Department of Biostatistics Seminar/Workshop Series

Confounding in Imaging-based Predictive Modeling

Kristin A. Linn, PhD, Post-doctoral Fellow, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania

The multivariate pattern analysis (MVPA) of neuroimaging data typically consists of one or more statistical learning models applied within a broader image analysis pipeline. The goal of MVPA is often to learn about patterns of variation encoded in magnetic resonance images (MRI) of the brain that are associated with brain disease incidence, progression, and response to therapy. Every model choice that is made during image processing and analysis can have implications with respect to the results of neuroimaging studies. Here, attention is given to two important steps within the MVPA framework: 1) the standardization of features prior to training a supervised learning model, and 2) the training of learning models in the presence of confounding. Specific examples focus on the use of the support vector machine, as it is a common model choice for MVPA, but the general concepts apply to a large set of models employed in the field. We propose novel methods that lead to improved classifier performance and interpretability, and we illustrate the methods on real neuroimaging data from a study of Alzheimer’s disease.