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.

翻译：深度学习（DL）模型日益广泛用于分析神经影像数据，以揭示大脑、脑部病理及心理特征的相关机制。然而，参与者年龄、性别或成像伪影等外部“混杂变量”可能使模型预测产生偏差，阻碍模型学习有效的脑-表型关联。本研究提出一种名为“DeepRepViz”框架的解决方案，使研究人员能够系统检测其DL模型预测中的混杂变量。该框架包含：（1）量化潜在混杂变量效应的指标；（2）帮助研究人员定性评估DL模型学习内容的可视化工具。通过在模拟数据集和神经影像数据集上的实验，我们展示了DeepRepViz与DL模型结合使用的优势。例如，神经影像实验表明，在预测慢性酒精使用者的DL模型中，性别是一个显著混杂变量（Con-score=0.35）；类似地，DeepRepViz识别出在预测参与者认知任务表现的DL模型中，年龄为混杂变量（Con-score=0.3）。总体而言，DeepRepViz使研究人员能够系统检验潜在混杂变量，并暴露依赖年龄、性别或成像伪影等外部信息的DL模型。