Deep Learning (DL) models have gained popularity in neuroimaging studies for predicting psychological behaviors, cognitive traits, and brain pathologies. However, these models can be biased by confounders such as age, sex, or imaging artifacts from the acquisition process. To address this, we introduce 'DeepRepViz', a two-part framework designed to identify confounders in DL model predictions. The first component is a visualization tool that can be used to qualitatively examine the final latent representation of the DL model. The second component is a metric called 'Con-score' that quantifies the confounder risk associated with a variable, using the final latent representation of the DL model. We demonstrate the effectiveness of the Con-score using a simple simulated setup by iteratively altering the strength of a simulated confounder and observing the corresponding change in the Con-score. Next, we validate the DeepRepViz framework on a large-scale neuroimaging dataset (n=12000) by performing three MRI-phenotype prediction tasks that include (a) predicting chronic alcohol users, (b) classifying participant sex, and (c) predicting performance speed on a cognitive task called 'trail making'. DeepRepViz identifies sex as a significant confounder in the DL model predicting chronic alcohol users (Con-score=0.35) and age as a confounder in the model predicting cognitive task performance (Con-score=0.3). In conclusion, the DeepRepViz framework provides a systematic approach to test for potential confounders such as age, sex, and imaging artifacts and improves the transparency of DL models for neuroimaging studies.

翻译：深度学习（DL）模型在神经影像学研究中日益普及，用于预测心理行为、认知特征和脑部病理。然而，这些模型可能受到年龄、性别或采集过程中的成像伪影等混杂因素的偏倚。为此，我们提出了“DeepRepViz”框架，这是一个由两部分组成的系统，旨在识别DL模型预测中的混杂因素。第一个组件是一个可视化工具，可用于定性检查DL模型的最终潜在表示。第二个组件是一个名为“Con-score”的指标，它利用DL模型的最终潜在表示来量化与某一变量相关的混杂风险。我们通过迭代改变模拟混杂因素的强度并观察Con-score的相应变化，在简单的模拟设置中证明了Con-score的有效性。接下来，我们在大规模神经影像数据集（n=12000）上验证了DeepRepViz框架，执行了三个MRI-表型预测任务，包括：(a) 预测慢性酒精使用者，(b) 对参与者性别进行分类，以及(c) 预测名为“轨迹连线”的认知任务的表现速度。DeepRepViz识别出性别是预测慢性酒精使用者的DL模型中的重要混杂因素（Con-score=0.35），而年龄是预测认知任务表现的模型中的混杂因素（Con-score=0.3）。总之，DeepRepViz框架提供了一种系统的方法来检测潜在的混杂因素（如年龄、性别和成像伪影），并提升了神经影像学研究中DL模型的透明度。