A Comparison of Strategies for Incorporating Nuisance Variables into Predictive Neuroimaging Models

In this paper we compare two different methods for dealing with so-called nuisance variables (NV) when training models to predict clinical/psychometric scales from neuroimaging data. In the first approach, the NV are used to adjust the imaging data by ´regressing out´ their contribution to the image features. In the second approach, the NV are included as additional predictors in the model with a separate kernel that controls their contribution to the prediction function. We evaluate these methods using data from an fMRI and a structural MRI study, and discuss the results and interpretation of the two modelling approaches.