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.

通过实验和模拟数据集的分析，我们展示了使用 DeepRepViz 与深度学习模型相结合的优势，该方案能帮助研究人员系统地检测潜在的混淆因素，并揭示依赖年龄、性别或图像伪迹等冗余信息的深度学习模型。