Identifying confounders in deep-learning-based model predictions using DeepRepViz

Deep Learning (DL) models are increasingly used to analyze neuroimaging data and uncover insights about the brain, brain pathologies, and psychological traits. However, extraneous `confounders' variables such as the age of the participants, sex, or imaging artifacts can bias model predictions, preventing the models from learning relevant brain-phenotype relationships. In this study, we provide a solution called the `DeepRepViz' framework that enables researchers to systematically detect confounders in their DL model predictions. The framework consists of (1) a metric that quantifies the effect of potential confounders and (2) a visualization tool that allows researchers to qualitatively inspect what the DL model is learning. By performing experiments on simulated and neuroimaging datasets, we demonstrate the benefits of using DeepRepViz in combination with DL models. For example, experiments on the neuroimaging datasets reveal that sex is a significant confounder in a DL model predicting chronic alcohol users (Con-score=0.35). Similarly, DeepRepViz identifies age as a confounder in a DL model predicting participants' performance on a cognitive task (Con-score=0.3). Overall, DeepRepViz enables researchers to systematically test for potential confounders and expose DL models that rely on extraneous information such as age, sex, or imaging artifacts.