Mixed-effects inference for classification group studies

micp_rMultivariate classification algorithms are powerful tools for predicting cognitive or pathophysiological states from neuroimaging data. However, meaningful inference requires models that explicitly account for fixed-effects (within-subjects) and random-effects (across-subjects) variance components. While models of this sort are standard practice in mass-univariate analyses of fMRI data, they have not yet received much attention in the decoding literature.

This software closes this gap by providing full Bayesian mixed-effects inference for multivariate classification studies. It includes step-by-step instructions and is extremely easy to use. The software includes:

  • a MATLAB toolbox which comprises both MCMC sampling algorithms and computationally more efficient variational Bayes (VB) approximations;
  • an R package which focuses on variational Bayesian algorithms.



  • K.H. Brodersen, J. Daunizeau, C. Mathys, J.R. Chumbley, J.M. Buhmann, & K.E. Stephan. Variational Bayesian mixed-effects inference for classification studies (2013). NeuroImage (in press). doi:10.1016/j.neuroimage.2013.03.008.
  • K.H. Brodersen, C. Mathys, J.R. Chumbley, J. Daunizeau, C.S. Ong, J.M. Buhmann, & K.E. Stephan. Bayesian mixed-effects inference on classification performance in hierarchical datasets (2012). Journal of Machine Learning Research, 13, 3133-3176.
  • K.H. Brodersen, C.S. Ong, J.M. Buhmann, & K.E. Stephan (2010). The balanced accuracy and its posterior distribution. ICPR, 3121-3124.