Preprocessing, Prediction and Significance: Framework and Application to Brain Imaging

Brain imaging studies have set the stage for measuring brain function in psychiatric disorders, such as depression, with the goal of developing effective treatment strategies. However, data arising from such studies are often hampered by noise confounds such as motion-related artifacts, affecting both the spatial and temporal correlation structure of the data. Failure to adequately control for these types of noise can have significant impact on subsequent statistical analyses. In this paper, we demonstrate a framework for extending the non-parametric testing of statistical significance in predictive modeling by including a plausible set of preprocessing strategies to measure the predictive power. Our approach adopts permutation tests to estimate how likely we are to obtain a given predictive performance in an independent sample, depending on the preprocessing strategy used to generate the data. We demonstrate and apply the framework on examples of longitudinal Positron Emission Tomography (PET) data following a pharmacological intervention.