data. MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient regularization. We detail these framework and validate our algorithm on simulated and real neuroimaging data sets, showing that it performs better than reference methods while yielding interpretable clusters of features.
Complete list of metadata

Cited literature [32 references]  Display  Hide  Download
Contributor : Bertrand Thirion Connect in order to contact the contributor
Submitted on : Monday, July 18, 2011 - 7:50:19 PM
Last modification on : Tuesday, October 25, 2022 - 4:19:55 PM
Long-term archiving on: : Monday, November 12, 2012 - 11:15:17 AM


Files produced by the author(s)



Vincent Michel, Evelyn Eger, Christine Keribin, Bertrand Thirion. Multiclass Sparse Bayesian Regression for fMRI-Based Prediction. International Journal of Biomedical Imaging, 2011, 2011, ⟨10.1155/2011/350838⟩. ⟨inria-00609365⟩



Record views


Files downloads