Section: New Results

Multiclass Sparse Bayesian Regression for fMRI-Based Prediction

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 work 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. See also [13] and Fig. 2 .

Figure 2. Mental representation of size - Inter-subject analysis. Histogram of the weights found by Gibbs-MCBR, and corresponding class membership values (each color of dots represents a different class), for the inter-subject analyzes on the mental representation of size. We can see that Gibbs-MCBR creates clusters of informative and non informative voxels, and that the different classes are regularized differently, according to the relevance of the features within them.