The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to fMRI data

Voxelwise modeling (VM) is a powerful framework for functionally mapping the brain. In the VM framework, features are extracted from the stimulus (or task) and used in an encoding model to predict brain activity. If the encoding model is able to predict brain activity in some part of the brain, then one may conclude that some information represented in the features is also encoded in the brain. In VM, a separate encoding model is fitted on each spatial sample (i.e. each voxel). VM has many benefits compared to other methods for analyzing and modeling neuroimaging data. Most importantly, VM can use large numbers of features simultaneously, which enables the analysis of complex naturalistic stimuli and tasks. Therefore, VM can produce high-dimensional functional maps that reflect the selectivity of each voxel to large numbers of features. Moreover, because model performance is estimated on a separate test dataset not used during fitting, VM minimizes overfitting and inflated Type I error confounds that plague other approaches, and the results of VM generalize to new subjects and new stimuli. Despite these benefits, VM is still not widely used in neuroimaging, partly because no tutorials on this method are available currently. To demystify the VM framework and ease its dissemination, this paper presents a series of hands-on tutorials accessible to novice practitioners. The VM tutorials are based on free open- source tools and public datasets, and reproduce the analysis presented in previously published work.